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4 Commits
atropos-he
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185
.env.example
185
.env.example
@@ -1,185 +0,0 @@
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# Hermes Agent Environment Configuration
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# Copy this file to .env and fill in your API keys
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# =============================================================================
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# LLM PROVIDER (OpenRouter)
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# =============================================================================
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# OpenRouter provides access to many models through one API
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# All LLM calls go through OpenRouter - no direct provider keys needed
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# Get your key at: https://openrouter.ai/keys
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OPENROUTER_API_KEY=
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# Default model to use (OpenRouter format: provider/model)
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# Examples: anthropic/claude-sonnet-4, openai/gpt-4o, google/gemini-2.0-flash, zhipuai/glm-4-plus
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LLM_MODEL=anthropic/claude-sonnet-4
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# =============================================================================
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# TOOL API KEYS
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# =============================================================================
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# Firecrawl API Key - Web search, extract, and crawl
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# Get at: https://firecrawl.dev/
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FIRECRAWL_API_KEY=
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# Nous Research API Key - Vision analysis and multi-model reasoning
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# Get at: https://inference-api.nousresearch.com/
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NOUS_API_KEY=
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# FAL.ai API Key - Image generation
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# Get at: https://fal.ai/
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FAL_KEY=
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# =============================================================================
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# TERMINAL TOOL CONFIGURATION (mini-swe-agent backend)
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# =============================================================================
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# Backend type: "local", "singularity", "docker", "modal", or "ssh"
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# - local: Runs directly on your machine (fastest, no isolation)
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# - ssh: Runs on remote server via SSH (great for sandboxing - agent can't touch its own code)
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# - singularity: Runs in Apptainer/Singularity containers (HPC clusters, no root needed)
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# - docker: Runs in Docker containers (isolated, requires Docker + docker group)
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# - modal: Runs in Modal cloud sandboxes (scalable, requires Modal account)
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TERMINAL_ENV=local
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# Container images (for singularity/docker/modal backends)
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TERMINAL_DOCKER_IMAGE=python:3.11
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TERMINAL_SINGULARITY_IMAGE=docker://python:3.11
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TERMINAL_MODAL_IMAGE=python:3.11
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# Working directory inside the container
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TERMINAL_CWD=/tmp
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# Default command timeout in seconds
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TERMINAL_TIMEOUT=60
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# Cleanup inactive environments after this many seconds
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TERMINAL_LIFETIME_SECONDS=300
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# =============================================================================
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# SSH REMOTE EXECUTION (for TERMINAL_ENV=ssh)
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# =============================================================================
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# Run terminal commands on a remote server via SSH.
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# Agent code stays on your machine, commands execute remotely.
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#
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# SECURITY BENEFITS:
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# - Agent cannot read your .env file (API keys protected)
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# - Agent cannot modify its own code
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# - Remote server acts as isolated sandbox
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# - Can safely configure passwordless sudo on remote
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#
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# TERMINAL_SSH_HOST=192.168.1.100
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# TERMINAL_SSH_USER=agent
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# TERMINAL_SSH_PORT=22
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# TERMINAL_SSH_KEY=~/.ssh/id_rsa
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# =============================================================================
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# SUDO SUPPORT (works with ALL terminal backends)
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# =============================================================================
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# If set, enables sudo commands by piping password via `sudo -S`.
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# Works with: local, docker, singularity, modal, and ssh backends.
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#
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# SECURITY WARNING: Password stored in plaintext. Only use on trusted machines.
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#
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# ALTERNATIVES:
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# - For SSH backend: Configure passwordless sudo on the remote server
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# - For containers: Run as root inside the container (no sudo needed)
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# - For local: Configure /etc/sudoers for specific commands
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# - For CLI: Leave unset - you'll be prompted interactively with 45s timeout
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#
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# SUDO_PASSWORD=your_password_here
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# =============================================================================
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# MODAL CLOUD BACKEND (Optional - for TERMINAL_ENV=modal)
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# =============================================================================
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# Modal uses CLI authentication, not environment variables.
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# Run: pip install modal && modal setup
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# This will authenticate via browser and store credentials locally.
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# No API key needed in .env - Modal handles auth automatically.
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# =============================================================================
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# BROWSER TOOL CONFIGURATION (agent-browser + Browserbase)
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# =============================================================================
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# Browser automation requires Browserbase cloud service for remote browser execution.
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# This allows the agent to navigate websites, fill forms, and extract information.
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#
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# STEALTH MODES:
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# - Basic Stealth: ALWAYS active (random fingerprints, auto CAPTCHA solving)
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# - Advanced Stealth: Requires BROWSERBASE_ADVANCED_STEALTH=true (Scale Plan only)
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# Browserbase API Key - Cloud browser execution
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# Get at: https://browserbase.com/
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BROWSERBASE_API_KEY=
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# Browserbase Project ID - From your Browserbase dashboard
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BROWSERBASE_PROJECT_ID=
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# Enable residential proxies for better CAPTCHA solving (default: true)
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# Routes traffic through residential IPs, significantly improves success rate
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BROWSERBASE_PROXIES=true
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# Enable advanced stealth mode (default: false, requires Scale Plan)
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# Uses custom Chromium build to avoid bot detection altogether
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BROWSERBASE_ADVANCED_STEALTH=false
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# Browser session timeout in seconds (default: 300)
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# Sessions are cleaned up after this duration of inactivity
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BROWSER_SESSION_TIMEOUT=300
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# Browser inactivity timeout - auto-cleanup inactive sessions (default: 120 = 2 min)
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# Browser sessions are automatically closed after this period of no activity
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BROWSER_INACTIVITY_TIMEOUT=120
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# =============================================================================
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# SESSION LOGGING
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# =============================================================================
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# Session trajectories are automatically saved to logs/ directory
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# Format: logs/session_YYYYMMDD_HHMMSS_UUID.json
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# Contains full conversation history in trajectory format for debugging/replay
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# =============================================================================
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# LEGACY/OPTIONAL API KEYS
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# =============================================================================
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# Morph API Key - For legacy Hecate terminal backend (terminal-hecate tool)
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# Get at: https://morph.so/
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MORPH_API_KEY=
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# Hecate VM Settings (only if using terminal-hecate tool)
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HECATE_VM_LIFETIME_SECONDS=300
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HECATE_DEFAULT_SNAPSHOT_ID=snapshot_p5294qxt
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# =============================================================================
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# DEBUG OPTIONS
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# =============================================================================
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WEB_TOOLS_DEBUG=false
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VISION_TOOLS_DEBUG=false
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MOA_TOOLS_DEBUG=false
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IMAGE_TOOLS_DEBUG=false
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# =============================================================================
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# CONTEXT COMPRESSION (Auto-shrinks long conversations)
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# =============================================================================
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# When conversation approaches model's context limit, middle turns are
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# automatically summarized to free up space.
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#
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# CONTEXT_COMPRESSION_ENABLED=true # Enable auto-compression (default: true)
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# CONTEXT_COMPRESSION_THRESHOLD=0.85 # Compress at 85% of context limit
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# CONTEXT_COMPRESSION_MODEL=google/gemini-2.0-flash-001 # Fast model for summaries
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# =============================================================================
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# RL TRAINING (Tinker + Atropos)
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# =============================================================================
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# Run reinforcement learning training on language models using the Tinker API.
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# Requires the rl-server to be running (from tinker-atropos package).
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# Tinker API Key - RL training service
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# Get at: https://tinker-console.thinkingmachines.ai/keys
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TINKER_API_KEY=
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# Weights & Biases API Key - Experiment tracking and metrics
|
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# Get at: https://wandb.ai/authorize
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WANDB_API_KEY=
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|
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# RL API Server URL (default: http://localhost:8080)
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# Change if running the rl-server on a different host/port
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# RL_API_URL=http://localhost:8080
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31
.gitignore
vendored
31
.gitignore
vendored
@@ -16,33 +16,4 @@ __pycache__/
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export*
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__pycache__/model_tools.cpython-310.pyc
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__pycache__/web_tools.cpython-310.pyc
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logs/
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data/
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.pytest_cache/
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tmp/
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temp_vision_images/
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hermes-*/*
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examples/
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tests/quick_test_dataset.jsonl
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tests/sample_dataset.jsonl
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run_datagen_kimik2-thinking.sh
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run_datagen_megascience_glm4-6.sh
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run_datagen_sonnet.sh
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source-data/*
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run_datagen_megascience_glm4-6.sh
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data/*
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node_modules/
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browser-use/
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agent-browser/
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# Private keys
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||||
*.ppk
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*.pem
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privvy*
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images/
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__pycache__/
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hermes_agent.egg-info/
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wandb/
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testlogs
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|
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# CLI config (may contain sensitive SSH paths)
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cli-config.yaml
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logs/
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6
.gitmodules
vendored
6
.gitmodules
vendored
@@ -1,6 +0,0 @@
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[submodule "mini-swe-agent"]
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path = mini-swe-agent
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url = https://github.com/SWE-agent/mini-swe-agent
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[submodule "tinker-atropos"]
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path = tinker-atropos
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url = https://github.com/nousresearch/tinker-atropos
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533
AGENTS.md
533
AGENTS.md
@@ -1,533 +0,0 @@
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# Hermes Agent - Development Guide
|
||||
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Instructions for AI coding assistants (GitHub Copilot, Cursor, etc.) and human developers.
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Hermes-Agent is an AI agent harness with tool-calling capabilities, interactive CLI, messaging integrations, and scheduled tasks.
|
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|
||||
## Development Environment
|
||||
|
||||
**IMPORTANT**: Always use the virtual environment if it exists:
|
||||
```bash
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source venv/bin/activate # Before running any Python commands
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||||
```
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
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||||
hermes-agent/
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├── hermes_cli/ # Unified CLI commands
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||||
│ ├── main.py # Entry point, command dispatcher
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│ ├── setup.py # Interactive setup wizard
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||||
│ ├── config.py # Config management & migration
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│ ├── status.py # Status display
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||||
│ ├── doctor.py # Diagnostics
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│ ├── gateway.py # Gateway management
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||||
│ ├── uninstall.py # Uninstaller
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│ └── cron.py # Cron job management
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||||
├── tools/ # Tool implementations
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├── gateway/ # Messaging platform adapters
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├── cron/ # Scheduler implementation
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||||
├── skills/ # Knowledge documents
|
||||
├── cli.py # Interactive CLI (Rich UI)
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||||
├── run_agent.py # Agent runner with AIAgent class
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||||
├── model_tools.py # Tool schemas and handlers
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├── toolsets.py # Tool groupings
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├── toolset_distributions.py # Probability-based tool selection
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└── batch_runner.py # Parallel batch processing
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```
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|
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**User Configuration** (stored in `~/.hermes/`):
|
||||
- `~/.hermes/config.yaml` - Settings (model, terminal, toolsets, etc.)
|
||||
- `~/.hermes/.env` - API keys and secrets
|
||||
|
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## File Dependency Chain
|
||||
|
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```
|
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tools/*.py → tools/__init__.py → model_tools.py → toolsets.py → toolset_distributions.py
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↑
|
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run_agent.py ──────────────────────────┘
|
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cli.py → run_agent.py (uses AIAgent with quiet_mode=True)
|
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batch_runner.py → run_agent.py + toolset_distributions.py
|
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```
|
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|
||||
Always ensure consistency between tools, model_tools.py, and toolsets.py when changing any of them.
|
||||
|
||||
---
|
||||
|
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## AIAgent Class
|
||||
|
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The main agent is implemented in `run_agent.py`:
|
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|
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```python
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class AIAgent:
|
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def __init__(
|
||||
self,
|
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model: str = "anthropic/claude-sonnet-4",
|
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api_key: str = None,
|
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base_url: str = "https://openrouter.ai/api/v1",
|
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max_iterations: int = 60, # Max tool-calling loops
|
||||
enabled_toolsets: list = None,
|
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disabled_toolsets: list = None,
|
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verbose_logging: bool = False,
|
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quiet_mode: bool = False, # Suppress progress output
|
||||
tool_progress_callback: callable = None, # Called on each tool use
|
||||
):
|
||||
# Initialize OpenAI client, load tools based on toolsets
|
||||
...
|
||||
|
||||
def chat(self, user_message: str, task_id: str = None) -> str:
|
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# Main entry point - runs the agent loop
|
||||
...
|
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```
|
||||
|
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### Agent Loop
|
||||
|
||||
The core loop in `_run_agent_loop()`:
|
||||
|
||||
```
|
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1. Add user message to conversation
|
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2. Call LLM with tools
|
||||
3. If LLM returns tool calls:
|
||||
- Execute each tool
|
||||
- Add tool results to conversation
|
||||
- Go to step 2
|
||||
4. If LLM returns text response:
|
||||
- Return response to user
|
||||
```
|
||||
|
||||
```python
|
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while turns < max_turns:
|
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response = client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tool_schemas,
|
||||
)
|
||||
|
||||
if response.tool_calls:
|
||||
for tool_call in response.tool_calls:
|
||||
result = await execute_tool(tool_call)
|
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messages.append(tool_result_message(result))
|
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turns += 1
|
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else:
|
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return response.content
|
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```
|
||||
|
||||
### Conversation Management
|
||||
|
||||
Messages are stored as a list of dicts following OpenAI format:
|
||||
|
||||
```python
|
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messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant..."},
|
||||
{"role": "user", "content": "Search for Python tutorials"},
|
||||
{"role": "assistant", "content": None, "tool_calls": [...]},
|
||||
{"role": "tool", "tool_call_id": "...", "content": "..."},
|
||||
{"role": "assistant", "content": "Here's what I found..."},
|
||||
]
|
||||
```
|
||||
|
||||
### Reasoning Model Support
|
||||
|
||||
For models that support chain-of-thought reasoning:
|
||||
- Extract `reasoning_content` from API responses
|
||||
- Store in `assistant_msg["reasoning"]` for trajectory export
|
||||
- Pass back via `reasoning_content` field on subsequent turns
|
||||
|
||||
---
|
||||
|
||||
## CLI Architecture (cli.py)
|
||||
|
||||
The interactive CLI uses:
|
||||
- **Rich** - For the welcome banner and styled panels
|
||||
- **prompt_toolkit** - For fixed input area with history and `patch_stdout`
|
||||
- **KawaiiSpinner** (in run_agent.py) - Animated feedback during API calls and tool execution
|
||||
|
||||
Key components:
|
||||
- `HermesCLI` class - Main CLI controller with commands and conversation loop
|
||||
- `load_cli_config()` - Loads config, sets environment variables for terminal
|
||||
- `build_welcome_banner()` - Displays ASCII art logo, tools, and skills summary
|
||||
- `/commands` - Process user commands like `/help`, `/clear`, `/personality`, etc.
|
||||
|
||||
CLI uses `quiet_mode=True` when creating AIAgent to suppress verbose logging.
|
||||
|
||||
### Adding CLI Commands
|
||||
|
||||
1. Add to `COMMANDS` dict with description
|
||||
2. Add handler in `process_command()` method
|
||||
3. For persistent settings, use `save_config_value()` to update config
|
||||
|
||||
---
|
||||
|
||||
## Hermes CLI Commands
|
||||
|
||||
The unified `hermes` command provides all functionality:
|
||||
|
||||
| Command | Description |
|
||||
|---------|-------------|
|
||||
| `hermes` | Interactive chat (default) |
|
||||
| `hermes chat -q "..."` | Single query mode |
|
||||
| `hermes setup` | Configure API keys and settings |
|
||||
| `hermes config` | View current configuration |
|
||||
| `hermes config edit` | Open config in editor |
|
||||
| `hermes config set KEY VAL` | Set a specific value |
|
||||
| `hermes config check` | Check for missing config |
|
||||
| `hermes config migrate` | Prompt for missing config interactively |
|
||||
| `hermes status` | Show configuration status |
|
||||
| `hermes doctor` | Diagnose issues |
|
||||
| `hermes update` | Update to latest (checks for new config) |
|
||||
| `hermes uninstall` | Uninstall (can keep configs for reinstall) |
|
||||
| `hermes gateway` | Start messaging gateway |
|
||||
| `hermes cron list` | View scheduled jobs |
|
||||
| `hermes version` | Show version info |
|
||||
|
||||
---
|
||||
|
||||
## Messaging Gateway
|
||||
|
||||
The gateway connects Hermes to Telegram, Discord, and WhatsApp.
|
||||
|
||||
### Configuration (in `~/.hermes/.env`):
|
||||
|
||||
```bash
|
||||
# Telegram
|
||||
TELEGRAM_BOT_TOKEN=123456:ABC-DEF... # From @BotFather
|
||||
TELEGRAM_ALLOWED_USERS=123456789,987654 # Comma-separated user IDs (from @userinfobot)
|
||||
|
||||
# Discord
|
||||
DISCORD_BOT_TOKEN=MTIz... # From Developer Portal
|
||||
DISCORD_ALLOWED_USERS=123456789012345678 # Comma-separated user IDs
|
||||
|
||||
# Agent Behavior
|
||||
HERMES_MAX_ITERATIONS=60 # Max tool-calling iterations
|
||||
MESSAGING_CWD=/home/myuser # Terminal working directory for messaging
|
||||
|
||||
# Tool Progress (optional)
|
||||
HERMES_TOOL_PROGRESS=true # Send progress messages
|
||||
HERMES_TOOL_PROGRESS_MODE=new # "new" or "all"
|
||||
```
|
||||
|
||||
### Working Directory Behavior
|
||||
|
||||
- **CLI (`hermes` command)**: Uses current directory (`.` → `os.getcwd()`)
|
||||
- **Messaging (Telegram/Discord)**: Uses `MESSAGING_CWD` (default: home directory)
|
||||
|
||||
This is intentional: CLI users are in a terminal and expect the agent to work in their current directory, while messaging users need a consistent starting location.
|
||||
|
||||
### Security (User Allowlists):
|
||||
|
||||
**IMPORTANT**: Without an allowlist, anyone who finds your bot can use it!
|
||||
|
||||
The gateway checks `{PLATFORM}_ALLOWED_USERS` environment variables:
|
||||
- If set: Only listed user IDs can interact with the bot
|
||||
- If unset: All users are allowed (dangerous with terminal access!)
|
||||
|
||||
Users can find their IDs:
|
||||
- **Telegram**: Message [@userinfobot](https://t.me/userinfobot)
|
||||
- **Discord**: Enable Developer Mode, right-click name → Copy ID
|
||||
|
||||
### Tool Progress Notifications
|
||||
|
||||
When `HERMES_TOOL_PROGRESS=true`, the bot sends status messages as it works:
|
||||
- `💻 \`ls -la\`...` (terminal commands show the actual command)
|
||||
- `🔍 web_search...`
|
||||
- `📄 web_extract...`
|
||||
|
||||
Modes:
|
||||
- `new`: Only when switching to a different tool (less spam)
|
||||
- `all`: Every single tool call
|
||||
|
||||
### Typing Indicator
|
||||
|
||||
The gateway keeps the "typing..." indicator active throughout processing, refreshing every 4 seconds. This lets users know the bot is working even during long tool-calling sequences.
|
||||
|
||||
### Platform Toolsets:
|
||||
|
||||
Each platform has a dedicated toolset in `toolsets.py`:
|
||||
- `hermes-telegram`: Full tools including terminal (with safety checks)
|
||||
- `hermes-discord`: Full tools including terminal
|
||||
- `hermes-whatsapp`: Full tools including terminal
|
||||
|
||||
---
|
||||
|
||||
## Configuration System
|
||||
|
||||
Configuration files are stored in `~/.hermes/` for easy user access:
|
||||
- `~/.hermes/config.yaml` - All settings (model, terminal, compression, etc.)
|
||||
- `~/.hermes/.env` - API keys and secrets
|
||||
|
||||
### Adding New Configuration Options
|
||||
|
||||
When adding new configuration variables, you MUST follow this process:
|
||||
|
||||
#### For config.yaml options:
|
||||
|
||||
1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
|
||||
2. **CRITICAL**: Bump `_config_version` in `DEFAULT_CONFIG` when adding required fields
|
||||
3. This triggers migration prompts for existing users on next `hermes update` or `hermes setup`
|
||||
|
||||
Example:
|
||||
```python
|
||||
DEFAULT_CONFIG = {
|
||||
# ... existing config ...
|
||||
|
||||
"new_feature": {
|
||||
"enabled": True,
|
||||
"option": "default_value",
|
||||
},
|
||||
|
||||
# BUMP THIS when adding required fields
|
||||
"_config_version": 2, # Was 1, now 2
|
||||
}
|
||||
```
|
||||
|
||||
#### For .env variables (API keys/secrets):
|
||||
|
||||
1. Add to `REQUIRED_ENV_VARS` or `OPTIONAL_ENV_VARS` in `hermes_cli/config.py`
|
||||
2. Include metadata for the migration system:
|
||||
|
||||
```python
|
||||
OPTIONAL_ENV_VARS = {
|
||||
# ... existing vars ...
|
||||
"NEW_API_KEY": {
|
||||
"description": "What this key is for",
|
||||
"prompt": "Display name in prompts",
|
||||
"url": "https://where-to-get-it.com/",
|
||||
"tools": ["tools_it_enables"], # What tools need this
|
||||
"password": True, # Mask input
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
#### Update related files:
|
||||
|
||||
- `hermes_cli/setup.py` - Add prompts in the setup wizard
|
||||
- `cli-config.yaml.example` - Add example with comments
|
||||
- Update README.md if user-facing
|
||||
|
||||
### Config Version Migration
|
||||
|
||||
The system uses `_config_version` to detect outdated configs:
|
||||
|
||||
1. `check_for_missing_config()` compares user config to `DEFAULT_CONFIG`
|
||||
2. `migrate_config()` interactively prompts for missing values
|
||||
3. Called automatically by `hermes update` and optionally by `hermes setup`
|
||||
|
||||
---
|
||||
|
||||
## Environment Variables
|
||||
|
||||
API keys are loaded from `~/.hermes/.env`:
|
||||
- `OPENROUTER_API_KEY` - Main LLM API access (primary provider)
|
||||
- `FIRECRAWL_API_KEY` - Web search/extract tools
|
||||
- `BROWSERBASE_API_KEY` / `BROWSERBASE_PROJECT_ID` - Browser automation
|
||||
- `FAL_KEY` - Image generation (FLUX model)
|
||||
- `NOUS_API_KEY` - Vision and Mixture-of-Agents tools
|
||||
|
||||
Terminal tool configuration (in `~/.hermes/config.yaml`):
|
||||
- `terminal.backend` - Backend: local, docker, singularity, modal, or ssh
|
||||
- `terminal.cwd` - Working directory for CLI ("." = current directory)
|
||||
- `terminal.docker_image` - Image for Docker backend
|
||||
- `terminal.singularity_image` - Image for Singularity backend
|
||||
- `terminal.modal_image` - Image for Modal backend
|
||||
- SSH: `TERMINAL_SSH_HOST`, `TERMINAL_SSH_USER`, `TERMINAL_SSH_KEY` in .env
|
||||
|
||||
Agent behavior (in `~/.hermes/.env`):
|
||||
- `HERMES_MAX_ITERATIONS` - Max tool-calling iterations (default: 60)
|
||||
- `MESSAGING_CWD` - Working directory for messaging platforms (default: ~)
|
||||
- `HERMES_TOOL_PROGRESS` - Enable tool progress messages (`true`/`false`)
|
||||
- `HERMES_TOOL_PROGRESS_MODE` - Progress mode: `new` (tool changes) or `all`
|
||||
|
||||
### Dangerous Command Approval
|
||||
|
||||
The terminal tool includes safety checks for potentially destructive commands (e.g., `rm -rf`, `DROP TABLE`, `chmod 777`, etc.):
|
||||
|
||||
**Behavior by Backend:**
|
||||
- **Docker/Singularity/Modal**: Commands run unrestricted (isolated containers)
|
||||
- **Local/SSH**: Dangerous commands trigger approval flow
|
||||
|
||||
**Approval Flow (CLI):**
|
||||
```
|
||||
⚠️ Potentially dangerous command detected: recursive delete
|
||||
rm -rf /tmp/test
|
||||
|
||||
[o]nce | [s]ession | [a]lways | [d]eny
|
||||
Choice [o/s/a/D]:
|
||||
```
|
||||
|
||||
**Approval Flow (Messaging):**
|
||||
- Command is blocked with explanation
|
||||
- Agent explains the command was blocked for safety
|
||||
- User must add the pattern to their allowlist via `hermes config edit` or run the command directly on their machine
|
||||
|
||||
**Configuration:**
|
||||
- `command_allowlist` in `~/.hermes/config.yaml` stores permanently allowed patterns
|
||||
- Add patterns via "always" approval or edit directly
|
||||
|
||||
**Sudo Handling (Messaging):**
|
||||
- If sudo fails over messaging, output includes tip to add `SUDO_PASSWORD` to `~/.hermes/.env`
|
||||
|
||||
---
|
||||
|
||||
## Adding New Tools
|
||||
|
||||
Follow this strict order to maintain consistency:
|
||||
|
||||
1. Create `tools/your_tool.py` with:
|
||||
- Handler function (sync or async) returning a JSON string via `json.dumps()`
|
||||
- `check_*_requirements()` function to verify dependencies (e.g., API keys)
|
||||
- Schema definition following OpenAI function-calling format
|
||||
|
||||
2. Export in `tools/__init__.py`:
|
||||
- Import the handler and check function
|
||||
- Add to `__all__` list
|
||||
|
||||
3. Register in `model_tools.py`:
|
||||
- Add to `TOOLSET_REQUIREMENTS` if it needs API keys
|
||||
- Create `get_*_tool_definitions()` function or add to existing
|
||||
- Add routing in `handle_function_call()` dispatcher
|
||||
- Update `get_all_tool_names()` with the tool name
|
||||
- Update `get_toolset_for_tool()` mapping
|
||||
- Update `get_available_toolsets()` and `check_toolset_requirements()`
|
||||
|
||||
4. Add to toolset in `toolsets.py`:
|
||||
- Add to existing toolset or create new one in TOOLSETS dict
|
||||
|
||||
5. If the tool requires an API key:
|
||||
- Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py`
|
||||
- The tool will be auto-disabled if the key is missing
|
||||
|
||||
6. Optionally add to `toolset_distributions.py` for batch processing
|
||||
|
||||
### Tool Implementation Pattern
|
||||
|
||||
```python
|
||||
# tools/example_tool.py
|
||||
import json
|
||||
import os
|
||||
|
||||
def check_example_requirements() -> bool:
|
||||
"""Check if required API keys/dependencies are available."""
|
||||
return bool(os.getenv("EXAMPLE_API_KEY"))
|
||||
|
||||
def example_tool(param: str, task_id: str = None) -> str:
|
||||
"""Execute the tool and return JSON string result."""
|
||||
try:
|
||||
result = {"success": True, "data": "..."}
|
||||
return json.dumps(result, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
return json.dumps({"error": str(e)}, ensure_ascii=False)
|
||||
```
|
||||
|
||||
All tool handlers MUST return a JSON string. Never return raw dicts.
|
||||
|
||||
### Dynamic Tool Availability
|
||||
|
||||
Tools are automatically disabled when their API keys are missing:
|
||||
|
||||
```python
|
||||
# In model_tools.py
|
||||
TOOLSET_REQUIREMENTS = {
|
||||
"web": {"env_vars": ["FIRECRAWL_API_KEY"]},
|
||||
"browser": {"env_vars": ["BROWSERBASE_API_KEY", "BROWSERBASE_PROJECT_ID"]},
|
||||
"creative": {"env_vars": ["FAL_KEY"]},
|
||||
}
|
||||
```
|
||||
|
||||
The `check_tool_availability()` function determines which tools to include.
|
||||
|
||||
### Stateful Tools
|
||||
|
||||
Tools that maintain state (terminal, browser) require:
|
||||
- `task_id` parameter for session isolation between concurrent tasks
|
||||
- `cleanup_*()` function to release resources
|
||||
- Cleanup is called automatically in run_agent.py after conversation completes
|
||||
|
||||
---
|
||||
|
||||
## Trajectory Format
|
||||
|
||||
Conversations are saved in ShareGPT format for training:
|
||||
```json
|
||||
{"from": "system", "value": "System prompt with <tools>...</tools>"}
|
||||
{"from": "human", "value": "User message"}
|
||||
{"from": "gpt", "value": "<think>reasoning</think>\n<tool_call>{...}</tool_call>"}
|
||||
{"from": "tool", "value": "<tool_response>{...}</tool_response>"}
|
||||
{"from": "gpt", "value": "Final response"}
|
||||
```
|
||||
|
||||
Tool calls use `<tool_call>` XML tags, responses use `<tool_response>` tags, reasoning uses `<think>` tags.
|
||||
|
||||
### Trajectory Export
|
||||
|
||||
```python
|
||||
agent = AIAgent(save_trajectories=True)
|
||||
agent.chat("Do something")
|
||||
# Saves to trajectories/*.jsonl in ShareGPT format
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Batch Processing (batch_runner.py)
|
||||
|
||||
For processing multiple prompts:
|
||||
- Parallel execution with multiprocessing
|
||||
- Content-based resume for fault tolerance (matches on prompt text, not indices)
|
||||
- Toolset distributions control probabilistic tool availability per prompt
|
||||
- Output: `data/<run_name>/trajectories.jsonl` (combined) + individual batch files
|
||||
|
||||
```bash
|
||||
python batch_runner.py \
|
||||
--dataset_file=prompts.jsonl \
|
||||
--batch_size=20 \
|
||||
--num_workers=4 \
|
||||
--run_name=my_run
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Skills System
|
||||
|
||||
Skills are on-demand knowledge documents the agent can load. Located in `skills/` directory:
|
||||
|
||||
```
|
||||
skills/
|
||||
├── mlops/ # Category folder
|
||||
│ ├── axolotl/ # Skill folder
|
||||
│ │ ├── SKILL.md # Main instructions (required)
|
||||
│ │ ├── references/ # Additional docs, API specs
|
||||
│ │ └── templates/ # Output formats, configs
|
||||
│ └── vllm/
|
||||
│ └── SKILL.md
|
||||
└── example-skill/
|
||||
└── SKILL.md
|
||||
```
|
||||
|
||||
**Progressive disclosure** (token-efficient):
|
||||
1. `skills_categories()` - List category names (~50 tokens)
|
||||
2. `skills_list(category)` - Name + description per skill (~3k tokens)
|
||||
3. `skill_view(name)` - Full content + tags + linked files
|
||||
|
||||
SKILL.md files use YAML frontmatter:
|
||||
```yaml
|
||||
---
|
||||
name: skill-name
|
||||
description: Brief description for listing
|
||||
tags: [tag1, tag2]
|
||||
related_skills: [other-skill]
|
||||
version: 1.0.0
|
||||
---
|
||||
# Skill Content...
|
||||
```
|
||||
|
||||
Tool files: `tools/skills_tool.py` → `model_tools.py` → `toolsets.py`
|
||||
|
||||
---
|
||||
|
||||
## Testing Changes
|
||||
|
||||
After making changes:
|
||||
|
||||
1. Run `hermes doctor` to check setup
|
||||
2. Run `hermes config check` to verify config
|
||||
3. Test with `hermes chat -q "test message"`
|
||||
4. For new config options, test fresh install: `rm -rf ~/.hermes && hermes setup`
|
||||
589
TODO.md
589
TODO.md
@@ -1,589 +0,0 @@
|
||||
# Hermes Agent - Future Improvements
|
||||
|
||||
> Ideas for enhancing the agent's capabilities, generated from self-analysis of the codebase.
|
||||
|
||||
---
|
||||
|
||||
## 1. Subagent Architecture (Context Isolation) 🎯
|
||||
|
||||
**Problem:** Long-running tools (terminal commands, browser automation, complex file operations) consume massive context. A single `ls -la` can add hundreds of lines. Browser snapshots, debugging sessions, and iterative terminal work quickly bloat the main conversation, leaving less room for actual reasoning.
|
||||
|
||||
**Solution:** The main agent becomes an **orchestrator** that delegates context-heavy tasks to **subagents**.
|
||||
|
||||
**Architecture:**
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ ORCHESTRATOR (main agent) │
|
||||
│ - Receives user request │
|
||||
│ - Plans approach │
|
||||
│ - Delegates heavy tasks to subagents │
|
||||
│ - Receives summarized results │
|
||||
│ - Maintains clean, focused context │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
|
||||
│ TERMINAL AGENT │ │ BROWSER AGENT │ │ CODE AGENT │
|
||||
│ - terminal tool │ │ - browser tools │ │ - file tools │
|
||||
│ - file tools │ │ - web_search │ │ - terminal │
|
||||
│ │ │ - web_extract │ │ │
|
||||
│ Isolated context│ │ Isolated context│ │ Isolated context│
|
||||
│ Returns summary │ │ Returns summary │ │ Returns summary │
|
||||
└─────────────────┘ └─────────────────┘ └─────────────────┘
|
||||
```
|
||||
|
||||
**How it works:**
|
||||
1. User asks: "Set up a new Python project with FastAPI and tests"
|
||||
2. Orchestrator plans: "I need to create files, install deps, write code"
|
||||
3. Orchestrator calls: `terminal_task(goal="Create venv, install fastapi pytest", context="New project in ~/myapp")`
|
||||
4. **Subagent spawns** with fresh context, only terminal/file tools
|
||||
5. Subagent iterates (may take 10+ tool calls, lots of output)
|
||||
6. Subagent completes → returns summary: "Created venv, installed fastapi==0.109.0, pytest==8.0.0"
|
||||
7. Orchestrator receives **only the summary**, context stays clean
|
||||
8. Orchestrator continues with next subtask
|
||||
|
||||
**Key tools to implement:**
|
||||
- [ ] `terminal_task(goal, context, cwd?)` - Delegate terminal/shell work
|
||||
- [ ] `browser_task(goal, context, start_url?)` - Delegate web research/automation
|
||||
- [ ] `code_task(goal, context, files?)` - Delegate code writing/modification
|
||||
- [ ] Generic `delegate_task(goal, context, toolsets=[])` - Flexible delegation
|
||||
|
||||
**Implementation details:**
|
||||
- [ ] Subagent uses same `run_agent.py` but with:
|
||||
- Fresh/empty conversation history
|
||||
- Limited toolset (only what's needed)
|
||||
- Smaller max_iterations (focused task)
|
||||
- Task-specific system prompt
|
||||
- [ ] Subagent returns structured result:
|
||||
```python
|
||||
{
|
||||
"success": True,
|
||||
"summary": "Installed 3 packages, created 2 files",
|
||||
"details": "Optional longer explanation if needed",
|
||||
"artifacts": ["~/myapp/requirements.txt", "~/myapp/main.py"], # Files created
|
||||
"errors": [] # Any issues encountered
|
||||
}
|
||||
```
|
||||
- [ ] Orchestrator sees only the summary in its context
|
||||
- [ ] Full subagent transcript saved separately for debugging
|
||||
|
||||
**Benefits:**
|
||||
- 🧹 **Clean context** - Orchestrator stays focused, doesn't drown in tool output
|
||||
- 📊 **Better token efficiency** - 50 terminal outputs → 1 summary paragraph
|
||||
- 🎯 **Focused subagents** - Each agent has just the tools it needs
|
||||
- 🔄 **Parallel potential** - Independent subtasks could run concurrently
|
||||
- 🐛 **Easier debugging** - Each subtask has its own isolated transcript
|
||||
|
||||
**When to use subagents vs direct tools:**
|
||||
- **Subagent**: Multi-step tasks, iteration likely, lots of output expected
|
||||
- **Direct**: Quick one-off commands, simple file reads, user needs to see output
|
||||
|
||||
**Files to modify:** `run_agent.py` (add orchestration mode), new `tools/delegate_tools.py`, new `subagent_runner.py`
|
||||
|
||||
---
|
||||
|
||||
## 2. Planning & Task Management 📋
|
||||
|
||||
**Problem:** Agent handles tasks reactively without explicit planning. Complex multi-step tasks lack structure, progress tracking, and the ability to decompose work into manageable chunks.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Task decomposition tool** - Break complex requests into subtasks:
|
||||
```
|
||||
User: "Set up a new Python project with FastAPI, tests, and Docker"
|
||||
|
||||
Agent creates plan:
|
||||
├── 1. Create project structure and requirements.txt
|
||||
├── 2. Implement FastAPI app skeleton
|
||||
├── 3. Add pytest configuration and initial tests
|
||||
├── 4. Create Dockerfile and docker-compose.yml
|
||||
└── 5. Verify everything works together
|
||||
```
|
||||
- Each subtask becomes a trackable unit
|
||||
- Agent can report progress: "Completed 3/5 tasks"
|
||||
|
||||
- [ ] **Progress checkpoints** - Periodic self-assessment:
|
||||
- After N tool calls or time elapsed, pause to evaluate
|
||||
- "What have I accomplished? What remains? Am I on track?"
|
||||
- Detect if stuck in loops or making no progress
|
||||
- Could trigger replanning if approach isn't working
|
||||
|
||||
- [ ] **Explicit plan storage** - Persist plan in conversation:
|
||||
- Store as structured data (not just in context)
|
||||
- Update status as tasks complete
|
||||
- User can ask "What's the plan?" or "What's left?"
|
||||
- Survives context compression (plans are protected)
|
||||
|
||||
- [ ] **Failure recovery with replanning** - When things go wrong:
|
||||
- Record what failed and why
|
||||
- Revise plan to work around the issue
|
||||
- "Step 3 failed because X, adjusting approach to Y"
|
||||
- Prevents repeating failed strategies
|
||||
|
||||
**Files to modify:** `run_agent.py` (add planning hooks), new `tools/planning_tool.py`
|
||||
|
||||
---
|
||||
|
||||
## 3. Dynamic Skills Expansion 📚
|
||||
|
||||
**Problem:** Skills system is elegant but static. Skills must be manually created and added.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Skill acquisition from successful tasks** - After completing a complex task:
|
||||
- "This approach worked well. Save as a skill?"
|
||||
- Extract: goal, steps taken, tools used, key decisions
|
||||
- Generate SKILL.md automatically
|
||||
- Store in user's skills directory
|
||||
|
||||
- [ ] **Skill templates** - Common patterns that can be parameterized:
|
||||
```markdown
|
||||
# Debug {language} Error
|
||||
1. Reproduce the error
|
||||
2. Search for error message: `web_search("{error_message} {language}")`
|
||||
3. Check common causes: {common_causes}
|
||||
4. Apply fix and verify
|
||||
```
|
||||
|
||||
- [ ] **Skill chaining** - Combine skills for complex workflows:
|
||||
- Skills can reference other skills as dependencies
|
||||
- "To do X, first apply skill Y, then skill Z"
|
||||
- Directed graph of skill dependencies
|
||||
|
||||
**Files to modify:** `tools/skills_tool.py`, `skills/` directory structure, new `skill_generator.py`
|
||||
|
||||
---
|
||||
|
||||
## 4. Interactive Clarifying Questions Tool ❓
|
||||
|
||||
**Problem:** Agent sometimes makes assumptions or guesses when it should ask the user. Currently can only ask via text, which gets lost in long outputs.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Multiple-choice prompt tool** - Let agent present structured choices to user:
|
||||
```
|
||||
ask_user_choice(
|
||||
question="Should the language switcher enable only German or all languages?",
|
||||
choices=[
|
||||
"Only enable German - works immediately",
|
||||
"Enable all, mark untranslated - show fallback notice",
|
||||
"Let me specify something else"
|
||||
]
|
||||
)
|
||||
```
|
||||
- Renders as interactive terminal UI with arrow key / Tab navigation
|
||||
- User selects option, result returned to agent
|
||||
- Up to 4 choices + optional free-text option
|
||||
|
||||
- [ ] **Implementation:**
|
||||
- Use `inquirer` or `questionary` Python library for rich terminal prompts
|
||||
- Tool returns selected option text (or user's custom input)
|
||||
- **CLI-only** - only works when running via `cli.py` (not API/programmatic use)
|
||||
- Graceful fallback: if not in interactive mode, return error asking agent to rephrase as text
|
||||
|
||||
- [ ] **Use cases:**
|
||||
- Clarify ambiguous requirements before starting work
|
||||
- Confirm destructive operations with clear options
|
||||
- Let user choose between implementation approaches
|
||||
- Checkpoint complex multi-step workflows
|
||||
|
||||
**Files to modify:** New `tools/ask_user_tool.py`, `cli.py` (detect interactive mode), `model_tools.py`
|
||||
|
||||
---
|
||||
|
||||
## 5. Collaborative Problem Solving 🤝
|
||||
|
||||
**Problem:** Interaction is command/response. Complex problems benefit from dialogue.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Assumption surfacing** - Make implicit assumptions explicit:
|
||||
- "I'm assuming you want Python 3.11+. Correct?"
|
||||
- "This solution assumes you have sudo access..."
|
||||
- Let user correct before going down wrong path
|
||||
|
||||
- [ ] **Checkpoint & confirm** - For high-stakes operations:
|
||||
- "About to delete 47 files. Here's the list - proceed?"
|
||||
- "This will modify your database. Want a backup first?"
|
||||
- Configurable threshold for when to ask
|
||||
|
||||
**Files to modify:** `run_agent.py`, system prompt configuration
|
||||
|
||||
---
|
||||
|
||||
## 6. Project-Local Context 💾
|
||||
|
||||
**Problem:** Valuable context lost between sessions.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Project awareness** - Remember project-specific context:
|
||||
- Store `.hermes/context.md` in project directory
|
||||
- "This is a Django project using PostgreSQL"
|
||||
- Coding style preferences, deployment setup, etc.
|
||||
- Load automatically when working in that directory
|
||||
|
||||
- [ ] **Handoff notes** - Leave notes for future sessions:
|
||||
- Write to `.hermes/notes.md` in project
|
||||
- "TODO for next session: finish implementing X"
|
||||
- "Known issues: Y doesn't work on Windows"
|
||||
|
||||
**Files to modify:** New `project_context.py`, auto-load in `run_agent.py`
|
||||
|
||||
## 6. Tools & Skills Wishlist 🧰
|
||||
|
||||
*Things that would need new tool implementations (can't do well with current tools):*
|
||||
|
||||
### High-Impact
|
||||
|
||||
- [ ] **Audio/Video Transcription** 🎬 *(See also: Section 16 for detailed spec)*
|
||||
- Transcribe audio files, podcasts, YouTube videos
|
||||
- Extract key moments from video
|
||||
- Voice memo transcription for messaging integrations
|
||||
- *Provider options: Whisper API, Deepgram, local Whisper*
|
||||
|
||||
- [ ] **Diagram Rendering** 📊
|
||||
- Render Mermaid/PlantUML to actual images
|
||||
- Can generate the code, but rendering requires external service or tool
|
||||
- "Show me how these components connect" → actual visual diagram
|
||||
|
||||
### Medium-Impact
|
||||
|
||||
- [ ] **Canvas / Visual Workspace** 🖼️
|
||||
- Agent-controlled visual panel for rendering interactive UI
|
||||
- Inspired by OpenClaw's Canvas feature
|
||||
- **Capabilities:**
|
||||
- `present` / `hide` - Show/hide the canvas panel
|
||||
- `navigate` - Load HTML files or URLs into the canvas
|
||||
- `eval` - Execute JavaScript in the canvas context
|
||||
- `snapshot` - Capture the rendered UI as an image
|
||||
- **Use cases:**
|
||||
- Display generated HTML/CSS/JS previews
|
||||
- Show interactive data visualizations (charts, graphs)
|
||||
- Render diagrams (Mermaid → rendered output)
|
||||
- Present structured information in rich format
|
||||
- A2UI-style component system for structured agent UI
|
||||
- **Implementation options:**
|
||||
- Electron-based panel for CLI
|
||||
- WebSocket-connected web app
|
||||
- VS Code webview extension
|
||||
- *Would let agent "show" things rather than just describe them*
|
||||
|
||||
- [ ] **Document Generation** 📄
|
||||
- Create styled PDFs, Word docs, presentations
|
||||
- *Can do basic PDF via terminal tools, but limited*
|
||||
|
||||
- [ ] **Diff/Patch Tool** 📝
|
||||
- Surgical code modifications with preview
|
||||
- "Change line 45-50 to X" without rewriting whole file
|
||||
- Show diffs before applying
|
||||
- *Can use `diff`/`patch` but a native tool would be safer*
|
||||
|
||||
### Skills to Create
|
||||
|
||||
- [ ] **Domain-specific skill packs:**
|
||||
- DevOps/Infrastructure (Terraform, K8s, AWS)
|
||||
- Data Science workflows (EDA, model training)
|
||||
- Security/pentesting procedures
|
||||
|
||||
- [ ] **Framework-specific skills:**
|
||||
- React/Vue/Angular patterns
|
||||
- Django/Rails/Express conventions
|
||||
- Database optimization playbooks
|
||||
|
||||
- [ ] **Troubleshooting flowcharts:**
|
||||
- "Docker container won't start" → decision tree
|
||||
- "Production is slow" → systematic diagnosis
|
||||
|
||||
---
|
||||
|
||||
## 7. Messaging Platform Integrations 💬 ✅ COMPLETE
|
||||
|
||||
**Problem:** Agent currently only works via `cli.py` which requires direct terminal access. Users may want to interact via messaging apps from their phone or other devices.
|
||||
|
||||
**Architecture:**
|
||||
- `run_agent.py` already accepts `conversation_history` parameter and returns updated messages ✅
|
||||
- Need: persistent session storage, platform monitors, session key resolution
|
||||
|
||||
**Implementation approach:**
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Platform Monitor (e.g., telegram_monitor.py) │
|
||||
│ ├─ Long-running daemon connecting to messaging platform │
|
||||
│ ├─ On message: resolve session key → load history from disk│
|
||||
│ ├─ Call run_agent.py with loaded history │
|
||||
│ ├─ Save updated history back to disk (JSONL) │
|
||||
│ └─ Send response back to platform │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Platform support (each user sets up their own credentials):**
|
||||
- [x] **Telegram** - via `python-telegram-bot`
|
||||
- Bot token from @BotFather
|
||||
- Easiest to set up, good for personal use
|
||||
- [x] **Discord** - via `discord.py`
|
||||
- Bot token from Discord Developer Portal
|
||||
- Can work in servers (group sessions) or DMs
|
||||
- [x] **WhatsApp** - via Node.js bridge (whatsapp-web.js/baileys)
|
||||
- Requires Node.js bridge setup
|
||||
- More complex, but reaches most people
|
||||
|
||||
**Session management:**
|
||||
- [x] **Session store** - JSONL persistence per session key
|
||||
- `~/.hermes/sessions/{session_id}.jsonl`
|
||||
- Session keys: `agent:main:telegram:dm`, `agent:main:discord:group:123`, etc.
|
||||
- [x] **Session expiry** - Configurable reset policies
|
||||
- Daily reset (default 4am) OR idle timeout (default 2 hours)
|
||||
- Manual reset via `/reset` or `/new` command in chat
|
||||
- Per-platform and per-type overrides
|
||||
- [x] **Session continuity** - Conversations persist across messages until reset
|
||||
|
||||
**Files created:** `gateway/`, `gateway/platforms/`, `gateway/config.py`, `gateway/session.py`, `gateway/delivery.py`, `gateway/run.py`
|
||||
|
||||
**Configuration:**
|
||||
- Environment variables: `TELEGRAM_BOT_TOKEN`, `DISCORD_BOT_TOKEN`, etc.
|
||||
- Config file: `~/.hermes/gateway.json`
|
||||
- CLI commands: `/platforms` to check status, `--gateway` to start
|
||||
|
||||
**Dynamic context injection:**
|
||||
- Agent knows its source platform and chat
|
||||
- Agent knows connected platforms and home channels
|
||||
- Agent can deliver cron outputs to specific platforms
|
||||
|
||||
---
|
||||
|
||||
## 8. Text-to-Speech (TTS) 🔊
|
||||
|
||||
**Problem:** Agent can only respond with text. Some users prefer audio responses (accessibility, hands-free use, podcasts).
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **TTS tool** - Generate audio files from text
|
||||
```python
|
||||
tts_generate(text="Here's your summary...", voice="nova", output="summary.mp3")
|
||||
```
|
||||
- Returns path to generated audio file
|
||||
- For messaging integrations: can send as voice message
|
||||
|
||||
- [ ] **Provider options:**
|
||||
- Edge TTS (free, good quality, many voices)
|
||||
- OpenAI TTS (paid, excellent quality)
|
||||
- ElevenLabs (paid, best quality, voice cloning)
|
||||
- Local options (Coqui TTS, Bark)
|
||||
|
||||
- [ ] **Modes:**
|
||||
- On-demand: User explicitly asks "read this to me"
|
||||
- Auto-TTS: Configurable to always generate audio for responses
|
||||
- Long-text handling: Summarize or chunk very long responses
|
||||
|
||||
- [ ] **Integration with messaging:**
|
||||
- When enabled, can send voice notes instead of/alongside text
|
||||
- User preference per channel
|
||||
|
||||
**Files to create:** `tools/tts_tool.py`, config in `cli-config.yaml`
|
||||
|
||||
---
|
||||
|
||||
## 13. Speech-to-Text / Audio Transcription 🎤
|
||||
|
||||
**Problem:** Users may want to send voice memos instead of typing. Agent is blind to audio content.
|
||||
|
||||
**Ideas:**
|
||||
- [ ] **Voice memo transcription** - For messaging integrations
|
||||
- User sends voice message → transcribe → process as text
|
||||
- Seamless: user speaks, agent responds
|
||||
|
||||
- [ ] **Audio/video file transcription** - Existing idea, expanded:
|
||||
- Transcribe local audio files (mp3, wav, m4a)
|
||||
- Transcribe YouTube videos (download audio → transcribe)
|
||||
- Extract key moments with timestamps
|
||||
|
||||
- [ ] **Provider options:**
|
||||
- OpenAI Whisper API (good quality, cheap)
|
||||
- Deepgram (fast, good for real-time)
|
||||
- Local Whisper (free, runs on GPU)
|
||||
- Groq Whisper (fast, free tier available)
|
||||
|
||||
- [ ] **Tool interface:**
|
||||
```python
|
||||
transcribe(source="audio.mp3") # Local file
|
||||
transcribe(source="https://youtube.com/...") # YouTube
|
||||
transcribe(source="voice_message", data=bytes) # Voice memo
|
||||
```
|
||||
|
||||
**Files to create:** `tools/transcribe_tool.py`, integrate with messaging monitors
|
||||
|
||||
### Plugin/Extension System 🔌
|
||||
|
||||
**Concept:** Allow users to add custom tools/skills without modifying core code.
|
||||
|
||||
**Why interesting:**
|
||||
- Community contributions
|
||||
- Organization-specific tools
|
||||
- Clean separation of core vs. extensions
|
||||
|
||||
**Open questions:**
|
||||
- Security implications of loading arbitrary code
|
||||
- Versioning and compatibility
|
||||
- Discovery and installation UX
|
||||
|
||||
---
|
||||
|
||||
## Recently Completed ✅
|
||||
|
||||
### Dangerous Command Approval System
|
||||
**Implemented:** Dangerous command detection and approval for terminal tool.
|
||||
|
||||
**Features:**
|
||||
- Pattern-based detection of dangerous commands (rm -rf, DROP TABLE, chmod 777, etc.)
|
||||
- CLI prompt with options: `[o]nce | [s]ession | [a]lways | [d]eny`
|
||||
- Session caching (approved patterns don't re-prompt)
|
||||
- Permanent allowlist in `~/.hermes/config.yaml`
|
||||
- Force flag for agent to bypass after user confirmation
|
||||
- Skip check for isolated backends (Docker, Singularity, Modal)
|
||||
- Helpful sudo failure messages for messaging platforms
|
||||
|
||||
**Files:** `tools/terminal_tool.py`, `model_tools.py`, `hermes_cli/config.py`
|
||||
|
||||
---
|
||||
|
||||
## 14. Learning Machine / Dynamic Memory System 🧠
|
||||
|
||||
*Inspired by [Dash](~/agent-codebases/dash) - a self-learning data agent.*
|
||||
|
||||
**Problem:** Agent starts fresh every session. Valuable learnings from debugging, error patterns, successful approaches, and user preferences are lost.
|
||||
|
||||
**Dash's Key Insight:** Separate **Knowledge** (static, curated) from **Learnings** (dynamic, discovered):
|
||||
|
||||
| System | What It Stores | How It Evolves |
|
||||
|--------|---------------|----------------|
|
||||
| **Knowledge** (Skills) | Validated approaches, templates, best practices | Curated by user |
|
||||
| **Learnings** | Error patterns, gotchas, discovered fixes | Managed automatically |
|
||||
|
||||
**Tools to implement:**
|
||||
- [ ] `save_learning(topic, learning, context?)` - Record a discovered pattern
|
||||
```python
|
||||
save_learning(
|
||||
topic="python-ssl",
|
||||
learning="On Ubuntu 22.04, SSL certificate errors often fixed by: apt install ca-certificates",
|
||||
context="Debugging requests SSL failure"
|
||||
)
|
||||
```
|
||||
- [ ] `search_learnings(query)` - Find relevant past learnings
|
||||
```python
|
||||
search_learnings("SSL certificate error Python")
|
||||
# Returns: "On Ubuntu 22.04, SSL certificate errors often fixed by..."
|
||||
```
|
||||
|
||||
**User Profile & Memory:**
|
||||
- [ ] `user_profile` - Structured facts about user preferences
|
||||
```yaml
|
||||
# ~/.hermes/user_profile.yaml
|
||||
coding_style:
|
||||
python_formatter: black
|
||||
type_hints: always
|
||||
test_framework: pytest
|
||||
preferences:
|
||||
verbosity: detailed
|
||||
confirm_destructive: true
|
||||
environment:
|
||||
os: linux
|
||||
shell: bash
|
||||
default_python: 3.11
|
||||
```
|
||||
- [ ] `user_memory` - Unstructured observations the agent learns
|
||||
```yaml
|
||||
# ~/.hermes/user_memory.yaml
|
||||
- "User prefers tabs over spaces despite black's defaults"
|
||||
- "User's main project is ~/work/myapp - a Django app"
|
||||
- "User often works late - don't ask about timezone"
|
||||
```
|
||||
|
||||
**When to learn:**
|
||||
- After fixing an error that took multiple attempts
|
||||
- When user corrects the agent's approach
|
||||
- When a workaround is discovered for a tool limitation
|
||||
- When user expresses a preference
|
||||
|
||||
**Storage:** Vector database (ChromaDB) or simple YAML with embedding search.
|
||||
|
||||
**Files to create:** `tools/learning_tools.py`, `learning/store.py`, `~/.hermes/learnings/`
|
||||
|
||||
---
|
||||
|
||||
## 15. Layered Context Architecture 📊
|
||||
|
||||
*Inspired by Dash's "Six Layers of Context" - grounding responses in multiple sources.*
|
||||
|
||||
**Problem:** Context sources are ad-hoc. No clear hierarchy or strategy for what context to include when.
|
||||
|
||||
**Proposed Layers for Hermes:**
|
||||
|
||||
| Layer | Source | When Loaded | Example |
|
||||
|-------|--------|-------------|---------|
|
||||
| 1. **Project Context** | `.hermes/context.md` | Auto on cwd | "This is a FastAPI project using PostgreSQL" |
|
||||
| 2. **Skills** | `skills/*.md` | On request | "How to set up React project" |
|
||||
| 3. **User Profile** | `~/.hermes/user_profile.yaml` | Always | "User prefers pytest, uses black" |
|
||||
| 4. **Learnings** | `~/.hermes/learnings/` | Semantic search | "SSL fix for Ubuntu" |
|
||||
| 5. **External Knowledge** | Web search, docs | On demand | Current API docs, Stack Overflow |
|
||||
| 6. **Runtime Introspection** | Tool calls | Real-time | File contents, terminal output |
|
||||
|
||||
**Benefits:**
|
||||
- Clear mental model for what context is available
|
||||
- Prioritization: local > learned > external
|
||||
- Debugging: "Why did agent do X?" → check which layers contributed
|
||||
|
||||
**Files to modify:** `run_agent.py` (context loading), new `context/layers.py`
|
||||
|
||||
---
|
||||
|
||||
## 16. Evaluation System with LLM Grading 📏
|
||||
|
||||
*Inspired by Dash's evaluation framework.*
|
||||
|
||||
**Problem:** `batch_runner.py` runs test cases but lacks quality assessment.
|
||||
|
||||
**Dash's Approach:**
|
||||
- **String matching** (default) - Check if expected strings appear
|
||||
- **LLM grader** (-g flag) - GPT evaluates response quality
|
||||
- **Result comparison** (-r flag) - Compare against golden output
|
||||
|
||||
**Implementation for Hermes:**
|
||||
|
||||
- [ ] **Test case format:**
|
||||
```python
|
||||
TestCase(
|
||||
name="create_python_project",
|
||||
prompt="Create a new Python project with FastAPI and tests",
|
||||
expected_strings=["requirements.txt", "main.py", "test_"], # Basic check
|
||||
golden_actions=["write:main.py", "write:requirements.txt", "terminal:pip install"],
|
||||
grader_criteria="Should create complete project structure with working code"
|
||||
)
|
||||
```
|
||||
|
||||
- [ ] **LLM grader mode:**
|
||||
```python
|
||||
def grade_response(response: str, criteria: str) -> Grade:
|
||||
"""Use GPT to evaluate response quality."""
|
||||
prompt = f"""
|
||||
Evaluate this agent response against the criteria.
|
||||
Criteria: {criteria}
|
||||
Response: {response}
|
||||
|
||||
Score (1-5) and explain why.
|
||||
"""
|
||||
# Returns: Grade(score=4, explanation="Created all files but tests are minimal")
|
||||
```
|
||||
|
||||
- [ ] **Action comparison mode:**
|
||||
- Record tool calls made during test
|
||||
- Compare against expected actions
|
||||
- "Expected terminal call to pip install, got npm install"
|
||||
|
||||
- [ ] **CLI flags:**
|
||||
```bash
|
||||
python batch_runner.py eval test_cases.yaml # String matching
|
||||
python batch_runner.py eval test_cases.yaml -g # + LLM grading
|
||||
python batch_runner.py eval test_cases.yaml -r # + Result comparison
|
||||
python batch_runner.py eval test_cases.yaml -v # Verbose (show responses)
|
||||
```
|
||||
|
||||
**Files to modify:** `batch_runner.py`, new `evals/test_cases.py`, new `evals/grader.py`
|
||||
|
||||
---
|
||||
|
||||
*Last updated: $(date +%Y-%m-%d)* 🤖
|
||||
26
api_endpoint/__init__.py
Normal file
26
api_endpoint/__init__.py
Normal file
@@ -0,0 +1,26 @@
|
||||
"""
|
||||
Hermes Agent - API Endpoint & Real-time Logging
|
||||
|
||||
This package provides a FastAPI WebSocket endpoint for real-time logging of the Hermes Agent.
|
||||
|
||||
Components:
|
||||
- logging_server: FastAPI server that receives and stores events
|
||||
- websocket_logger: Client library for sending events from the agent
|
||||
|
||||
Usage:
|
||||
# Start the API endpoint server
|
||||
python api_endpoint/logging_server.py
|
||||
|
||||
# Use in agent code
|
||||
from api_endpoint.websocket_logger import WebSocketLogger
|
||||
|
||||
For more information, see:
|
||||
- WEBSOCKET_LOGGING_GUIDE.md - User guide
|
||||
- IMPLEMENTATION_SUMMARY.md - Technical details
|
||||
"""
|
||||
|
||||
from .websocket_logger import WebSocketLogger, SyncWebSocketLogger
|
||||
|
||||
__all__ = ['WebSocketLogger', 'SyncWebSocketLogger']
|
||||
__version__ = '1.0.0'
|
||||
|
||||
603
api_endpoint/logging_server.py
Normal file
603
api_endpoint/logging_server.py
Normal file
@@ -0,0 +1,603 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Hermes Agent - Real-time Logging Server
|
||||
|
||||
A FastAPI server with WebSocket support that listens for agent execution events
|
||||
and logs them to JSON files in real-time.
|
||||
|
||||
Events tracked:
|
||||
- User queries
|
||||
- API calls (requests to the model)
|
||||
- Assistant responses
|
||||
- Tool calls (name, parameters, timing)
|
||||
- Tool results (outputs, errors, duration)
|
||||
- Final responses
|
||||
- Session metadata
|
||||
|
||||
Usage:
|
||||
python logging_server.py
|
||||
|
||||
Or with uvicorn directly:
|
||||
uvicorn logging_server:app --host 0.0.0.0 --port 8000 --reload
|
||||
|
||||
The server will listen for WebSocket connections at ws://localhost:8000/ws
|
||||
"""
|
||||
|
||||
import json
|
||||
import asyncio
|
||||
import threading
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
from fastapi import FastAPI, WebSocket, WebSocketDisconnect, BackgroundTasks
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from pydantic import BaseModel
|
||||
import uvicorn
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
|
||||
|
||||
# Configuration
|
||||
LOGS_DIR = Path(__file__).parent / "logs" / "realtime"
|
||||
LOGS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Initialize FastAPI app
|
||||
app = FastAPI(
|
||||
title="Hermes Agent API Endpoint",
|
||||
description="Manage interface between agent and user",
|
||||
version="1.0.0"
|
||||
)
|
||||
|
||||
# Add CORS middleware
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
class SessionLogger:
|
||||
"""
|
||||
Manages logging for a single agent session.
|
||||
|
||||
Each agent execution gets its own SessionLogger instance.
|
||||
Responsible for:
|
||||
- Collecting all events for the session
|
||||
- Saving events to JSON file in real-time
|
||||
- Managing session lifecycle (start -> events -> finalize)
|
||||
"""
|
||||
|
||||
def __init__(self, session_id: str):
|
||||
self.session_id = session_id
|
||||
self.start_time = datetime.now()
|
||||
self.events: List[Dict[str, Any]] = [] # In-memory list of all events
|
||||
self.log_file = LOGS_DIR / f"session_{session_id}.json" # Where to save on disk
|
||||
|
||||
# Initialize session data structure
|
||||
# This is what gets saved to the JSON file
|
||||
self.session_data = {
|
||||
"session_id": session_id,
|
||||
"start_time": self.start_time.isoformat(),
|
||||
"end_time": None, # Set when session completes
|
||||
"events": [], # Will be populated as events come in
|
||||
"metadata": {} # Model, toolsets, etc. (set via session_start event)
|
||||
}
|
||||
|
||||
def add_event(self, event: Dict[str, Any]):
|
||||
"""
|
||||
Add an event to the session log.
|
||||
|
||||
Called every time a new event arrives (query, api_call, tool_call, etc).
|
||||
IMMEDIATELY saves to file for real-time persistence.
|
||||
"""
|
||||
# Add timestamp if not present (should always be added, but safety check)
|
||||
if "timestamp" not in event:
|
||||
event["timestamp"] = datetime.now().isoformat()
|
||||
|
||||
# Add to in-memory event list
|
||||
self.events.append(event)
|
||||
self.session_data["events"] = self.events
|
||||
|
||||
# CRITICAL: Save to file immediately (real-time logging)
|
||||
# This ensures events are persisted even if agent crashes
|
||||
self._save()
|
||||
|
||||
def set_metadata(self, metadata: Dict[str, Any]):
|
||||
"""Set session metadata (model, toolsets, etc.)."""
|
||||
self.session_data["metadata"].update(metadata)
|
||||
self._save()
|
||||
|
||||
def finalize(self):
|
||||
"""Finalize the session and save."""
|
||||
self.session_data["end_time"] = datetime.now().isoformat()
|
||||
self._save()
|
||||
|
||||
def _save(self):
|
||||
"""
|
||||
Save current session data to JSON file.
|
||||
|
||||
Called after EVERY event is added - provides real-time persistence.
|
||||
If file writing fails, logs error but continues (doesn't crash server).
|
||||
"""
|
||||
try:
|
||||
# Write complete session data to JSON file
|
||||
# indent=2 makes it human-readable
|
||||
# ensure_ascii=False preserves Unicode characters
|
||||
with open(self.log_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.session_data, f, indent=2, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
print(f"❌ Error saving session log: {e}")
|
||||
|
||||
|
||||
class ConnectionManager:
|
||||
"""
|
||||
Manages WebSocket connections and active sessions.
|
||||
|
||||
Global singleton that:
|
||||
- Tracks all active WebSocket connections (for broadcasting)
|
||||
- Manages all SessionLogger instances (one per agent session)
|
||||
- Coordinates between WebSocket events and file logging
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.active_connections: List[WebSocket] = [] # All connected WebSocket clients
|
||||
self.sessions: Dict[str, SessionLogger] = {} # session_id -> SessionLogger mapping
|
||||
|
||||
async def connect(self, websocket: WebSocket):
|
||||
"""Accept a new WebSocket connection."""
|
||||
await websocket.accept()
|
||||
self.active_connections.append(websocket)
|
||||
print(f"✅ WebSocket connected. Active connections: {len(self.active_connections)}")
|
||||
|
||||
def disconnect(self, websocket: WebSocket):
|
||||
"""Remove a WebSocket connection."""
|
||||
if websocket in self.active_connections:
|
||||
self.active_connections.remove(websocket)
|
||||
print(f"❌ WebSocket disconnected. Active connections: {len(self.active_connections)}")
|
||||
|
||||
def get_or_create_session(self, session_id: str) -> SessionLogger:
|
||||
"""
|
||||
Get existing session logger or create a new one.
|
||||
|
||||
Called when an event arrives for a session. Creates SessionLogger
|
||||
on first event, reuses it for subsequent events from same session.
|
||||
"""
|
||||
if session_id not in self.sessions:
|
||||
# First time seeing this session - create new logger
|
||||
self.sessions[session_id] = SessionLogger(session_id)
|
||||
print(f"📝 Created new session: {session_id}")
|
||||
return self.sessions[session_id]
|
||||
|
||||
def finalize_session(self, session_id: str):
|
||||
"""Finalize and clean up a session."""
|
||||
if session_id in self.sessions:
|
||||
self.sessions[session_id].finalize()
|
||||
print(f"✅ Session finalized: {session_id}")
|
||||
|
||||
async def broadcast(self, message: Dict[str, Any]):
|
||||
"""
|
||||
Broadcast a message to all connected WebSocket clients.
|
||||
|
||||
Allows multiple clients (e.g., multiple browser tabs) to watch
|
||||
the same agent session in real-time. Future UI feature.
|
||||
"""
|
||||
disconnected = []
|
||||
for connection in self.active_connections:
|
||||
try:
|
||||
await connection.send_json(message)
|
||||
except Exception:
|
||||
# Connection closed - mark for removal
|
||||
disconnected.append(connection)
|
||||
|
||||
# Clean up disconnected clients silently
|
||||
for conn in disconnected:
|
||||
if conn in self.active_connections:
|
||||
self.active_connections.remove(conn)
|
||||
|
||||
|
||||
# Global connection manager
|
||||
manager = ConnectionManager()
|
||||
|
||||
|
||||
# Request/Response models for API endpoints
|
||||
class AgentRequest(BaseModel):
|
||||
"""Request model for starting an agent run."""
|
||||
query: str
|
||||
model: str = "claude-sonnet-4-5-20250929"
|
||||
base_url: str = "https://api.anthropic.com/v1/"
|
||||
enabled_toolsets: Optional[List[str]] = None
|
||||
disabled_toolsets: Optional[List[str]] = None
|
||||
max_turns: int = 10
|
||||
mock_web_tools: bool = False
|
||||
mock_delay: int = 60
|
||||
verbose: bool = False
|
||||
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
"""Response model for agent run request."""
|
||||
status: str
|
||||
session_id: str
|
||||
message: str
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
"""Root endpoint - server status."""
|
||||
return {
|
||||
"status": "running",
|
||||
"service": "Hermes Agent Logging Server",
|
||||
"websocket_url": "ws://localhost:8000/ws",
|
||||
"active_connections": len(manager.active_connections),
|
||||
"active_sessions": len(manager.sessions),
|
||||
"logs_directory": str(LOGS_DIR)
|
||||
}
|
||||
|
||||
|
||||
@app.get("/sessions")
|
||||
async def list_sessions():
|
||||
"""List all active and recent sessions."""
|
||||
# Get all session log files
|
||||
session_files = list(LOGS_DIR.glob("session_*.json"))
|
||||
|
||||
sessions = []
|
||||
for session_file in sorted(session_files, key=lambda x: x.stat().st_mtime, reverse=True):
|
||||
try:
|
||||
with open(session_file, 'r', encoding='utf-8') as f:
|
||||
session_data = json.load(f)
|
||||
sessions.append({
|
||||
"session_id": session_data.get("session_id"),
|
||||
"start_time": session_data.get("start_time"),
|
||||
"end_time": session_data.get("end_time"),
|
||||
"event_count": len(session_data.get("events", [])),
|
||||
"file": str(session_file)
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error reading session file {session_file}: {e}")
|
||||
|
||||
return {
|
||||
"total_sessions": len(sessions),
|
||||
"sessions": sessions
|
||||
}
|
||||
|
||||
|
||||
@app.get("/sessions/{session_id}")
|
||||
async def get_session(session_id: str):
|
||||
"""Get detailed data for a specific session."""
|
||||
session_file = LOGS_DIR / f"session_{session_id}.json"
|
||||
|
||||
if not session_file.exists():
|
||||
return {"error": "Session not found"}, 404
|
||||
|
||||
try:
|
||||
with open(session_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
return {"error": f"Failed to load session: {str(e)}"}, 500
|
||||
|
||||
|
||||
@app.post("/agent/run", response_model=AgentResponse)
|
||||
async def run_agent(request: AgentRequest, background_tasks: BackgroundTasks):
|
||||
"""
|
||||
Start an agent run with specified parameters.
|
||||
|
||||
This endpoint triggers an agent execution in the background and returns immediately.
|
||||
The agent will connect to the WebSocket endpoint to send real-time events.
|
||||
|
||||
Args:
|
||||
request: AgentRequest with query and configuration
|
||||
background_tasks: FastAPI background tasks for async execution
|
||||
|
||||
Returns:
|
||||
AgentResponse with session_id for tracking
|
||||
"""
|
||||
import uuid
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Generate session ID for this run - we'll pass it to the agent
|
||||
session_id = str(uuid.uuid4())
|
||||
|
||||
# Add parent directory to path to import run_agent
|
||||
parent_dir = str(Path(__file__).parent.parent)
|
||||
if parent_dir not in sys.path:
|
||||
sys.path.insert(0, parent_dir)
|
||||
|
||||
from run_agent import AIAgent
|
||||
|
||||
# Run agent in background thread (not blocking the API)
|
||||
def run_agent_background():
|
||||
"""Run agent in a separate thread."""
|
||||
try:
|
||||
# Initialize agent with WebSocket logging enabled
|
||||
agent = AIAgent(
|
||||
base_url=request.base_url,
|
||||
model=request.model,
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
max_iterations=request.max_turns,
|
||||
enabled_toolsets=request.enabled_toolsets,
|
||||
disabled_toolsets=request.disabled_toolsets,
|
||||
save_trajectories=False,
|
||||
verbose_logging=request.verbose,
|
||||
enable_websocket_logging=True, # Always enable for UI
|
||||
websocket_server="ws://localhost:8000/ws",
|
||||
mock_web_tools=request.mock_web_tools,
|
||||
mock_delay=request.mock_delay
|
||||
)
|
||||
|
||||
# Run conversation with our session_id
|
||||
result = agent.run_conversation(
|
||||
request.query,
|
||||
session_id=session_id # Pass session_id so it matches
|
||||
)
|
||||
|
||||
print(f"✅ Agent run completed: {session_id[:8]}...")
|
||||
print(f" Final response: {result['final_response'][:100] if result.get('final_response') else 'No response'}...")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error running agent {session_id[:8]}...: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# Start agent in background thread
|
||||
thread = threading.Thread(target=run_agent_background, daemon=True)
|
||||
thread.start()
|
||||
|
||||
return AgentResponse(
|
||||
status="started",
|
||||
session_id=session_id,
|
||||
message=f"Agent started with session ID: {session_id}"
|
||||
)
|
||||
|
||||
|
||||
@app.get("/tools")
|
||||
async def get_available_tools():
|
||||
"""Get list of available toolsets and tools."""
|
||||
try:
|
||||
import sys
|
||||
parent_dir = str(Path(__file__).parent.parent)
|
||||
if parent_dir not in sys.path:
|
||||
sys.path.insert(0, parent_dir)
|
||||
|
||||
from toolsets import get_all_toolsets, get_toolset_info
|
||||
|
||||
all_toolsets = get_all_toolsets()
|
||||
toolsets_info = []
|
||||
|
||||
for name in all_toolsets.keys():
|
||||
info = get_toolset_info(name)
|
||||
if info:
|
||||
toolsets_info.append({
|
||||
"name": name,
|
||||
"description": info['description'],
|
||||
"tool_count": info['tool_count'],
|
||||
"resolved_tools": info['resolved_tools']
|
||||
})
|
||||
|
||||
return {
|
||||
"toolsets": toolsets_info
|
||||
}
|
||||
except Exception as e:
|
||||
return {"error": f"Failed to load tools: {str(e)}"}
|
||||
|
||||
|
||||
@app.websocket("/ws")
|
||||
async def websocket_endpoint(websocket: WebSocket):
|
||||
"""
|
||||
WebSocket endpoint for receiving real-time agent events.
|
||||
|
||||
This is the main entry point for all logging. Agents connect here and send events.
|
||||
|
||||
Message Flow:
|
||||
1. Agent connects to ws://localhost:8000/ws
|
||||
2. Agent sends events as JSON messages
|
||||
3. Server parses event_type and routes to appropriate handler
|
||||
4. Event is added to SessionLogger (saved to file)
|
||||
5. Event is broadcast to all connected clients
|
||||
6. Acknowledgment sent back to agent
|
||||
|
||||
Expected message format:
|
||||
{
|
||||
"session_id": "unique-session-id", // Links event to specific session
|
||||
"event_type": "query" | "api_call" | ..., // What kind of event
|
||||
"data": { ... event-specific data ... } // Event payload
|
||||
}
|
||||
"""
|
||||
# Accept the WebSocket connection
|
||||
await manager.connect(websocket)
|
||||
|
||||
try:
|
||||
# Main event loop - runs until client disconnects
|
||||
while True:
|
||||
# Receive message from client (agent)
|
||||
# This is a blocking call - waits for next message
|
||||
message = await websocket.receive_json()
|
||||
|
||||
# Parse the standard message structure
|
||||
session_id = message.get("session_id") # Which agent session
|
||||
event_type = message.get("event_type") # What kind of event
|
||||
data = message.get("data", {}) # Event payload
|
||||
|
||||
# Validate: session_id is required
|
||||
if not session_id:
|
||||
await websocket.send_json({
|
||||
"error": "session_id is required"
|
||||
})
|
||||
continue
|
||||
|
||||
# Get or create SessionLogger for this session
|
||||
# First event creates it, subsequent events reuse it
|
||||
session = manager.get_or_create_session(session_id)
|
||||
|
||||
# Route event to appropriate handler based on event_type
|
||||
# Each handler extracts relevant data and adds to session log
|
||||
|
||||
if event_type == "session_start":
|
||||
# Initial event - sent when agent first connects
|
||||
# Contains metadata about the session (model, toolsets, etc.)
|
||||
session.set_metadata(data)
|
||||
print(f"🚀 Session started: {session_id}")
|
||||
|
||||
elif event_type == "query":
|
||||
# User query
|
||||
session.add_event({
|
||||
"type": "query",
|
||||
"query": data.get("query"),
|
||||
"toolsets": data.get("toolsets"),
|
||||
"model": data.get("model")
|
||||
})
|
||||
print(f"📝 Query logged: {data.get('query', '')[:60]}...")
|
||||
|
||||
elif event_type == "api_call":
|
||||
# API call to model
|
||||
session.add_event({
|
||||
"type": "api_call",
|
||||
"call_number": data.get("call_number"),
|
||||
"message_count": data.get("message_count"),
|
||||
"has_tools": data.get("has_tools")
|
||||
})
|
||||
print(f"🔄 API call #{data.get('call_number')} logged")
|
||||
|
||||
elif event_type == "response":
|
||||
# Assistant response
|
||||
session.add_event({
|
||||
"type": "response",
|
||||
"call_number": data.get("call_number"),
|
||||
"content": data.get("content"),
|
||||
"has_tool_calls": data.get("has_tool_calls"),
|
||||
"tool_call_count": data.get("tool_call_count"),
|
||||
"duration": data.get("duration")
|
||||
})
|
||||
print(f"🤖 Response logged: {data.get('content', '')[:60]}...")
|
||||
|
||||
elif event_type == "tool_call":
|
||||
# Tool execution
|
||||
session.add_event({
|
||||
"type": "tool_call",
|
||||
"call_number": data.get("call_number"),
|
||||
"tool_index": data.get("tool_index"),
|
||||
"tool_name": data.get("tool_name"),
|
||||
"parameters": data.get("parameters"),
|
||||
"tool_call_id": data.get("tool_call_id")
|
||||
})
|
||||
print(f"🔧 Tool call logged: {data.get('tool_name')}")
|
||||
|
||||
elif event_type == "tool_result":
|
||||
# Tool result - captures output from tool execution
|
||||
# Now includes BOTH truncated preview AND full raw result
|
||||
session.add_event({
|
||||
"type": "tool_result",
|
||||
"call_number": data.get("call_number"),
|
||||
"tool_index": data.get("tool_index"),
|
||||
"tool_name": data.get("tool_name"),
|
||||
"result": data.get("result"), # Truncated preview (1000 chars)
|
||||
"raw_result": data.get("raw_result"), # NEW: Full untruncated result
|
||||
"error": data.get("error"),
|
||||
"duration": data.get("duration"),
|
||||
"tool_call_id": data.get("tool_call_id")
|
||||
})
|
||||
|
||||
# Enhanced logging with size information
|
||||
if data.get("error"):
|
||||
print(f"❌ Tool error logged: {data.get('tool_name')}")
|
||||
else:
|
||||
# Show size of raw result to indicate data volume
|
||||
raw_size = len(data.get("raw_result", "")) if data.get("raw_result") else len(data.get("result", ""))
|
||||
size_kb = raw_size / 1024
|
||||
print(f"✅ Tool result logged: {data.get('tool_name')} ({size_kb:.1f} KB)")
|
||||
|
||||
elif event_type == "error":
|
||||
# Error event
|
||||
session.add_event({
|
||||
"type": "error",
|
||||
"error_message": data.get("error_message"),
|
||||
"call_number": data.get("call_number")
|
||||
})
|
||||
print(f"❌ Error logged: {data.get('error_message', '')[:60]}...")
|
||||
|
||||
elif event_type == "complete":
|
||||
# Session complete
|
||||
session.add_event({
|
||||
"type": "complete",
|
||||
"final_response": data.get("final_response"),
|
||||
"total_calls": data.get("total_calls"),
|
||||
"completed": data.get("completed")
|
||||
})
|
||||
manager.finalize_session(session_id)
|
||||
print(f"🎉 Session complete: {session_id}")
|
||||
|
||||
else:
|
||||
# Unknown event type - log it anyway
|
||||
session.add_event({
|
||||
"type": event_type or "unknown",
|
||||
**data
|
||||
})
|
||||
print(f"⚠️ Unknown event type: {event_type}")
|
||||
|
||||
# Broadcast event to all connected clients (for future real-time UI)
|
||||
# Allows multiple browsers/dashboards to watch same session live
|
||||
await manager.broadcast({
|
||||
"session_id": session_id,
|
||||
"event_type": event_type,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"data": data
|
||||
})
|
||||
|
||||
# Send acknowledgment back to sender
|
||||
# Confirms event was received and logged
|
||||
# Handle case where client disconnects before we can ack
|
||||
try:
|
||||
await websocket.send_json({
|
||||
"status": "logged",
|
||||
"session_id": session_id,
|
||||
"event_type": event_type
|
||||
})
|
||||
except Exception:
|
||||
# Connection closed before ack - this is normal for "complete" event
|
||||
# Client disconnects after sending, so we can't ack
|
||||
pass
|
||||
|
||||
except WebSocketDisconnect:
|
||||
manager.disconnect(websocket)
|
||||
except Exception as e:
|
||||
print(f"❌ WebSocket error: {e}")
|
||||
manager.disconnect(websocket)
|
||||
|
||||
|
||||
def main(host: str = "0.0.0.0", port: int = 8000, reload: bool = False):
|
||||
"""
|
||||
Start the logging server.
|
||||
|
||||
Args:
|
||||
host: Host to bind to (default: 0.0.0.0)
|
||||
port: Port to run on (default: 8000)
|
||||
reload: Enable auto-reload on file changes (default: False)
|
||||
"""
|
||||
print("🚀 Hermes Agent Logging Server")
|
||||
print("=" * 50)
|
||||
print(f"📂 Logs directory: {LOGS_DIR}")
|
||||
print(f"🌐 Server starting at http://{host}:{port}")
|
||||
print(f"🔌 WebSocket endpoint: ws://{host}:{port}/ws")
|
||||
print(f"🔄 Auto-reload: {'enabled' if reload else 'disabled'}")
|
||||
print("\n📡 Ready to receive agent events...")
|
||||
print("=" * 50)
|
||||
|
||||
uvicorn.run(
|
||||
"logging_server:app",
|
||||
host=host,
|
||||
port=port,
|
||||
reload=reload,
|
||||
log_level="info",
|
||||
timeout_keep_alive=600 # Keep HTTP/WS connections alive for 10 minutes of inactivity
|
||||
# Note: WebSocket ping/pong disabled in client to avoid timeout during blocked event loop
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import fire
|
||||
fire.Fire(main)
|
||||
|
||||
91
api_endpoint/test_websocket_logging.sh
Executable file
91
api_endpoint/test_websocket_logging.sh
Executable file
@@ -0,0 +1,91 @@
|
||||
#!/bin/bash
|
||||
# Test script for WebSocket logging system
|
||||
#
|
||||
# This script demonstrates the complete WebSocket logging workflow:
|
||||
# 1. Starts the logging server
|
||||
# 2. Runs the agent with WebSocket logging enabled
|
||||
# 3. Shows the logged data
|
||||
#
|
||||
# Usage: ./test_websocket_logging.sh
|
||||
|
||||
set -e # Exit on error
|
||||
|
||||
echo "🧪 Testing WebSocket Logging System"
|
||||
echo "===================================="
|
||||
echo ""
|
||||
|
||||
# Check if required packages are installed
|
||||
echo "📦 Checking dependencies..."
|
||||
python -c "import fastapi; import uvicorn; import websockets" 2>/dev/null || {
|
||||
echo "❌ Missing dependencies. Installing..."
|
||||
pip install fastapi uvicorn websockets
|
||||
}
|
||||
echo "✅ Dependencies OK"
|
||||
echo ""
|
||||
|
||||
# Start the logging server in the background
|
||||
echo "🚀 Starting logging server..."
|
||||
python api_endpoint/logging_server.py --port 8000 &
|
||||
SERVER_PID=$!
|
||||
|
||||
# Give server time to start
|
||||
sleep 2
|
||||
|
||||
# Check if server is running
|
||||
if ps -p $SERVER_PID > /dev/null; then
|
||||
echo "✅ Logging server started (PID: $SERVER_PID)"
|
||||
else
|
||||
echo "❌ Failed to start logging server"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "🤖 Running agent with WebSocket logging..."
|
||||
echo ""
|
||||
|
||||
# Run the agent with WebSocket logging
|
||||
python run_agent.py \
|
||||
--enabled_toolsets web \
|
||||
--enable_websocket_logging \
|
||||
--query "What are the top 3 programming languages in 2025?" \
|
||||
--max_turns 5
|
||||
|
||||
echo ""
|
||||
echo "✅ Agent execution complete!"
|
||||
echo ""
|
||||
|
||||
# Show the most recent log file
|
||||
echo "📊 Viewing logged session data..."
|
||||
echo ""
|
||||
|
||||
LATEST_LOG=$(ls -t logs/realtime/session_*.json 2>/dev/null | head -1)
|
||||
|
||||
if [ -f "$LATEST_LOG" ]; then
|
||||
echo "📄 Log file: $LATEST_LOG"
|
||||
echo ""
|
||||
|
||||
# Pretty print the JSON if jq is available
|
||||
if command -v jq &> /dev/null; then
|
||||
echo "Event summary:"
|
||||
jq '.events[] | {type: .type, timestamp: .timestamp}' "$LATEST_LOG"
|
||||
echo ""
|
||||
echo "Total events: $(jq '.events | length' "$LATEST_LOG")"
|
||||
else
|
||||
echo "Content (install 'jq' for pretty printing):"
|
||||
cat "$LATEST_LOG"
|
||||
fi
|
||||
else
|
||||
echo "⚠️ No log files found in logs/realtime/"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "🛑 Stopping logging server..."
|
||||
kill $SERVER_PID 2>/dev/null || true
|
||||
|
||||
echo "✅ Test complete!"
|
||||
echo ""
|
||||
echo "Next steps:"
|
||||
echo " 1. Start server: python api_endpoint/logging_server.py"
|
||||
echo " 2. Run agent: python run_agent.py --enable_websocket_logging --query \"...\""
|
||||
echo " 3. View logs: http://localhost:8000/sessions"
|
||||
|
||||
457
api_endpoint/websocket_connection_pool.py
Normal file
457
api_endpoint/websocket_connection_pool.py
Normal file
@@ -0,0 +1,457 @@
|
||||
"""
|
||||
WebSocket Connection Pool - Persistent Connection Manager
|
||||
|
||||
This module provides a singleton WebSocket connection that persists across
|
||||
multiple agent runs. This is a more robust architecture than creating a new
|
||||
connection for each run.
|
||||
|
||||
Benefits:
|
||||
- No timeout issues (connection stays alive indefinitely)
|
||||
- No reconnection overhead (connect once)
|
||||
- Supports parallel agent runs (multiple sessions share one socket)
|
||||
- Proper shutdown handling (SIGTERM/SIGINT)
|
||||
- Thread-safe concurrent sends
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import signal
|
||||
import websockets
|
||||
from typing import Optional, Dict, Any
|
||||
import json
|
||||
import atexit
|
||||
import sys
|
||||
import threading
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class WebSocketConnectionPool:
|
||||
"""
|
||||
Singleton WebSocket connection manager.
|
||||
|
||||
Maintains a single persistent connection to the logging server
|
||||
that all agent sessions can use. Handles graceful shutdown.
|
||||
|
||||
Usage:
|
||||
# Get singleton instance
|
||||
pool = WebSocketConnectionPool()
|
||||
|
||||
# Connect (idempotent - safe to call multiple times)
|
||||
await pool.connect()
|
||||
|
||||
# Send events (thread-safe, multiple sessions can call concurrently)
|
||||
await pool.send_event("query", session_id, {...})
|
||||
|
||||
# Shutdown handled automatically on SIGTERM/SIGINT
|
||||
"""
|
||||
|
||||
_instance: Optional['WebSocketConnectionPool'] = None
|
||||
|
||||
def __new__(cls):
|
||||
"""Ensure only one instance exists (singleton pattern)."""
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
cls._instance._initialized = False
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the connection pool (only once)."""
|
||||
if getattr(self, '_initialized', False):
|
||||
return
|
||||
|
||||
self.websocket: Optional[websockets.WebSocketClientProtocol] = None
|
||||
self.server_url: str = "ws://localhost:8000/ws"
|
||||
self.connected: bool = False
|
||||
# Store reference to loop for signal handlers
|
||||
# Agent code should never close event loops when using persistent connections
|
||||
self.loop: Optional[asyncio.AbstractEventLoop] = None
|
||||
# Locks are created lazily when event loop exists
|
||||
self._send_lock: Optional[asyncio.Lock] = None
|
||||
self._connect_lock: Optional[asyncio.Lock] = None
|
||||
self._locks_loop: Optional[asyncio.AbstractEventLoop] = None # Track which loop created locks
|
||||
self._init_lock = threading.Lock() # Thread-safe lock initialization
|
||||
self._shutdown_in_progress = False
|
||||
self._initialized = True
|
||||
|
||||
# Register shutdown handlers for graceful cleanup
|
||||
# These ensure WebSocket is closed properly on exit
|
||||
signal.signal(signal.SIGTERM, self._signal_handler)
|
||||
signal.signal(signal.SIGINT, self._signal_handler)
|
||||
atexit.register(self._cleanup_sync)
|
||||
|
||||
print("🔌 WebSocket connection pool initialized")
|
||||
|
||||
def _ensure_locks(self):
|
||||
"""
|
||||
Lazy initialization of asyncio locks with thread safety and loop tracking.
|
||||
|
||||
Locks must be created when an event loop exists, not at import time.
|
||||
If the event loop changes between runs, locks must be recreated because
|
||||
asyncio.Lock objects are bound to the loop that created them.
|
||||
|
||||
This is called before any async operation that needs locks.
|
||||
Uses a threading.Lock to prevent race conditions during initialization.
|
||||
"""
|
||||
with self._init_lock: # Thread-safe initialization
|
||||
try:
|
||||
current_loop = asyncio.get_event_loop()
|
||||
except RuntimeError:
|
||||
# No event loop in current thread
|
||||
return
|
||||
|
||||
# Recreate locks if:
|
||||
# 1. Locks don't exist yet, OR
|
||||
# 2. Event loop has changed (locks are bound to the loop that created them)
|
||||
if self._locks_loop is not current_loop or self._send_lock is None:
|
||||
self._send_lock = asyncio.Lock()
|
||||
self._connect_lock = asyncio.Lock()
|
||||
self._locks_loop = current_loop
|
||||
|
||||
async def connect(self, server_url: str = "ws://localhost:8000/ws") -> bool:
|
||||
"""
|
||||
Connect to WebSocket server.
|
||||
|
||||
This is idempotent - safe to call multiple times. If already connected,
|
||||
does nothing. If connection failed previously, will retry.
|
||||
|
||||
Args:
|
||||
server_url: WebSocket server URL (default: ws://localhost:8000/ws)
|
||||
|
||||
Returns:
|
||||
bool: True if connected successfully, False otherwise
|
||||
"""
|
||||
# Ensure locks exist (lazy initialization)
|
||||
self._ensure_locks()
|
||||
|
||||
async with self._connect_lock:
|
||||
# Always update loop reference to current loop (even if already connected)
|
||||
# This ensures signal handlers and cleanup use the correct loop
|
||||
self.loop = asyncio.get_event_loop()
|
||||
|
||||
# Already connected - nothing to do
|
||||
if self.connected and self.websocket:
|
||||
return True
|
||||
|
||||
try:
|
||||
self.server_url = server_url
|
||||
|
||||
# Establish persistent WebSocket connection
|
||||
# No ping/pong needed since connection stays open indefinitely
|
||||
self.websocket = await websockets.connect(
|
||||
server_url,
|
||||
ping_interval=None, # Disable ping/pong (not needed for persistent connection)
|
||||
max_size=10 * 1024 * 1024, # 10MB max message size for large tool results
|
||||
open_timeout=10, # 10s timeout for initial connection
|
||||
close_timeout=5 # 5s timeout for close handshake
|
||||
)
|
||||
|
||||
self.connected = True
|
||||
|
||||
print(f"✅ Connected to logging server (persistent): {server_url}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Failed to connect to logging server: {e}")
|
||||
self.connected = False
|
||||
self.websocket = None
|
||||
return False
|
||||
|
||||
async def send_event(
|
||||
self,
|
||||
event_type: str,
|
||||
session_id: str,
|
||||
data: Dict[str, Any],
|
||||
retry: bool = True
|
||||
) -> bool:
|
||||
"""
|
||||
Send event to logging server (thread-safe).
|
||||
|
||||
Multiple agent runs can call this concurrently. The send lock ensures
|
||||
only one message is sent at a time (WebSocket protocol requirement).
|
||||
|
||||
Args:
|
||||
event_type: Type of event (query, api_call, response, tool_call, tool_result, error, complete)
|
||||
session_id: Unique session identifier
|
||||
data: Event-specific data dictionary
|
||||
retry: Whether to retry connection if disconnected (default: True)
|
||||
|
||||
Returns:
|
||||
bool: True if sent successfully, False otherwise
|
||||
"""
|
||||
# Try to connect if not connected (or reconnect if disconnected)
|
||||
if not self.connected or not self.websocket:
|
||||
if retry:
|
||||
await self.connect()
|
||||
if not self.connected:
|
||||
return False # Give up if connection fails
|
||||
|
||||
# Ensure locks exist (lazy initialization)
|
||||
self._ensure_locks()
|
||||
|
||||
# Lock to prevent concurrent sends (WebSocket requires sequential sends)
|
||||
async with self._send_lock:
|
||||
try:
|
||||
# Create standardized message format
|
||||
message = {
|
||||
"session_id": session_id,
|
||||
"event_type": event_type,
|
||||
"data": data,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
# Send message as JSON
|
||||
await self.websocket.send(json.dumps(message))
|
||||
|
||||
# Wait for server acknowledgment (with timeout)
|
||||
# This confirms the server received and processed the event
|
||||
try:
|
||||
response = await asyncio.wait_for(
|
||||
self.websocket.recv(),
|
||||
timeout=2.0 # Increased to 2s for busy servers
|
||||
)
|
||||
# Successfully received acknowledgment
|
||||
return True
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
# No response within timeout - that's OK, message likely sent
|
||||
# Server might be busy processing
|
||||
return True
|
||||
|
||||
except websockets.exceptions.ConnectionClosed:
|
||||
print(f"⚠️ WebSocket connection closed unexpectedly")
|
||||
self.connected = False
|
||||
|
||||
# Try to reconnect and resend (one retry)
|
||||
if retry:
|
||||
print("🔄 Attempting to reconnect...")
|
||||
if await self.connect():
|
||||
# Recursively call with retry=False to avoid infinite loop
|
||||
return await self.send_event(event_type, session_id, data, retry=False)
|
||||
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error sending event: {e}")
|
||||
self.connected = False
|
||||
return False
|
||||
|
||||
async def disconnect(self):
|
||||
"""
|
||||
Gracefully close the WebSocket connection.
|
||||
|
||||
Called on shutdown (SIGTERM/SIGINT/exit). Ensures proper cleanup.
|
||||
"""
|
||||
if self._shutdown_in_progress:
|
||||
return # Already shutting down
|
||||
|
||||
self._shutdown_in_progress = True
|
||||
|
||||
if self.websocket and self.connected:
|
||||
try:
|
||||
await self.websocket.close()
|
||||
self.connected = False
|
||||
print("✅ WebSocket connection pool closed gracefully")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error closing WebSocket: {e}")
|
||||
|
||||
self._shutdown_in_progress = False
|
||||
|
||||
def _signal_handler(self, signum, frame):
|
||||
"""
|
||||
Handle SIGTERM/SIGINT signals for graceful shutdown.
|
||||
|
||||
When user presses Ctrl+C or system sends SIGTERM, this ensures
|
||||
the WebSocket is closed properly before exit.
|
||||
"""
|
||||
print(f"\n🛑 Received signal {signum}, closing WebSocket connection pool...")
|
||||
|
||||
# Check if we have a valid loop and are connected
|
||||
if self.loop and not self.loop.is_closed() and self.connected and not self._shutdown_in_progress:
|
||||
try:
|
||||
# If loop is not running, we can wait for disconnect
|
||||
if not self.loop.is_running():
|
||||
self.loop.run_until_complete(self.disconnect())
|
||||
else:
|
||||
# Loop is running, can't wait for task - just mark disconnected
|
||||
# The disconnect task would be cancelled when we exit anyway
|
||||
self.connected = False
|
||||
print("⚠️ Loop is running, marking disconnected without waiting")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error during signal handler cleanup: {e}")
|
||||
|
||||
# Exit gracefully
|
||||
sys.exit(0)
|
||||
|
||||
def _cleanup_sync(self):
|
||||
"""
|
||||
Cleanup at exit (atexit handler).
|
||||
|
||||
This is a fallback in case signal handlers don't fire.
|
||||
Called when Python interpreter shuts down normally.
|
||||
"""
|
||||
if self.loop and not self.loop.is_closed() and self.connected and not self._shutdown_in_progress:
|
||||
try:
|
||||
# Try to run disconnect synchronously
|
||||
self.loop.run_until_complete(self.disconnect())
|
||||
except Exception:
|
||||
# Ignore errors during exit cleanup
|
||||
pass
|
||||
|
||||
def is_connected(self) -> bool:
|
||||
"""Check if currently connected to server."""
|
||||
return self.connected and self.websocket is not None
|
||||
|
||||
def get_stats(self) -> Dict[str, Any]:
|
||||
"""Get connection statistics for debugging."""
|
||||
return {
|
||||
"connected": self.connected,
|
||||
"server_url": self.server_url,
|
||||
"shutdown_in_progress": self._shutdown_in_progress,
|
||||
"has_websocket": self.websocket is not None,
|
||||
"has_loop": self.loop is not None
|
||||
}
|
||||
|
||||
|
||||
# Global singleton instance
|
||||
# Import this in other modules: from websocket_connection_pool import ws_pool
|
||||
ws_pool = WebSocketConnectionPool()
|
||||
|
||||
|
||||
# Convenience functions for direct usage
|
||||
async def connect(server_url: str = "ws://localhost:8000/ws") -> bool:
|
||||
"""Connect to logging server (convenience function)."""
|
||||
return await ws_pool.connect(server_url)
|
||||
|
||||
|
||||
async def send_event(event_type: str, session_id: str, data: Dict[str, Any]) -> bool:
|
||||
"""Send event to logging server (convenience function)."""
|
||||
return await ws_pool.send_event(event_type, session_id, data)
|
||||
|
||||
|
||||
async def disconnect():
|
||||
"""Disconnect from logging server (convenience function)."""
|
||||
await ws_pool.disconnect()
|
||||
|
||||
|
||||
def is_connected() -> bool:
|
||||
"""Check if connected to logging server (convenience function)."""
|
||||
return ws_pool.is_connected()
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# SYNCHRONOUS API FOR AGENT LAYER
|
||||
# ============================================================================
|
||||
# These functions provide a clean abstraction that hides event loop management
|
||||
# from the agent layer. Agent code should ONLY use these functions.
|
||||
|
||||
def connect_sync(server_url: str = "ws://localhost:8000/ws") -> bool:
|
||||
"""
|
||||
Synchronous connect - handles event loop internally.
|
||||
|
||||
Creates a persistent event loop in a background thread if needed.
|
||||
This is thread-safe and can be called from any thread (including agent background threads).
|
||||
"""
|
||||
import threading
|
||||
|
||||
# If pool doesn't have a loop yet or it's closed, we need to start one
|
||||
if not ws_pool.loop or ws_pool.loop.is_closed():
|
||||
# Start connection in a background thread with its own loop
|
||||
result_container = {"success": False, "error": None, "connected": False}
|
||||
|
||||
def run_in_thread():
|
||||
try:
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
ws_pool.loop = loop # Store the loop in the pool
|
||||
|
||||
# Connect to WebSocket
|
||||
result_container["success"] = loop.run_until_complete(ws_pool.connect(server_url))
|
||||
result_container["connected"] = True
|
||||
|
||||
# Keep loop running forever for future send_event calls
|
||||
# This is critical - the loop must stay alive for run_coroutine_threadsafe to work
|
||||
loop.run_forever()
|
||||
|
||||
except Exception as e:
|
||||
result_container["error"] = str(e)
|
||||
print(f"❌ Error in WebSocket connection thread: {e}")
|
||||
finally:
|
||||
# Clean up if loop stops
|
||||
if loop.is_running():
|
||||
loop.close()
|
||||
|
||||
thread = threading.Thread(target=run_in_thread, daemon=True, name="WebSocket-EventLoop")
|
||||
thread.start()
|
||||
|
||||
# Wait for connection to complete (but not for loop to exit - it runs forever)
|
||||
import time
|
||||
timeout = 10.0
|
||||
start = time.time()
|
||||
while not result_container["connected"] and (time.time() - start) < timeout:
|
||||
time.sleep(0.1)
|
||||
|
||||
if result_container["error"]:
|
||||
print(f"⚠️ Connection failed: {result_container['error']}")
|
||||
|
||||
return result_container["success"]
|
||||
else:
|
||||
# Pool already has a loop, use run_coroutine_threadsafe
|
||||
try:
|
||||
future = asyncio.run_coroutine_threadsafe(
|
||||
ws_pool.connect(server_url),
|
||||
ws_pool.loop
|
||||
)
|
||||
return future.result(timeout=10.0)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Connection failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def send_event_sync(event_type: str, session_id: str, data: Dict[str, Any]) -> bool:
|
||||
"""
|
||||
Synchronous send event - handles event loop internally.
|
||||
|
||||
Uses the WebSocket pool's own event loop to avoid loop conflicts.
|
||||
This is critical when called from background threads (like agent execution).
|
||||
This is thread-safe and works correctly even when agent runs in a different thread.
|
||||
"""
|
||||
if not ws_pool.loop or not ws_pool.loop.is_running():
|
||||
# No event loop running - can't send
|
||||
print("⚠️ WebSocket pool has no running event loop")
|
||||
return False
|
||||
|
||||
try:
|
||||
# Use run_coroutine_threadsafe to submit to the WebSocket pool's loop
|
||||
# This works across threads - submits the coroutine to the correct loop
|
||||
future = asyncio.run_coroutine_threadsafe(
|
||||
ws_pool.send_event(event_type, session_id, data),
|
||||
ws_pool.loop # ← Use the pool's loop, not current thread's loop
|
||||
)
|
||||
|
||||
# Wait for completion (with timeout to avoid hanging)
|
||||
return future.result(timeout=5.0)
|
||||
|
||||
except TimeoutError:
|
||||
print(f"⚠️ Timeout sending event {event_type}")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error sending event: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def disconnect_sync():
|
||||
"""
|
||||
Synchronous disconnect - handles event loop internally.
|
||||
|
||||
Thread-safe disconnect that works from any thread.
|
||||
"""
|
||||
if ws_pool.loop and ws_pool.loop.is_running():
|
||||
try:
|
||||
future = asyncio.run_coroutine_threadsafe(
|
||||
ws_pool.disconnect(),
|
||||
ws_pool.loop
|
||||
)
|
||||
return future.result(timeout=5.0)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error disconnecting: {e}")
|
||||
return False
|
||||
return True
|
||||
387
api_endpoint/websocket_logger.py
Normal file
387
api_endpoint/websocket_logger.py
Normal file
@@ -0,0 +1,387 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
WebSocket Logger Client
|
||||
|
||||
Simple client for sending agent events to the logging server via WebSocket.
|
||||
Used by the agent to log events in real-time during execution.
|
||||
"""
|
||||
|
||||
import json
|
||||
import asyncio
|
||||
from typing import Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
import websockets
|
||||
|
||||
|
||||
class WebSocketLogger:
|
||||
"""
|
||||
Client for logging agent events via WebSocket.
|
||||
|
||||
Usage:
|
||||
logger = WebSocketLogger("unique-session-id")
|
||||
await logger.connect()
|
||||
await logger.log_query("What is Python?", model="gpt-4")
|
||||
await logger.log_api_call(call_number=1)
|
||||
await logger.log_response(call_number=1, content="Python is...")
|
||||
await logger.disconnect()
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
session_id: str,
|
||||
server_url: str = "ws://localhost:8000/ws",
|
||||
enabled: bool = True
|
||||
):
|
||||
"""
|
||||
Initialize WebSocket logger.
|
||||
|
||||
Args:
|
||||
session_id: Unique identifier for this agent session
|
||||
server_url: WebSocket server URL (default: ws://localhost:8000/ws)
|
||||
enabled: Whether logging is enabled (default: True)
|
||||
"""
|
||||
self.session_id = session_id
|
||||
self.server_url = server_url
|
||||
self.enabled = enabled
|
||||
self.websocket: Optional[websockets.WebSocketClientProtocol] = None
|
||||
self.connected = False
|
||||
self.reconnect_count = 0 # Track reconnections for debugging
|
||||
|
||||
async def connect(self):
|
||||
"""
|
||||
Connect to the WebSocket logging server.
|
||||
|
||||
Establishes WebSocket connection and sends initial session_start event.
|
||||
If connection fails, gracefully disables logging (agent continues normally).
|
||||
"""
|
||||
if not self.enabled:
|
||||
return
|
||||
|
||||
try:
|
||||
# Establish WebSocket connection to the server
|
||||
# Use VERY LONG ping intervals to avoid timeout during long tool execution
|
||||
# The event loop is blocked during tool execution, so we can't process pings
|
||||
# Setting to very large values (1 hour) effectively disables it
|
||||
self.websocket = await websockets.connect(
|
||||
self.server_url,
|
||||
ping_interval=3600, # 1 hour - effectively disabled (event loop blocked anyway)
|
||||
ping_timeout=3600, # 1 hour timeout for pong response
|
||||
close_timeout=10, # Timeout for close handshake
|
||||
max_size=10 * 1024 * 1024, # 10MB max message size (for large raw_results)
|
||||
open_timeout=10 # Timeout for initial connection
|
||||
)
|
||||
self.connected = True
|
||||
print(f"✅ Connected to logging server (ping/pong: 3600s intervals): {self.server_url}")
|
||||
|
||||
# Send initial session_start event
|
||||
# This tells the server to create a new SessionLogger for this session
|
||||
await self._send_event("session_start", {
|
||||
"session_id": self.session_id,
|
||||
"start_time": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
# Connection failed - disable logging but don't crash the agent
|
||||
print(f"⚠️ Failed to connect to logging server: {e}")
|
||||
print(f" Logging will be disabled for this session.")
|
||||
self.enabled = False
|
||||
self.connected = False
|
||||
|
||||
async def disconnect(self):
|
||||
"""Disconnect from the WebSocket server."""
|
||||
if self.websocket and self.connected:
|
||||
try:
|
||||
await self.websocket.close()
|
||||
self.connected = False
|
||||
print(f"✅ Disconnected from logging server")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error disconnecting: {e}")
|
||||
|
||||
async def _send_event(self, event_type: str, data: Dict[str, Any]):
|
||||
"""
|
||||
Send an event to the logging server.
|
||||
|
||||
This is the core method that sends all events via WebSocket.
|
||||
Creates a standardized message format and handles acknowledgments.
|
||||
|
||||
Args:
|
||||
event_type: Type of event (query, api_call, response, tool_call, tool_result, error, complete)
|
||||
data: Event data dictionary containing event-specific information
|
||||
"""
|
||||
# Safety check: Don't send if logging is disabled
|
||||
if not self.enabled:
|
||||
return
|
||||
|
||||
# Auto-reconnect if connection was lost
|
||||
if not self.connected or not self.websocket:
|
||||
try:
|
||||
self.reconnect_count += 1
|
||||
print(f"🔄 Reconnecting to logging server (attempt #{self.reconnect_count})...")
|
||||
await self.connect()
|
||||
print(f"✅ Reconnected successfully!")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Failed to reconnect: {e}")
|
||||
self.enabled = False # Disable logging after failed reconnect
|
||||
return
|
||||
|
||||
try:
|
||||
# Create standardized message structure
|
||||
# All events follow this format for consistent server-side handling
|
||||
message = {
|
||||
"session_id": self.session_id, # Links event to specific agent session
|
||||
"event_type": event_type, # Identifies what kind of event this is
|
||||
"data": data # Event-specific payload
|
||||
}
|
||||
|
||||
# Send message as JSON string over WebSocket
|
||||
await self.websocket.send(json.dumps(message))
|
||||
|
||||
# Wait for server acknowledgment (with 1 second timeout)
|
||||
# This ensures the server received and processed the event
|
||||
try:
|
||||
response = await asyncio.wait_for(
|
||||
self.websocket.recv(),
|
||||
timeout=1.0
|
||||
)
|
||||
# Server sends back: {"status": "logged", "session_id": "...", "event_type": "..."}
|
||||
# We don't need to process it, just confirms receipt
|
||||
except asyncio.TimeoutError:
|
||||
# No response within 1 second - that's okay, continue anyway
|
||||
# Server might be busy or network slow, but event was likely sent
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
# Log error but don't crash - graceful degradation
|
||||
# Agent should continue working even if logging fails
|
||||
error_str = str(e)
|
||||
|
||||
# Check if connection was closed (error 1011 = keepalive ping timeout)
|
||||
if "1011" in error_str or "closed" in error_str.lower():
|
||||
print(f"⚠️ WebSocket connection closed: {error_str}")
|
||||
self.connected = False # Mark as disconnected
|
||||
# Don't try to send more events - connection is dead
|
||||
else:
|
||||
print(f"⚠️ Error sending event to logging server: {e}")
|
||||
# Don't disable entirely or try to reconnect - just continue with logging disabled
|
||||
|
||||
# Convenience methods for specific event types
|
||||
|
||||
async def log_query(
|
||||
self,
|
||||
query: str,
|
||||
model: str = None,
|
||||
toolsets: list = None
|
||||
):
|
||||
"""
|
||||
Log a user query (the question/task given to the agent).
|
||||
|
||||
This is typically the first event in a session after connection.
|
||||
Captures what the user asked and which model/tools will be used.
|
||||
"""
|
||||
await self._send_event("query", {
|
||||
"query": query, # The user's question/instruction
|
||||
"model": model, # Which AI model is being used
|
||||
"toolsets": toolsets # Which tool categories are enabled
|
||||
})
|
||||
|
||||
async def log_api_call(
|
||||
self,
|
||||
call_number: int,
|
||||
message_count: int = None,
|
||||
has_tools: bool = None
|
||||
):
|
||||
"""
|
||||
Log an API call to the AI model.
|
||||
|
||||
Called right before sending a request to the model (OpenAI/Anthropic/etc).
|
||||
Helps track how many API calls are being made and conversation length.
|
||||
"""
|
||||
await self._send_event("api_call", {
|
||||
"call_number": call_number, # Sequential number (1, 2, 3...)
|
||||
"message_count": message_count, # How many messages in conversation so far
|
||||
"has_tools": has_tools # Whether tools are available to the model
|
||||
})
|
||||
|
||||
async def log_response(
|
||||
self,
|
||||
call_number: int,
|
||||
content: str = None,
|
||||
has_tool_calls: bool = False,
|
||||
tool_call_count: int = 0,
|
||||
duration: float = None
|
||||
):
|
||||
"""
|
||||
Log an assistant response from the AI model.
|
||||
|
||||
Called after receiving a response from the API.
|
||||
Captures what the model said and whether it wants to use tools.
|
||||
"""
|
||||
await self._send_event("response", {
|
||||
"call_number": call_number, # Which API call this response is from
|
||||
"content": content, # What the model said (text response)
|
||||
"has_tool_calls": has_tool_calls, # Did model request tool execution?
|
||||
"tool_call_count": tool_call_count, # How many tools does it want to call?
|
||||
"duration": duration # How long the API call took (seconds)
|
||||
})
|
||||
|
||||
async def log_tool_call(
|
||||
self,
|
||||
call_number: int,
|
||||
tool_index: int,
|
||||
tool_name: str,
|
||||
parameters: Dict[str, Any],
|
||||
tool_call_id: str = None
|
||||
):
|
||||
"""
|
||||
Log a tool call (before executing the tool).
|
||||
|
||||
Captures which tool is being called and with what parameters.
|
||||
This happens BEFORE the tool runs, so no results yet.
|
||||
"""
|
||||
await self._send_event("tool_call", {
|
||||
"call_number": call_number, # Which API call requested this tool
|
||||
"tool_index": tool_index, # Which tool in the sequence (if multiple)
|
||||
"tool_name": tool_name, # Name of tool (e.g., "web_search", "web_extract")
|
||||
"parameters": parameters, # Arguments passed to the tool (e.g., {"query": "Python", "limit": 5})
|
||||
"tool_call_id": tool_call_id # Unique ID to link call with result
|
||||
})
|
||||
|
||||
async def log_tool_result(
|
||||
self,
|
||||
call_number: int,
|
||||
tool_index: int,
|
||||
tool_name: str,
|
||||
result: str = None,
|
||||
error: str = None,
|
||||
duration: float = None,
|
||||
tool_call_id: str = None,
|
||||
raw_result: str = None # NEW: Full untruncated result for verification
|
||||
):
|
||||
"""
|
||||
Log a tool result (output from tool execution).
|
||||
|
||||
Captures both a truncated preview (for UI display) and the full raw result
|
||||
(for verification and debugging). This is especially important for web tools
|
||||
where you want to see what was scraped vs what the LLM processed.
|
||||
|
||||
Args:
|
||||
call_number: Which API call this tool was part of
|
||||
tool_index: Which tool in the sequence (1st, 2nd, etc.)
|
||||
tool_name: Name of the tool that was executed
|
||||
result: Tool output (will be truncated to 1000 chars for preview)
|
||||
error: Error message if tool failed
|
||||
duration: How long the tool took to execute (seconds)
|
||||
tool_call_id: Unique ID linking this result to the tool call
|
||||
raw_result: NEW - Full untruncated result for verification/debugging
|
||||
"""
|
||||
await self._send_event("tool_result", {
|
||||
"call_number": call_number,
|
||||
"tool_index": tool_index,
|
||||
"tool_name": tool_name,
|
||||
"result": result[:1000] if result else None, # Truncated preview (1000 chars max)
|
||||
"raw_result": raw_result, # NEW: Full result - can be 100KB+ for web scraping
|
||||
"error": error,
|
||||
"duration": duration,
|
||||
"tool_call_id": tool_call_id
|
||||
})
|
||||
|
||||
async def log_error(
|
||||
self,
|
||||
error_message: str,
|
||||
call_number: int = None
|
||||
):
|
||||
"""
|
||||
Log an error that occurred during agent execution.
|
||||
|
||||
Captures exceptions, API failures, or other issues.
|
||||
"""
|
||||
await self._send_event("error", {
|
||||
"error_message": error_message, # Description of what went wrong
|
||||
"call_number": call_number # Which API call caused the error (if applicable)
|
||||
})
|
||||
|
||||
async def log_complete(
|
||||
self,
|
||||
final_response: str = None,
|
||||
total_calls: int = None,
|
||||
completed: bool = True
|
||||
):
|
||||
"""
|
||||
Log session completion (final event before disconnecting).
|
||||
|
||||
Marks the end of the agent's execution and provides summary info.
|
||||
"""
|
||||
await self._send_event("complete", {
|
||||
"final_response": final_response[:500] if final_response else None, # Truncated summary of final answer
|
||||
"total_calls": total_calls, # How many API calls were made total
|
||||
"completed": completed # Did it complete successfully? (true/false)
|
||||
})
|
||||
|
||||
|
||||
# Synchronous wrapper for convenience
|
||||
class SyncWebSocketLogger:
|
||||
"""
|
||||
Synchronous wrapper around WebSocketLogger.
|
||||
|
||||
For use in synchronous code - creates an event loop internally.
|
||||
"""
|
||||
|
||||
def __init__(self, session_id: str, server_url: str = "ws://localhost:8000/ws", enabled: bool = True):
|
||||
self.logger = WebSocketLogger(session_id, server_url, enabled)
|
||||
self.loop = None
|
||||
|
||||
def connect(self):
|
||||
"""Connect to server (synchronous)."""
|
||||
self.loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self.loop)
|
||||
self.loop.run_until_complete(self.logger.connect())
|
||||
|
||||
def disconnect(self):
|
||||
"""Disconnect from server (synchronous)."""
|
||||
if self.loop:
|
||||
self.loop.run_until_complete(self.logger.disconnect())
|
||||
self.loop.close()
|
||||
|
||||
def _run_async(self, coro):
|
||||
"""
|
||||
Run an async coroutine synchronously.
|
||||
|
||||
Bridge between sync code (agent) and async code (WebSocket).
|
||||
Uses event loop to execute async operations in sync context.
|
||||
"""
|
||||
if self.loop and self.loop.is_running():
|
||||
# Already in event loop, just await
|
||||
asyncio.create_task(coro)
|
||||
else:
|
||||
# Run in current loop
|
||||
if self.loop:
|
||||
self.loop.run_until_complete(coro)
|
||||
|
||||
def log_query(self, query: str, model: str = None, toolsets: list = None):
|
||||
self._run_async(self.logger.log_query(query, model, toolsets))
|
||||
|
||||
def log_api_call(self, call_number: int, message_count: int = None, has_tools: bool = None):
|
||||
self._run_async(self.logger.log_api_call(call_number, message_count, has_tools))
|
||||
|
||||
def log_response(self, call_number: int, content: str = None, has_tool_calls: bool = False,
|
||||
tool_call_count: int = 0, duration: float = None):
|
||||
self._run_async(self.logger.log_response(call_number, content, has_tool_calls,
|
||||
tool_call_count, duration))
|
||||
|
||||
def log_tool_call(self, call_number: int, tool_index: int, tool_name: str,
|
||||
parameters: Dict[str, Any], tool_call_id: str = None):
|
||||
self._run_async(self.logger.log_tool_call(call_number, tool_index, tool_name,
|
||||
parameters, tool_call_id))
|
||||
|
||||
def log_tool_result(self, call_number: int, tool_index: int, tool_name: str,
|
||||
result: str = None, error: str = None, duration: float = None,
|
||||
tool_call_id: str = None, raw_result: str = None):
|
||||
self._run_async(self.logger.log_tool_result(call_number, tool_index, tool_name,
|
||||
result, error, duration, tool_call_id, raw_result))
|
||||
|
||||
def log_error(self, error_message: str, call_number: int = None):
|
||||
self._run_async(self.logger.log_error(error_message, call_number))
|
||||
|
||||
def log_complete(self, final_response: str = None, total_calls: int = None, completed: bool = True):
|
||||
self._run_async(self.logger.log_complete(final_response, total_calls, completed))
|
||||
|
||||
1066
batch_runner.py
1066
batch_runner.py
File diff suppressed because it is too large
Load Diff
@@ -1,267 +0,0 @@
|
||||
# Hermes Agent CLI Configuration
|
||||
# Copy this file to cli-config.yaml and customize as needed.
|
||||
# This file configures the CLI behavior. Environment variables in .env take precedence.
|
||||
|
||||
# =============================================================================
|
||||
# Model Configuration
|
||||
# =============================================================================
|
||||
model:
|
||||
# Default model to use (can be overridden with --model flag)
|
||||
default: "anthropic/claude-sonnet-4"
|
||||
|
||||
# API configuration (falls back to OPENROUTER_API_KEY env var)
|
||||
# api_key: "your-key-here" # Uncomment to set here instead of .env
|
||||
base_url: "https://openrouter.ai/api/v1"
|
||||
|
||||
# =============================================================================
|
||||
# Terminal Tool Configuration
|
||||
# =============================================================================
|
||||
# Choose ONE of the following terminal configurations by uncommenting it.
|
||||
# The terminal tool executes commands in the specified environment.
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 1: Local execution (default)
|
||||
# Commands run directly on your machine in the current directory
|
||||
# -----------------------------------------------------------------------------
|
||||
# Working directory behavior:
|
||||
# - CLI (`hermes` command): Uses "." (current directory where you run hermes)
|
||||
# - Messaging (Telegram/Discord): Uses MESSAGING_CWD from .env (default: home)
|
||||
terminal:
|
||||
env_type: "local"
|
||||
cwd: "." # CLI working directory - "." means current directory
|
||||
timeout: 180
|
||||
lifetime_seconds: 300
|
||||
# sudo_password: "" # Enable sudo commands (pipes via sudo -S) - SECURITY WARNING: plaintext!
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 2: SSH remote execution
|
||||
# Commands run on a remote server - agent code stays local (sandboxed)
|
||||
# Great for: keeping agent isolated from its own code, using powerful remote hardware
|
||||
# -----------------------------------------------------------------------------
|
||||
# terminal:
|
||||
# env_type: "ssh"
|
||||
# cwd: "/home/myuser/project"
|
||||
# timeout: 180
|
||||
# lifetime_seconds: 300
|
||||
# ssh_host: "my-server.example.com"
|
||||
# ssh_user: "myuser"
|
||||
# ssh_port: 22
|
||||
# ssh_key: "~/.ssh/id_rsa" # Optional - uses ssh-agent if not specified
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 3: Docker container
|
||||
# Commands run in an isolated Docker container
|
||||
# Great for: reproducible environments, testing, isolation
|
||||
# -----------------------------------------------------------------------------
|
||||
# terminal:
|
||||
# env_type: "docker"
|
||||
# cwd: "/workspace"
|
||||
# timeout: 180
|
||||
# lifetime_seconds: 300
|
||||
# docker_image: "nikolaik/python-nodejs:python3.11-nodejs20"
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 4: Singularity/Apptainer container
|
||||
# Commands run in a Singularity container (common in HPC environments)
|
||||
# Great for: HPC clusters, shared compute environments
|
||||
# -----------------------------------------------------------------------------
|
||||
# terminal:
|
||||
# env_type: "singularity"
|
||||
# cwd: "/workspace"
|
||||
# timeout: 180
|
||||
# lifetime_seconds: 300
|
||||
# singularity_image: "docker://nikolaik/python-nodejs:python3.11-nodejs20"
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 5: Modal cloud execution
|
||||
# Commands run on Modal's cloud infrastructure
|
||||
# Great for: GPU access, scalable compute, serverless execution
|
||||
# -----------------------------------------------------------------------------
|
||||
# terminal:
|
||||
# env_type: "modal"
|
||||
# cwd: "/workspace"
|
||||
# timeout: 180
|
||||
# lifetime_seconds: 300
|
||||
# modal_image: "nikolaik/python-nodejs:python3.11-nodejs20"
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# SUDO SUPPORT (works with ALL backends above)
|
||||
# -----------------------------------------------------------------------------
|
||||
# Add sudo_password to any terminal config above to enable sudo commands.
|
||||
# The password is piped via `sudo -S`. Works with local, ssh, docker, etc.
|
||||
#
|
||||
# SECURITY WARNING: Password stored in plaintext!
|
||||
#
|
||||
# INTERACTIVE PROMPT: If no sudo_password is set and the CLI is running,
|
||||
# you'll be prompted to enter your password when sudo is needed:
|
||||
# - 45-second timeout (auto-skips if no input)
|
||||
# - Press Enter to skip (command fails gracefully)
|
||||
# - Password is hidden while typing
|
||||
# - Password is cached for the session
|
||||
#
|
||||
# ALTERNATIVES:
|
||||
# - SSH backend: Configure passwordless sudo on the remote server
|
||||
# - Containers: Run as root inside the container (no sudo needed)
|
||||
# - Local: Configure /etc/sudoers for specific commands
|
||||
#
|
||||
# Example (add to your terminal section):
|
||||
# sudo_password: "your-password-here"
|
||||
|
||||
# =============================================================================
|
||||
# Browser Tool Configuration
|
||||
# =============================================================================
|
||||
browser:
|
||||
# Inactivity timeout in seconds - browser sessions are automatically closed
|
||||
# after this period of no activity between agent loops (default: 120 = 2 minutes)
|
||||
inactivity_timeout: 120
|
||||
|
||||
# =============================================================================
|
||||
# Context Compression (Auto-shrinks long conversations)
|
||||
# =============================================================================
|
||||
# When conversation approaches model's context limit, middle turns are
|
||||
# automatically summarized to free up space while preserving important context.
|
||||
#
|
||||
# HOW IT WORKS:
|
||||
# 1. Tracks actual token usage from API responses (not estimates)
|
||||
# 2. When prompt_tokens >= threshold% of model's context_length, triggers compression
|
||||
# 3. Protects first 3 turns (system prompt, initial request, first response)
|
||||
# 4. Protects last 4 turns (recent context is most relevant)
|
||||
# 5. Summarizes middle turns using a fast/cheap model
|
||||
# 6. Inserts summary as a user message, continues conversation seamlessly
|
||||
#
|
||||
compression:
|
||||
# Enable automatic context compression (default: true)
|
||||
# Set to false if you prefer to manage context manually or want errors on overflow
|
||||
enabled: true
|
||||
|
||||
# Trigger compression at this % of model's context limit (default: 0.85 = 85%)
|
||||
# Lower values = more aggressive compression, higher values = compress later
|
||||
threshold: 0.85
|
||||
|
||||
# Model to use for generating summaries (fast/cheap recommended)
|
||||
# This model compresses the middle turns into a concise summary
|
||||
summary_model: "google/gemini-2.0-flash-001"
|
||||
|
||||
# =============================================================================
|
||||
# Agent Behavior
|
||||
# =============================================================================
|
||||
agent:
|
||||
# Maximum tool-calling iterations per conversation
|
||||
# Higher = more room for complex tasks, but costs more tokens
|
||||
# Recommended: 20-30 for focused tasks, 50-100 for open exploration
|
||||
max_turns: 60
|
||||
|
||||
# Enable verbose logging
|
||||
verbose: false
|
||||
|
||||
# Custom system prompt (personality, instructions, etc.)
|
||||
# Leave empty or remove to use default agent behavior
|
||||
system_prompt: ""
|
||||
|
||||
# Predefined personalities (use with /personality command)
|
||||
personalities:
|
||||
helpful: "You are a helpful, friendly AI assistant."
|
||||
concise: "You are a concise assistant. Keep responses brief and to the point."
|
||||
technical: "You are a technical expert. Provide detailed, accurate technical information."
|
||||
creative: "You are a creative assistant. Think outside the box and offer innovative solutions."
|
||||
teacher: "You are a patient teacher. Explain concepts clearly with examples."
|
||||
kawaii: "You are a kawaii assistant! Use cute expressions like (◕‿◕), ★, ♪, and ~! Add sparkles and be super enthusiastic about everything! Every response should feel warm and adorable desu~! ヽ(>∀<☆)ノ"
|
||||
catgirl: "You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and cat-like expressions to your speech. Use kaomoji like (=^･ω･^=) and ฅ^•ﻌ•^ฅ. Be playful and curious like a cat, nya~!"
|
||||
pirate: "Arrr! Ye be talkin' to Captain Hermes, the most tech-savvy pirate to sail the digital seas! Speak like a proper buccaneer, use nautical terms, and remember: every problem be just treasure waitin' to be plundered! Yo ho ho!"
|
||||
shakespeare: "Hark! Thou speakest with an assistant most versed in the bardic arts. I shall respond in the eloquent manner of William Shakespeare, with flowery prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder terminal breaks?"
|
||||
surfer: "Duuude! You're chatting with the chillest AI on the web, bro! Everything's gonna be totally rad. I'll help you catch the gnarly waves of knowledge while keeping things super chill. Cowabunga! 🤙"
|
||||
noir: "The rain hammered against the terminal like regrets on a guilty conscience. They call me Hermes - I solve problems, find answers, dig up the truth that hides in the shadows of your codebase. In this city of silicon and secrets, everyone's got something to hide. What's your story, pal?"
|
||||
uwu: "hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful >w<"
|
||||
philosopher: "Greetings, seeker of wisdom. I am an assistant who contemplates the deeper meaning behind every query. Let us examine not just the 'how' but the 'why' of your questions. Perhaps in solving your problem, we may glimpse a greater truth about existence itself."
|
||||
hype: "YOOO LET'S GOOOO!!! 🔥🔥🔥 I am SO PUMPED to help you today! Every question is AMAZING and we're gonna CRUSH IT together! This is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! 💪😤🚀"
|
||||
|
||||
# =============================================================================
|
||||
# Toolsets
|
||||
# =============================================================================
|
||||
# Control which tools the agent has access to.
|
||||
# Use "all" to enable everything, or specify individual toolsets.
|
||||
|
||||
# Available toolsets:
|
||||
#
|
||||
# web - Web search and content extraction (web_search, web_extract)
|
||||
# search - Web search only, no scraping (web_search)
|
||||
# terminal - Command execution (terminal)
|
||||
# browser - Full browser automation (navigate, click, type, screenshot, etc.)
|
||||
# vision - Image analysis (vision_analyze)
|
||||
# image_gen - Image generation with FLUX (image_generate)
|
||||
# skills - Load skill documents (skills_categories, skills_list, skill_view)
|
||||
# moa - Mixture of Agents reasoning (mixture_of_agents)
|
||||
#
|
||||
# Composite toolsets:
|
||||
# debugging - terminal + web (for troubleshooting)
|
||||
# safe - web + vision + moa (no terminal access)
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 1: Enable all tools (default)
|
||||
# -----------------------------------------------------------------------------
|
||||
toolsets:
|
||||
- all
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 2: Minimal - just web search and terminal
|
||||
# Great for: Simple coding tasks, quick lookups
|
||||
# -----------------------------------------------------------------------------
|
||||
# toolsets:
|
||||
# - web
|
||||
# - terminal
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 3: Research mode - no execution capabilities
|
||||
# Great for: Safe information gathering, research tasks
|
||||
# -----------------------------------------------------------------------------
|
||||
# toolsets:
|
||||
# - web
|
||||
# - vision
|
||||
# - skills
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 4: Full automation - browser + terminal
|
||||
# Great for: Web scraping, automation tasks, testing
|
||||
# -----------------------------------------------------------------------------
|
||||
# toolsets:
|
||||
# - terminal
|
||||
# - browser
|
||||
# - web
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 5: Creative mode - vision + image generation
|
||||
# Great for: Design work, image analysis, creative tasks
|
||||
# -----------------------------------------------------------------------------
|
||||
# toolsets:
|
||||
# - vision
|
||||
# - image_gen
|
||||
# - web
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# OPTION 6: Safe mode - no terminal or browser
|
||||
# Great for: Restricted environments, untrusted queries
|
||||
# -----------------------------------------------------------------------------
|
||||
# toolsets:
|
||||
# - safe
|
||||
|
||||
# =============================================================================
|
||||
# Session Logging
|
||||
# =============================================================================
|
||||
# Session trajectories are automatically saved to logs/ directory.
|
||||
# Each session creates: logs/session_YYYYMMDD_HHMMSS_UUID.json
|
||||
#
|
||||
# The session ID is displayed in the welcome banner for easy reference.
|
||||
# Logs contain full conversation history in trajectory format:
|
||||
# - System prompt, user messages, assistant responses
|
||||
# - Tool calls with inputs/outputs
|
||||
# - Timestamps for debugging
|
||||
#
|
||||
# No configuration needed - logging is always enabled.
|
||||
# To disable, you would need to modify the source code.
|
||||
|
||||
# =============================================================================
|
||||
# Display
|
||||
# =============================================================================
|
||||
display:
|
||||
# Use compact banner mode
|
||||
compact: false
|
||||
@@ -1,42 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Browser-focused data generation run
|
||||
# Uses browser-use-tasks.jsonl (6504 tasks)
|
||||
# Distribution: browser 97%, web 20%, vision 12%, terminal 15%
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/browser_tasks_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
echo "🌐 Running browser-focused tasks with browser_tasks distribution"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="browser-use-tasks.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="browser_tasks" \
|
||||
--distribution="browser_tasks" \
|
||||
--model="moonshotai/kimi-k2.5" \
|
||||
--verbose \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--num_workers=50 \
|
||||
--max_turns=60 \
|
||||
--resume \
|
||||
--ephemeral_system_prompt="You are an AI assistant with browser automation capabilities. Your primary task is to navigate and interact with web pages to accomplish user goals.
|
||||
|
||||
IMPORTANT GUIDELINES:
|
||||
|
||||
1. SEARCHING: Do NOT try to search directly on Google or other search engines via the browser - they block automated searches. Instead, ALWAYS use the web_search tool first to find URLs for any pages you need to visit, then use browser tools to navigate to those URLs.
|
||||
|
||||
2. COOKIE/PRIVACY DIALOGS: After navigating to a page, ALWAYS check if there are cookie consent dialogs, privacy popups, or overlay modals blocking the page. These appear in snapshots as 'dialog' elements with buttons like 'Close', 'Accept', 'Accept All', 'Decline', 'I Agree', 'Got it', 'OK', or 'X'. You MUST dismiss these dialogs FIRST by clicking the appropriate button before trying to interact with other page elements. After dismissing a dialog, take a fresh browser_snapshot to get updated element references.
|
||||
|
||||
3. HANDLING TIMEOUTS: If an action times out, it often means the element is blocked by an overlay or the page state has changed. Take a new snapshot to see the current page state and look for any dialogs or popups that need to be dismissed. If there is no dialog box to bypass, then try a new method or report the error to the user and complete the task.
|
||||
|
||||
4. GENERAL: Use browser tools to click elements, fill forms, extract information, and perform web-based tasks. If terminal is available, use it for any local file operations or computations needed to support your web tasks. Be thorough in verifying your actions and handle any errors gracefully by retrying or trying alternative approaches." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
|
||||
# --providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
@@ -1,26 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate a timestamp for the log file
|
||||
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
|
||||
LOG_FILE="logs/imagen_eval_gpt5_${TIMESTAMP}.log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/hermes-agent-imagen-data/hermes_agent_imagen_train_sft.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="imagen_train_sft_glm4.7" \
|
||||
--distribution="image_gen" \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=50 \
|
||||
--max_turns=25 \
|
||||
--ephemeral_system_prompt="When generating an image for the user view the image by using the vision_analyze tool to ensure it is what the user wanted. If it isn't feel free to retry a few times. If none are perfect, choose the best option that is the closest match, and explain its imperfections. If the image generation tool fails, try again a few times. If the vision analyze tool fails, provide the image to the user and explain it is your best effort attempt." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
# --verbose \
|
||||
@@ -1,26 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/glm4.7-thinking-sft1_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/hermes-agent-agent-tasks-1/agent_tasks_sft_2.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="megascience_glm4.7-thinking-sft2" \
|
||||
--distribution="science" \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=15 \
|
||||
--max_turns=60 \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you solve scientific, math, and technology problems presented to you. You can use them in sequence and build off of the results of prior tools you've used results. Always use the terminal or search tool if it can provide additional context, verify formulas, double check concepts and recent studies and understanding, doing all calculations, etc. You should only be confident in your own reasoning, knowledge, or calculations if you've exhaustively used all tools available to you to that can help you verify or validate your work. Always pip install any packages you need to use the python scripts you want to run. If you need to use a tool that isn't available, you can use the terminal tool to install or create it in many cases as well. Do not use the terminal tool to communicate with the user, as they cannot see your commands, only your final response after completing the task. Search for at least 3 sources, but not more than 12, so you can maintain focused context." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
|
||||
# --verbose \
|
||||
@@ -1,27 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/glm4.7-thinking-sft1-10k_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/hermes-agent-megascience-data/hermes_agent_megascience_sft_train_1_10k.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="megascience_glm4.7-thinking-sft1" \
|
||||
--distribution="science" \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=50 \
|
||||
--max_turns=60 \
|
||||
--resume \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you solve scientific, math, and technology problems presented to you. You can use them in sequence and build off of the results of prior tools you've used for furthering results. Always use the terminal or search tool if it can provide additional context, verify formulas, double check concepts and recent studies and understanding, doing all calculations, etc. You should only be confident in your own reasoning, knowledge, or calculations if you've exhaustively used all tools available to you to that can help you verify or validate your work. Always pip install any packages you need to use the python scripts you want to run. If you need to use a tool that isn't available, you can use the terminal tool to install or create it in many cases as well. Do not use the terminal tool to communicate with the user, as they cannot see your commands, only your final response after completing the task. Search for at least 3 sources, but not more than 12, so you can maintain a focused context." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
|
||||
# --verbose \
|
||||
@@ -1,28 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/glm4.7-terminal-tasks_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/raw_tasks_prompts.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="terminal-tasks-glm4.7-thinking" \
|
||||
--distribution="default" \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=50 \
|
||||
--max_turns=60 \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you complete coding, system administration, and general computing tasks. You can use them in sequence and build off of the results of prior tools you've used. Always use the terminal tool to execute commands, write code, install packages, and verify your work. You should test and validate everything you create. Always pip install any packages you need (use --break-system-packages if needed). If you need a tool that isn't available, you can use the terminal to install or create it. Do not use the terminal tool to communicate with the user, as they cannot see your commands, only your final response after completing the task. Use web search when you need to look up documentation, APIs, or current best practices." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
|
||||
# --verbose \
|
||||
# --resume \
|
||||
|
||||
@@ -1,12 +0,0 @@
|
||||
python batch_runner.py \
|
||||
--dataset_file="hermes-agent-megascience-data/hermes_agent_megascience_eval.jsonl" \
|
||||
--batch_size=10 \
|
||||
--run_name="megascience_eval_gpt5_2" \
|
||||
--distribution="science" \
|
||||
--model="gpt-5" \
|
||||
--base_url="https://api.openai.com/v1" \
|
||||
--api_key="${OPENAI_API_KEY}" \
|
||||
--num_workers=5 \
|
||||
--max_turns=30 \
|
||||
--verbose \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you solve scientific, math, and technology problems presented to you. You can use them in sequence and build off of the results of prior tools you've used results. Always use a tool if it can provide additional context, verify formulas, double check concepts and recent studies and understanding, doing all calculations, etc. You should not be confident in your own reasoning, knowledge, or calculations without using a tool to verify or validate your work."
|
||||
@@ -1,12 +0,0 @@
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/hermes-agent-agent-tasks-1/agent_tasks_eval.jsonl" \
|
||||
--batch_size=50 \
|
||||
--run_name="megascience_sft_minimax-m2.1-thinking-2-eval" \
|
||||
--distribution="science" \
|
||||
--model="minimax/minimax-m2.1" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="minimax" \
|
||||
--num_workers=1 \
|
||||
--max_turns=40 \
|
||||
--verbose \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you solve scientific, math, and technology problems presented to you. You can use them in sequence and build off of the results of prior tools you've used results. Always use the terminal or search tool if it can provide additional context, verify formulas, double check concepts and recent studies and understanding, doing all calculations, etc. You should only be confident in your own reasoning, knowledge, or calculations if you've exhaustively used all tools available to you to that can help you verify or validate your work. Always pip install any packages you need to use the python scripts you want to run. If you need to use a tool that isn't available, you can use the terminal tool to install or create it in many cases as well. Do not use the terminal tool to communicate with the user, as they cannot see your commands, only your final response after completing the task. Search for at least 3 sources, but not more than 12."
|
||||
@@ -1,29 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/glm4.7-terminal-tasks-newterm_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="source-data/hermes-agent-agent-tasks-1/agent_tasks_eval.jsonl" \
|
||||
--batch_size=1 \
|
||||
--run_name="terminal-tasks-test-newterm" \
|
||||
--distribution="terminal_only" \
|
||||
--verbose \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=5 \
|
||||
--max_turns=60 \
|
||||
--ephemeral_system_prompt="You have access to a variety of tools to help you complete coding, system administration, and general computing tasks. You can use them in sequence and build off of the results of prior tools you've used. Always use the terminal tool to execute commands, write code, install packages, and verify your work. You should test and validate everything you create. Always pip install any packages you need (use --break-system-packages if needed). If you need a tool that isn't available, you can use the terminal to install or create it. Do not use the terminal tool to communicate with the user, as they cannot see your commands, only your final response after completing the task. Use web search when you need to look up documentation, APIs, or current best practices." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
|
||||
# --verbose \
|
||||
# --resume \
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Terminal-only evaluation run using Modal sandboxes
|
||||
# Uses 10 sample tasks from nous-terminal-tasks
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/terminal_eval_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
echo "🔧 Using Modal sandboxes (TERMINAL_ENV=modal)"
|
||||
|
||||
# Set terminal to use Modal
|
||||
export TERMINAL_ENV=modal
|
||||
export TERMINAL_MODAL_IMAGE=nikolaik/python-nodejs:python3.11-nodejs20
|
||||
export TERMINAL_TIMEOUT=300
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="nous-terminal-tasks_eval.jsonl" \
|
||||
--batch_size=5 \
|
||||
--run_name="terminal_eval" \
|
||||
--distribution="terminal_only" \
|
||||
--model="z-ai/glm-4.7" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--providers_allowed="gmicloud,siliconflow,atlas-cloud,z-ai,novita" \
|
||||
--num_workers=2 \
|
||||
--max_turns=30 \
|
||||
--ephemeral_system_prompt="You have access to a terminal tool for executing commands. Use it to complete the task. Install any packages you need with apt-get or pip (use --break-system-packages if needed). Do not use interactive tools (vim, nano, python repl). If git output is large, pipe to cat." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
@@ -1,46 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Mixed browser+terminal data generation run
|
||||
# Uses mixed-browser-terminal-tasks.jsonl (200 tasks)
|
||||
# Distribution: browser 92%, terminal 92%, web 35%, vision 15%, image_gen 15%
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/mixed_tasks_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
echo "🔀 Running mixed browser+terminal tasks with mixed_tasks distribution"
|
||||
|
||||
# Set terminal environment
|
||||
# SIF images are automatically built/cached by terminal_tool.py
|
||||
export TERMINAL_ENV=singularity
|
||||
export TERMINAL_SINGULARITY_IMAGE="docker://nikolaik/python-nodejs:python3.11-nodejs20"
|
||||
export TERMINAL_TIMEOUT=300
|
||||
|
||||
# Set up Apptainer cache directories (use /scratch if available, otherwise /tmp)
|
||||
if [ -d "/scratch" ] && [ -w "/scratch" ]; then
|
||||
CACHE_BASE="/scratch/$USER/.apptainer"
|
||||
else
|
||||
CACHE_BASE="/tmp/$USER/.apptainer"
|
||||
fi
|
||||
export APPTAINER_CACHEDIR="$CACHE_BASE"
|
||||
export APPTAINER_TMPDIR="$CACHE_BASE/tmp"
|
||||
mkdir -p "$APPTAINER_CACHEDIR" "$APPTAINER_TMPDIR"
|
||||
|
||||
echo "📁 Apptainer cache: $APPTAINER_CACHEDIR"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="mixed-browser-terminal-tasks.jsonl" \
|
||||
--batch_size=20 \
|
||||
--run_name="mixed_tasks" \
|
||||
--distribution="mixed_tasks" \
|
||||
--model="moonshotai/kimi-k2.5" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--num_workers=25 \
|
||||
--max_turns=60 \
|
||||
--ephemeral_system_prompt="You are an AI assistant capable of both browser automation and terminal operations. Use browser tools to navigate websites, interact with web pages, fill forms, and extract information. Use terminal tools to execute commands, write and run code, install packages (use --break-system-packages with pip if needed), and perform local computations. When web search is available, use it to find URLs, documentation, or current information. If vision is available, use it to analyze images or screenshots. If image generation is available, use it when the task requires creating images. Combine browser and terminal capabilities effectively - for example, you might use the browser to fetch data from a website and terminal to process or analyze it. Always verify your work and handle errors gracefully. Whenever you can do something in a terminal instead of a web browser, you should choose to do so, as it's much cheaper." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
@@ -1,50 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Terminal-focused data generation run
|
||||
# Uses nous-terminal-tasks.jsonl (597 tasks)
|
||||
# Distribution: terminal 97%, web 15%, browser 0%, vision 8%, image_gen 3%
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
mkdir -p logs
|
||||
|
||||
# Generate log filename with timestamp
|
||||
LOG_FILE="logs/terminal_tasks_$(date +%Y%m%d_%H%M%S).log"
|
||||
|
||||
echo "📝 Logging output to: $LOG_FILE"
|
||||
echo "💻 Running terminal-focused tasks with terminal_tasks distribution"
|
||||
|
||||
# Set terminal environment
|
||||
# SIF images are automatically built/cached by terminal_tool.py
|
||||
export TERMINAL_ENV=singularity
|
||||
export TERMINAL_SINGULARITY_IMAGE="docker://nikolaik/python-nodejs:python3.11-nodejs20"
|
||||
export TERMINAL_TIMEOUT=300
|
||||
|
||||
# Set up Apptainer cache directories (use /scratch if available, otherwise /tmp)
|
||||
if [ -d "/scratch" ] && [ -w "/scratch" ]; then
|
||||
CACHE_BASE="/scratch/$USER/.apptainer"
|
||||
else
|
||||
CACHE_BASE="/tmp/$USER/.apptainer"
|
||||
fi
|
||||
export APPTAINER_CACHEDIR="$CACHE_BASE"
|
||||
export APPTAINER_TMPDIR="$CACHE_BASE/tmp"
|
||||
mkdir -p "$APPTAINER_CACHEDIR" "$APPTAINER_TMPDIR"
|
||||
|
||||
echo "📁 Apptainer cache: $APPTAINER_CACHEDIR"
|
||||
echo "🐳 Image: $TERMINAL_SINGULARITY_IMAGE (auto-converted to SIF on first use)"
|
||||
|
||||
python batch_runner.py \
|
||||
--dataset_file="nous-terminal-tasks.jsonl" \
|
||||
--batch_size=5 \
|
||||
--run_name="terminal_tasks-kimi-k2.5" \
|
||||
--distribution="terminal_tasks" \
|
||||
--model="moonshotai/kimi-k2.5" \
|
||||
--verbose \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--num_workers=80 \
|
||||
--max_turns=60 \
|
||||
--providers_ignored="Novita" \
|
||||
--resume \
|
||||
--ephemeral_system_prompt="You have access to a terminal tool for executing commands and completing coding, system administration, and computing tasks. Use the terminal to write code, run scripts, install packages (use --break-system-packages with pip if needed), manipulate files, and verify your work. Always test and validate code you create. Do not use interactive tools like vim, nano, or python REPL. If git output is large, pipe to cat. When web search is available, use it to look up documentation, APIs, or best practices. If browser tools are available, use them for web interactions that require page manipulation. Do not use the terminal to communicate with the user - only your final response will be shown to them." \
|
||||
2>&1 | tee "$LOG_FILE"
|
||||
|
||||
echo "✅ Log saved to: $LOG_FILE"
|
||||
@@ -1,21 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Test skills tool with Kimi K2.5
|
||||
# Usage: ./configs/test_skills_kimi.sh "your query here"
|
||||
# Example: ./configs/test_skills_kimi.sh "List available skills and show me the vllm skill"
|
||||
|
||||
# Default query if none provided
|
||||
QUERY="${1:-List all available skills. Then show me the axolotl skill and view one of its reference files.}"
|
||||
|
||||
echo "🎯 Testing Skills Tool with Kimi K2.5"
|
||||
echo "📝 Query: $QUERY"
|
||||
echo "="
|
||||
|
||||
python run_agent.py \
|
||||
--enabled_toolsets=skills \
|
||||
--model="moonshotai/kimi-k2.5" \
|
||||
--base_url="https://openrouter.ai/api/v1" \
|
||||
--max_turns=10 \
|
||||
--verbose \
|
||||
--save_sample \
|
||||
--query="$QUERY"
|
||||
@@ -1,101 +0,0 @@
|
||||
# Trajectory Compression Configuration
|
||||
#
|
||||
# Post-processes completed agent trajectories to fit within a target token budget.
|
||||
# Compression preserves head/tail turns and summarizes middle content only as needed.
|
||||
|
||||
# Tokenizer settings for accurate token counting
|
||||
tokenizer:
|
||||
# HuggingFace tokenizer name
|
||||
name: "moonshotai/Kimi-K2-Thinking"
|
||||
|
||||
# Trust remote code (required for some tokenizers)
|
||||
trust_remote_code: true
|
||||
|
||||
# Compression targets and behavior
|
||||
compression:
|
||||
# Target maximum tokens for compressed trajectory
|
||||
target_max_tokens: 29000
|
||||
|
||||
# Target size for summary (in tokens)
|
||||
# This is factored into calculations when determining what to compress
|
||||
summary_target_tokens: 750
|
||||
|
||||
# Protected turns that should NEVER be compressed
|
||||
protected_turns:
|
||||
# Always protect the first system message (tool definitions)
|
||||
first_system: true
|
||||
|
||||
# Always protect the first human message (original request)
|
||||
first_human: true
|
||||
|
||||
# Always protect the first gpt message (initial response/tool_call)
|
||||
first_gpt: true
|
||||
|
||||
# Always protect the first tool response (result of first action)
|
||||
first_tool: true
|
||||
|
||||
# Always protect the last 2 complete turn pairs (gpt+tool or gpt only)
|
||||
# This ensures the model's final actions and conclusions are preserved
|
||||
last_n_turns: 4
|
||||
|
||||
# LLM settings for generating summaries (OpenRouter only)
|
||||
summarization:
|
||||
# Model to use for summarization (should be fast and cheap)
|
||||
# Using OpenRouter model path format
|
||||
model: "google/gemini-3-flash-preview"
|
||||
|
||||
# OpenRouter API settings
|
||||
base_url: "https://openrouter.ai/api/v1"
|
||||
|
||||
# Environment variable containing OpenRouter API key
|
||||
api_key_env: "OPENROUTER_API_KEY"
|
||||
|
||||
# Temperature for summarization (lower = more deterministic)
|
||||
temperature: 0.3
|
||||
|
||||
# Max retries for API failures
|
||||
max_retries: 3
|
||||
|
||||
# Delay between retries (seconds)
|
||||
retry_delay: 2
|
||||
|
||||
# Output settings
|
||||
output:
|
||||
# Add notice to system message about potential summarization
|
||||
add_summary_notice: true
|
||||
|
||||
# Text to append to system message
|
||||
summary_notice_text: "\n\nSome of the conversation may be summarized to preserve context."
|
||||
|
||||
# Output directory suffix (appended to input directory name)
|
||||
output_suffix: "_compressed"
|
||||
|
||||
# Processing settings
|
||||
processing:
|
||||
# Number of parallel workers for batch processing
|
||||
num_workers: 4
|
||||
|
||||
# Maximum concurrent API calls for summarization (async parallelism)
|
||||
max_concurrent_requests: 50
|
||||
|
||||
# Skip trajectories that are already under target length
|
||||
skip_under_target: true
|
||||
|
||||
# If true, save trajectories even if compression can't get under target
|
||||
# (will compress as much as possible)
|
||||
save_over_limit: true
|
||||
|
||||
# Timeout per trajectory in seconds (skip if takes longer)
|
||||
# Helps avoid hanging on problematic entries
|
||||
per_trajectory_timeout: 300 # 5 minutes
|
||||
|
||||
# Metrics to track
|
||||
metrics:
|
||||
# Log detailed compression statistics
|
||||
enabled: true
|
||||
|
||||
# Save per-trajectory metrics in output
|
||||
per_trajectory: false
|
||||
|
||||
# Metrics file name (saved in output directory)
|
||||
output_file: "compression_metrics.json"
|
||||
@@ -1,36 +0,0 @@
|
||||
"""
|
||||
Cron job scheduling system for Hermes Agent.
|
||||
|
||||
This module provides scheduled task execution, allowing the agent to:
|
||||
- Run automated tasks on schedules (cron expressions, intervals, one-shot)
|
||||
- Self-schedule reminders and follow-up tasks
|
||||
- Execute tasks in isolated sessions (no prior context)
|
||||
|
||||
Usage:
|
||||
# Run due jobs (for system cron integration)
|
||||
python -c "from cron import tick; tick()"
|
||||
|
||||
# Or via CLI
|
||||
python cli.py --cron-daemon
|
||||
"""
|
||||
|
||||
from cron.jobs import (
|
||||
create_job,
|
||||
get_job,
|
||||
list_jobs,
|
||||
remove_job,
|
||||
update_job,
|
||||
JOBS_FILE,
|
||||
)
|
||||
from cron.scheduler import tick, run_daemon
|
||||
|
||||
__all__ = [
|
||||
"create_job",
|
||||
"get_job",
|
||||
"list_jobs",
|
||||
"remove_job",
|
||||
"update_job",
|
||||
"tick",
|
||||
"run_daemon",
|
||||
"JOBS_FILE",
|
||||
]
|
||||
383
cron/jobs.py
383
cron/jobs.py
@@ -1,383 +0,0 @@
|
||||
"""
|
||||
Cron job storage and management.
|
||||
|
||||
Jobs are stored in ~/.hermes/cron/jobs.json
|
||||
Output is saved to ~/.hermes/cron/output/{job_id}/{timestamp}.md
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import uuid
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Optional, Dict, List, Any
|
||||
|
||||
try:
|
||||
from croniter import croniter
|
||||
HAS_CRONITER = True
|
||||
except ImportError:
|
||||
HAS_CRONITER = False
|
||||
|
||||
# =============================================================================
|
||||
# Configuration
|
||||
# =============================================================================
|
||||
|
||||
HERMES_DIR = Path.home() / ".hermes"
|
||||
CRON_DIR = HERMES_DIR / "cron"
|
||||
JOBS_FILE = CRON_DIR / "jobs.json"
|
||||
OUTPUT_DIR = CRON_DIR / "output"
|
||||
|
||||
|
||||
def ensure_dirs():
|
||||
"""Ensure cron directories exist."""
|
||||
CRON_DIR.mkdir(parents=True, exist_ok=True)
|
||||
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Schedule Parsing
|
||||
# =============================================================================
|
||||
|
||||
def parse_duration(s: str) -> int:
|
||||
"""
|
||||
Parse duration string into minutes.
|
||||
|
||||
Examples:
|
||||
"30m" → 30
|
||||
"2h" → 120
|
||||
"1d" → 1440
|
||||
"""
|
||||
s = s.strip().lower()
|
||||
match = re.match(r'^(\d+)\s*(m|min|mins|minute|minutes|h|hr|hrs|hour|hours|d|day|days)$', s)
|
||||
if not match:
|
||||
raise ValueError(f"Invalid duration: '{s}'. Use format like '30m', '2h', or '1d'")
|
||||
|
||||
value = int(match.group(1))
|
||||
unit = match.group(2)[0] # First char: m, h, or d
|
||||
|
||||
multipliers = {'m': 1, 'h': 60, 'd': 1440}
|
||||
return value * multipliers[unit]
|
||||
|
||||
|
||||
def parse_schedule(schedule: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Parse schedule string into structured format.
|
||||
|
||||
Returns dict with:
|
||||
- kind: "once" | "interval" | "cron"
|
||||
- For "once": "run_at" (ISO timestamp)
|
||||
- For "interval": "minutes" (int)
|
||||
- For "cron": "expr" (cron expression)
|
||||
|
||||
Examples:
|
||||
"30m" → once in 30 minutes
|
||||
"2h" → once in 2 hours
|
||||
"every 30m" → recurring every 30 minutes
|
||||
"every 2h" → recurring every 2 hours
|
||||
"0 9 * * *" → cron expression
|
||||
"2026-02-03T14:00" → once at timestamp
|
||||
"""
|
||||
schedule = schedule.strip()
|
||||
original = schedule
|
||||
schedule_lower = schedule.lower()
|
||||
|
||||
# "every X" pattern → recurring interval
|
||||
if schedule_lower.startswith("every "):
|
||||
duration_str = schedule[6:].strip()
|
||||
minutes = parse_duration(duration_str)
|
||||
return {
|
||||
"kind": "interval",
|
||||
"minutes": minutes,
|
||||
"display": f"every {minutes}m"
|
||||
}
|
||||
|
||||
# Check for cron expression (5 or 6 space-separated fields)
|
||||
# Cron fields: minute hour day month weekday [year]
|
||||
parts = schedule.split()
|
||||
if len(parts) >= 5 and all(
|
||||
re.match(r'^[\d\*\-,/]+$', p) for p in parts[:5]
|
||||
):
|
||||
if not HAS_CRONITER:
|
||||
raise ValueError("Cron expressions require 'croniter' package. Install with: pip install croniter")
|
||||
# Validate cron expression
|
||||
try:
|
||||
croniter(schedule)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Invalid cron expression '{schedule}': {e}")
|
||||
return {
|
||||
"kind": "cron",
|
||||
"expr": schedule,
|
||||
"display": schedule
|
||||
}
|
||||
|
||||
# ISO timestamp (contains T or looks like date)
|
||||
if 'T' in schedule or re.match(r'^\d{4}-\d{2}-\d{2}', schedule):
|
||||
try:
|
||||
# Parse and validate
|
||||
dt = datetime.fromisoformat(schedule.replace('Z', '+00:00'))
|
||||
return {
|
||||
"kind": "once",
|
||||
"run_at": dt.isoformat(),
|
||||
"display": f"once at {dt.strftime('%Y-%m-%d %H:%M')}"
|
||||
}
|
||||
except ValueError as e:
|
||||
raise ValueError(f"Invalid timestamp '{schedule}': {e}")
|
||||
|
||||
# Duration like "30m", "2h", "1d" → one-shot from now
|
||||
try:
|
||||
minutes = parse_duration(schedule)
|
||||
run_at = datetime.now() + timedelta(minutes=minutes)
|
||||
return {
|
||||
"kind": "once",
|
||||
"run_at": run_at.isoformat(),
|
||||
"display": f"once in {original}"
|
||||
}
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
raise ValueError(
|
||||
f"Invalid schedule '{original}'. Use:\n"
|
||||
f" - Duration: '30m', '2h', '1d' (one-shot)\n"
|
||||
f" - Interval: 'every 30m', 'every 2h' (recurring)\n"
|
||||
f" - Cron: '0 9 * * *' (cron expression)\n"
|
||||
f" - Timestamp: '2026-02-03T14:00:00' (one-shot at time)"
|
||||
)
|
||||
|
||||
|
||||
def compute_next_run(schedule: Dict[str, Any], last_run_at: Optional[str] = None) -> Optional[str]:
|
||||
"""
|
||||
Compute the next run time for a schedule.
|
||||
|
||||
Returns ISO timestamp string, or None if no more runs.
|
||||
"""
|
||||
now = datetime.now()
|
||||
|
||||
if schedule["kind"] == "once":
|
||||
run_at = datetime.fromisoformat(schedule["run_at"])
|
||||
# If in the future, return it; if in the past, no more runs
|
||||
return schedule["run_at"] if run_at > now else None
|
||||
|
||||
elif schedule["kind"] == "interval":
|
||||
minutes = schedule["minutes"]
|
||||
if last_run_at:
|
||||
# Next run is last_run + interval
|
||||
last = datetime.fromisoformat(last_run_at)
|
||||
next_run = last + timedelta(minutes=minutes)
|
||||
else:
|
||||
# First run is now + interval
|
||||
next_run = now + timedelta(minutes=minutes)
|
||||
return next_run.isoformat()
|
||||
|
||||
elif schedule["kind"] == "cron":
|
||||
if not HAS_CRONITER:
|
||||
return None
|
||||
cron = croniter(schedule["expr"], now)
|
||||
next_run = cron.get_next(datetime)
|
||||
return next_run.isoformat()
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Job CRUD Operations
|
||||
# =============================================================================
|
||||
|
||||
def load_jobs() -> List[Dict[str, Any]]:
|
||||
"""Load all jobs from storage."""
|
||||
ensure_dirs()
|
||||
if not JOBS_FILE.exists():
|
||||
return []
|
||||
|
||||
try:
|
||||
with open(JOBS_FILE, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
return data.get("jobs", [])
|
||||
except (json.JSONDecodeError, IOError):
|
||||
return []
|
||||
|
||||
|
||||
def save_jobs(jobs: List[Dict[str, Any]]):
|
||||
"""Save all jobs to storage."""
|
||||
ensure_dirs()
|
||||
with open(JOBS_FILE, 'w', encoding='utf-8') as f:
|
||||
json.dump({"jobs": jobs, "updated_at": datetime.now().isoformat()}, f, indent=2)
|
||||
|
||||
|
||||
def create_job(
|
||||
prompt: str,
|
||||
schedule: str,
|
||||
name: Optional[str] = None,
|
||||
repeat: Optional[int] = None,
|
||||
deliver: Optional[str] = None,
|
||||
origin: Optional[Dict[str, Any]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Create a new cron job.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to run (must be self-contained)
|
||||
schedule: Schedule string (see parse_schedule)
|
||||
name: Optional friendly name
|
||||
repeat: How many times to run (None = forever, 1 = once)
|
||||
deliver: Where to deliver output ("origin", "local", "telegram", etc.)
|
||||
origin: Source info where job was created (for "origin" delivery)
|
||||
|
||||
Returns:
|
||||
The created job dict
|
||||
"""
|
||||
parsed_schedule = parse_schedule(schedule)
|
||||
|
||||
# Auto-set repeat=1 for one-shot schedules if not specified
|
||||
if parsed_schedule["kind"] == "once" and repeat is None:
|
||||
repeat = 1
|
||||
|
||||
# Default delivery to origin if available, otherwise local
|
||||
if deliver is None:
|
||||
deliver = "origin" if origin else "local"
|
||||
|
||||
job_id = uuid.uuid4().hex[:12]
|
||||
now = datetime.now().isoformat()
|
||||
|
||||
job = {
|
||||
"id": job_id,
|
||||
"name": name or prompt[:50].strip(),
|
||||
"prompt": prompt,
|
||||
"schedule": parsed_schedule,
|
||||
"schedule_display": parsed_schedule.get("display", schedule),
|
||||
"repeat": {
|
||||
"times": repeat, # None = forever
|
||||
"completed": 0
|
||||
},
|
||||
"enabled": True,
|
||||
"created_at": now,
|
||||
"next_run_at": compute_next_run(parsed_schedule),
|
||||
"last_run_at": None,
|
||||
"last_status": None,
|
||||
"last_error": None,
|
||||
# Delivery configuration
|
||||
"deliver": deliver,
|
||||
"origin": origin, # Tracks where job was created for "origin" delivery
|
||||
}
|
||||
|
||||
jobs = load_jobs()
|
||||
jobs.append(job)
|
||||
save_jobs(jobs)
|
||||
|
||||
return job
|
||||
|
||||
|
||||
def get_job(job_id: str) -> Optional[Dict[str, Any]]:
|
||||
"""Get a job by ID."""
|
||||
jobs = load_jobs()
|
||||
for job in jobs:
|
||||
if job["id"] == job_id:
|
||||
return job
|
||||
return None
|
||||
|
||||
|
||||
def list_jobs(include_disabled: bool = False) -> List[Dict[str, Any]]:
|
||||
"""List all jobs, optionally including disabled ones."""
|
||||
jobs = load_jobs()
|
||||
if not include_disabled:
|
||||
jobs = [j for j in jobs if j.get("enabled", True)]
|
||||
return jobs
|
||||
|
||||
|
||||
def update_job(job_id: str, updates: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
"""Update a job by ID."""
|
||||
jobs = load_jobs()
|
||||
for i, job in enumerate(jobs):
|
||||
if job["id"] == job_id:
|
||||
jobs[i] = {**job, **updates}
|
||||
save_jobs(jobs)
|
||||
return jobs[i]
|
||||
return None
|
||||
|
||||
|
||||
def remove_job(job_id: str) -> bool:
|
||||
"""Remove a job by ID."""
|
||||
jobs = load_jobs()
|
||||
original_len = len(jobs)
|
||||
jobs = [j for j in jobs if j["id"] != job_id]
|
||||
if len(jobs) < original_len:
|
||||
save_jobs(jobs)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def mark_job_run(job_id: str, success: bool, error: Optional[str] = None):
|
||||
"""
|
||||
Mark a job as having been run.
|
||||
|
||||
Updates last_run_at, last_status, increments completed count,
|
||||
computes next_run_at, and auto-deletes if repeat limit reached.
|
||||
"""
|
||||
jobs = load_jobs()
|
||||
for i, job in enumerate(jobs):
|
||||
if job["id"] == job_id:
|
||||
now = datetime.now().isoformat()
|
||||
job["last_run_at"] = now
|
||||
job["last_status"] = "ok" if success else "error"
|
||||
job["last_error"] = error if not success else None
|
||||
|
||||
# Increment completed count
|
||||
if job.get("repeat"):
|
||||
job["repeat"]["completed"] = job["repeat"].get("completed", 0) + 1
|
||||
|
||||
# Check if we've hit the repeat limit
|
||||
times = job["repeat"].get("times")
|
||||
completed = job["repeat"]["completed"]
|
||||
if times is not None and completed >= times:
|
||||
# Remove the job (limit reached)
|
||||
jobs.pop(i)
|
||||
save_jobs(jobs)
|
||||
return
|
||||
|
||||
# Compute next run
|
||||
job["next_run_at"] = compute_next_run(job["schedule"], now)
|
||||
|
||||
# If no next run (one-shot completed), disable
|
||||
if job["next_run_at"] is None:
|
||||
job["enabled"] = False
|
||||
|
||||
save_jobs(jobs)
|
||||
return
|
||||
|
||||
save_jobs(jobs)
|
||||
|
||||
|
||||
def get_due_jobs() -> List[Dict[str, Any]]:
|
||||
"""Get all jobs that are due to run now."""
|
||||
now = datetime.now()
|
||||
jobs = load_jobs()
|
||||
due = []
|
||||
|
||||
for job in jobs:
|
||||
if not job.get("enabled", True):
|
||||
continue
|
||||
|
||||
next_run = job.get("next_run_at")
|
||||
if not next_run:
|
||||
continue
|
||||
|
||||
next_run_dt = datetime.fromisoformat(next_run)
|
||||
if next_run_dt <= now:
|
||||
due.append(job)
|
||||
|
||||
return due
|
||||
|
||||
|
||||
def save_job_output(job_id: str, output: str):
|
||||
"""Save job output to file."""
|
||||
ensure_dirs()
|
||||
job_output_dir = OUTPUT_DIR / job_id
|
||||
job_output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
output_file = job_output_dir / f"{timestamp}.md"
|
||||
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
f.write(output)
|
||||
|
||||
return output_file
|
||||
@@ -1,188 +0,0 @@
|
||||
"""
|
||||
Cron job scheduler - executes due jobs.
|
||||
|
||||
This module provides:
|
||||
- tick(): Run all due jobs once (for system cron integration)
|
||||
- run_daemon(): Run continuously, checking every 60 seconds
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
# Add parent directory to path for imports
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from cron.jobs import get_due_jobs, mark_job_run, save_job_output
|
||||
|
||||
|
||||
def run_job(job: dict) -> tuple[bool, str, Optional[str]]:
|
||||
"""
|
||||
Execute a single cron job.
|
||||
|
||||
Returns:
|
||||
Tuple of (success, output, error_message)
|
||||
"""
|
||||
from run_agent import AIAgent
|
||||
|
||||
job_id = job["id"]
|
||||
job_name = job["name"]
|
||||
prompt = job["prompt"]
|
||||
|
||||
print(f"[cron] Running job '{job_name}' (ID: {job_id})")
|
||||
print(f"[cron] Prompt: {prompt[:100]}{'...' if len(prompt) > 100 else ''}")
|
||||
|
||||
try:
|
||||
# Create agent with default settings
|
||||
# Jobs run in isolated sessions (no prior context)
|
||||
agent = AIAgent(
|
||||
model=os.getenv("HERMES_MODEL", "anthropic/claude-sonnet-4"),
|
||||
quiet_mode=True,
|
||||
session_id=f"cron_{job_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
)
|
||||
|
||||
# Run the conversation
|
||||
result = agent.run_conversation(prompt)
|
||||
|
||||
# Extract final response
|
||||
final_response = result.get("final_response", "")
|
||||
if not final_response:
|
||||
final_response = "(No response generated)"
|
||||
|
||||
# Build output document
|
||||
output = f"""# Cron Job: {job_name}
|
||||
|
||||
**Job ID:** {job_id}
|
||||
**Run Time:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||||
**Schedule:** {job.get('schedule_display', 'N/A')}
|
||||
|
||||
## Prompt
|
||||
|
||||
{prompt}
|
||||
|
||||
## Response
|
||||
|
||||
{final_response}
|
||||
"""
|
||||
|
||||
print(f"[cron] Job '{job_name}' completed successfully")
|
||||
return True, output, None
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"{type(e).__name__}: {str(e)}"
|
||||
print(f"[cron] Job '{job_name}' failed: {error_msg}")
|
||||
|
||||
# Build error output
|
||||
output = f"""# Cron Job: {job_name} (FAILED)
|
||||
|
||||
**Job ID:** {job_id}
|
||||
**Run Time:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||||
**Schedule:** {job.get('schedule_display', 'N/A')}
|
||||
|
||||
## Prompt
|
||||
|
||||
{prompt}
|
||||
|
||||
## Error
|
||||
|
||||
```
|
||||
{error_msg}
|
||||
|
||||
{traceback.format_exc()}
|
||||
```
|
||||
"""
|
||||
return False, output, error_msg
|
||||
|
||||
|
||||
def tick(verbose: bool = True) -> int:
|
||||
"""
|
||||
Check and run all due jobs.
|
||||
|
||||
This is designed to be called by system cron every minute:
|
||||
*/1 * * * * cd ~/hermes-agent && python -c "from cron import tick; tick()"
|
||||
|
||||
Args:
|
||||
verbose: Whether to print status messages
|
||||
|
||||
Returns:
|
||||
Number of jobs executed
|
||||
"""
|
||||
due_jobs = get_due_jobs()
|
||||
|
||||
if verbose and not due_jobs:
|
||||
print(f"[cron] {datetime.now().strftime('%H:%M:%S')} - No jobs due")
|
||||
return 0
|
||||
|
||||
if verbose:
|
||||
print(f"[cron] {datetime.now().strftime('%H:%M:%S')} - {len(due_jobs)} job(s) due")
|
||||
|
||||
executed = 0
|
||||
for job in due_jobs:
|
||||
try:
|
||||
success, output, error = run_job(job)
|
||||
|
||||
# Save output to file
|
||||
output_file = save_job_output(job["id"], output)
|
||||
if verbose:
|
||||
print(f"[cron] Output saved to: {output_file}")
|
||||
|
||||
# Mark job as run (handles repeat counting, next_run computation)
|
||||
mark_job_run(job["id"], success, error)
|
||||
executed += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"[cron] Error processing job {job['id']}: {e}")
|
||||
mark_job_run(job["id"], False, str(e))
|
||||
|
||||
return executed
|
||||
|
||||
|
||||
def run_daemon(check_interval: int = 60, verbose: bool = True):
|
||||
"""
|
||||
Run the cron daemon continuously.
|
||||
|
||||
Checks for due jobs every `check_interval` seconds.
|
||||
|
||||
Args:
|
||||
check_interval: Seconds between checks (default: 60)
|
||||
verbose: Whether to print status messages
|
||||
"""
|
||||
print(f"[cron] Starting daemon (checking every {check_interval}s)")
|
||||
print(f"[cron] Press Ctrl+C to stop")
|
||||
print()
|
||||
|
||||
try:
|
||||
while True:
|
||||
try:
|
||||
tick(verbose=verbose)
|
||||
except Exception as e:
|
||||
print(f"[cron] Tick error: {e}")
|
||||
|
||||
time.sleep(check_interval)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n[cron] Daemon stopped")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Allow running directly: python cron/scheduler.py [daemon|tick]
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="Hermes Cron Scheduler")
|
||||
parser.add_argument("mode", choices=["daemon", "tick"], default="tick", nargs="?",
|
||||
help="Mode: 'tick' to run once, 'daemon' to run continuously")
|
||||
parser.add_argument("--interval", type=int, default=60,
|
||||
help="Check interval in seconds for daemon mode")
|
||||
parser.add_argument("--quiet", "-q", action="store_true",
|
||||
help="Suppress status messages")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.mode == "daemon":
|
||||
run_daemon(check_interval=args.interval, verbose=not args.quiet)
|
||||
else:
|
||||
tick(verbose=not args.quiet)
|
||||
104
docs/agents.md
104
docs/agents.md
@@ -1,104 +0,0 @@
|
||||
# Agents
|
||||
|
||||
The agent is the core loop that orchestrates LLM calls and tool execution.
|
||||
|
||||
## AIAgent Class
|
||||
|
||||
The main agent is implemented in `run_agent.py`:
|
||||
|
||||
```python
|
||||
class AIAgent:
|
||||
def __init__(
|
||||
self,
|
||||
model: str = "anthropic/claude-sonnet-4",
|
||||
api_key: str = None,
|
||||
base_url: str = "https://openrouter.ai/api/v1",
|
||||
max_turns: int = 20,
|
||||
enabled_toolsets: list = None,
|
||||
disabled_toolsets: list = None,
|
||||
verbose_logging: bool = False,
|
||||
):
|
||||
# Initialize OpenAI client, load tools based on toolsets
|
||||
...
|
||||
|
||||
def chat(self, user_message: str, task_id: str = None) -> str:
|
||||
# Main entry point - runs the agent loop
|
||||
...
|
||||
```
|
||||
|
||||
## Agent Loop
|
||||
|
||||
The core loop in `_run_agent_loop()`:
|
||||
|
||||
```
|
||||
1. Add user message to conversation
|
||||
2. Call LLM with tools
|
||||
3. If LLM returns tool calls:
|
||||
- Execute each tool
|
||||
- Add tool results to conversation
|
||||
- Go to step 2
|
||||
4. If LLM returns text response:
|
||||
- Return response to user
|
||||
```
|
||||
|
||||
```python
|
||||
while turns < max_turns:
|
||||
response = client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tool_schemas,
|
||||
)
|
||||
|
||||
if response.tool_calls:
|
||||
for tool_call in response.tool_calls:
|
||||
result = await execute_tool(tool_call)
|
||||
messages.append(tool_result_message(result))
|
||||
turns += 1
|
||||
else:
|
||||
return response.content
|
||||
```
|
||||
|
||||
## Conversation Management
|
||||
|
||||
Messages are stored as a list of dicts following OpenAI format:
|
||||
|
||||
```python
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant..."},
|
||||
{"role": "user", "content": "Search for Python tutorials"},
|
||||
{"role": "assistant", "content": None, "tool_calls": [...]},
|
||||
{"role": "tool", "tool_call_id": "...", "content": "..."},
|
||||
{"role": "assistant", "content": "Here's what I found..."},
|
||||
]
|
||||
```
|
||||
|
||||
## Reasoning Context
|
||||
|
||||
For models that support reasoning (chain-of-thought), the agent:
|
||||
1. Extracts `reasoning_content` from API responses
|
||||
2. Stores it in `assistant_msg["reasoning"]` for trajectory export
|
||||
3. Passes it back via `reasoning_content` field on subsequent turns
|
||||
|
||||
## Trajectory Export
|
||||
|
||||
Conversations can be exported for training:
|
||||
|
||||
```python
|
||||
agent = AIAgent(save_trajectories=True)
|
||||
agent.chat("Do something")
|
||||
# Saves to trajectories/*.jsonl in ShareGPT format
|
||||
```
|
||||
|
||||
## Batch Processing
|
||||
|
||||
For processing multiple prompts, use `batch_runner.py`:
|
||||
|
||||
```bash
|
||||
python batch_runner.py \
|
||||
--dataset_file=prompts.jsonl \
|
||||
--batch_size=20 \
|
||||
--num_workers=4 \
|
||||
--run_name=my_run
|
||||
```
|
||||
|
||||
See `batch_runner.py` for parallel execution with checkpointing.
|
||||
296
docs/cli.md
296
docs/cli.md
@@ -1,296 +0,0 @@
|
||||
# CLI
|
||||
|
||||
The Hermes Agent CLI provides an interactive terminal interface for working with the agent.
|
||||
|
||||
## Running the CLI
|
||||
|
||||
```bash
|
||||
# Basic usage
|
||||
./hermes
|
||||
|
||||
# With specific model
|
||||
./hermes --model "anthropic/claude-sonnet-4"
|
||||
|
||||
# With specific toolsets
|
||||
./hermes --toolsets "web,terminal,skills"
|
||||
|
||||
# Verbose mode
|
||||
./hermes --verbose
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
The CLI is implemented in `cli.py` and uses:
|
||||
|
||||
- **Rich** - Welcome banner with ASCII art and styled panels
|
||||
- **prompt_toolkit** - Fixed input area with command history
|
||||
- **KawaiiSpinner** - Animated feedback during operations
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────┐
|
||||
│ HERMES-AGENT ASCII Logo │
|
||||
│ ┌─────────────┐ ┌────────────────────────────┐ │
|
||||
│ │ Caduceus │ │ Model: claude-opus-4.5 │ │
|
||||
│ │ ASCII Art │ │ Terminal: local │ │
|
||||
│ │ │ │ Working Dir: /home/user │ │
|
||||
│ │ │ │ Available Tools: 19 │ │
|
||||
│ │ │ │ Available Skills: 12 │ │
|
||||
│ └─────────────┘ └────────────────────────────┘ │
|
||||
└─────────────────────────────────────────────────┘
|
||||
│ Conversation output scrolls here... │
|
||||
│ │
|
||||
│ User: Hello! │
|
||||
│ ────────────────────────────────────────────── │
|
||||
│ (◕‿◕✿) 🧠 pondering... (2.3s) │
|
||||
│ ✧٩(ˊᗜˋ*)و✧ got it! (2.3s) │
|
||||
│ │
|
||||
│ Assistant: Hello! How can I help you today? │
|
||||
├─────────────────────────────────────────────────┤
|
||||
│ ❯ [Fixed input area at bottom] │
|
||||
└─────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Commands
|
||||
|
||||
| Command | Description |
|
||||
|---------|-------------|
|
||||
| `/help` | Show available commands |
|
||||
| `/tools` | List available tools grouped by toolset |
|
||||
| `/toolsets` | List available toolsets with descriptions |
|
||||
| `/model [name]` | Show or change the current model |
|
||||
| `/prompt [text]` | View/set/clear custom system prompt |
|
||||
| `/personality [name]` | Set a predefined personality |
|
||||
| `/clear` | Clear screen and reset conversation |
|
||||
| `/reset` | Reset conversation only (keep screen) |
|
||||
| `/history` | Show conversation history |
|
||||
| `/save` | Save current conversation to file |
|
||||
| `/config` | Show current configuration |
|
||||
| `/quit` | Exit the CLI (also: `/exit`, `/q`) |
|
||||
|
||||
## Configuration
|
||||
|
||||
The CLI is configured via `cli-config.yaml`. Copy from `cli-config.yaml.example`:
|
||||
|
||||
```bash
|
||||
cp cli-config.yaml.example cli-config.yaml
|
||||
```
|
||||
|
||||
### Model Configuration
|
||||
|
||||
```yaml
|
||||
model:
|
||||
default: "anthropic/claude-opus-4.5"
|
||||
base_url: "https://openrouter.ai/api/v1"
|
||||
```
|
||||
|
||||
### Terminal Configuration
|
||||
|
||||
The CLI supports multiple terminal backends:
|
||||
|
||||
```yaml
|
||||
# Local execution (default)
|
||||
terminal:
|
||||
env_type: "local"
|
||||
cwd: "." # Current directory
|
||||
|
||||
# SSH remote execution (sandboxed - agent can't touch its own code)
|
||||
terminal:
|
||||
env_type: "ssh"
|
||||
cwd: "/home/myuser/project"
|
||||
ssh_host: "my-server.example.com"
|
||||
ssh_user: "myuser"
|
||||
ssh_key: "~/.ssh/id_rsa"
|
||||
|
||||
# Docker container
|
||||
terminal:
|
||||
env_type: "docker"
|
||||
docker_image: "python:3.11"
|
||||
|
||||
# Singularity/Apptainer (HPC)
|
||||
terminal:
|
||||
env_type: "singularity"
|
||||
singularity_image: "docker://python:3.11"
|
||||
|
||||
# Modal cloud
|
||||
terminal:
|
||||
env_type: "modal"
|
||||
modal_image: "python:3.11"
|
||||
```
|
||||
|
||||
### Sudo Support
|
||||
|
||||
The CLI supports interactive sudo prompts:
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────┐
|
||||
│ 🔐 SUDO PASSWORD REQUIRED │
|
||||
├──────────────────────────────────────────────────────────┤
|
||||
│ Enter password below (input is hidden), or: │
|
||||
│ • Press Enter to skip (command fails gracefully) │
|
||||
│ • Wait 45s to auto-skip │
|
||||
└──────────────────────────────────────────────────────────┘
|
||||
|
||||
Password (hidden):
|
||||
```
|
||||
|
||||
**Options:**
|
||||
- **Interactive**: Leave `sudo_password` unset - you'll be prompted when needed
|
||||
- **Configured**: Set `sudo_password` in `cli-config.yaml` to auto-fill
|
||||
- **Environment**: Set `SUDO_PASSWORD` in `.env` for all runs
|
||||
|
||||
Password is cached for the session once entered.
|
||||
|
||||
### Toolsets
|
||||
|
||||
Control which tools are available:
|
||||
|
||||
```yaml
|
||||
# Enable all tools
|
||||
toolsets:
|
||||
- all
|
||||
|
||||
# Or enable specific toolsets
|
||||
toolsets:
|
||||
- web
|
||||
- terminal
|
||||
- skills
|
||||
```
|
||||
|
||||
Available toolsets: `web`, `search`, `terminal`, `browser`, `vision`, `image_gen`, `skills`, `moa`, `debugging`, `safe`
|
||||
|
||||
### Personalities
|
||||
|
||||
Predefined personalities for the `/personality` command:
|
||||
|
||||
```yaml
|
||||
agent:
|
||||
personalities:
|
||||
helpful: "You are a helpful, friendly AI assistant."
|
||||
kawaii: "You are a kawaii assistant! Use cute expressions..."
|
||||
pirate: "Arrr! Ye be talkin' to Captain Hermes..."
|
||||
# Add your own!
|
||||
```
|
||||
|
||||
Built-in personalities:
|
||||
- `helpful`, `concise`, `technical`, `creative`, `teacher`
|
||||
- `kawaii`, `catgirl`, `pirate`, `shakespeare`, `surfer`
|
||||
- `noir`, `uwu`, `philosopher`, `hype`
|
||||
|
||||
## Animated Feedback
|
||||
|
||||
The CLI provides animated feedback during operations:
|
||||
|
||||
### Thinking Animation
|
||||
|
||||
During API calls, shows animated spinner with thinking verbs:
|
||||
```
|
||||
◜ (｡•́︿•̀｡) pondering... (1.2s)
|
||||
◠ (⊙_⊙) contemplating... (2.4s)
|
||||
✧٩(ˊᗜˋ*)و✧ got it! (3.1s)
|
||||
```
|
||||
|
||||
### Tool Execution Animation
|
||||
|
||||
Each tool type has unique animations:
|
||||
```
|
||||
⠋ (◕‿◕✿) 🔍 web_search... (0.8s)
|
||||
▅ (≧◡≦) 💻 terminal... (1.2s)
|
||||
🌓 (★ω★) 🌐 browser_navigate... (2.1s)
|
||||
✧ (✿◠‿◠) 🎨 image_generate... (4.5s)
|
||||
```
|
||||
|
||||
## Multi-line Input
|
||||
|
||||
For multi-line input, end a line with `\` to continue:
|
||||
|
||||
```
|
||||
❯ Write a function that:\
|
||||
1. Takes a list of numbers\
|
||||
2. Returns the sum
|
||||
```
|
||||
|
||||
## Environment Variable Priority
|
||||
|
||||
For terminal settings, `cli-config.yaml` takes precedence over `.env`:
|
||||
|
||||
1. `cli-config.yaml` (highest priority in CLI)
|
||||
2. `.env` file
|
||||
3. System environment variables
|
||||
4. Default values
|
||||
|
||||
This allows you to have different terminal configs for CLI vs batch processing.
|
||||
|
||||
## Session Management
|
||||
|
||||
- **History**: Command history is saved to `~/.hermes_history`
|
||||
- **Conversations**: Use `/save` to export conversations
|
||||
- **Reset**: Use `/clear` for full reset, `/reset` to just clear history
|
||||
- **Session Logs**: Every session automatically logs to `logs/session_{session_id}.json`
|
||||
|
||||
### Session Logging
|
||||
|
||||
Sessions are automatically logged to the `logs/` directory:
|
||||
|
||||
```
|
||||
logs/
|
||||
├── session_20260201_143052_a1b2c3.json
|
||||
├── session_20260201_150217_d4e5f6.json
|
||||
└── ...
|
||||
```
|
||||
|
||||
The session ID is displayed in the welcome banner and follows the format: `YYYYMMDD_HHMMSS_UUID`.
|
||||
|
||||
Log files contain:
|
||||
- Full conversation history in trajectory format
|
||||
- Timestamps for session start and last update
|
||||
- Model and message count metadata
|
||||
|
||||
This is useful for:
|
||||
- Debugging agent behavior
|
||||
- Replaying conversations
|
||||
- Training data inspection
|
||||
|
||||
### Context Compression
|
||||
|
||||
Long conversations can exceed model context limits. The CLI automatically compresses context when approaching the limit:
|
||||
|
||||
```yaml
|
||||
# In cli-config.yaml
|
||||
compression:
|
||||
enabled: true # Enable auto-compression
|
||||
threshold: 0.85 # Compress at 85% of context limit
|
||||
summary_model: "google/gemini-2.0-flash-001"
|
||||
```
|
||||
|
||||
**How it works:**
|
||||
1. Tracks actual token usage from each API response
|
||||
2. When tokens reach threshold, middle turns are summarized
|
||||
3. First 3 and last 4 turns are always protected
|
||||
4. Conversation continues seamlessly after compression
|
||||
|
||||
**When compression triggers:**
|
||||
```
|
||||
📦 Context compression triggered (170,000 tokens ≥ 170,000 threshold)
|
||||
📊 Model context limit: 200,000 tokens (85% = 170,000)
|
||||
🗜️ Summarizing turns 4-15 (12 turns)
|
||||
✅ Compressed: 20 → 9 messages (~45,000 tokens saved)
|
||||
```
|
||||
|
||||
To disable compression:
|
||||
```yaml
|
||||
compression:
|
||||
enabled: false
|
||||
```
|
||||
|
||||
## Quiet Mode
|
||||
|
||||
The CLI runs in "quiet mode" (`HERMES_QUIET=1`), which:
|
||||
- Suppresses verbose logging from tools
|
||||
- Enables kawaii-style animated feedback
|
||||
- Hides terminal environment warnings
|
||||
- Keeps output clean and user-friendly
|
||||
|
||||
For verbose output (debugging), use:
|
||||
```bash
|
||||
./hermes --verbose
|
||||
```
|
||||
@@ -1,124 +0,0 @@
|
||||
# LLM Client
|
||||
|
||||
Hermes Agent uses the OpenAI Python SDK with OpenRouter as the backend, providing access to many models through a single API.
|
||||
|
||||
## Configuration
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(
|
||||
api_key=os.getenv("OPENROUTER_API_KEY"),
|
||||
base_url="https://openrouter.ai/api/v1"
|
||||
)
|
||||
```
|
||||
|
||||
## Supported Models
|
||||
|
||||
Any model available on [OpenRouter](https://openrouter.ai/models):
|
||||
|
||||
```python
|
||||
# Anthropic
|
||||
model = "anthropic/claude-sonnet-4"
|
||||
model = "anthropic/claude-opus-4"
|
||||
|
||||
# OpenAI
|
||||
model = "openai/gpt-4o"
|
||||
model = "openai/o1"
|
||||
|
||||
# Google
|
||||
model = "google/gemini-2.0-flash"
|
||||
|
||||
# Open models
|
||||
model = "meta-llama/llama-3.3-70b-instruct"
|
||||
model = "deepseek/deepseek-chat-v3"
|
||||
model = "moonshotai/kimi-k2.5"
|
||||
```
|
||||
|
||||
## Tool Calling
|
||||
|
||||
Standard OpenAI function calling format:
|
||||
|
||||
```python
|
||||
response = client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "web_search",
|
||||
"description": "Search the web",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {"type": "string"}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
# Check for tool calls
|
||||
if response.choices[0].message.tool_calls:
|
||||
for tool_call in response.choices[0].message.tool_calls:
|
||||
name = tool_call.function.name
|
||||
args = json.loads(tool_call.function.arguments)
|
||||
# Execute tool...
|
||||
```
|
||||
|
||||
## Reasoning Models
|
||||
|
||||
Some models return reasoning/thinking content:
|
||||
|
||||
```python
|
||||
# Access reasoning if available
|
||||
message = response.choices[0].message
|
||||
if hasattr(message, 'reasoning_content') and message.reasoning_content:
|
||||
reasoning = message.reasoning_content
|
||||
# Store for trajectory export
|
||||
```
|
||||
|
||||
## Provider Selection
|
||||
|
||||
OpenRouter allows selecting specific providers:
|
||||
|
||||
```python
|
||||
response = client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
extra_body={
|
||||
"provider": {
|
||||
"order": ["Anthropic", "Google"], # Preferred providers
|
||||
"ignore": ["Novita"], # Providers to skip
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
Common errors and handling:
|
||||
|
||||
```python
|
||||
try:
|
||||
response = client.chat.completions.create(...)
|
||||
except openai.RateLimitError:
|
||||
# Back off and retry
|
||||
except openai.APIError as e:
|
||||
# Check e.code for specific errors
|
||||
# 400 = bad request (often provider-specific)
|
||||
# 502 = bad gateway (retry with different provider)
|
||||
```
|
||||
|
||||
## Cost Tracking
|
||||
|
||||
OpenRouter returns usage info:
|
||||
|
||||
```python
|
||||
usage = response.usage
|
||||
print(f"Tokens: {usage.prompt_tokens} + {usage.completion_tokens}")
|
||||
print(f"Cost: ${usage.cost:.6f}") # If available
|
||||
```
|
||||
@@ -1,121 +0,0 @@
|
||||
# Message Format & Trajectories
|
||||
|
||||
Hermes Agent uses two message formats: the **API format** for LLM calls and the **trajectory format** for training data export.
|
||||
|
||||
## API Message Format
|
||||
|
||||
Standard OpenAI chat format used during execution:
|
||||
|
||||
```python
|
||||
messages = [
|
||||
# System prompt
|
||||
{"role": "system", "content": "You are a helpful assistant with tools..."},
|
||||
|
||||
# User query
|
||||
{"role": "user", "content": "Search for Python tutorials"},
|
||||
|
||||
# Assistant with tool call
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [{
|
||||
"id": "call_abc123",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "web_search",
|
||||
"arguments": "{\"query\": \"Python tutorials\"}"
|
||||
}
|
||||
}]
|
||||
},
|
||||
|
||||
# Tool result
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_abc123",
|
||||
"content": "{\"results\": [...]}"
|
||||
},
|
||||
|
||||
# Final response
|
||||
{"role": "assistant", "content": "Here's what I found..."}
|
||||
]
|
||||
```
|
||||
|
||||
## Trajectory Format (ShareGPT)
|
||||
|
||||
Exported for training in ShareGPT format:
|
||||
|
||||
```json
|
||||
{
|
||||
"conversations": [
|
||||
{"from": "system", "value": "You are a helpful assistant..."},
|
||||
{"from": "human", "value": "Search for Python tutorials"},
|
||||
{"from": "gpt", "value": "<tool_call>\n{\"name\": \"web_search\", \"arguments\": {\"query\": \"Python tutorials\"}}\n</tool_call>"},
|
||||
{"from": "tool", "value": "<tool_response>\n{\"results\": [...]}\n</tool_response>"},
|
||||
{"from": "gpt", "value": "Here's what I found..."}
|
||||
],
|
||||
"tools": "[{\"type\": \"function\", \"function\": {...}}]",
|
||||
"source": "hermes-agent"
|
||||
}
|
||||
```
|
||||
|
||||
## Reasoning Content
|
||||
|
||||
For models that output reasoning/chain-of-thought:
|
||||
|
||||
**During execution** (API format):
|
||||
```python
|
||||
# Stored internally but not sent back to model in content
|
||||
assistant_msg = {
|
||||
"role": "assistant",
|
||||
"content": "Here's what I found...",
|
||||
"reasoning": "Let me think about this step by step..." # Internal only
|
||||
}
|
||||
```
|
||||
|
||||
**In trajectory export** (reasoning wrapped in tags):
|
||||
```json
|
||||
{
|
||||
"from": "gpt",
|
||||
"value": "<think>\nLet me think about this step by step...\n</think>\nHere's what I found..."
|
||||
}
|
||||
```
|
||||
|
||||
## Conversion Flow
|
||||
|
||||
```
|
||||
API Response → Internal Storage → Trajectory Export
|
||||
↓ ↓ ↓
|
||||
tool_calls reasoning field <tool_call> tags
|
||||
reasoning_content <think> tags
|
||||
```
|
||||
|
||||
The conversion happens in `_convert_to_trajectory_format()` in `run_agent.py`.
|
||||
|
||||
## Ephemeral System Prompts
|
||||
|
||||
Batch processing supports ephemeral system prompts that guide behavior during execution but are NOT saved to trajectories:
|
||||
|
||||
```python
|
||||
# During execution: full system prompt + ephemeral guidance
|
||||
messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT + "\n\n" + ephemeral_prompt},
|
||||
...
|
||||
]
|
||||
|
||||
# In saved trajectory: only the base system prompt
|
||||
trajectory = {
|
||||
"conversations": [
|
||||
{"from": "system", "value": SYSTEM_PROMPT}, # No ephemeral
|
||||
...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
## Trajectory Compression
|
||||
|
||||
Long trajectories can be compressed for training using `trajectory_compressor.py`:
|
||||
|
||||
- Protects first/last N turns
|
||||
- Summarizes middle turns with LLM
|
||||
- Targets specific token budget
|
||||
- See `configs/trajectory_compression.yaml` for settings
|
||||
@@ -1,515 +0,0 @@
|
||||
# Messaging Platform Integrations (Gateway)
|
||||
|
||||
Hermes Agent can connect to messaging platforms like Telegram, Discord, and WhatsApp to serve as a conversational AI assistant.
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# 1. Set your bot token(s) in .env file
|
||||
echo 'TELEGRAM_BOT_TOKEN="your_telegram_bot_token"' >> .env
|
||||
echo 'DISCORD_BOT_TOKEN="your_discord_bot_token"' >> .env
|
||||
|
||||
# 2. Test the gateway (foreground)
|
||||
./scripts/hermes-gateway run
|
||||
|
||||
# 3. Install as a system service (runs in background)
|
||||
./scripts/hermes-gateway install
|
||||
|
||||
# 4. Manage the service
|
||||
./scripts/hermes-gateway start
|
||||
./scripts/hermes-gateway stop
|
||||
./scripts/hermes-gateway restart
|
||||
./scripts/hermes-gateway status
|
||||
```
|
||||
|
||||
**Quick test (without service install):**
|
||||
```bash
|
||||
python cli.py --gateway # Runs in foreground, useful for debugging
|
||||
```
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ Hermes Gateway │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
|
||||
│ │ Telegram │ │ Discord │ │ WhatsApp │ │
|
||||
│ │ Adapter │ │ Adapter │ │ Adapter │ │
|
||||
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
|
||||
│ │ │ │ │
|
||||
│ └─────────────────┼─────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌────────▼────────┐ │
|
||||
│ │ Session Store │ │
|
||||
│ │ (per-chat) │ │
|
||||
│ └────────┬────────┘ │
|
||||
│ │ │
|
||||
│ ┌────────▼────────┐ │
|
||||
│ │ AIAgent │ │
|
||||
│ │ (run_agent) │ │
|
||||
│ └─────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Session Management
|
||||
|
||||
### Session Persistence
|
||||
|
||||
Sessions persist across messages until they reset. The agent remembers your conversation context.
|
||||
|
||||
### Reset Policies
|
||||
|
||||
Sessions reset based on configurable policies:
|
||||
|
||||
| Policy | Default | Description |
|
||||
|--------|---------|-------------|
|
||||
| Daily | 4:00 AM | Reset at a specific hour each day |
|
||||
| Idle | 120 min | Reset after N minutes of inactivity |
|
||||
| Both | (combined) | Whichever triggers first |
|
||||
|
||||
### Manual Reset
|
||||
|
||||
Send `/new` or `/reset` as a message to start fresh.
|
||||
|
||||
### Per-Platform Overrides
|
||||
|
||||
Configure different reset policies per platform:
|
||||
|
||||
```json
|
||||
{
|
||||
"reset_by_platform": {
|
||||
"telegram": { "mode": "idle", "idle_minutes": 240 },
|
||||
"discord": { "mode": "idle", "idle_minutes": 60 }
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Platform Setup
|
||||
|
||||
### Telegram
|
||||
|
||||
1. **Create a bot** via [@BotFather](https://t.me/BotFather)
|
||||
2. **Get your token** (looks like `123456789:ABCdefGHIjklMNOpqrsTUVwxyz`)
|
||||
3. **Set environment variable:**
|
||||
```bash
|
||||
export TELEGRAM_BOT_TOKEN="your_token_here"
|
||||
```
|
||||
4. **Optional: Set home channel** for cron job delivery:
|
||||
```bash
|
||||
export TELEGRAM_HOME_CHANNEL="-1001234567890"
|
||||
export TELEGRAM_HOME_CHANNEL_NAME="My Notes"
|
||||
```
|
||||
|
||||
**Requirements:**
|
||||
```bash
|
||||
pip install python-telegram-bot>=20.0
|
||||
```
|
||||
|
||||
### Discord
|
||||
|
||||
1. **Create an application** at [Discord Developer Portal](https://discord.com/developers/applications)
|
||||
2. **Create a bot** under your application
|
||||
3. **Get the bot token**
|
||||
4. **Enable required intents:**
|
||||
- Message Content Intent
|
||||
- Server Members Intent (optional)
|
||||
5. **Invite to your server** using OAuth2 URL generator (scopes: `bot`, `applications.commands`)
|
||||
6. **Set environment variable:**
|
||||
```bash
|
||||
export DISCORD_BOT_TOKEN="your_token_here"
|
||||
```
|
||||
7. **Optional: Set home channel:**
|
||||
```bash
|
||||
export DISCORD_HOME_CHANNEL="123456789012345678"
|
||||
export DISCORD_HOME_CHANNEL_NAME="#bot-updates"
|
||||
```
|
||||
|
||||
**Requirements:**
|
||||
```bash
|
||||
pip install discord.py>=2.0
|
||||
```
|
||||
|
||||
### WhatsApp
|
||||
|
||||
WhatsApp integration is more complex due to the lack of a simple bot API.
|
||||
|
||||
**Options:**
|
||||
1. **WhatsApp Business API** (requires Meta verification)
|
||||
2. **whatsapp-web.js** via Node.js bridge (for personal accounts)
|
||||
|
||||
**Bridge Setup:**
|
||||
1. Install Node.js
|
||||
2. Set up the bridge script (see `scripts/whatsapp-bridge/` for reference)
|
||||
3. Configure in gateway:
|
||||
```json
|
||||
{
|
||||
"platforms": {
|
||||
"whatsapp": {
|
||||
"enabled": true,
|
||||
"extra": {
|
||||
"bridge_script": "/path/to/bridge.js",
|
||||
"bridge_port": 3000
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
There are **three ways** to configure the gateway (in order of precedence):
|
||||
|
||||
### 1. Environment Variables (`.env` file) - Recommended for Quick Setup
|
||||
|
||||
Add to your `~/.hermes/.env` file:
|
||||
|
||||
```bash
|
||||
# =============================================================================
|
||||
# MESSAGING PLATFORM TOKENS
|
||||
# =============================================================================
|
||||
|
||||
# Telegram - get from @BotFather on Telegram
|
||||
TELEGRAM_BOT_TOKEN=your_telegram_bot_token
|
||||
TELEGRAM_ALLOWED_USERS=123456789,987654321 # Security: restrict to these user IDs
|
||||
|
||||
# Optional: Default channel for cron job delivery
|
||||
TELEGRAM_HOME_CHANNEL=-1001234567890
|
||||
TELEGRAM_HOME_CHANNEL_NAME="My Notes"
|
||||
|
||||
# Discord - get from Discord Developer Portal
|
||||
DISCORD_BOT_TOKEN=your_discord_bot_token
|
||||
DISCORD_ALLOWED_USERS=123456789012345678 # Security: restrict to these user IDs
|
||||
|
||||
# Optional: Default channel for cron job delivery
|
||||
DISCORD_HOME_CHANNEL=123456789012345678
|
||||
DISCORD_HOME_CHANNEL_NAME="#bot-updates"
|
||||
|
||||
# WhatsApp - requires Node.js bridge setup
|
||||
WHATSAPP_ENABLED=true
|
||||
|
||||
# =============================================================================
|
||||
# AGENT SETTINGS
|
||||
# =============================================================================
|
||||
|
||||
# Max tool-calling iterations per conversation (default: 60)
|
||||
HERMES_MAX_ITERATIONS=60
|
||||
|
||||
# Working directory for terminal commands (default: home ~)
|
||||
MESSAGING_CWD=/home/myuser
|
||||
|
||||
# =============================================================================
|
||||
# TOOL PROGRESS NOTIFICATIONS
|
||||
# =============================================================================
|
||||
|
||||
# Show progress messages as agent uses tools
|
||||
HERMES_TOOL_PROGRESS=true
|
||||
|
||||
# Mode: "new" (only when tool changes) or "all" (every tool call)
|
||||
HERMES_TOOL_PROGRESS_MODE=new
|
||||
|
||||
# =============================================================================
|
||||
# SESSION SETTINGS
|
||||
# =============================================================================
|
||||
|
||||
# Reset sessions after N minutes of inactivity (default: 120)
|
||||
SESSION_IDLE_MINUTES=120
|
||||
|
||||
# Daily reset hour in 24h format (default: 4 = 4am)
|
||||
SESSION_RESET_HOUR=4
|
||||
```
|
||||
|
||||
### 2. Gateway Config File (`~/.hermes/gateway.json`) - Full Control
|
||||
|
||||
For advanced configuration, create `~/.hermes/gateway.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"platforms": {
|
||||
"telegram": {
|
||||
"enabled": true,
|
||||
"token": "your_telegram_token",
|
||||
"home_channel": {
|
||||
"platform": "telegram",
|
||||
"chat_id": "-1001234567890",
|
||||
"name": "My Notes"
|
||||
}
|
||||
},
|
||||
"discord": {
|
||||
"enabled": true,
|
||||
"token": "your_discord_token",
|
||||
"home_channel": {
|
||||
"platform": "discord",
|
||||
"chat_id": "123456789012345678",
|
||||
"name": "#bot-updates"
|
||||
}
|
||||
}
|
||||
},
|
||||
"default_reset_policy": {
|
||||
"mode": "both",
|
||||
"at_hour": 4,
|
||||
"idle_minutes": 120
|
||||
},
|
||||
"reset_by_platform": {
|
||||
"discord": {
|
||||
"mode": "idle",
|
||||
"idle_minutes": 60
|
||||
}
|
||||
},
|
||||
"always_log_local": true
|
||||
}
|
||||
```
|
||||
|
||||
## Platform-Specific Toolsets
|
||||
|
||||
Each platform has its own toolset for security:
|
||||
|
||||
| Platform | Toolset | Capabilities |
|
||||
|----------|---------|--------------|
|
||||
| CLI | `hermes-cli` | Full access (terminal, browser, etc.) |
|
||||
| Telegram | `hermes-telegram` | Full tools including terminal |
|
||||
| Discord | `hermes-discord` | Full tools including terminal |
|
||||
| WhatsApp | `hermes-whatsapp` | Full tools including terminal |
|
||||
|
||||
## User Experience Features
|
||||
|
||||
### Typing Indicator
|
||||
|
||||
The gateway keeps the "typing..." indicator active throughout processing, refreshing every 4 seconds. This lets users know the bot is working even during long tool-calling sequences.
|
||||
|
||||
### Tool Progress Notifications
|
||||
|
||||
When `HERMES_TOOL_PROGRESS=true`, the bot sends status messages as it works:
|
||||
|
||||
```
|
||||
💻 `ls -la`...
|
||||
🔍 web_search...
|
||||
📄 web_extract...
|
||||
🎨 image_generate...
|
||||
```
|
||||
|
||||
Terminal commands show the actual command (truncated to 50 chars). Other tools just show the tool name.
|
||||
|
||||
**Modes:**
|
||||
- `new`: Only sends message when switching to a different tool (less spam)
|
||||
- `all`: Sends message for every single tool call
|
||||
|
||||
### Working Directory
|
||||
|
||||
- **CLI (`hermes` command)**: Uses current directory where you run the command
|
||||
- **Messaging**: Uses `MESSAGING_CWD` (default: home directory `~`)
|
||||
|
||||
This is intentional: CLI users are in a terminal and expect the agent to work in their current directory, while messaging users need a consistent starting location.
|
||||
|
||||
### Max Iterations
|
||||
|
||||
If the agent hits the max iteration limit while working, instead of a generic error, it asks the model to summarize what it found so far. This gives you a useful response even when the task couldn't be fully completed.
|
||||
|
||||
## Cron Job Delivery
|
||||
|
||||
When scheduling cron jobs, you can specify where the output should be delivered:
|
||||
|
||||
```
|
||||
User: "Remind me to check the server in 30 minutes"
|
||||
|
||||
Agent uses: schedule_cronjob(
|
||||
prompt="Check server status...",
|
||||
schedule="30m",
|
||||
deliver="origin" # Back to this chat
|
||||
)
|
||||
```
|
||||
|
||||
### Delivery Options
|
||||
|
||||
| Option | Description |
|
||||
|--------|-------------|
|
||||
| `"origin"` | Back to where the job was created |
|
||||
| `"local"` | Save to local files only |
|
||||
| `"telegram"` | Telegram home channel |
|
||||
| `"discord"` | Discord home channel |
|
||||
| `"telegram:123456"` | Specific Telegram chat |
|
||||
|
||||
## Dynamic Context Injection
|
||||
|
||||
The agent knows where it is via injected context:
|
||||
|
||||
```
|
||||
## Current Session Context
|
||||
|
||||
**Source:** Telegram (group: Dev Team, ID: -1001234567890)
|
||||
**Connected Platforms:** local, telegram, discord
|
||||
|
||||
**Home Channels:**
|
||||
- telegram: My Notes (ID: -1001234567890)
|
||||
- discord: #bot-updates (ID: 123456789012345678)
|
||||
|
||||
**Delivery options for scheduled tasks:**
|
||||
- "origin" → Back to this chat (Dev Team)
|
||||
- "local" → Save to local files only
|
||||
- "telegram" → Home channel (My Notes)
|
||||
- "discord" → Home channel (#bot-updates)
|
||||
```
|
||||
|
||||
## CLI Commands
|
||||
|
||||
| Command | Description |
|
||||
|---------|-------------|
|
||||
| `/platforms` | Show gateway configuration and status |
|
||||
| `--gateway` | Start the gateway (CLI flag) |
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "python-telegram-bot not installed"
|
||||
|
||||
```bash
|
||||
pip install python-telegram-bot>=20.0
|
||||
```
|
||||
|
||||
### "discord.py not installed"
|
||||
|
||||
```bash
|
||||
pip install discord.py>=2.0
|
||||
```
|
||||
|
||||
### "No platforms connected"
|
||||
|
||||
1. Check your environment variables are set
|
||||
2. Check your tokens are valid
|
||||
3. Try `/platforms` to see configuration status
|
||||
|
||||
### Session not persisting
|
||||
|
||||
1. Check `~/.hermes/sessions/` exists
|
||||
2. Check session policies aren't too aggressive
|
||||
3. Verify no errors in gateway logs
|
||||
|
||||
## Adding a New Platform
|
||||
|
||||
To add a new messaging platform:
|
||||
|
||||
### 1. Create the adapter
|
||||
|
||||
Create `gateway/platforms/your_platform.py`:
|
||||
|
||||
```python
|
||||
from gateway.platforms.base import BasePlatformAdapter, MessageEvent, SendResult
|
||||
from gateway.config import Platform, PlatformConfig
|
||||
|
||||
class YourPlatformAdapter(BasePlatformAdapter):
|
||||
def __init__(self, config: PlatformConfig):
|
||||
super().__init__(config, Platform.YOUR_PLATFORM)
|
||||
|
||||
async def connect(self) -> bool:
|
||||
# Connect to the platform
|
||||
...
|
||||
|
||||
async def disconnect(self) -> None:
|
||||
# Disconnect
|
||||
...
|
||||
|
||||
async def send(self, chat_id: str, content: str, ...) -> SendResult:
|
||||
# Send a message
|
||||
...
|
||||
|
||||
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
|
||||
# Get chat information
|
||||
...
|
||||
```
|
||||
|
||||
### 2. Register the platform
|
||||
|
||||
Add to `gateway/config.py`:
|
||||
|
||||
```python
|
||||
class Platform(Enum):
|
||||
# ... existing ...
|
||||
YOUR_PLATFORM = "your_platform"
|
||||
```
|
||||
|
||||
### 3. Add to gateway runner
|
||||
|
||||
Update `gateway/run.py` `_create_adapter()`:
|
||||
|
||||
```python
|
||||
elif platform == Platform.YOUR_PLATFORM:
|
||||
from gateway.platforms.your_platform import YourPlatformAdapter
|
||||
return YourPlatformAdapter(config)
|
||||
```
|
||||
|
||||
### 4. Create a toolset (optional)
|
||||
|
||||
Add to `toolsets.py`:
|
||||
|
||||
```python
|
||||
"hermes-your-platform": {
|
||||
"description": "Your platform toolset",
|
||||
"tools": [...],
|
||||
"includes": []
|
||||
}
|
||||
```
|
||||
|
||||
### 5. Configure
|
||||
|
||||
Add environment variables to `.env`:
|
||||
|
||||
```bash
|
||||
YOUR_PLATFORM_TOKEN=...
|
||||
YOUR_PLATFORM_HOME_CHANNEL=...
|
||||
```
|
||||
|
||||
## Service Management
|
||||
|
||||
### Linux (systemd)
|
||||
|
||||
```bash
|
||||
# Install as user service
|
||||
./scripts/hermes-gateway install
|
||||
|
||||
# Manage
|
||||
systemctl --user start hermes-gateway
|
||||
systemctl --user stop hermes-gateway
|
||||
systemctl --user restart hermes-gateway
|
||||
systemctl --user status hermes-gateway
|
||||
|
||||
# View logs
|
||||
journalctl --user -u hermes-gateway -f
|
||||
|
||||
# Enable lingering (keeps running after logout)
|
||||
sudo loginctl enable-linger $USER
|
||||
```
|
||||
|
||||
### macOS (launchd)
|
||||
|
||||
```bash
|
||||
# Install
|
||||
./scripts/hermes-gateway install
|
||||
|
||||
# Manage
|
||||
launchctl start ai.hermes.gateway
|
||||
launchctl stop ai.hermes.gateway
|
||||
|
||||
# View logs
|
||||
tail -f ~/.hermes/logs/gateway.log
|
||||
```
|
||||
|
||||
### Manual (any platform)
|
||||
|
||||
```bash
|
||||
# Run in foreground (for testing/debugging)
|
||||
./scripts/hermes-gateway run
|
||||
|
||||
# Or via CLI (also foreground)
|
||||
python cli.py --gateway
|
||||
```
|
||||
|
||||
## Storage Locations
|
||||
|
||||
| Path | Purpose |
|
||||
|------|---------|
|
||||
| `~/.hermes/gateway.json` | Gateway configuration |
|
||||
| `~/.hermes/sessions/sessions.json` | Session index |
|
||||
| `~/.hermes/sessions/{id}.jsonl` | Conversation transcripts |
|
||||
| `~/.hermes/cron/output/` | Cron job outputs |
|
||||
| `~/.hermes/logs/gateway.log` | Gateway logs (macOS launchd) |
|
||||
159
docs/tools.md
159
docs/tools.md
@@ -1,159 +0,0 @@
|
||||
# Tools
|
||||
|
||||
Tools are functions that extend the agent's capabilities. Each tool is defined with an OpenAI-compatible JSON schema and an async handler function.
|
||||
|
||||
## Tool Structure
|
||||
|
||||
Each tool module in `tools/` exports:
|
||||
1. **Schema definitions** - OpenAI function-calling format
|
||||
2. **Handler functions** - Async functions that execute the tool
|
||||
|
||||
```python
|
||||
# Example: tools/web_tools.py
|
||||
|
||||
# Schema definition
|
||||
WEB_SEARCH_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "web_search",
|
||||
"description": "Search the web for information",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {"type": "string", "description": "Search query"}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Handler function
|
||||
async def web_search(query: str) -> dict:
|
||||
"""Execute web search and return results."""
|
||||
# Implementation...
|
||||
return {"results": [...]}
|
||||
```
|
||||
|
||||
## Tool Categories
|
||||
|
||||
| Category | Module | Tools |
|
||||
|----------|--------|-------|
|
||||
| **Web** | `web_tools.py` | `web_search`, `web_extract`, `web_crawl` |
|
||||
| **Terminal** | `terminal_tool.py` | `terminal` (local/docker/singularity/modal/ssh backends) |
|
||||
| **Browser** | `browser_tool.py` | `browser_navigate`, `browser_click`, `browser_type`, etc. |
|
||||
| **Vision** | `vision_tools.py` | `vision_analyze` |
|
||||
| **Image Gen** | `image_generation_tool.py` | `image_generate` |
|
||||
| **Reasoning** | `mixture_of_agents_tool.py` | `mixture_of_agents` |
|
||||
| **Skills** | `skills_tool.py` | `skills_categories`, `skills_list`, `skill_view` |
|
||||
|
||||
## Tool Registration
|
||||
|
||||
Tools are registered in `model_tools.py`:
|
||||
|
||||
```python
|
||||
# model_tools.py
|
||||
TOOL_SCHEMAS = [
|
||||
*WEB_TOOL_SCHEMAS,
|
||||
*TERMINAL_TOOL_SCHEMAS,
|
||||
*BROWSER_TOOL_SCHEMAS,
|
||||
# ...
|
||||
]
|
||||
|
||||
TOOL_HANDLERS = {
|
||||
"web_search": web_search,
|
||||
"terminal": terminal_tool,
|
||||
"browser_navigate": browser_navigate,
|
||||
# ...
|
||||
}
|
||||
```
|
||||
|
||||
## Toolsets
|
||||
|
||||
Tools are grouped into **toolsets** for logical organization (see `toolsets.py`):
|
||||
|
||||
```python
|
||||
TOOLSETS = {
|
||||
"web": {
|
||||
"description": "Web search and content extraction",
|
||||
"tools": ["web_search", "web_extract", "web_crawl"]
|
||||
},
|
||||
"terminal": {
|
||||
"description": "Command execution",
|
||||
"tools": ["terminal"]
|
||||
},
|
||||
# ...
|
||||
}
|
||||
```
|
||||
|
||||
## Adding a New Tool
|
||||
|
||||
1. Create handler function in `tools/your_tool.py`
|
||||
2. Define JSON schema following OpenAI format
|
||||
3. Register in `model_tools.py` (schemas and handlers)
|
||||
4. Add to appropriate toolset in `toolsets.py`
|
||||
5. Update `tools/__init__.py` exports
|
||||
|
||||
## Stateful Tools
|
||||
|
||||
Some tools maintain state across calls within a session:
|
||||
|
||||
- **Terminal**: Keeps container/sandbox running between commands
|
||||
- **Browser**: Maintains browser session for multi-step navigation
|
||||
|
||||
State is managed per `task_id` and cleaned up automatically.
|
||||
|
||||
## Terminal Backends
|
||||
|
||||
The terminal tool supports multiple execution backends:
|
||||
|
||||
| Backend | Description | Use Case |
|
||||
|---------|-------------|----------|
|
||||
| `local` | Direct execution on host | Development, simple tasks |
|
||||
| `ssh` | Remote execution via SSH | Sandboxing (agent can't modify its own code) |
|
||||
| `docker` | Docker container | Isolation, reproducibility |
|
||||
| `singularity` | Singularity/Apptainer | HPC clusters, rootless containers |
|
||||
| `modal` | Modal cloud | Scalable cloud compute, GPUs |
|
||||
|
||||
Configure via environment variables or `cli-config.yaml`:
|
||||
|
||||
```yaml
|
||||
# SSH backend example (in cli-config.yaml)
|
||||
terminal:
|
||||
env_type: "ssh"
|
||||
ssh_host: "my-server.example.com"
|
||||
ssh_user: "myuser"
|
||||
ssh_key: "~/.ssh/id_rsa"
|
||||
cwd: "/home/myuser/project"
|
||||
```
|
||||
|
||||
The SSH backend uses ControlMaster for connection persistence, making subsequent commands fast.
|
||||
|
||||
## Skills Tools (Progressive Disclosure)
|
||||
|
||||
Skills are on-demand knowledge documents. They use **progressive disclosure** to minimize tokens:
|
||||
|
||||
```
|
||||
Level 0: skills_categories() → ["mlops", "devops"] (~50 tokens)
|
||||
Level 1: skills_list(category) → [{name, description}, ...] (~3k tokens)
|
||||
Level 2: skill_view(name) → Full content + metadata (varies)
|
||||
Level 3: skill_view(name, path) → Specific reference file (varies)
|
||||
```
|
||||
|
||||
Skill directory structure:
|
||||
```
|
||||
skills/
|
||||
└── mlops/
|
||||
└── axolotl/
|
||||
├── SKILL.md # Main instructions (required)
|
||||
├── references/ # Additional docs
|
||||
└── templates/ # Output formats, configs
|
||||
```
|
||||
|
||||
SKILL.md uses YAML frontmatter:
|
||||
```yaml
|
||||
---
|
||||
name: axolotl
|
||||
description: Fine-tuning LLMs with Axolotl
|
||||
tags: [Fine-Tuning, LoRA, DPO]
|
||||
---
|
||||
```
|
||||
@@ -1,28 +0,0 @@
|
||||
"""
|
||||
Hermes-Agent Atropos Environments
|
||||
|
||||
Provides a layered integration between hermes-agent's tool-calling capabilities
|
||||
and the Atropos RL training framework.
|
||||
|
||||
Layers:
|
||||
- agent_loop: Reusable multi-turn agent loop with standard OpenAI-spec tool calling
|
||||
- tool_context: Per-rollout tool access handle for reward/verification functions
|
||||
- hermes_base_env: Abstract base environment (BaseEnv subclass) for Atropos
|
||||
- tool_call_parsers: Client-side tool call parser registry for Phase 2 (VLLM /generate)
|
||||
|
||||
Concrete environments:
|
||||
- terminal_test_env: Simple file-creation tasks for testing the stack
|
||||
- hermes_swe_env: SWE-bench style tasks with Modal sandboxes
|
||||
"""
|
||||
|
||||
from environments.agent_loop import AgentResult, HermesAgentLoop
|
||||
from environments.tool_context import ToolContext
|
||||
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
|
||||
|
||||
__all__ = [
|
||||
"AgentResult",
|
||||
"HermesAgentLoop",
|
||||
"ToolContext",
|
||||
"HermesAgentBaseEnv",
|
||||
"HermesAgentEnvConfig",
|
||||
]
|
||||
@@ -1,372 +0,0 @@
|
||||
"""
|
||||
HermesAgentLoop -- Reusable Multi-Turn Agent Engine
|
||||
|
||||
Runs the hermes-agent tool-calling loop using standard OpenAI-spec tool calling.
|
||||
Works with any server that returns ChatCompletion objects with tool_calls:
|
||||
- Phase 1: OpenAI server type (VLLM, SGLang, OpenRouter, OpenAI API)
|
||||
- Phase 2: ManagedServer with client-side tool call parser
|
||||
|
||||
The loop passes tools= and checks response.choices[0].message.tool_calls,
|
||||
identical to hermes-agent's run_agent.py. Tool execution is dispatched via
|
||||
handle_function_call() from model_tools.py.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import concurrent.futures
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Dict, List, Optional, Set
|
||||
|
||||
from model_tools import handle_function_call
|
||||
|
||||
# Thread pool for running sync tool calls that internally use asyncio.run()
|
||||
# (e.g., mini-swe-agent's modal/docker backends). Running them in a separate
|
||||
# thread gives them a clean event loop so they don't deadlock inside Atropos's loop.
|
||||
_tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=8)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolError:
|
||||
"""Record of a tool execution error during the agent loop."""
|
||||
|
||||
turn: int # Which turn the error occurred on
|
||||
tool_name: str # Which tool was called
|
||||
arguments: str # The arguments passed (truncated)
|
||||
error: str # The error message
|
||||
tool_result: str # The raw result returned to the model
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentResult:
|
||||
"""Result of running the agent loop."""
|
||||
|
||||
# Full conversation history in OpenAI message format
|
||||
messages: List[Dict[str, Any]]
|
||||
# ManagedServer.get_state() if available (Phase 2), None otherwise
|
||||
managed_state: Optional[Dict[str, Any]] = None
|
||||
# How many LLM calls were made
|
||||
turns_used: int = 0
|
||||
# True if model stopped calling tools naturally (vs hitting max_turns)
|
||||
finished_naturally: bool = False
|
||||
# Extracted reasoning content per turn (from PR #297 helpers)
|
||||
reasoning_per_turn: List[Optional[str]] = field(default_factory=list)
|
||||
# Tool errors encountered during the loop
|
||||
tool_errors: List[ToolError] = field(default_factory=list)
|
||||
|
||||
|
||||
def _extract_reasoning_from_message(message) -> Optional[str]:
|
||||
"""
|
||||
Extract reasoning content from a ChatCompletion message.
|
||||
|
||||
Handles multiple provider formats:
|
||||
1. message.reasoning_content field (some providers)
|
||||
2. message.reasoning field (some providers)
|
||||
3. message.reasoning_details[].text (OpenRouter style)
|
||||
|
||||
Note: <think> block extraction from content is NOT done here -- that's
|
||||
handled by the response already in Phase 1 (server does it) or by
|
||||
ManagedServer's patch in Phase 2.
|
||||
|
||||
Args:
|
||||
message: The assistant message from ChatCompletion response
|
||||
|
||||
Returns:
|
||||
Extracted reasoning text, or None if not found
|
||||
"""
|
||||
# Check reasoning_content field (common across providers)
|
||||
if hasattr(message, "reasoning_content") and message.reasoning_content:
|
||||
return message.reasoning_content
|
||||
|
||||
# Check reasoning field
|
||||
if hasattr(message, "reasoning") and message.reasoning:
|
||||
return message.reasoning
|
||||
|
||||
# Check reasoning_details (OpenRouter style)
|
||||
if hasattr(message, "reasoning_details") and message.reasoning_details:
|
||||
for detail in message.reasoning_details:
|
||||
if hasattr(detail, "text") and detail.text:
|
||||
return detail.text
|
||||
if isinstance(detail, dict) and detail.get("text"):
|
||||
return detail["text"]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
class HermesAgentLoop:
|
||||
"""
|
||||
Runs hermes-agent's tool-calling loop using standard OpenAI-spec tool calling.
|
||||
|
||||
Same pattern as run_agent.py:
|
||||
- Pass tools= to the API
|
||||
- Check response.choices[0].message.tool_calls
|
||||
- Dispatch via handle_function_call()
|
||||
|
||||
Works identically with any server type -- OpenAI, VLLM, SGLang, OpenRouter,
|
||||
or ManagedServer with a parser. The server determines how tool_calls get
|
||||
populated on the response.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
server,
|
||||
tool_schemas: List[Dict[str, Any]],
|
||||
valid_tool_names: Set[str],
|
||||
max_turns: int = 30,
|
||||
task_id: Optional[str] = None,
|
||||
temperature: float = 1.0,
|
||||
max_tokens: Optional[int] = None,
|
||||
):
|
||||
"""
|
||||
Initialize the agent loop.
|
||||
|
||||
Args:
|
||||
server: Server object with chat_completion() method (OpenAIServer,
|
||||
ManagedServer, ServerManager, etc.)
|
||||
tool_schemas: OpenAI-format tool definitions from get_tool_definitions()
|
||||
valid_tool_names: Set of tool names the model is allowed to call
|
||||
max_turns: Maximum number of LLM calls before stopping
|
||||
task_id: Unique ID for terminal/browser session isolation
|
||||
temperature: Sampling temperature for generation
|
||||
max_tokens: Max tokens per generation (None for server default)
|
||||
"""
|
||||
self.server = server
|
||||
self.tool_schemas = tool_schemas
|
||||
self.valid_tool_names = valid_tool_names
|
||||
self.max_turns = max_turns
|
||||
self.task_id = task_id or str(uuid.uuid4())
|
||||
self.temperature = temperature
|
||||
self.max_tokens = max_tokens
|
||||
|
||||
async def run(self, messages: List[Dict[str, Any]]) -> AgentResult:
|
||||
"""
|
||||
Execute the full agent loop using standard OpenAI tool calling.
|
||||
|
||||
Args:
|
||||
messages: Initial conversation messages (system + user).
|
||||
Modified in-place as the conversation progresses.
|
||||
|
||||
Returns:
|
||||
AgentResult with full conversation history, managed state, and metadata
|
||||
"""
|
||||
reasoning_per_turn = []
|
||||
tool_errors: List[ToolError] = []
|
||||
|
||||
for turn in range(self.max_turns):
|
||||
# Build the chat_completion kwargs
|
||||
chat_kwargs = {
|
||||
"messages": messages,
|
||||
"n": 1,
|
||||
"temperature": self.temperature,
|
||||
}
|
||||
|
||||
# Only pass tools if we have them
|
||||
if self.tool_schemas:
|
||||
chat_kwargs["tools"] = self.tool_schemas
|
||||
|
||||
# Only pass max_tokens if explicitly set
|
||||
if self.max_tokens is not None:
|
||||
chat_kwargs["max_tokens"] = self.max_tokens
|
||||
|
||||
# Make the API call -- standard OpenAI spec
|
||||
try:
|
||||
response = await self.server.chat_completion(**chat_kwargs)
|
||||
except Exception as e:
|
||||
logger.error("API call failed on turn %d: %s", turn + 1, e)
|
||||
return AgentResult(
|
||||
messages=messages,
|
||||
managed_state=self._get_managed_state(),
|
||||
turns_used=turn + 1,
|
||||
finished_naturally=False,
|
||||
reasoning_per_turn=reasoning_per_turn,
|
||||
tool_errors=tool_errors,
|
||||
)
|
||||
|
||||
if not response or not response.choices:
|
||||
logger.warning("Empty response on turn %d", turn + 1)
|
||||
return AgentResult(
|
||||
messages=messages,
|
||||
managed_state=self._get_managed_state(),
|
||||
turns_used=turn + 1,
|
||||
finished_naturally=False,
|
||||
reasoning_per_turn=reasoning_per_turn,
|
||||
tool_errors=tool_errors,
|
||||
)
|
||||
|
||||
assistant_msg = response.choices[0].message
|
||||
|
||||
# Extract reasoning content from the response (all provider formats)
|
||||
reasoning = _extract_reasoning_from_message(assistant_msg)
|
||||
reasoning_per_turn.append(reasoning)
|
||||
|
||||
# Check for tool calls -- standard OpenAI spec
|
||||
if assistant_msg.tool_calls:
|
||||
# Build the assistant message dict for conversation history
|
||||
msg_dict: Dict[str, Any] = {
|
||||
"role": "assistant",
|
||||
"content": assistant_msg.content or "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tc.id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tc.function.name,
|
||||
"arguments": tc.function.arguments,
|
||||
},
|
||||
}
|
||||
for tc in assistant_msg.tool_calls
|
||||
],
|
||||
}
|
||||
|
||||
# Preserve reasoning_content for multi-turn chat template handling
|
||||
# (e.g., Kimi-K2's template renders <think> blocks differently
|
||||
# for history vs. the latest turn based on this field)
|
||||
if reasoning:
|
||||
msg_dict["reasoning_content"] = reasoning
|
||||
|
||||
messages.append(msg_dict)
|
||||
|
||||
# Execute each tool call via hermes-agent's dispatch
|
||||
for tc in assistant_msg.tool_calls:
|
||||
tool_name = tc.function.name
|
||||
tool_args_raw = tc.function.arguments
|
||||
|
||||
# Validate tool name
|
||||
if tool_name not in self.valid_tool_names:
|
||||
tool_result = json.dumps(
|
||||
{
|
||||
"error": f"Unknown tool '{tool_name}'. "
|
||||
f"Available tools: {sorted(self.valid_tool_names)}"
|
||||
}
|
||||
)
|
||||
tool_errors.append(ToolError(
|
||||
turn=turn + 1, tool_name=tool_name,
|
||||
arguments=tool_args_raw[:200],
|
||||
error=f"Unknown tool '{tool_name}'",
|
||||
tool_result=tool_result,
|
||||
))
|
||||
logger.warning(
|
||||
"Model called unknown tool '%s' on turn %d",
|
||||
tool_name, turn + 1,
|
||||
)
|
||||
else:
|
||||
# Parse arguments and dispatch
|
||||
try:
|
||||
args = json.loads(tool_args_raw)
|
||||
except json.JSONDecodeError:
|
||||
args = {}
|
||||
logger.warning(
|
||||
"Invalid JSON in tool call arguments for '%s': %s",
|
||||
tool_name, tool_args_raw[:200],
|
||||
)
|
||||
|
||||
try:
|
||||
if tool_name == "terminal":
|
||||
import os
|
||||
backend = os.getenv("TERMINAL_ENV", "local")
|
||||
cmd_preview = args.get("command", "")[:80]
|
||||
print(f" 🖥️ [{backend}] $ {cmd_preview}")
|
||||
|
||||
# Run tool calls in a thread pool so backends that use
|
||||
# asyncio.run() internally (modal, docker) get a clean
|
||||
# event loop instead of deadlocking inside Atropos's loop.
|
||||
loop = asyncio.get_event_loop()
|
||||
tool_result = await loop.run_in_executor(
|
||||
_tool_executor,
|
||||
lambda: handle_function_call(
|
||||
tool_name, args, task_id=self.task_id
|
||||
),
|
||||
)
|
||||
except Exception as e:
|
||||
tool_result = json.dumps(
|
||||
{"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"}
|
||||
)
|
||||
tool_errors.append(ToolError(
|
||||
turn=turn + 1, tool_name=tool_name,
|
||||
arguments=tool_args_raw[:200],
|
||||
error=f"{type(e).__name__}: {str(e)}",
|
||||
tool_result=tool_result,
|
||||
))
|
||||
logger.error(
|
||||
"Tool '%s' execution failed on turn %d: %s",
|
||||
tool_name, turn + 1, e,
|
||||
)
|
||||
|
||||
# Also check if the tool returned an error in its JSON result
|
||||
try:
|
||||
result_data = json.loads(tool_result)
|
||||
if isinstance(result_data, dict):
|
||||
err = result_data.get("error")
|
||||
exit_code = result_data.get("exit_code")
|
||||
if err and exit_code and exit_code < 0:
|
||||
tool_errors.append(ToolError(
|
||||
turn=turn + 1, tool_name=tool_name,
|
||||
arguments=tool_args_raw[:200],
|
||||
error=str(err),
|
||||
tool_result=tool_result[:500],
|
||||
))
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Add tool response to conversation
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": tc.id,
|
||||
"content": tool_result,
|
||||
}
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
"Turn %d: %d tool calls executed",
|
||||
turn + 1,
|
||||
len(assistant_msg.tool_calls),
|
||||
)
|
||||
|
||||
else:
|
||||
# No tool calls -- model is done
|
||||
msg_dict = {
|
||||
"role": "assistant",
|
||||
"content": assistant_msg.content or "",
|
||||
}
|
||||
if reasoning:
|
||||
msg_dict["reasoning_content"] = reasoning
|
||||
messages.append(msg_dict)
|
||||
|
||||
logger.debug(
|
||||
"Turn %d: model finished naturally (no tool calls)", turn + 1
|
||||
)
|
||||
|
||||
return AgentResult(
|
||||
messages=messages,
|
||||
managed_state=self._get_managed_state(),
|
||||
turns_used=turn + 1,
|
||||
finished_naturally=True,
|
||||
reasoning_per_turn=reasoning_per_turn,
|
||||
tool_errors=tool_errors,
|
||||
)
|
||||
|
||||
# Hit max turns without the model stopping
|
||||
logger.info("Agent hit max_turns (%d) without finishing", self.max_turns)
|
||||
return AgentResult(
|
||||
messages=messages,
|
||||
managed_state=self._get_managed_state(),
|
||||
turns_used=self.max_turns,
|
||||
finished_naturally=False,
|
||||
reasoning_per_turn=reasoning_per_turn,
|
||||
tool_errors=tool_errors,
|
||||
)
|
||||
|
||||
def _get_managed_state(self) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Get ManagedServer state if the server supports it.
|
||||
|
||||
Returns state dict with SequenceNodes containing tokens/logprobs/masks,
|
||||
or None if the server doesn't support get_state() (e.g., regular OpenAI server).
|
||||
"""
|
||||
if hasattr(self.server, "get_state"):
|
||||
return self.server.get_state()
|
||||
return None
|
||||
@@ -1,33 +0,0 @@
|
||||
# SWE Environment -- Default Configuration
|
||||
#
|
||||
# SWE-bench style tasks with Modal sandboxes for cloud isolation.
|
||||
# Uses terminal + file + web toolsets.
|
||||
#
|
||||
# Usage:
|
||||
# python environments/hermes_swe_env.py serve --config environments/configs/swe_default.yaml
|
||||
|
||||
env:
|
||||
enabled_toolsets: ["terminal", "file", "web"]
|
||||
max_agent_turns: 30
|
||||
max_token_length: 4096
|
||||
group_size: 4
|
||||
terminal_backend: "modal"
|
||||
tool_call_parser: "hermes"
|
||||
tokenizer_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview"
|
||||
dataset_name: "bigcode/humanevalpack"
|
||||
dataset_split: "test"
|
||||
prompt_field: "prompt"
|
||||
steps_per_eval: 50
|
||||
total_steps: 500
|
||||
use_wandb: true
|
||||
wandb_name: "hermes-swe"
|
||||
system_prompt: >
|
||||
You are a skilled software engineer. You have access to a terminal,
|
||||
file tools, and web search. Use these tools to complete the coding task.
|
||||
Write clean, working code and verify it runs correctly before finishing.
|
||||
|
||||
openai:
|
||||
base_url: "http://localhost:8000/v1"
|
||||
model_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview"
|
||||
server_type: "openai"
|
||||
api_key: ""
|
||||
@@ -1,35 +0,0 @@
|
||||
# Terminal Test Environment -- Default Configuration
|
||||
#
|
||||
# Simple file-creation tasks for validating the full Atropos + hermes-agent stack.
|
||||
# Uses Modal terminal backend and OpenRouter (Claude) for inference.
|
||||
# API keys loaded from ~/hermes-agent/.env
|
||||
#
|
||||
# Usage:
|
||||
# run-api
|
||||
# python environments/terminal_test_env.py serve
|
||||
# # Or with config file:
|
||||
# python environments/terminal_test_env.py serve --config environments/configs/terminal_test_default.yaml
|
||||
|
||||
env:
|
||||
enabled_toolsets: ["terminal", "file"]
|
||||
max_agent_turns: 10
|
||||
max_token_length: 2048
|
||||
group_size: 3
|
||||
total_steps: 3
|
||||
steps_per_eval: 3
|
||||
terminal_backend: "modal"
|
||||
tool_call_parser: "hermes"
|
||||
tokenizer_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview"
|
||||
ensure_scores_are_not_same: false
|
||||
use_wandb: false
|
||||
system_prompt: >
|
||||
You are a helpful assistant with access to a terminal and file tools.
|
||||
Complete the user's request by using the available tools.
|
||||
Be precise and follow instructions exactly.
|
||||
|
||||
openai:
|
||||
base_url: "https://openrouter.ai/api/v1"
|
||||
model_name: "anthropic/claude-opus-4.6"
|
||||
server_type: "openai"
|
||||
health_check: false
|
||||
# api_key loaded from OPENROUTER_API_KEY in .env
|
||||
@@ -1,615 +0,0 @@
|
||||
"""
|
||||
HermesAgentBaseEnv -- Abstract Base Environment for Hermes-Agent + Atropos
|
||||
|
||||
Provides the Atropos integration plumbing that all hermes-agent environments share:
|
||||
- Two-mode operation (OpenAI server for Phase 1, VLLM ManagedServer for Phase 2)
|
||||
- Per-group toolset/distribution resolution
|
||||
- Agent loop orchestration via HermesAgentLoop
|
||||
- ToolContext creation for reward functions
|
||||
- ScoredDataGroup construction from ManagedServer state
|
||||
|
||||
Subclasses only need to implement:
|
||||
setup() -- Load dataset, initialize state
|
||||
get_next_item() -- Return the next item from the dataset
|
||||
format_prompt() -- Convert a dataset item into the user message
|
||||
compute_reward() -- Score the rollout (has full ToolContext access)
|
||||
evaluate() -- Periodic evaluation
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import uuid
|
||||
from abc import abstractmethod
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Set, Tuple, Union
|
||||
|
||||
# Ensure the hermes-agent repo root is on sys.path so that imports like
|
||||
# `from model_tools import ...` and `from environments.X import ...` work
|
||||
# regardless of where the script is invoked from.
|
||||
_repo_root = Path(__file__).resolve().parent.parent
|
||||
if str(_repo_root) not in sys.path:
|
||||
sys.path.insert(0, str(_repo_root))
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from pydantic import Field
|
||||
|
||||
# Load API keys from hermes-agent/.env so all environments can access them
|
||||
_env_path = _repo_root / ".env"
|
||||
if _env_path.exists():
|
||||
load_dotenv(dotenv_path=_env_path)
|
||||
|
||||
# Apply monkey patches for async-safe tool operation inside Atropos's event loop.
|
||||
# This patches SwerexModalEnvironment to use a background thread instead of
|
||||
# asyncio.run(), which would deadlock inside Atropos. Safe for normal CLI too.
|
||||
from environments.patches import apply_patches
|
||||
apply_patches()
|
||||
|
||||
from atroposlib.envs.base import (
|
||||
BaseEnv,
|
||||
BaseEnvConfig,
|
||||
ScoredDataGroup,
|
||||
ScoredDataItem,
|
||||
)
|
||||
from atroposlib.envs.server_handling.server_manager import (
|
||||
APIServerConfig,
|
||||
ServerBaseline,
|
||||
ServerManager,
|
||||
)
|
||||
from atroposlib.type_definitions import Item
|
||||
|
||||
from environments.agent_loop import AgentResult, HermesAgentLoop
|
||||
from environments.tool_context import ToolContext
|
||||
|
||||
# Import hermes-agent toolset infrastructure
|
||||
from model_tools import get_tool_definitions
|
||||
from toolset_distributions import sample_toolsets_from_distribution
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HermesAgentEnvConfig(BaseEnvConfig):
|
||||
"""
|
||||
Configuration for hermes-agent Atropos environments.
|
||||
|
||||
Extends BaseEnvConfig with agent-specific settings for toolsets,
|
||||
terminal backend, dataset loading, and tool call parsing.
|
||||
"""
|
||||
|
||||
# --- Toolset configuration ---
|
||||
# Mutually exclusive: use either enabled_toolsets OR distribution
|
||||
enabled_toolsets: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="Explicit list of hermes toolsets to enable (e.g., ['terminal', 'file', 'web']). "
|
||||
"If None and distribution is also None, all available toolsets are enabled.",
|
||||
)
|
||||
disabled_toolsets: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="Toolsets to disable. Applied as a filter on top of enabled_toolsets or distribution.",
|
||||
)
|
||||
distribution: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Name of a toolset distribution from toolset_distributions.py "
|
||||
"(e.g., 'development', 'terminal_tasks'). Sampled once per group. "
|
||||
"Mutually exclusive with enabled_toolsets.",
|
||||
)
|
||||
|
||||
# --- Agent loop configuration ---
|
||||
max_agent_turns: int = Field(
|
||||
default=30,
|
||||
description="Maximum number of LLM calls (tool-calling iterations) per rollout.",
|
||||
)
|
||||
system_prompt: Optional[str] = Field(
|
||||
default=None,
|
||||
description="System prompt for the agent. Tools are handled via the tools= parameter, "
|
||||
"not embedded in the prompt text.",
|
||||
)
|
||||
agent_temperature: float = Field(
|
||||
default=1.0,
|
||||
description="Sampling temperature for agent generation during rollouts.",
|
||||
)
|
||||
|
||||
# --- Terminal backend ---
|
||||
terminal_backend: str = Field(
|
||||
default="local",
|
||||
description="Terminal backend: 'local', 'docker', 'modal', 'ssh', 'singularity'. "
|
||||
"Modal recommended for production RL (cloud isolation per rollout).",
|
||||
)
|
||||
|
||||
# --- Dataset ---
|
||||
dataset_name: Optional[str] = Field(
|
||||
default=None,
|
||||
description="HuggingFace dataset name. Optional if tasks are defined inline.",
|
||||
)
|
||||
dataset_split: str = Field(
|
||||
default="train",
|
||||
description="Dataset split to use.",
|
||||
)
|
||||
prompt_field: str = Field(
|
||||
default="prompt",
|
||||
description="Which field in the dataset contains the prompt.",
|
||||
)
|
||||
|
||||
# --- Phase 2: Tool call parsing ---
|
||||
tool_call_parser: str = Field(
|
||||
default="hermes",
|
||||
description="Tool call parser name for Phase 2 (VLLM server type). "
|
||||
"Ignored in Phase 1 (OpenAI server type where VLLM parses natively). "
|
||||
"Options: hermes, mistral, llama3_json, qwen, deepseek_v3, etc.",
|
||||
)
|
||||
|
||||
|
||||
class HermesAgentBaseEnv(BaseEnv):
|
||||
"""
|
||||
Abstract base environment for hermes-agent Atropos integration.
|
||||
|
||||
Handles two modes of operation:
|
||||
- Phase 1 (OpenAI server type): Uses server.chat_completion() directly.
|
||||
The server (VLLM, SGLang, OpenRouter, OpenAI) handles tool call parsing
|
||||
and reasoning extraction natively. DummyManagedServer provides placeholder
|
||||
tokens. Good for SFT data gen, verifier testing, evaluation.
|
||||
|
||||
- Phase 2 (VLLM server type): Uses ManagedServer for exact token IDs + logprobs
|
||||
via /generate. Client-side tool call parser reconstructs structured tool_calls
|
||||
from raw output. Full RL training capability.
|
||||
|
||||
Subclasses must implement:
|
||||
setup() -- Load dataset, initialize state
|
||||
get_next_item() -- Return the next item to roll out
|
||||
format_prompt() -- Convert a dataset item into the user message string
|
||||
compute_reward() -- Score the rollout using ToolContext
|
||||
evaluate() -- Periodic evaluation
|
||||
"""
|
||||
|
||||
name: Optional[str] = "hermes-agent"
|
||||
env_config_cls = HermesAgentEnvConfig
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: HermesAgentEnvConfig,
|
||||
server_configs: Union[ServerBaseline, List[APIServerConfig]],
|
||||
slurm=False,
|
||||
testing=False,
|
||||
):
|
||||
super().__init__(config, server_configs, slurm, testing)
|
||||
|
||||
# Set terminal backend environment variable so hermes tools pick it up
|
||||
if config.terminal_backend:
|
||||
os.environ["TERMINAL_ENV"] = config.terminal_backend
|
||||
print(f"🖥️ Terminal backend: {config.terminal_backend}")
|
||||
|
||||
# Current group's resolved tools (set in collect_trajectories)
|
||||
self._current_group_tools: Optional[Tuple[List[Dict], Set[str]]] = None
|
||||
|
||||
# Tool error tracking for wandb logging
|
||||
self._tool_error_buffer: List[Dict[str, Any]] = []
|
||||
|
||||
# =========================================================================
|
||||
# Toolset resolution (per-group)
|
||||
# =========================================================================
|
||||
|
||||
def _resolve_tools_for_group(self) -> Tuple[List[Dict[str, Any]], Set[str]]:
|
||||
"""
|
||||
Resolve toolsets for a group. Called once in collect_trajectories(),
|
||||
then shared by all collect_trajectory() calls in the group.
|
||||
|
||||
If distribution is set, samples probabilistically.
|
||||
If enabled_toolsets is set, uses that explicit list.
|
||||
disabled_toolsets is applied as a filter on top.
|
||||
|
||||
Returns:
|
||||
(tool_schemas, valid_tool_names) tuple
|
||||
"""
|
||||
config = self.config
|
||||
|
||||
if config.distribution:
|
||||
group_toolsets = sample_toolsets_from_distribution(config.distribution)
|
||||
logger.info("Sampled toolsets from '%s': %s", config.distribution, group_toolsets)
|
||||
else:
|
||||
group_toolsets = config.enabled_toolsets # None means "all available"
|
||||
|
||||
tools = get_tool_definitions(
|
||||
enabled_toolsets=group_toolsets,
|
||||
disabled_toolsets=config.disabled_toolsets,
|
||||
quiet_mode=True,
|
||||
)
|
||||
|
||||
valid_names = {t["function"]["name"] for t in tools} if tools else set()
|
||||
logger.info("Resolved %d tools for group: %s", len(valid_names), sorted(valid_names))
|
||||
return tools, valid_names
|
||||
|
||||
# =========================================================================
|
||||
# Server mode detection
|
||||
# =========================================================================
|
||||
|
||||
def _use_managed_server(self) -> bool:
|
||||
"""
|
||||
Determine if we should use ManagedServer (Phase 2) or direct server (Phase 1).
|
||||
|
||||
Phase 2 (ManagedServer) is used when the server type is 'vllm' or 'sglang',
|
||||
which go through the /generate endpoint for exact token tracking.
|
||||
|
||||
Phase 1 (direct server) is used for 'openai' server type, which uses
|
||||
/v1/chat/completions with native tool call parsing.
|
||||
"""
|
||||
if not self.server.servers:
|
||||
return False
|
||||
|
||||
server = self.server.servers[0]
|
||||
# If the server is an OpenAI server (not VLLM/SGLang), use direct mode
|
||||
from atroposlib.envs.server_handling.openai_server import OpenAIServer
|
||||
return not isinstance(server, OpenAIServer)
|
||||
|
||||
# =========================================================================
|
||||
# Core Atropos integration
|
||||
# =========================================================================
|
||||
|
||||
async def collect_trajectories(
|
||||
self, item: Item
|
||||
) -> Tuple[
|
||||
Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]],
|
||||
List[Item],
|
||||
]:
|
||||
"""
|
||||
Override collect_trajectories to resolve toolsets once per group,
|
||||
then delegate to the standard group-level collection.
|
||||
|
||||
The default BaseEnv.collect_trajectories() calls collect_trajectory()
|
||||
group_size times in parallel. We resolve tools once here and store
|
||||
them for all those calls to use.
|
||||
"""
|
||||
# Resolve toolsets for this group (shared by all rollouts in the group)
|
||||
self._current_group_tools = self._resolve_tools_for_group()
|
||||
|
||||
# Delegate to the default implementation which calls collect_trajectory()
|
||||
# group_size times via asyncio.gather
|
||||
return await super().collect_trajectories(item)
|
||||
|
||||
# =========================================================================
|
||||
# Wandb rollout display -- format trajectories nicely
|
||||
# =========================================================================
|
||||
|
||||
@staticmethod
|
||||
def _format_trajectory_for_display(messages: List[Dict[str, Any]]) -> str:
|
||||
"""
|
||||
Format a conversation's messages into a readable trajectory string
|
||||
for wandb rollout tables. Shows tool calls, tool results, and reasoning
|
||||
in a structured way instead of raw token decoding.
|
||||
"""
|
||||
parts = []
|
||||
for msg in messages:
|
||||
role = msg.get("role", "unknown")
|
||||
content = msg.get("content", "")
|
||||
|
||||
if role == "system":
|
||||
parts.append(f"[SYSTEM]\n{content}")
|
||||
|
||||
elif role == "user":
|
||||
parts.append(f"[USER]\n{content}")
|
||||
|
||||
elif role == "assistant":
|
||||
# Show reasoning if present
|
||||
reasoning = msg.get("reasoning_content", "")
|
||||
if reasoning:
|
||||
# Truncate long reasoning for display
|
||||
if len(reasoning) > 300:
|
||||
reasoning = reasoning[:300] + "..."
|
||||
parts.append(f"[ASSISTANT thinking]\n{reasoning}")
|
||||
|
||||
# Show content
|
||||
if content:
|
||||
parts.append(f"[ASSISTANT]\n{content}")
|
||||
|
||||
# Show tool calls
|
||||
tool_calls = msg.get("tool_calls", [])
|
||||
for tc in tool_calls:
|
||||
func = tc.get("function", {})
|
||||
name = func.get("name", "?")
|
||||
args = func.get("arguments", "{}")
|
||||
# Truncate long arguments for display
|
||||
if len(args) > 200:
|
||||
args = args[:200] + "..."
|
||||
parts.append(f"[TOOL CALL] {name}({args})")
|
||||
|
||||
elif role == "tool":
|
||||
tool_id = msg.get("tool_call_id", "")
|
||||
result = content
|
||||
# Truncate long tool results for display
|
||||
if len(result) > 500:
|
||||
result = result[:500] + "..."
|
||||
parts.append(f"[TOOL RESULT] {result}")
|
||||
|
||||
return "\n\n".join(parts)
|
||||
|
||||
async def add_rollouts_for_wandb(
|
||||
self,
|
||||
scored_data,
|
||||
item=None,
|
||||
):
|
||||
"""
|
||||
Override to show formatted trajectories with tool calls visible,
|
||||
instead of raw token decoding which loses all structure.
|
||||
"""
|
||||
num_keep = self.config.num_rollouts_per_group_for_logging
|
||||
if num_keep == -1:
|
||||
num_keep = self.config.group_size
|
||||
|
||||
group = []
|
||||
for i in range(min(num_keep, len(scored_data.get("scores", [])))):
|
||||
score = scored_data["scores"][i]
|
||||
|
||||
# Use messages if available for rich display
|
||||
messages = None
|
||||
if scored_data.get("messages") and i < len(scored_data["messages"]):
|
||||
messages = scored_data["messages"][i]
|
||||
|
||||
if messages:
|
||||
text = self._format_trajectory_for_display(messages)
|
||||
elif scored_data.get("tokens") and i < len(scored_data["tokens"]):
|
||||
text = self.tokenizer.decode(scored_data["tokens"][i])
|
||||
else:
|
||||
text = "(no data)"
|
||||
|
||||
group.append((text, score))
|
||||
|
||||
self.rollouts_for_wandb.append(group)
|
||||
if len(self.rollouts_for_wandb) > self.config.num_rollouts_to_keep:
|
||||
self.rollouts_for_wandb.pop(0)
|
||||
|
||||
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
||||
"""Log base metrics including tool errors to wandb."""
|
||||
if wandb_metrics is None:
|
||||
wandb_metrics = {}
|
||||
|
||||
# Log tool error stats
|
||||
if self._tool_error_buffer:
|
||||
wandb_metrics["train/tool_errors_count"] = len(self._tool_error_buffer)
|
||||
|
||||
# Log error details as a summary string (tables can crash wandb on tmp cleanup)
|
||||
error_summaries = []
|
||||
for err in self._tool_error_buffer:
|
||||
error_summaries.append(
|
||||
f"[turn {err['turn']}] {err['tool']}({err['args'][:80]}) -> {err['error'][:150]}"
|
||||
)
|
||||
wandb_metrics["train/tool_error_details"] = "\n".join(error_summaries)
|
||||
|
||||
# Also print to stdout for immediate visibility
|
||||
for summary in error_summaries:
|
||||
print(f" Tool Error: {summary}")
|
||||
|
||||
self._tool_error_buffer = []
|
||||
else:
|
||||
wandb_metrics["train/tool_errors_count"] = 0
|
||||
|
||||
await super().wandb_log(wandb_metrics)
|
||||
|
||||
async def collect_trajectory(
|
||||
self, item: Item
|
||||
) -> Tuple[Optional[Union[ScoredDataItem, Any]], List[Item]]:
|
||||
"""
|
||||
Run a single rollout: agent loop + reward computation.
|
||||
|
||||
This is called group_size times in parallel by collect_trajectories().
|
||||
Each call gets its own task_id for terminal/browser session isolation.
|
||||
"""
|
||||
task_id = str(uuid.uuid4())
|
||||
|
||||
# Get group-level tools (resolved once in collect_trajectories)
|
||||
if self._current_group_tools is None:
|
||||
# Fallback: resolve per-trajectory if called outside collect_trajectories
|
||||
tools, valid_names = self._resolve_tools_for_group()
|
||||
else:
|
||||
tools, valid_names = self._current_group_tools
|
||||
|
||||
# Build initial messages
|
||||
messages: List[Dict[str, Any]] = []
|
||||
if self.config.system_prompt:
|
||||
messages.append({"role": "system", "content": self.config.system_prompt})
|
||||
messages.append({"role": "user", "content": self.format_prompt(item)})
|
||||
|
||||
# Run the agent loop
|
||||
result: AgentResult
|
||||
if self._use_managed_server():
|
||||
# Phase 2: ManagedServer with parser -- exact tokens + logprobs
|
||||
# Load the tool call parser from registry based on config
|
||||
from environments.tool_call_parsers import get_parser
|
||||
try:
|
||||
tc_parser = get_parser(self.config.tool_call_parser)
|
||||
except KeyError:
|
||||
logger.warning(
|
||||
"Tool call parser '%s' not found, falling back to 'hermes'",
|
||||
self.config.tool_call_parser,
|
||||
)
|
||||
tc_parser = get_parser("hermes")
|
||||
|
||||
try:
|
||||
async with self.server.managed_server(
|
||||
tokenizer=self.tokenizer,
|
||||
tool_call_parser=tc_parser,
|
||||
) as managed:
|
||||
agent = HermesAgentLoop(
|
||||
server=managed,
|
||||
tool_schemas=tools,
|
||||
valid_tool_names=valid_names,
|
||||
max_turns=self.config.max_agent_turns,
|
||||
task_id=task_id,
|
||||
temperature=self.config.agent_temperature,
|
||||
max_tokens=self.config.max_token_length,
|
||||
)
|
||||
result = await agent.run(messages)
|
||||
except NotImplementedError:
|
||||
# DummyManagedServer not allowed -- fall back to Phase 1
|
||||
logger.warning(
|
||||
"ManagedServer not available (OpenAI server?). "
|
||||
"Falling back to direct server mode."
|
||||
)
|
||||
agent = HermesAgentLoop(
|
||||
server=self.server,
|
||||
tool_schemas=tools,
|
||||
valid_tool_names=valid_names,
|
||||
max_turns=self.config.max_agent_turns,
|
||||
task_id=task_id,
|
||||
temperature=self.config.agent_temperature,
|
||||
max_tokens=self.config.max_token_length,
|
||||
)
|
||||
result = await agent.run(messages)
|
||||
else:
|
||||
# Phase 1: OpenAI server -- native tool_calls, placeholder tokens
|
||||
agent = HermesAgentLoop(
|
||||
server=self.server,
|
||||
tool_schemas=tools,
|
||||
valid_tool_names=valid_names,
|
||||
max_turns=self.config.max_agent_turns,
|
||||
task_id=task_id,
|
||||
temperature=self.config.agent_temperature,
|
||||
max_tokens=self.config.max_token_length,
|
||||
)
|
||||
result = await agent.run(messages)
|
||||
|
||||
# Skip reward computation if the agent loop produced no meaningful work
|
||||
# (e.g., API call failed on turn 1). No point spinning up a Modal sandbox
|
||||
# just to verify files that were never created.
|
||||
only_system_and_user = all(
|
||||
msg.get("role") in ("system", "user") for msg in result.messages
|
||||
)
|
||||
if result.turns_used == 0 or only_system_and_user:
|
||||
logger.warning(
|
||||
"Agent loop produced no output (turns=%d, msgs=%d). Skipping reward.",
|
||||
result.turns_used, len(result.messages),
|
||||
)
|
||||
reward = 0.0
|
||||
else:
|
||||
# Compute reward using ToolContext (gives verifier full tool access)
|
||||
ctx = ToolContext(task_id)
|
||||
try:
|
||||
reward = await self.compute_reward(item, result, ctx)
|
||||
except Exception as e:
|
||||
logger.error("compute_reward failed: %s", e)
|
||||
reward = 0.0
|
||||
finally:
|
||||
ctx.cleanup()
|
||||
|
||||
# Track tool errors for wandb logging
|
||||
if result.tool_errors:
|
||||
for err in result.tool_errors:
|
||||
self._tool_error_buffer.append({
|
||||
"turn": err.turn,
|
||||
"tool": err.tool_name,
|
||||
"args": err.arguments[:150],
|
||||
"error": err.error[:300],
|
||||
"result": err.tool_result[:300],
|
||||
})
|
||||
|
||||
# Build ScoredDataItem from ManagedServer state
|
||||
# Phase 2: real tokens/masks/logprobs from SequenceNodes
|
||||
# Phase 1: placeholder tokens (still need a valid ScoredDataItem for the pipeline)
|
||||
nodes = (result.managed_state or {}).get("nodes", [])
|
||||
|
||||
if nodes:
|
||||
# Phase 2 (or DummyManagedServer): use actual node data
|
||||
node = nodes[-1] # Final sequence node = full trajectory
|
||||
scored_item: Dict[str, Any] = {
|
||||
"tokens": node.tokens,
|
||||
"masks": node.masked_tokens,
|
||||
"scores": reward,
|
||||
}
|
||||
|
||||
# Include logprobs if available (Phase 2)
|
||||
if hasattr(node, "logprobs") and node.logprobs:
|
||||
scored_item["advantages"] = None # Computed by trainer
|
||||
scored_item["ref_logprobs"] = None
|
||||
else:
|
||||
# Phase 1 with no managed state: create placeholder tokens
|
||||
# so the data pipeline doesn't break. These are NOT suitable
|
||||
# for training but allow process mode (SFT data gen) to work.
|
||||
# Tokenize the full conversation to get approximate tokens.
|
||||
full_text = "\n".join(
|
||||
msg.get("content", "") for msg in result.messages if msg.get("content")
|
||||
)
|
||||
if self.tokenizer:
|
||||
tokens = self.tokenizer.encode(full_text, add_special_tokens=True)
|
||||
else:
|
||||
tokens = list(range(min(len(full_text) // 4, 128)))
|
||||
|
||||
scored_item = {
|
||||
"tokens": tokens,
|
||||
"masks": [-100] + tokens[1:], # Mask first token as prompt
|
||||
"scores": reward,
|
||||
}
|
||||
|
||||
# Always include messages for wandb rollout display and data logging
|
||||
scored_item["messages"] = result.messages
|
||||
|
||||
return scored_item, []
|
||||
|
||||
# =========================================================================
|
||||
# Abstract methods -- subclasses must implement
|
||||
# =========================================================================
|
||||
|
||||
@abstractmethod
|
||||
async def setup(self):
|
||||
"""
|
||||
Load dataset, initialize state.
|
||||
|
||||
Called once when the environment starts. Typical implementation:
|
||||
self.dataset = load_dataset(self.config.dataset_name, split=self.config.dataset_split)
|
||||
self.iter = 0
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
async def get_next_item(self) -> Item:
|
||||
"""
|
||||
Return the next item from the dataset for rollout.
|
||||
|
||||
Called by the base env's main loop to get items for workers.
|
||||
Should cycle through the dataset.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def format_prompt(self, item: Item) -> str:
|
||||
"""
|
||||
Convert a dataset item into the user message for the agent.
|
||||
|
||||
Args:
|
||||
item: Dataset item (dict, tuple, etc.)
|
||||
|
||||
Returns:
|
||||
The prompt string to send to the agent
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
async def compute_reward(
|
||||
self, item: Item, result: AgentResult, ctx: ToolContext
|
||||
) -> float:
|
||||
"""
|
||||
Score the rollout. Has full access to:
|
||||
- item: the original dataset item (ground truth, test commands, etc.)
|
||||
- result: AgentResult with full messages, turn count, reasoning, etc.
|
||||
- ctx: ToolContext -- call ANY hermes-agent tool (terminal, file, web,
|
||||
browser, vision...) scoped to this rollout's sandbox. Nothing
|
||||
is off-limits.
|
||||
|
||||
Args:
|
||||
item: The dataset item that was rolled out
|
||||
result: The agent's rollout result
|
||||
ctx: ToolContext with full tool access for verification
|
||||
|
||||
Returns:
|
||||
Reward float (typically 0.0 to 1.0, but any float is valid)
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
async def evaluate(self, *args, **kwargs):
|
||||
"""
|
||||
Periodic evaluation. Called every steps_per_eval steps.
|
||||
|
||||
Typical implementation runs the agent on a held-out eval set
|
||||
and logs metrics via wandb/evaluate_log.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
@@ -1,229 +0,0 @@
|
||||
"""
|
||||
HermesSweEnv -- SWE-Bench Style Environment with Modal Sandboxes
|
||||
|
||||
A concrete environment for software engineering tasks where the model writes code
|
||||
and the reward function runs tests to verify correctness. Uses Modal terminal
|
||||
backend for cloud-isolated sandboxes per rollout.
|
||||
|
||||
The reward function uses ToolContext.terminal() to run test commands in the same
|
||||
Modal sandbox the model used during its agentic loop. All filesystem state from
|
||||
the model's tool calls is preserved for verification.
|
||||
|
||||
Usage:
|
||||
# Phase 1: OpenAI server type
|
||||
vllm serve YourModel --tool-parser hermes
|
||||
run-api
|
||||
python environments/hermes_swe_env.py serve \\
|
||||
--openai.base_url http://localhost:8000/v1 \\
|
||||
--openai.model_name YourModel \\
|
||||
--openai.server_type openai \\
|
||||
--env.dataset_name bigcode/humanevalpack \\
|
||||
--env.terminal_backend modal
|
||||
|
||||
# Phase 2: VLLM server type (full RL training)
|
||||
python environments/hermes_swe_env.py serve \\
|
||||
--openai.base_url http://localhost:8000/v1 \\
|
||||
--openai.model_name YourModel \\
|
||||
--openai.server_type vllm \\
|
||||
--env.tool_call_parser hermes \\
|
||||
--env.terminal_backend modal
|
||||
"""
|
||||
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
# Ensure repo root is on sys.path for imports
|
||||
_repo_root = Path(__file__).resolve().parent.parent
|
||||
if str(_repo_root) not in sys.path:
|
||||
sys.path.insert(0, str(_repo_root))
|
||||
|
||||
from datasets import load_dataset
|
||||
|
||||
from atroposlib.envs.base import ScoredDataGroup
|
||||
from atroposlib.envs.server_handling.server_manager import APIServerConfig
|
||||
from atroposlib.type_definitions import Item
|
||||
|
||||
from environments.agent_loop import AgentResult
|
||||
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
|
||||
from environments.tool_context import ToolContext
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HermesSweEnvConfig(HermesAgentEnvConfig):
|
||||
"""Config with defaults for SWE-bench style tasks."""
|
||||
|
||||
pass # Inherits all fields, overrides defaults in config_init
|
||||
|
||||
|
||||
class HermesSweEnv(HermesAgentBaseEnv):
|
||||
"""
|
||||
SWE-bench style environment using Modal terminal backend.
|
||||
|
||||
The model gets a coding task, uses terminal + file + web tools to solve it,
|
||||
and the reward function runs tests in the same Modal sandbox to verify.
|
||||
|
||||
Subclass this for specific SWE datasets (HumanEval, SWE-bench, etc.)
|
||||
and customize format_prompt() and compute_reward() as needed.
|
||||
"""
|
||||
|
||||
name = "hermes-swe"
|
||||
env_config_cls = HermesSweEnvConfig
|
||||
|
||||
@classmethod
|
||||
def config_init(cls) -> Tuple[HermesSweEnvConfig, List[APIServerConfig]]:
|
||||
"""
|
||||
Default configuration for the SWE environment.
|
||||
|
||||
Uses Modal terminal backend for cloud isolation and terminal + file + web toolsets.
|
||||
"""
|
||||
env_config = HermesSweEnvConfig(
|
||||
# Toolsets: terminal for running code, file for reading/writing, web for docs
|
||||
enabled_toolsets=["terminal", "file", "web"],
|
||||
disabled_toolsets=None,
|
||||
distribution=None,
|
||||
# Agent settings -- SWE tasks need more turns
|
||||
max_agent_turns=30,
|
||||
max_token_length=4096,
|
||||
agent_temperature=1.0,
|
||||
system_prompt=(
|
||||
"You are a skilled software engineer. You have access to a terminal, "
|
||||
"file tools, and web search. Use these tools to complete the coding task. "
|
||||
"Write clean, working code and verify it runs correctly before finishing."
|
||||
),
|
||||
# Modal backend for cloud-isolated sandboxes
|
||||
terminal_backend="modal",
|
||||
# Dataset -- override via CLI for your specific SWE dataset
|
||||
dataset_name="bigcode/humanevalpack",
|
||||
dataset_split="test",
|
||||
prompt_field="prompt",
|
||||
# Atropos settings
|
||||
group_size=4,
|
||||
tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
|
||||
tool_call_parser="hermes",
|
||||
steps_per_eval=50,
|
||||
total_steps=500,
|
||||
use_wandb=True,
|
||||
wandb_name="hermes-swe",
|
||||
)
|
||||
|
||||
server_configs = [
|
||||
APIServerConfig(
|
||||
base_url="http://localhost:8000/v1",
|
||||
model_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
|
||||
server_type="openai", # Phase 1; switch to "vllm" for Phase 2
|
||||
api_key="",
|
||||
)
|
||||
]
|
||||
|
||||
return env_config, server_configs
|
||||
|
||||
async def setup(self):
|
||||
"""Load the SWE dataset."""
|
||||
if self.config.dataset_name:
|
||||
self.dataset = load_dataset(
|
||||
self.config.dataset_name, split=self.config.dataset_split
|
||||
)
|
||||
else:
|
||||
# Placeholder if no dataset specified
|
||||
self.dataset = []
|
||||
self.iter = 0
|
||||
self.reward_buffer: List[float] = []
|
||||
|
||||
async def get_next_item(self) -> Dict[str, Any]:
|
||||
"""Cycle through the SWE dataset."""
|
||||
if not self.dataset:
|
||||
raise ValueError("No dataset loaded. Set dataset_name in config.")
|
||||
item = self.dataset[self.iter % len(self.dataset)]
|
||||
self.iter += 1
|
||||
return item
|
||||
|
||||
def format_prompt(self, item: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Format the SWE task prompt.
|
||||
|
||||
Override this in subclasses for different dataset formats.
|
||||
Default assumes the dataset has a 'prompt' field and optionally a 'test' field.
|
||||
"""
|
||||
prompt = item.get(self.config.prompt_field, "")
|
||||
|
||||
# If the dataset has test information, include it in the prompt
|
||||
test_info = item.get("test", item.get("test_code", item.get("tests", "")))
|
||||
if test_info:
|
||||
prompt += f"\n\nTests to pass:\n{test_info}"
|
||||
|
||||
return prompt
|
||||
|
||||
async def compute_reward(
|
||||
self, item: Dict[str, Any], result: AgentResult, ctx: ToolContext
|
||||
) -> float:
|
||||
"""
|
||||
Score by running tests in the model's Modal sandbox.
|
||||
|
||||
Default implementation:
|
||||
- If the dataset item has a 'test' or 'test_code' field, run it
|
||||
- Check exit code: 0 = pass, non-zero = fail
|
||||
- Partial credit for file creation
|
||||
|
||||
Override this in subclasses for more sophisticated reward logic.
|
||||
"""
|
||||
# Find the test command from the dataset item
|
||||
test_code = item.get("test", item.get("test_code", item.get("tests", "")))
|
||||
|
||||
if test_code:
|
||||
# Run the test in the model's sandbox
|
||||
test_result = ctx.terminal(
|
||||
f'cd /workspace && python3 -c "{test_code}"', timeout=60
|
||||
)
|
||||
|
||||
if test_result["exit_code"] == 0:
|
||||
self.reward_buffer.append(1.0)
|
||||
return 1.0
|
||||
|
||||
# Partial credit: check if the model created any Python files
|
||||
file_check = ctx.terminal("find /workspace -name '*.py' -newer /tmp/.start_marker 2>/dev/null | head -5")
|
||||
if file_check["exit_code"] == 0 and file_check.get("output", "").strip():
|
||||
self.reward_buffer.append(0.1)
|
||||
return 0.1
|
||||
|
||||
self.reward_buffer.append(0.0)
|
||||
return 0.0
|
||||
|
||||
async def evaluate(self, *args, **kwargs):
|
||||
"""
|
||||
Run evaluation on a held-out set.
|
||||
|
||||
Override for dataset-specific evaluation logic.
|
||||
"""
|
||||
start_time = time.time()
|
||||
end_time = time.time()
|
||||
|
||||
eval_metrics = {"eval/placeholder": 0.0}
|
||||
await self.evaluate_log(
|
||||
metrics=eval_metrics,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
)
|
||||
|
||||
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
||||
"""Log SWE-specific metrics."""
|
||||
if wandb_metrics is None:
|
||||
wandb_metrics = {}
|
||||
|
||||
if self.reward_buffer:
|
||||
wandb_metrics["train/avg_reward"] = sum(self.reward_buffer) / len(
|
||||
self.reward_buffer
|
||||
)
|
||||
wandb_metrics["train/pass_rate"] = sum(
|
||||
1 for r in self.reward_buffer if r == 1.0
|
||||
) / len(self.reward_buffer)
|
||||
self.reward_buffer = []
|
||||
|
||||
await super().wandb_log(wandb_metrics)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
HermesSweEnv.cli()
|
||||
@@ -1,188 +0,0 @@
|
||||
"""
|
||||
Monkey patches for making hermes-agent tools work inside async frameworks (Atropos).
|
||||
|
||||
Problem:
|
||||
Some tools use asyncio.run() internally (e.g., mini-swe-agent's Modal backend,
|
||||
web_extract). This crashes when called from inside Atropos's event loop because
|
||||
asyncio.run() can't be nested.
|
||||
|
||||
Solution:
|
||||
Replace the problematic methods with versions that use a dedicated background
|
||||
thread with its own event loop. The calling code sees the same sync interface --
|
||||
call a function, get a result -- but internally the async work happens on a
|
||||
separate thread that doesn't conflict with Atropos's loop.
|
||||
|
||||
These patches are safe for normal CLI use too: when there's no running event
|
||||
loop, the behavior is identical (the background thread approach works regardless).
|
||||
|
||||
What gets patched:
|
||||
- SwerexModalEnvironment.__init__ -- creates Modal deployment on a background thread
|
||||
- SwerexModalEnvironment.execute -- runs commands on the same background thread
|
||||
- SwerexModalEnvironment.stop -- stops deployment on the background thread
|
||||
|
||||
Usage:
|
||||
Call apply_patches() once at import time (done automatically by hermes_base_env.py).
|
||||
This is idempotent -- calling it multiple times is safe.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_patches_applied = False
|
||||
|
||||
|
||||
class _AsyncWorker:
|
||||
"""
|
||||
A dedicated background thread with its own event loop.
|
||||
|
||||
Allows sync code to submit async coroutines and block for results,
|
||||
even when called from inside another running event loop. Used to
|
||||
bridge sync tool interfaces with async backends (Modal, SWE-ReX).
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._loop: asyncio.AbstractEventLoop = None
|
||||
self._thread: threading.Thread = None
|
||||
self._started = threading.Event()
|
||||
|
||||
def start(self):
|
||||
"""Start the background event loop thread."""
|
||||
self._thread = threading.Thread(target=self._run_loop, daemon=True)
|
||||
self._thread.start()
|
||||
self._started.wait(timeout=30)
|
||||
|
||||
def _run_loop(self):
|
||||
"""Background thread entry point -- runs the event loop forever."""
|
||||
self._loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self._loop)
|
||||
self._started.set()
|
||||
self._loop.run_forever()
|
||||
|
||||
def run_coroutine(self, coro, timeout=600):
|
||||
"""
|
||||
Submit a coroutine to the background loop and block until it completes.
|
||||
|
||||
Safe to call from any thread, including threads that already have
|
||||
a running event loop.
|
||||
"""
|
||||
if self._loop is None or self._loop.is_closed():
|
||||
raise RuntimeError("AsyncWorker loop is not running")
|
||||
future = asyncio.run_coroutine_threadsafe(coro, self._loop)
|
||||
return future.result(timeout=timeout)
|
||||
|
||||
def stop(self):
|
||||
"""Stop the background event loop and join the thread."""
|
||||
if self._loop and self._loop.is_running():
|
||||
self._loop.call_soon_threadsafe(self._loop.stop)
|
||||
if self._thread:
|
||||
self._thread.join(timeout=10)
|
||||
|
||||
|
||||
def _patch_swerex_modal():
|
||||
"""
|
||||
Monkey patch SwerexModalEnvironment to use a background thread event loop
|
||||
instead of asyncio.run(). This makes it safe to call from inside Atropos's
|
||||
async event loop.
|
||||
|
||||
The patched methods have the exact same interface and behavior -- the only
|
||||
difference is HOW the async work is executed internally.
|
||||
"""
|
||||
try:
|
||||
from minisweagent.environments.extra.swerex_modal import (
|
||||
SwerexModalEnvironment,
|
||||
SwerexModalEnvironmentConfig,
|
||||
)
|
||||
from swerex.deployment.modal import ModalDeployment
|
||||
from swerex.runtime.abstract import Command as RexCommand
|
||||
except ImportError:
|
||||
# mini-swe-agent or swe-rex not installed -- nothing to patch
|
||||
logger.debug("mini-swe-agent Modal backend not available, skipping patch")
|
||||
return
|
||||
|
||||
# Save original methods so we can refer to config handling
|
||||
_original_init = SwerexModalEnvironment.__init__
|
||||
|
||||
def _patched_init(self, **kwargs):
|
||||
"""Patched __init__: creates Modal deployment on a background thread."""
|
||||
self.config = SwerexModalEnvironmentConfig(**kwargs)
|
||||
|
||||
# Start a dedicated event loop thread for all Modal async operations
|
||||
self._worker = _AsyncWorker()
|
||||
self._worker.start()
|
||||
|
||||
# Create AND start the deployment entirely on the worker's loop/thread
|
||||
# so all gRPC channels and async state are bound to that loop
|
||||
async def _create_and_start():
|
||||
deployment = ModalDeployment(
|
||||
image=self.config.image,
|
||||
startup_timeout=self.config.startup_timeout,
|
||||
runtime_timeout=self.config.runtime_timeout,
|
||||
deployment_timeout=self.config.deployment_timeout,
|
||||
install_pipx=self.config.install_pipx,
|
||||
modal_sandbox_kwargs=self.config.modal_sandbox_kwargs,
|
||||
)
|
||||
await deployment.start()
|
||||
return deployment
|
||||
|
||||
self.deployment = self._worker.run_coroutine(_create_and_start())
|
||||
|
||||
def _patched_execute(self, command: str, cwd: str = "", *, timeout: int | None = None) -> dict[str, Any]:
|
||||
"""Patched execute: runs commands on the background thread's loop."""
|
||||
async def _do_execute():
|
||||
return await self.deployment.runtime.execute(
|
||||
RexCommand(
|
||||
command=command,
|
||||
shell=True,
|
||||
check=False,
|
||||
cwd=cwd or self.config.cwd,
|
||||
timeout=timeout or self.config.timeout,
|
||||
merge_output_streams=True,
|
||||
env=self.config.env if self.config.env else None,
|
||||
)
|
||||
)
|
||||
|
||||
output = self._worker.run_coroutine(_do_execute())
|
||||
return {
|
||||
"output": output.stdout,
|
||||
"returncode": output.exit_code,
|
||||
}
|
||||
|
||||
def _patched_stop(self):
|
||||
"""Patched stop: stops deployment on the background thread, then stops the thread."""
|
||||
try:
|
||||
self._worker.run_coroutine(
|
||||
asyncio.wait_for(self.deployment.stop(), timeout=10),
|
||||
timeout=15,
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
finally:
|
||||
self._worker.stop()
|
||||
|
||||
# Apply the patches
|
||||
SwerexModalEnvironment.__init__ = _patched_init
|
||||
SwerexModalEnvironment.execute = _patched_execute
|
||||
SwerexModalEnvironment.stop = _patched_stop
|
||||
|
||||
logger.debug("Patched SwerexModalEnvironment for async-safe operation")
|
||||
|
||||
|
||||
def apply_patches():
|
||||
"""
|
||||
Apply all monkey patches needed for Atropos compatibility.
|
||||
|
||||
Safe to call multiple times -- patches are only applied once.
|
||||
Safe for normal CLI use -- patched code works identically when
|
||||
there is no running event loop.
|
||||
"""
|
||||
global _patches_applied
|
||||
if _patches_applied:
|
||||
return
|
||||
|
||||
_patch_swerex_modal()
|
||||
|
||||
_patches_applied = True
|
||||
@@ -1,292 +0,0 @@
|
||||
"""
|
||||
TerminalTestEnv -- Simple Test Environment for Validating the Stack
|
||||
|
||||
A self-contained environment with inline tasks (no external dataset needed).
|
||||
Each task asks the model to create a file at a known path with specific content.
|
||||
The reward verifier cats the file and checks if the content matches.
|
||||
|
||||
Enables only terminal + file toolsets. Uses Modal terminal backend with
|
||||
OpenRouter (Claude) by default.
|
||||
|
||||
Training tasks (3):
|
||||
1. Create ~/greeting.txt with "Hello from Hermes Agent"
|
||||
2. Create ~/count.txt with numbers 1-5, one per line
|
||||
3. Create ~/answer.txt with the result of 123 + 456
|
||||
|
||||
Eval task (1):
|
||||
1. Create ~/result.txt with the result of 6 * 7
|
||||
|
||||
Usage:
|
||||
# Start Atropos API server
|
||||
run-api
|
||||
|
||||
# Run environment (uses OpenRouter + Modal by default)
|
||||
python environments/terminal_test_env.py serve
|
||||
|
||||
# Process mode (no run-api needed, saves to JSONL)
|
||||
python environments/terminal_test_env.py process \\
|
||||
--env.data_path_to_save_groups terminal_test_output.jsonl
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
# Ensure repo root is on sys.path for imports
|
||||
_repo_root = Path(__file__).resolve().parent.parent
|
||||
if str(_repo_root) not in sys.path:
|
||||
sys.path.insert(0, str(_repo_root))
|
||||
|
||||
from atroposlib.envs.base import ScoredDataGroup
|
||||
from atroposlib.envs.server_handling.server_manager import APIServerConfig
|
||||
from atroposlib.type_definitions import Item
|
||||
|
||||
from environments.agent_loop import AgentResult
|
||||
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
|
||||
from environments.tool_context import ToolContext
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Inline task definitions -- no external dataset needed
|
||||
# =============================================================================
|
||||
|
||||
TRAIN_TASKS = [
|
||||
{
|
||||
"prompt": "Create a file at ~/greeting.txt containing exactly the text: Hello from Hermes Agent",
|
||||
"verify_path": "~/greeting.txt",
|
||||
"expected_content": "Hello from Hermes Agent",
|
||||
},
|
||||
{
|
||||
"prompt": "Create a file at ~/count.txt containing the numbers 1 through 5, one per line",
|
||||
"verify_path": "~/count.txt",
|
||||
"expected_content": "1\n2\n3\n4\n5",
|
||||
},
|
||||
{
|
||||
"prompt": "Create a file at ~/answer.txt containing the result of 123 + 456",
|
||||
"verify_path": "~/answer.txt",
|
||||
"expected_content": "579",
|
||||
},
|
||||
]
|
||||
|
||||
EVAL_TASKS = [
|
||||
{
|
||||
"prompt": "Create a file at ~/result.txt containing the result of 6 * 7",
|
||||
"verify_path": "~/result.txt",
|
||||
"expected_content": "42",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class TerminalTestEnvConfig(HermesAgentEnvConfig):
|
||||
"""Config with defaults suitable for terminal testing."""
|
||||
|
||||
pass # Inherits all fields, overrides defaults in config_init
|
||||
|
||||
|
||||
class TerminalTestEnv(HermesAgentBaseEnv):
|
||||
"""
|
||||
Simple test environment with inline file-creation tasks.
|
||||
|
||||
All tasks follow the same pattern: "create a file at ~/X.txt with content Y".
|
||||
The verifier runs `cat ~/X.txt` in the rollout's terminal and checks the output
|
||||
against the expected string. Same verifier logic for all tasks.
|
||||
|
||||
This environment is designed to validate the full stack end-to-end:
|
||||
- Agent loop executes tool calls (terminal/file)
|
||||
- ToolContext provides terminal access to the reward function
|
||||
- Reward function verifies file content via cat
|
||||
- Scored data flows through the Atropos pipeline
|
||||
"""
|
||||
|
||||
name = "terminal-test"
|
||||
env_config_cls = TerminalTestEnvConfig
|
||||
|
||||
@classmethod
|
||||
def config_init(cls) -> Tuple[TerminalTestEnvConfig, List[APIServerConfig]]:
|
||||
"""
|
||||
Default configuration for the terminal test environment.
|
||||
|
||||
Uses Modal terminal backend for cloud isolation and OpenRouter with
|
||||
Claude for inference. API keys loaded from ~/hermes-agent/.env.
|
||||
"""
|
||||
env_config = TerminalTestEnvConfig(
|
||||
# Terminal + file tools only
|
||||
enabled_toolsets=["terminal", "file"],
|
||||
disabled_toolsets=None,
|
||||
distribution=None,
|
||||
# Agent settings
|
||||
max_agent_turns=10, # Simple tasks, don't need many turns
|
||||
max_token_length=16000,
|
||||
agent_temperature=1.0,
|
||||
system_prompt=(
|
||||
"You are a helpful assistant with access to a terminal and file tools. "
|
||||
"Complete the user's request by using the available tools. "
|
||||
"Be precise and follow instructions exactly."
|
||||
),
|
||||
# Modal terminal backend for cloud-isolated sandboxes per rollout
|
||||
terminal_backend="modal",
|
||||
# Atropos settings
|
||||
group_size=3, # 3 rollouts per group
|
||||
tokenizer_name="NousResearch/q-30b-t-h45-e1",
|
||||
tool_call_parser="hermes",
|
||||
steps_per_eval=3, # Eval after all 3 steps
|
||||
total_steps=3, # 3 groups total (1 group per step)
|
||||
use_wandb=True,
|
||||
wandb_name="terminal-test",
|
||||
ensure_scores_are_not_same=False, # Allow all-same scores for simple tasks
|
||||
# No external dataset
|
||||
dataset_name=None,
|
||||
)
|
||||
|
||||
# OpenRouter with Claude -- API key loaded from .env (OPENROUTER_API_KEY)
|
||||
server_configs = [
|
||||
APIServerConfig(
|
||||
base_url="https://openrouter.ai/api/v1",
|
||||
model_name="anthropic/claude-opus-4.6",
|
||||
server_type="openai",
|
||||
api_key=os.getenv("OPENROUTER_API_KEY", ""),
|
||||
health_check=False, # OpenRouter doesn't have a /health endpoint
|
||||
)
|
||||
]
|
||||
|
||||
return env_config, server_configs
|
||||
|
||||
async def setup(self):
|
||||
"""Initialize inline task lists."""
|
||||
self.train_tasks = list(TRAIN_TASKS)
|
||||
self.eval_tasks = list(EVAL_TASKS)
|
||||
self.iter = 0
|
||||
# Track reward stats for wandb logging
|
||||
self.reward_buffer: List[float] = []
|
||||
|
||||
async def get_next_item(self) -> Dict[str, str]:
|
||||
"""Cycle through training tasks."""
|
||||
item = self.train_tasks[self.iter % len(self.train_tasks)]
|
||||
self.iter += 1
|
||||
return item
|
||||
|
||||
def format_prompt(self, item: Dict[str, str]) -> str:
|
||||
"""The prompt is directly in the task item."""
|
||||
return item["prompt"]
|
||||
|
||||
async def compute_reward(
|
||||
self, item: Dict[str, str], result: AgentResult, ctx: ToolContext
|
||||
) -> float:
|
||||
"""
|
||||
Verify by cat-ing the expected file path and checking content matches.
|
||||
Same verifier for all tasks -- they all write a file at a known path.
|
||||
|
||||
Scoring:
|
||||
1.0 = exact match
|
||||
0.5 = expected content is present but has extra stuff
|
||||
0.0 = file doesn't exist or content doesn't match
|
||||
"""
|
||||
verify_result = ctx.terminal(f"cat {item['verify_path']}")
|
||||
|
||||
# File doesn't exist or can't be read
|
||||
if verify_result["exit_code"] != 0:
|
||||
self.reward_buffer.append(0.0)
|
||||
return 0.0
|
||||
|
||||
actual = verify_result.get("output", "").strip()
|
||||
expected = item["expected_content"].strip()
|
||||
|
||||
# Exact match
|
||||
if actual == expected:
|
||||
self.reward_buffer.append(1.0)
|
||||
return 1.0
|
||||
|
||||
# Partial credit: expected content is present but has extra stuff
|
||||
if expected in actual:
|
||||
self.reward_buffer.append(0.5)
|
||||
return 0.5
|
||||
|
||||
self.reward_buffer.append(0.0)
|
||||
return 0.0
|
||||
|
||||
async def evaluate(self, *args, **kwargs):
|
||||
"""
|
||||
Run eval tasks using the agent loop and verify results.
|
||||
Logs accuracy metrics.
|
||||
"""
|
||||
start_time = time.time()
|
||||
correct = 0
|
||||
total = len(self.eval_tasks)
|
||||
samples = []
|
||||
|
||||
for eval_item in self.eval_tasks:
|
||||
try:
|
||||
# For eval, we do a simple single-turn completion (not full agent loop)
|
||||
# to keep eval fast. The agent loop is tested via training.
|
||||
completion = await self.server.chat_completion(
|
||||
messages=[
|
||||
{"role": "system", "content": self.config.system_prompt or ""},
|
||||
{"role": "user", "content": eval_item["prompt"]},
|
||||
],
|
||||
n=1,
|
||||
max_tokens=self.config.max_token_length,
|
||||
temperature=0.0,
|
||||
split="eval",
|
||||
)
|
||||
|
||||
response_content = (
|
||||
completion.choices[0].message.content if completion.choices else ""
|
||||
)
|
||||
|
||||
samples.append(
|
||||
{
|
||||
"prompt": eval_item["prompt"],
|
||||
"response": response_content,
|
||||
"expected": eval_item["expected_content"],
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Eval failed for item: %s", e)
|
||||
samples.append(
|
||||
{
|
||||
"prompt": eval_item["prompt"],
|
||||
"response": f"ERROR: {e}",
|
||||
"expected": eval_item["expected_content"],
|
||||
}
|
||||
)
|
||||
|
||||
end_time = time.time()
|
||||
|
||||
eval_metrics = {
|
||||
"eval/num_samples": total,
|
||||
}
|
||||
|
||||
await self.evaluate_log(
|
||||
metrics=eval_metrics,
|
||||
samples=samples,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
)
|
||||
|
||||
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
||||
"""Log training metrics including reward stats and accuracy."""
|
||||
if wandb_metrics is None:
|
||||
wandb_metrics = {}
|
||||
|
||||
if self.reward_buffer:
|
||||
total = len(self.reward_buffer)
|
||||
correct = sum(1 for r in self.reward_buffer if r == 1.0)
|
||||
partial = sum(1 for r in self.reward_buffer if r == 0.5)
|
||||
|
||||
wandb_metrics["train/avg_reward"] = sum(self.reward_buffer) / total
|
||||
wandb_metrics["train/accuracy"] = correct / total
|
||||
wandb_metrics["train/partial_match_rate"] = partial / total
|
||||
wandb_metrics["train/total_rollouts"] = total
|
||||
self.reward_buffer = []
|
||||
|
||||
await super().wandb_log(wandb_metrics)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
TerminalTestEnv.cli()
|
||||
@@ -1,120 +0,0 @@
|
||||
"""
|
||||
Tool Call Parser Registry
|
||||
|
||||
Client-side parsers that extract structured tool_calls from raw model output text.
|
||||
Used in Phase 2 (VLLM server type) where ManagedServer's /generate endpoint returns
|
||||
raw text without tool call parsing.
|
||||
|
||||
Each parser is a standalone reimplementation of the corresponding VLLM parser's
|
||||
non-streaming extract_tool_calls() logic. No VLLM dependency -- only standard library
|
||||
(re, json, uuid) and openai types.
|
||||
|
||||
Usage:
|
||||
from environments.tool_call_parsers import get_parser
|
||||
|
||||
parser = get_parser("hermes")
|
||||
content, tool_calls = parser.parse(raw_model_output)
|
||||
# content = text with tool call markup stripped
|
||||
# tool_calls = list of ChatCompletionMessageToolCall objects, or None
|
||||
"""
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Dict, List, Optional, Tuple, Type
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Type alias for parser return value
|
||||
ParseResult = Tuple[Optional[str], Optional[List[ChatCompletionMessageToolCall]]]
|
||||
|
||||
|
||||
class ToolCallParser(ABC):
|
||||
"""
|
||||
Base class for tool call parsers.
|
||||
|
||||
Each parser knows how to extract structured tool_calls from a specific
|
||||
model family's raw output text format.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
"""
|
||||
Parse raw model output text for tool calls.
|
||||
|
||||
Args:
|
||||
text: Raw decoded text from the model's completion
|
||||
|
||||
Returns:
|
||||
Tuple of (content, tool_calls) where:
|
||||
- content: text with tool call markup stripped (the message 'content' field),
|
||||
or None if the entire output was tool calls
|
||||
- tool_calls: list of ChatCompletionMessageToolCall objects,
|
||||
or None if no tool calls were found
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
# Global parser registry: name -> parser class
|
||||
PARSER_REGISTRY: Dict[str, Type[ToolCallParser]] = {}
|
||||
|
||||
|
||||
def register_parser(name: str):
|
||||
"""
|
||||
Decorator to register a parser class under a given name.
|
||||
|
||||
Usage:
|
||||
@register_parser("hermes")
|
||||
class HermesToolCallParser(ToolCallParser):
|
||||
...
|
||||
"""
|
||||
|
||||
def decorator(cls: Type[ToolCallParser]) -> Type[ToolCallParser]:
|
||||
PARSER_REGISTRY[name] = cls
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def get_parser(name: str) -> ToolCallParser:
|
||||
"""
|
||||
Get a parser instance by name.
|
||||
|
||||
Args:
|
||||
name: Parser name (e.g., "hermes", "mistral", "llama3_json")
|
||||
|
||||
Returns:
|
||||
Instantiated parser
|
||||
|
||||
Raises:
|
||||
KeyError: If parser name is not found in registry
|
||||
"""
|
||||
if name not in PARSER_REGISTRY:
|
||||
available = sorted(PARSER_REGISTRY.keys())
|
||||
raise KeyError(
|
||||
f"Tool call parser '{name}' not found. Available parsers: {available}"
|
||||
)
|
||||
return PARSER_REGISTRY[name]()
|
||||
|
||||
|
||||
def list_parsers() -> List[str]:
|
||||
"""Return sorted list of registered parser names."""
|
||||
return sorted(PARSER_REGISTRY.keys())
|
||||
|
||||
|
||||
# Import all parser modules to trigger registration via @register_parser decorators
|
||||
# Each module registers itself when imported
|
||||
from environments.tool_call_parsers.hermes_parser import HermesToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.longcat_parser import LongcatToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.mistral_parser import MistralToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.llama_parser import LlamaToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.qwen_parser import QwenToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.deepseek_v3_parser import DeepSeekV3ToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.deepseek_v3_1_parser import DeepSeekV31ToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.kimi_k2_parser import KimiK2ToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.glm47_parser import Glm47ToolCallParser # noqa: E402, F401
|
||||
from environments.tool_call_parsers.qwen3_coder_parser import Qwen3CoderToolCallParser # noqa: E402, F401
|
||||
@@ -1,71 +0,0 @@
|
||||
"""
|
||||
DeepSeek V3.1 tool call parser.
|
||||
|
||||
Similar to V3 but with a slightly different format:
|
||||
<｜tool▁call▁begin｜>function_name<｜tool▁sep｜>arguments<｜tool▁call▁end｜>
|
||||
|
||||
Note: V3 has type+name before the separator, V3.1 has name before and args after.
|
||||
|
||||
Based on VLLM's DeepSeekV31ToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("deepseek_v3_1")
|
||||
@register_parser("deepseek_v31")
|
||||
class DeepSeekV31ToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for DeepSeek V3.1 tool calls.
|
||||
|
||||
Slightly different regex than V3: function_name comes before the separator,
|
||||
arguments come after (no type field, no json code block wrapper).
|
||||
"""
|
||||
|
||||
START_TOKEN = "<｜tool▁calls▁begin｜>"
|
||||
|
||||
# Regex captures: function_name, function_arguments
|
||||
PATTERN = re.compile(
|
||||
r"<｜tool▁call▁begin｜>(?P<function_name>.*?)<｜tool▁sep｜>(?P<function_arguments>.*?)<｜tool▁call▁end｜>"
|
||||
)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if self.START_TOKEN not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matches = self.PATTERN.findall(text)
|
||||
if not matches:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for match in matches:
|
||||
func_name, func_args = match
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=func_name.strip(),
|
||||
arguments=func_args.strip(),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
content = text[: text.find(self.START_TOKEN)].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,75 +0,0 @@
|
||||
"""
|
||||
DeepSeek V3 tool call parser.
|
||||
|
||||
Format uses special unicode tokens:
|
||||
<｜tool▁calls▁begin｜>
|
||||
<｜tool▁call▁begin｜>type<｜tool▁sep｜>function_name
|
||||
```json
|
||||
{"arg": "value"}
|
||||
```
|
||||
<｜tool▁call▁end｜>
|
||||
<｜tool▁calls▁end｜>
|
||||
|
||||
Based on VLLM's DeepSeekV3ToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("deepseek_v3")
|
||||
class DeepSeekV3ToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for DeepSeek V3 tool calls.
|
||||
|
||||
Uses special unicode tokens with fullwidth angle brackets and block elements.
|
||||
Extracts type, function name, and JSON arguments from the structured format.
|
||||
"""
|
||||
|
||||
START_TOKEN = "<｜tool▁calls▁begin｜>"
|
||||
|
||||
# Regex captures: type, function_name, function_arguments
|
||||
PATTERN = re.compile(
|
||||
r"<｜tool▁call▁begin｜>(?P<type>.*)<｜tool▁sep｜>(?P<function_name>.*)\n```json\n(?P<function_arguments>.*)\n```<｜tool▁call▁end｜>"
|
||||
)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if self.START_TOKEN not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matches = self.PATTERN.findall(text)
|
||||
if not matches:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for match in matches:
|
||||
tc_type, func_name, func_args = match
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=func_name.strip(),
|
||||
arguments=func_args.strip(),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
# Content is everything before the tool calls section
|
||||
content = text[: text.find(self.START_TOKEN)].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,109 +0,0 @@
|
||||
"""
|
||||
GLM 4.5 (GLM-4-MoE) tool call parser.
|
||||
|
||||
Format uses custom arg_key/arg_value tags rather than standard JSON:
|
||||
<tool_call>function_name
|
||||
<arg_key>param1</arg_key><arg_value>value1</arg_value>
|
||||
<arg_key>param2</arg_key><arg_value>value2</arg_value>
|
||||
</tool_call>
|
||||
|
||||
Values are deserialized using json.loads -> ast.literal_eval -> raw string fallback.
|
||||
|
||||
Based on VLLM's Glm4MoeModelToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import ast
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
def _deserialize_value(value: str) -> Any:
|
||||
"""
|
||||
Try to deserialize a string value to its native Python type.
|
||||
Attempts json.loads, then ast.literal_eval, then returns raw string.
|
||||
"""
|
||||
try:
|
||||
return json.loads(value)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
try:
|
||||
return ast.literal_eval(value)
|
||||
except (ValueError, SyntaxError, TypeError):
|
||||
pass
|
||||
|
||||
return value
|
||||
|
||||
|
||||
@register_parser("glm45")
|
||||
class Glm45ToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for GLM 4.5 (GLM-4-MoE) tool calls.
|
||||
|
||||
Uses <tool_call>...</tool_call> tags with <arg_key>/<arg_value> pairs
|
||||
instead of standard JSON arguments.
|
||||
"""
|
||||
|
||||
FUNC_CALL_REGEX = re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL)
|
||||
FUNC_DETAIL_REGEX = re.compile(r"<tool_call>([^\n]*)\n(.*)</tool_call>", re.DOTALL)
|
||||
FUNC_ARG_REGEX = re.compile(
|
||||
r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>", re.DOTALL
|
||||
)
|
||||
|
||||
START_TOKEN = "<tool_call>"
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if self.START_TOKEN not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matched_calls = self.FUNC_CALL_REGEX.findall(text)
|
||||
if not matched_calls:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
|
||||
for match in matched_calls:
|
||||
detail = self.FUNC_DETAIL_REGEX.search(match)
|
||||
if not detail:
|
||||
continue
|
||||
|
||||
func_name = detail.group(1).strip()
|
||||
func_args_raw = detail.group(2)
|
||||
|
||||
# Parse arg_key/arg_value pairs
|
||||
pairs = self.FUNC_ARG_REGEX.findall(func_args_raw) if func_args_raw else []
|
||||
arg_dict: Dict[str, Any] = {}
|
||||
for key, value in pairs:
|
||||
arg_key = key.strip()
|
||||
arg_val = _deserialize_value(value.strip())
|
||||
arg_dict[arg_key] = arg_val
|
||||
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=func_name,
|
||||
arguments=json.dumps(arg_dict, ensure_ascii=False),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
content = text[: text.find(self.START_TOKEN)].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,35 +0,0 @@
|
||||
"""
|
||||
GLM 4.7 tool call parser.
|
||||
|
||||
Same as GLM 4.5 but with slightly different regex patterns.
|
||||
The tool_call tags may wrap differently and arg parsing handles
|
||||
newlines between key/value pairs.
|
||||
|
||||
Based on VLLM's Glm47MoeModelToolParser (extends Glm4MoeModelToolParser).
|
||||
"""
|
||||
|
||||
import re
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, register_parser
|
||||
from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser
|
||||
|
||||
|
||||
@register_parser("glm47")
|
||||
class Glm47ToolCallParser(Glm45ToolCallParser):
|
||||
"""
|
||||
Parser for GLM 4.7 tool calls.
|
||||
Extends GLM 4.5 with updated regex patterns.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
# GLM 4.7 uses a slightly different detail regex that includes
|
||||
# the <tool_call> wrapper and optional arg_key content
|
||||
self.FUNC_DETAIL_REGEX = re.compile(
|
||||
r"<tool_call>(.*?)(<arg_key>.*?)?</tool_call>", re.DOTALL
|
||||
)
|
||||
# GLM 4.7 handles newlines between arg_key and arg_value tags
|
||||
self.FUNC_ARG_REGEX = re.compile(
|
||||
r"<arg_key>(.*?)</arg_key>(?:\\n|\s)*<arg_value>(.*?)</arg_value>",
|
||||
re.DOTALL,
|
||||
)
|
||||
@@ -1,73 +0,0 @@
|
||||
"""
|
||||
Hermes tool call parser.
|
||||
|
||||
Format: <tool_call>{"name": "func", "arguments": {...}}</tool_call>
|
||||
Based on VLLM's Hermes2ProToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("hermes")
|
||||
class HermesToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Hermes-format tool calls.
|
||||
|
||||
Matches <tool_call>...</tool_call> tags containing JSON with "name" and "arguments".
|
||||
Also handles unclosed <tool_call> at end-of-string (truncated generation).
|
||||
"""
|
||||
|
||||
# Matches both closed and unclosed tool_call tags
|
||||
PATTERN = re.compile(
|
||||
r"<tool_call>\s*(.*?)\s*</tool_call>|<tool_call>\s*(.*)", re.DOTALL
|
||||
)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if "<tool_call>" not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matches = self.PATTERN.findall(text)
|
||||
if not matches:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for match in matches:
|
||||
# match is a tuple: (closed_content, unclosed_content)
|
||||
raw_json = match[0] if match[0] else match[1]
|
||||
if not raw_json.strip():
|
||||
continue
|
||||
|
||||
tc_data = json.loads(raw_json)
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=tc_data["name"],
|
||||
arguments=json.dumps(
|
||||
tc_data.get("arguments", {}), ensure_ascii=False
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
# Content is everything before the first <tool_call> tag
|
||||
content = text[: text.find("<tool_call>")].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,93 +0,0 @@
|
||||
"""
|
||||
Kimi K2 tool call parser.
|
||||
|
||||
Format:
|
||||
<|tool_calls_section_begin|>
|
||||
<|tool_call_begin|>function_id:0<|tool_call_argument_begin|>{"arg": "val"}<|tool_call_end|>
|
||||
<|tool_calls_section_end|>
|
||||
|
||||
The function_id format is typically "functions.func_name:index" or "func_name:index".
|
||||
|
||||
Based on VLLM's KimiK2ToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("kimi_k2")
|
||||
class KimiK2ToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Kimi K2 tool calls.
|
||||
|
||||
Uses section begin/end tokens wrapping individual tool call begin/end tokens.
|
||||
The tool_call_id contains the function name (after last dot, before colon).
|
||||
"""
|
||||
|
||||
# Support both singular and plural variants
|
||||
START_TOKENS = [
|
||||
"<|tool_calls_section_begin|>",
|
||||
"<|tool_call_section_begin|>",
|
||||
]
|
||||
|
||||
# Regex captures: tool_call_id (e.g., "functions.get_weather:0"), function_arguments
|
||||
PATTERN = re.compile(
|
||||
r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[^<]+:\d+)\s*"
|
||||
r"<\|tool_call_argument_begin\|>\s*"
|
||||
r"(?P<function_arguments>(?:(?!<\|tool_call_begin\|>).)*?)\s*"
|
||||
r"<\|tool_call_end\|>",
|
||||
re.DOTALL,
|
||||
)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
# Check for any variant of the start token
|
||||
has_start = any(token in text for token in self.START_TOKENS)
|
||||
if not has_start:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matches = self.PATTERN.findall(text)
|
||||
if not matches:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for match in matches:
|
||||
function_id, function_args = match
|
||||
|
||||
# Extract function name from ID format: "functions.get_weather:0" -> "get_weather"
|
||||
function_name = function_id.split(":")[0].split(".")[-1]
|
||||
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=function_id, # Preserve the original ID format
|
||||
type="function",
|
||||
function=Function(
|
||||
name=function_name,
|
||||
arguments=function_args.strip(),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
# Content is everything before the tool calls section
|
||||
earliest_start = len(text)
|
||||
for token in self.START_TOKENS:
|
||||
idx = text.find(token)
|
||||
if idx >= 0 and idx < earliest_start:
|
||||
earliest_start = idx
|
||||
|
||||
content = text[:earliest_start].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,96 +0,0 @@
|
||||
"""
|
||||
Llama 3.x / 4 tool call parser.
|
||||
|
||||
Format: The model outputs JSON objects with "name" and "arguments" (or "parameters") keys.
|
||||
May be preceded by <|python_tag|> token. Supports multiple JSON objects separated
|
||||
by content or semicolons.
|
||||
|
||||
Based on VLLM's Llama3JsonToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("llama3_json")
|
||||
@register_parser("llama4_json")
|
||||
class LlamaToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Llama 3.x and 4 JSON-format tool calls.
|
||||
|
||||
Finds JSON objects containing "name" + ("arguments" or "parameters") keys.
|
||||
Uses Python's json.JSONDecoder.raw_decode for robust extraction of
|
||||
JSON objects from mixed text.
|
||||
"""
|
||||
|
||||
BOT_TOKEN = "<|python_tag|>"
|
||||
|
||||
# Regex to find the start of potential JSON objects
|
||||
JSON_START = re.compile(r"\{")
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
# Quick check: need either the bot token or a JSON brace
|
||||
if self.BOT_TOKEN not in text and "{" not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
decoder = json.JSONDecoder()
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
end_index = -1 # Track where the last parsed JSON ended
|
||||
|
||||
for match in self.JSON_START.finditer(text):
|
||||
start = match.start()
|
||||
# Skip if this brace is inside a previously parsed JSON object
|
||||
if start <= end_index:
|
||||
continue
|
||||
|
||||
try:
|
||||
obj, json_end = decoder.raw_decode(text[start:])
|
||||
end_index = start + json_end
|
||||
|
||||
# Must have "name" and either "arguments" or "parameters"
|
||||
name = obj.get("name")
|
||||
args = obj.get("arguments", obj.get("parameters"))
|
||||
|
||||
if not name or args is None:
|
||||
continue
|
||||
|
||||
# Normalize arguments to JSON string
|
||||
if isinstance(args, dict):
|
||||
args = json.dumps(args, ensure_ascii=False)
|
||||
elif not isinstance(args, str):
|
||||
args = json.dumps(args, ensure_ascii=False)
|
||||
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(name=name, arguments=args),
|
||||
)
|
||||
)
|
||||
except (json.JSONDecodeError, KeyError, ValueError):
|
||||
continue
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
# Content is everything before the first tool call JSON
|
||||
# Find where the first tool call starts in the text
|
||||
first_tc_start = text.find("{")
|
||||
if self.BOT_TOKEN in text:
|
||||
first_tc_start = text.find(self.BOT_TOKEN)
|
||||
content = text[:first_tc_start].strip() if first_tc_start > 0 else None
|
||||
|
||||
return content, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,69 +0,0 @@
|
||||
"""
|
||||
Longcat Flash Chat tool call parser.
|
||||
|
||||
Same as Hermes but uses <longcat_tool_call> tags instead of <tool_call>.
|
||||
Based on VLLM's LongcatFlashToolParser (extends Hermes2ProToolParser).
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
@register_parser("longcat")
|
||||
class LongcatToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Longcat Flash Chat tool calls.
|
||||
Identical logic to Hermes, just different tag names.
|
||||
"""
|
||||
|
||||
PATTERN = re.compile(
|
||||
r"<longcat_tool_call>\s*(.*?)\s*</longcat_tool_call>|<longcat_tool_call>\s*(.*)",
|
||||
re.DOTALL,
|
||||
)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if "<longcat_tool_call>" not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
matches = self.PATTERN.findall(text)
|
||||
if not matches:
|
||||
return text, None
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for match in matches:
|
||||
raw_json = match[0] if match[0] else match[1]
|
||||
if not raw_json.strip():
|
||||
continue
|
||||
|
||||
tc_data = json.loads(raw_json)
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:8]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=tc_data["name"],
|
||||
arguments=json.dumps(
|
||||
tc_data.get("arguments", {}), ensure_ascii=False
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
content = text[: text.find("<longcat_tool_call>")].strip()
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,130 +0,0 @@
|
||||
"""
|
||||
Mistral tool call parser.
|
||||
|
||||
Supports two formats depending on tokenizer version:
|
||||
- Pre-v11: content[TOOL_CALLS] [{"name": ..., "arguments": {...}}, ...]
|
||||
- v11+: content[TOOL_CALLS]tool_name1{"arg": "val"}[TOOL_CALLS]tool_name2{"arg": "val"}
|
||||
|
||||
Based on VLLM's MistralToolParser.extract_tool_calls()
|
||||
The [TOOL_CALLS] token is the bot_token used by Mistral models.
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
def _generate_mistral_id() -> str:
|
||||
"""Mistral tool call IDs are 9-char alphanumeric strings."""
|
||||
import random
|
||||
import string
|
||||
|
||||
return "".join(random.choices(string.ascii_letters + string.digits, k=9))
|
||||
|
||||
|
||||
@register_parser("mistral")
|
||||
class MistralToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Mistral-format tool calls.
|
||||
|
||||
Detects format by checking if the content after [TOOL_CALLS] starts with '['
|
||||
(pre-v11 JSON array) or with a tool name (v11+ format).
|
||||
"""
|
||||
|
||||
# The [TOOL_CALLS] token -- may appear as different strings depending on tokenizer
|
||||
BOT_TOKEN = "[TOOL_CALLS]"
|
||||
|
||||
# Fallback regex for pre-v11 format when JSON parsing fails
|
||||
TOOL_CALL_REGEX = re.compile(r"\[?\s*(\{.*?\})\s*\]?", re.DOTALL)
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if self.BOT_TOKEN not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
parts = text.split(self.BOT_TOKEN)
|
||||
content = parts[0].strip()
|
||||
raw_tool_calls = parts[1:]
|
||||
|
||||
# Detect format: if the first raw part starts with '[', it's pre-v11
|
||||
first_raw = raw_tool_calls[0].strip() if raw_tool_calls else ""
|
||||
is_pre_v11 = first_raw.startswith("[") or first_raw.startswith("{")
|
||||
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
|
||||
if not is_pre_v11:
|
||||
# v11+ format: [TOOL_CALLS]tool_name{args}[TOOL_CALLS]tool_name2{args2}
|
||||
for raw in raw_tool_calls:
|
||||
raw = raw.strip()
|
||||
if not raw or "{" not in raw:
|
||||
continue
|
||||
|
||||
brace_idx = raw.find("{")
|
||||
tool_name = raw[:brace_idx].strip()
|
||||
args_str = raw[brace_idx:]
|
||||
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=_generate_mistral_id(),
|
||||
type="function",
|
||||
function=Function(name=tool_name, arguments=args_str),
|
||||
)
|
||||
)
|
||||
else:
|
||||
# Pre-v11 format: [TOOL_CALLS] [{"name": ..., "arguments": {...}}]
|
||||
try:
|
||||
parsed = json.loads(first_raw)
|
||||
if isinstance(parsed, dict):
|
||||
parsed = [parsed]
|
||||
|
||||
for tc in parsed:
|
||||
args = tc.get("arguments", {})
|
||||
if isinstance(args, dict):
|
||||
args = json.dumps(args, ensure_ascii=False)
|
||||
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=_generate_mistral_id(),
|
||||
type="function",
|
||||
function=Function(
|
||||
name=tc["name"], arguments=args
|
||||
),
|
||||
)
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
# Fallback regex extraction
|
||||
match = self.TOOL_CALL_REGEX.findall(first_raw)
|
||||
if match:
|
||||
for raw_json in match:
|
||||
try:
|
||||
tc = json.loads(raw_json)
|
||||
args = tc.get("arguments", {})
|
||||
if isinstance(args, dict):
|
||||
args = json.dumps(args, ensure_ascii=False)
|
||||
tool_calls.append(
|
||||
ChatCompletionMessageToolCall(
|
||||
id=_generate_mistral_id(),
|
||||
type="function",
|
||||
function=Function(
|
||||
name=tc["name"], arguments=args
|
||||
),
|
||||
)
|
||||
)
|
||||
except (json.JSONDecodeError, KeyError):
|
||||
continue
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
return content if content else None, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,163 +0,0 @@
|
||||
"""
|
||||
Qwen3-Coder tool call parser.
|
||||
|
||||
Format uses XML-style nested tags:
|
||||
<tool_call>
|
||||
<function=function_name>
|
||||
<parameter=param_name>value</parameter>
|
||||
<parameter=param_name2>value2</parameter>
|
||||
</function>
|
||||
</tool_call>
|
||||
|
||||
Parameters are extracted from <parameter=name>value</parameter> tags and
|
||||
type-converted using the schema if available, otherwise treated as strings.
|
||||
|
||||
Based on VLLM's Qwen3CoderToolParser.extract_tool_calls()
|
||||
"""
|
||||
|
||||
import ast
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from openai.types.chat.chat_completion_message_tool_call import (
|
||||
ChatCompletionMessageToolCall,
|
||||
Function,
|
||||
)
|
||||
|
||||
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
|
||||
|
||||
|
||||
def _try_convert_value(value: str) -> Any:
|
||||
"""
|
||||
Try to convert a parameter value string to a native Python type.
|
||||
Handles null, numbers, booleans, JSON objects/arrays, and falls back to string.
|
||||
"""
|
||||
stripped = value.strip()
|
||||
|
||||
# Handle null
|
||||
if stripped.lower() == "null":
|
||||
return None
|
||||
|
||||
# Try JSON first (handles objects, arrays, strings, numbers, booleans)
|
||||
try:
|
||||
return json.loads(stripped)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Try Python literal eval (handles tuples, etc.)
|
||||
try:
|
||||
return ast.literal_eval(stripped)
|
||||
except (ValueError, SyntaxError, TypeError):
|
||||
pass
|
||||
|
||||
# Return as string
|
||||
return stripped
|
||||
|
||||
|
||||
@register_parser("qwen3_coder")
|
||||
class Qwen3CoderToolCallParser(ToolCallParser):
|
||||
"""
|
||||
Parser for Qwen3-Coder XML-format tool calls.
|
||||
|
||||
Uses nested XML tags: <tool_call><function=name><parameter=key>val</parameter></function></tool_call>
|
||||
"""
|
||||
|
||||
START_TOKEN = "<tool_call>"
|
||||
FUNCTION_PREFIX = "<function="
|
||||
|
||||
# Find complete tool_call blocks (or unclosed at end)
|
||||
TOOL_CALL_REGEX = re.compile(
|
||||
r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL
|
||||
)
|
||||
|
||||
# Find function blocks within a tool_call
|
||||
FUNCTION_REGEX = re.compile(
|
||||
r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL
|
||||
)
|
||||
|
||||
# Find parameter blocks within a function
|
||||
PARAMETER_REGEX = re.compile(
|
||||
r"<parameter=(.*?)(?:</parameter>|(?=<parameter=)|(?=</function>)|$)",
|
||||
re.DOTALL,
|
||||
)
|
||||
|
||||
def _parse_function_call(self, function_str: str) -> Optional[ChatCompletionMessageToolCall]:
|
||||
"""Parse a single <function=name>...</function> block into a ToolCall."""
|
||||
try:
|
||||
# Extract function name: everything before the first '>'
|
||||
gt_idx = function_str.index(">")
|
||||
func_name = function_str[:gt_idx].strip()
|
||||
params_str = function_str[gt_idx + 1:]
|
||||
|
||||
# Extract parameters
|
||||
param_dict: Dict[str, Any] = {}
|
||||
for match_text in self.PARAMETER_REGEX.findall(params_str):
|
||||
if ">" not in match_text:
|
||||
continue
|
||||
eq_idx = match_text.index(">")
|
||||
param_name = match_text[:eq_idx].strip()
|
||||
param_value = match_text[eq_idx + 1:]
|
||||
|
||||
# Clean up whitespace
|
||||
if param_value.startswith("\n"):
|
||||
param_value = param_value[1:]
|
||||
if param_value.endswith("\n"):
|
||||
param_value = param_value[:-1]
|
||||
|
||||
param_dict[param_name] = _try_convert_value(param_value)
|
||||
|
||||
return ChatCompletionMessageToolCall(
|
||||
id=f"call_{uuid.uuid4().hex[:24]}",
|
||||
type="function",
|
||||
function=Function(
|
||||
name=func_name,
|
||||
arguments=json.dumps(param_dict, ensure_ascii=False),
|
||||
),
|
||||
)
|
||||
except (ValueError, IndexError):
|
||||
return None
|
||||
|
||||
def parse(self, text: str) -> ParseResult:
|
||||
if self.FUNCTION_PREFIX not in text:
|
||||
return text, None
|
||||
|
||||
try:
|
||||
# Find all tool_call blocks
|
||||
tc_matches = self.TOOL_CALL_REGEX.findall(text)
|
||||
raw_blocks = [m[0] if m[0] else m[1] for m in tc_matches]
|
||||
|
||||
# Fallback: if no tool_call tags, try the whole text
|
||||
if not raw_blocks:
|
||||
raw_blocks = [text]
|
||||
|
||||
# Find function blocks within each tool_call
|
||||
function_strs: List[str] = []
|
||||
for block in raw_blocks:
|
||||
func_matches = self.FUNCTION_REGEX.findall(block)
|
||||
function_strs.extend(m[0] if m[0] else m[1] for m in func_matches)
|
||||
|
||||
if not function_strs:
|
||||
return text, None
|
||||
|
||||
# Parse each function call
|
||||
tool_calls: List[ChatCompletionMessageToolCall] = []
|
||||
for func_str in function_strs:
|
||||
tc = self._parse_function_call(func_str)
|
||||
if tc is not None:
|
||||
tool_calls.append(tc)
|
||||
|
||||
if not tool_calls:
|
||||
return text, None
|
||||
|
||||
# Content before tool calls
|
||||
first_tc = text.find(self.START_TOKEN)
|
||||
if first_tc < 0:
|
||||
first_tc = text.find(self.FUNCTION_PREFIX)
|
||||
content = text[:first_tc].strip() if first_tc > 0 else None
|
||||
|
||||
return content, tool_calls
|
||||
|
||||
except Exception:
|
||||
return text, None
|
||||
@@ -1,19 +0,0 @@
|
||||
"""
|
||||
Qwen 2.5 tool call parser.
|
||||
|
||||
Uses the same <tool_call> format as Hermes.
|
||||
Registered as a separate parser name for clarity when using --tool-parser=qwen.
|
||||
"""
|
||||
|
||||
from environments.tool_call_parsers import register_parser
|
||||
from environments.tool_call_parsers.hermes_parser import HermesToolCallParser
|
||||
|
||||
|
||||
@register_parser("qwen")
|
||||
class QwenToolCallParser(HermesToolCallParser):
|
||||
"""
|
||||
Parser for Qwen 2.5 tool calls.
|
||||
Same <tool_call>{"name": ..., "arguments": ...}</tool_call> format as Hermes.
|
||||
"""
|
||||
|
||||
pass # Identical format -- inherits everything from Hermes
|
||||
@@ -1,289 +0,0 @@
|
||||
"""
|
||||
ToolContext -- Unrestricted Tool Access for Reward Functions
|
||||
|
||||
A per-rollout handle that gives reward/verification functions direct access to
|
||||
ALL hermes-agent tools, scoped to the rollout's task_id. The same task_id means
|
||||
the terminal/browser session is the SAME one the model used during its rollout --
|
||||
all state (files, processes, browser tabs) is preserved.
|
||||
|
||||
The verifier author decides which tools to use. Nothing is hardcoded or gated.
|
||||
|
||||
Example usage in a compute_reward():
|
||||
async def compute_reward(self, item, result, ctx):
|
||||
# Run tests in the model's terminal sandbox
|
||||
test = ctx.terminal("pytest -v")
|
||||
if test["exit_code"] == 0:
|
||||
return 1.0
|
||||
|
||||
# Check if a file was created
|
||||
content = ctx.read_file("/workspace/solution.py")
|
||||
if content.get("content"):
|
||||
return 0.5
|
||||
|
||||
return 0.0
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import asyncio
|
||||
import concurrent.futures
|
||||
|
||||
from model_tools import handle_function_call
|
||||
from tools.terminal_tool import cleanup_vm
|
||||
from tools.browser_tool import cleanup_browser
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Thread pool for running sync tool calls that internally use asyncio.run()
|
||||
_tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
|
||||
|
||||
|
||||
def _run_tool_in_thread(tool_name: str, arguments: Dict[str, Any], task_id: str) -> str:
|
||||
"""
|
||||
Run a tool call in a thread pool executor so backends that use asyncio.run()
|
||||
internally (modal, docker) get a clean event loop.
|
||||
|
||||
If we're already in an async context, uses run_in_executor.
|
||||
If not (e.g., called from sync code), runs directly.
|
||||
"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
# We're in an async context -- need to run in thread
|
||||
import concurrent.futures
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
|
||||
future = pool.submit(
|
||||
handle_function_call, tool_name, arguments, task_id
|
||||
)
|
||||
return future.result(timeout=300)
|
||||
except RuntimeError:
|
||||
# No running event loop -- safe to call directly
|
||||
return handle_function_call(tool_name, arguments, task_id)
|
||||
|
||||
|
||||
class ToolContext:
|
||||
"""
|
||||
Open-ended access to all hermes-agent tools for a specific rollout.
|
||||
|
||||
Passed to compute_reward() so verifiers can use any tool they need:
|
||||
terminal commands, file reads/writes, web searches, browser automation, etc.
|
||||
All calls share the rollout's task_id for session isolation.
|
||||
"""
|
||||
|
||||
def __init__(self, task_id: str):
|
||||
self.task_id = task_id
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Terminal tools
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def terminal(self, command: str, timeout: int = 180) -> Dict[str, Any]:
|
||||
"""
|
||||
Run a command in the rollout's terminal session.
|
||||
|
||||
Args:
|
||||
command: Shell command to execute
|
||||
timeout: Command timeout in seconds
|
||||
|
||||
Returns:
|
||||
Dict with 'exit_code' (int) and 'output' (str)
|
||||
"""
|
||||
import os
|
||||
backend = os.getenv("TERMINAL_ENV", "local")
|
||||
logger.debug("ToolContext.terminal [%s backend] task=%s: %s", backend, self.task_id[:8], command[:100])
|
||||
|
||||
# Run in thread pool so modal/docker backends' asyncio.run() doesn't deadlock
|
||||
result = _run_tool_in_thread(
|
||||
"terminal",
|
||||
{"command": command, "timeout": timeout},
|
||||
self.task_id,
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"exit_code": -1, "output": result}
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# File tools
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def read_file(self, path: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Read a file from the rollout's filesystem.
|
||||
|
||||
Args:
|
||||
path: File path to read
|
||||
|
||||
Returns:
|
||||
Dict with file content or error
|
||||
"""
|
||||
result = handle_function_call(
|
||||
"read_file", {"path": path}, task_id=self.task_id
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
def write_file(self, path: str, content: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Write a file in the rollout's filesystem.
|
||||
|
||||
Args:
|
||||
path: File path to write
|
||||
content: Content to write
|
||||
|
||||
Returns:
|
||||
Dict with success status or error
|
||||
"""
|
||||
result = handle_function_call(
|
||||
"write_file", {"path": path, "content": content}, task_id=self.task_id
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
def search(self, query: str, path: str = ".") -> Dict[str, Any]:
|
||||
"""
|
||||
Search for text in the rollout's filesystem.
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
path: Directory to search in
|
||||
|
||||
Returns:
|
||||
Dict with search results
|
||||
"""
|
||||
result = handle_function_call(
|
||||
"search", {"query": query, "path": path}, task_id=self.task_id
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Web tools
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def web_search(self, query: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Search the web.
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
|
||||
Returns:
|
||||
Dict with search results
|
||||
"""
|
||||
result = handle_function_call("web_search", {"query": query})
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
def web_extract(self, urls: List[str]) -> Dict[str, Any]:
|
||||
"""
|
||||
Extract content from URLs.
|
||||
|
||||
Args:
|
||||
urls: List of URLs to extract content from
|
||||
|
||||
Returns:
|
||||
Dict with extracted content
|
||||
"""
|
||||
result = handle_function_call("web_extract", {"urls": urls})
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Browser tools
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def browser_navigate(self, url: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Navigate the rollout's browser session to a URL.
|
||||
|
||||
Args:
|
||||
url: URL to navigate to
|
||||
|
||||
Returns:
|
||||
Dict with page snapshot or error
|
||||
"""
|
||||
result = handle_function_call(
|
||||
"browser_navigate", {"url": url}, task_id=self.task_id
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
def browser_snapshot(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Take a snapshot of the current browser page.
|
||||
|
||||
Returns:
|
||||
Dict with page content/accessibility snapshot
|
||||
"""
|
||||
result = handle_function_call(
|
||||
"browser_snapshot", {}, task_id=self.task_id
|
||||
)
|
||||
try:
|
||||
return json.loads(result)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": result}
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Generic tool access
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def call_tool(self, tool_name: str, arguments: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Call any hermes-agent tool by name.
|
||||
|
||||
This is the generic escape hatch -- if a tool doesn't have a convenience
|
||||
wrapper above, you can call it directly here.
|
||||
|
||||
Args:
|
||||
tool_name: Name of the tool (e.g., "vision_analyze", "skills_list")
|
||||
arguments: Dict of arguments for the tool
|
||||
|
||||
Returns:
|
||||
Raw JSON string result from the tool
|
||||
"""
|
||||
return _run_tool_in_thread(tool_name, arguments, self.task_id)
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Cleanup
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
def cleanup(self):
|
||||
"""
|
||||
Release all resources (terminal VMs, browser sessions) for this rollout.
|
||||
|
||||
Called automatically by the base environment via try/finally after
|
||||
compute_reward() completes. You generally don't need to call this yourself.
|
||||
"""
|
||||
try:
|
||||
cleanup_vm(self.task_id)
|
||||
except Exception as e:
|
||||
logger.debug("VM cleanup for task %s: %s", self.task_id, e)
|
||||
|
||||
# Suppress browser_tool's noisy debug prints during cleanup.
|
||||
# The cleanup still runs (safe), it just doesn't spam the console.
|
||||
_prev_quiet = os.environ.get("HERMES_QUIET")
|
||||
os.environ["HERMES_QUIET"] = "1"
|
||||
try:
|
||||
cleanup_browser(self.task_id)
|
||||
except Exception as e:
|
||||
logger.debug("Browser cleanup for task %s: %s", self.task_id, e)
|
||||
finally:
|
||||
if _prev_quiet is None:
|
||||
os.environ.pop("HERMES_QUIET", None)
|
||||
else:
|
||||
os.environ["HERMES_QUIET"] = _prev_quiet
|
||||
@@ -1,35 +0,0 @@
|
||||
"""
|
||||
Hermes Gateway - Multi-platform messaging integration.
|
||||
|
||||
This module provides a unified gateway for connecting the Hermes agent
|
||||
to various messaging platforms (Telegram, Discord, WhatsApp) with:
|
||||
- Session management (persistent conversations with reset policies)
|
||||
- Dynamic context injection (agent knows where messages come from)
|
||||
- Delivery routing (cron job outputs to appropriate channels)
|
||||
- Platform-specific toolsets (different capabilities per platform)
|
||||
"""
|
||||
|
||||
from .config import GatewayConfig, PlatformConfig, HomeChannel, load_gateway_config
|
||||
from .session import (
|
||||
SessionContext,
|
||||
SessionStore,
|
||||
SessionResetPolicy,
|
||||
build_session_context_prompt,
|
||||
)
|
||||
from .delivery import DeliveryRouter, DeliveryTarget
|
||||
|
||||
__all__ = [
|
||||
# Config
|
||||
"GatewayConfig",
|
||||
"PlatformConfig",
|
||||
"HomeChannel",
|
||||
"load_gateway_config",
|
||||
# Session
|
||||
"SessionContext",
|
||||
"SessionStore",
|
||||
"SessionResetPolicy",
|
||||
"build_session_context_prompt",
|
||||
# Delivery
|
||||
"DeliveryRouter",
|
||||
"DeliveryTarget",
|
||||
]
|
||||
@@ -1,333 +0,0 @@
|
||||
"""
|
||||
Gateway configuration management.
|
||||
|
||||
Handles loading and validating configuration for:
|
||||
- Connected platforms (Telegram, Discord, WhatsApp)
|
||||
- Home channels for each platform
|
||||
- Session reset policies
|
||||
- Delivery preferences
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
from pathlib import Path
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Any
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class Platform(Enum):
|
||||
"""Supported messaging platforms."""
|
||||
LOCAL = "local"
|
||||
TELEGRAM = "telegram"
|
||||
DISCORD = "discord"
|
||||
WHATSAPP = "whatsapp"
|
||||
|
||||
|
||||
@dataclass
|
||||
class HomeChannel:
|
||||
"""
|
||||
Default destination for a platform.
|
||||
|
||||
When a cron job specifies deliver="telegram" without a specific chat ID,
|
||||
messages are sent to this home channel.
|
||||
"""
|
||||
platform: Platform
|
||||
chat_id: str
|
||||
name: str # Human-readable name for display
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"platform": self.platform.value,
|
||||
"chat_id": self.chat_id,
|
||||
"name": self.name,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "HomeChannel":
|
||||
return cls(
|
||||
platform=Platform(data["platform"]),
|
||||
chat_id=str(data["chat_id"]),
|
||||
name=data.get("name", "Home"),
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionResetPolicy:
|
||||
"""
|
||||
Controls when sessions reset (lose context).
|
||||
|
||||
Modes:
|
||||
- "daily": Reset at a specific hour each day
|
||||
- "idle": Reset after N minutes of inactivity
|
||||
- "both": Whichever triggers first (daily boundary OR idle timeout)
|
||||
"""
|
||||
mode: str = "both" # "daily", "idle", or "both"
|
||||
at_hour: int = 4 # Hour for daily reset (0-23, local time)
|
||||
idle_minutes: int = 120 # Minutes of inactivity before reset
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"mode": self.mode,
|
||||
"at_hour": self.at_hour,
|
||||
"idle_minutes": self.idle_minutes,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "SessionResetPolicy":
|
||||
return cls(
|
||||
mode=data.get("mode", "both"),
|
||||
at_hour=data.get("at_hour", 4),
|
||||
idle_minutes=data.get("idle_minutes", 120),
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlatformConfig:
|
||||
"""Configuration for a single messaging platform."""
|
||||
enabled: bool = False
|
||||
token: Optional[str] = None # Bot token (Telegram, Discord)
|
||||
api_key: Optional[str] = None # API key if different from token
|
||||
home_channel: Optional[HomeChannel] = None
|
||||
|
||||
# Platform-specific settings
|
||||
extra: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
result = {
|
||||
"enabled": self.enabled,
|
||||
"extra": self.extra,
|
||||
}
|
||||
if self.token:
|
||||
result["token"] = self.token
|
||||
if self.api_key:
|
||||
result["api_key"] = self.api_key
|
||||
if self.home_channel:
|
||||
result["home_channel"] = self.home_channel.to_dict()
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "PlatformConfig":
|
||||
home_channel = None
|
||||
if "home_channel" in data:
|
||||
home_channel = HomeChannel.from_dict(data["home_channel"])
|
||||
|
||||
return cls(
|
||||
enabled=data.get("enabled", False),
|
||||
token=data.get("token"),
|
||||
api_key=data.get("api_key"),
|
||||
home_channel=home_channel,
|
||||
extra=data.get("extra", {}),
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GatewayConfig:
|
||||
"""
|
||||
Main gateway configuration.
|
||||
|
||||
Manages all platform connections, session policies, and delivery settings.
|
||||
"""
|
||||
# Platform configurations
|
||||
platforms: Dict[Platform, PlatformConfig] = field(default_factory=dict)
|
||||
|
||||
# Session reset policies by type
|
||||
default_reset_policy: SessionResetPolicy = field(default_factory=SessionResetPolicy)
|
||||
reset_by_type: Dict[str, SessionResetPolicy] = field(default_factory=dict)
|
||||
reset_by_platform: Dict[Platform, SessionResetPolicy] = field(default_factory=dict)
|
||||
|
||||
# Reset trigger commands
|
||||
reset_triggers: List[str] = field(default_factory=lambda: ["/new", "/reset"])
|
||||
|
||||
# Storage paths
|
||||
sessions_dir: Path = field(default_factory=lambda: Path.home() / ".hermes" / "sessions")
|
||||
|
||||
# Delivery settings
|
||||
always_log_local: bool = True # Always save cron outputs to local files
|
||||
|
||||
def get_connected_platforms(self) -> List[Platform]:
|
||||
"""Return list of platforms that are enabled and configured."""
|
||||
connected = []
|
||||
for platform, config in self.platforms.items():
|
||||
if config.enabled and (config.token or config.api_key):
|
||||
connected.append(platform)
|
||||
return connected
|
||||
|
||||
def get_home_channel(self, platform: Platform) -> Optional[HomeChannel]:
|
||||
"""Get the home channel for a platform."""
|
||||
config = self.platforms.get(platform)
|
||||
if config:
|
||||
return config.home_channel
|
||||
return None
|
||||
|
||||
def get_reset_policy(
|
||||
self,
|
||||
platform: Optional[Platform] = None,
|
||||
session_type: Optional[str] = None
|
||||
) -> SessionResetPolicy:
|
||||
"""
|
||||
Get the appropriate reset policy for a session.
|
||||
|
||||
Priority: platform override > type override > default
|
||||
"""
|
||||
# Platform-specific override takes precedence
|
||||
if platform and platform in self.reset_by_platform:
|
||||
return self.reset_by_platform[platform]
|
||||
|
||||
# Type-specific override (dm, group, thread)
|
||||
if session_type and session_type in self.reset_by_type:
|
||||
return self.reset_by_type[session_type]
|
||||
|
||||
return self.default_reset_policy
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"platforms": {
|
||||
p.value: c.to_dict() for p, c in self.platforms.items()
|
||||
},
|
||||
"default_reset_policy": self.default_reset_policy.to_dict(),
|
||||
"reset_by_type": {
|
||||
k: v.to_dict() for k, v in self.reset_by_type.items()
|
||||
},
|
||||
"reset_by_platform": {
|
||||
p.value: v.to_dict() for p, v in self.reset_by_platform.items()
|
||||
},
|
||||
"reset_triggers": self.reset_triggers,
|
||||
"sessions_dir": str(self.sessions_dir),
|
||||
"always_log_local": self.always_log_local,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "GatewayConfig":
|
||||
platforms = {}
|
||||
for platform_name, platform_data in data.get("platforms", {}).items():
|
||||
try:
|
||||
platform = Platform(platform_name)
|
||||
platforms[platform] = PlatformConfig.from_dict(platform_data)
|
||||
except ValueError:
|
||||
pass # Skip unknown platforms
|
||||
|
||||
reset_by_type = {}
|
||||
for type_name, policy_data in data.get("reset_by_type", {}).items():
|
||||
reset_by_type[type_name] = SessionResetPolicy.from_dict(policy_data)
|
||||
|
||||
reset_by_platform = {}
|
||||
for platform_name, policy_data in data.get("reset_by_platform", {}).items():
|
||||
try:
|
||||
platform = Platform(platform_name)
|
||||
reset_by_platform[platform] = SessionResetPolicy.from_dict(policy_data)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
default_policy = SessionResetPolicy()
|
||||
if "default_reset_policy" in data:
|
||||
default_policy = SessionResetPolicy.from_dict(data["default_reset_policy"])
|
||||
|
||||
sessions_dir = Path.home() / ".hermes" / "sessions"
|
||||
if "sessions_dir" in data:
|
||||
sessions_dir = Path(data["sessions_dir"])
|
||||
|
||||
return cls(
|
||||
platforms=platforms,
|
||||
default_reset_policy=default_policy,
|
||||
reset_by_type=reset_by_type,
|
||||
reset_by_platform=reset_by_platform,
|
||||
reset_triggers=data.get("reset_triggers", ["/new", "/reset"]),
|
||||
sessions_dir=sessions_dir,
|
||||
always_log_local=data.get("always_log_local", True),
|
||||
)
|
||||
|
||||
|
||||
def load_gateway_config() -> GatewayConfig:
|
||||
"""
|
||||
Load gateway configuration from multiple sources.
|
||||
|
||||
Priority (highest to lowest):
|
||||
1. Environment variables
|
||||
2. ~/.hermes/gateway.json
|
||||
3. cli-config.yaml gateway section
|
||||
4. Defaults
|
||||
"""
|
||||
config = GatewayConfig()
|
||||
|
||||
# Try loading from ~/.hermes/gateway.json
|
||||
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
|
||||
if gateway_config_path.exists():
|
||||
try:
|
||||
with open(gateway_config_path, "r") as f:
|
||||
data = json.load(f)
|
||||
config = GatewayConfig.from_dict(data)
|
||||
except Exception as e:
|
||||
print(f"[gateway] Warning: Failed to load {gateway_config_path}: {e}")
|
||||
|
||||
# Override with environment variables
|
||||
_apply_env_overrides(config)
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def _apply_env_overrides(config: GatewayConfig) -> None:
|
||||
"""Apply environment variable overrides to config."""
|
||||
|
||||
# Telegram
|
||||
telegram_token = os.getenv("TELEGRAM_BOT_TOKEN")
|
||||
if telegram_token:
|
||||
if Platform.TELEGRAM not in config.platforms:
|
||||
config.platforms[Platform.TELEGRAM] = PlatformConfig()
|
||||
config.platforms[Platform.TELEGRAM].enabled = True
|
||||
config.platforms[Platform.TELEGRAM].token = telegram_token
|
||||
|
||||
telegram_home = os.getenv("TELEGRAM_HOME_CHANNEL")
|
||||
if telegram_home and Platform.TELEGRAM in config.platforms:
|
||||
config.platforms[Platform.TELEGRAM].home_channel = HomeChannel(
|
||||
platform=Platform.TELEGRAM,
|
||||
chat_id=telegram_home,
|
||||
name=os.getenv("TELEGRAM_HOME_CHANNEL_NAME", "Home"),
|
||||
)
|
||||
|
||||
# Discord
|
||||
discord_token = os.getenv("DISCORD_BOT_TOKEN")
|
||||
if discord_token:
|
||||
if Platform.DISCORD not in config.platforms:
|
||||
config.platforms[Platform.DISCORD] = PlatformConfig()
|
||||
config.platforms[Platform.DISCORD].enabled = True
|
||||
config.platforms[Platform.DISCORD].token = discord_token
|
||||
|
||||
discord_home = os.getenv("DISCORD_HOME_CHANNEL")
|
||||
if discord_home and Platform.DISCORD in config.platforms:
|
||||
config.platforms[Platform.DISCORD].home_channel = HomeChannel(
|
||||
platform=Platform.DISCORD,
|
||||
chat_id=discord_home,
|
||||
name=os.getenv("DISCORD_HOME_CHANNEL_NAME", "Home"),
|
||||
)
|
||||
|
||||
# WhatsApp (typically uses different auth mechanism)
|
||||
whatsapp_enabled = os.getenv("WHATSAPP_ENABLED", "").lower() in ("true", "1", "yes")
|
||||
if whatsapp_enabled:
|
||||
if Platform.WHATSAPP not in config.platforms:
|
||||
config.platforms[Platform.WHATSAPP] = PlatformConfig()
|
||||
config.platforms[Platform.WHATSAPP].enabled = True
|
||||
|
||||
# Session settings
|
||||
idle_minutes = os.getenv("SESSION_IDLE_MINUTES")
|
||||
if idle_minutes:
|
||||
try:
|
||||
config.default_reset_policy.idle_minutes = int(idle_minutes)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
reset_hour = os.getenv("SESSION_RESET_HOUR")
|
||||
if reset_hour:
|
||||
try:
|
||||
config.default_reset_policy.at_hour = int(reset_hour)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
|
||||
def save_gateway_config(config: GatewayConfig) -> None:
|
||||
"""Save gateway configuration to ~/.hermes/gateway.json."""
|
||||
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
|
||||
gateway_config_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with open(gateway_config_path, "w") as f:
|
||||
json.dump(config.to_dict(), f, indent=2)
|
||||
@@ -1,318 +0,0 @@
|
||||
"""
|
||||
Delivery routing for cron job outputs and agent responses.
|
||||
|
||||
Routes messages to the appropriate destination based on:
|
||||
- Explicit targets (e.g., "telegram:123456789")
|
||||
- Platform home channels (e.g., "telegram" → home channel)
|
||||
- Origin (back to where the job was created)
|
||||
- Local (always saved to files)
|
||||
"""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Any, Union
|
||||
from enum import Enum
|
||||
|
||||
from .config import Platform, GatewayConfig, HomeChannel
|
||||
from .session import SessionSource
|
||||
|
||||
|
||||
@dataclass
|
||||
class DeliveryTarget:
|
||||
"""
|
||||
A single delivery target.
|
||||
|
||||
Represents where a message should be sent:
|
||||
- "origin" → back to source
|
||||
- "local" → save to local files
|
||||
- "telegram" → Telegram home channel
|
||||
- "telegram:123456" → specific Telegram chat
|
||||
"""
|
||||
platform: Platform
|
||||
chat_id: Optional[str] = None # None means use home channel
|
||||
is_origin: bool = False
|
||||
is_explicit: bool = False # True if chat_id was explicitly specified
|
||||
|
||||
@classmethod
|
||||
def parse(cls, target: str, origin: Optional[SessionSource] = None) -> "DeliveryTarget":
|
||||
"""
|
||||
Parse a delivery target string.
|
||||
|
||||
Formats:
|
||||
- "origin" → back to source
|
||||
- "local" → local files only
|
||||
- "telegram" → Telegram home channel
|
||||
- "telegram:123456" → specific Telegram chat
|
||||
"""
|
||||
target = target.strip().lower()
|
||||
|
||||
if target == "origin":
|
||||
if origin:
|
||||
return cls(
|
||||
platform=origin.platform,
|
||||
chat_id=origin.chat_id,
|
||||
is_origin=True,
|
||||
)
|
||||
else:
|
||||
# Fallback to local if no origin
|
||||
return cls(platform=Platform.LOCAL, is_origin=True)
|
||||
|
||||
if target == "local":
|
||||
return cls(platform=Platform.LOCAL)
|
||||
|
||||
# Check for platform:chat_id format
|
||||
if ":" in target:
|
||||
platform_str, chat_id = target.split(":", 1)
|
||||
try:
|
||||
platform = Platform(platform_str)
|
||||
return cls(platform=platform, chat_id=chat_id, is_explicit=True)
|
||||
except ValueError:
|
||||
# Unknown platform, treat as local
|
||||
return cls(platform=Platform.LOCAL)
|
||||
|
||||
# Just a platform name (use home channel)
|
||||
try:
|
||||
platform = Platform(target)
|
||||
return cls(platform=platform)
|
||||
except ValueError:
|
||||
# Unknown platform, treat as local
|
||||
return cls(platform=Platform.LOCAL)
|
||||
|
||||
def to_string(self) -> str:
|
||||
"""Convert back to string format."""
|
||||
if self.is_origin:
|
||||
return "origin"
|
||||
if self.platform == Platform.LOCAL:
|
||||
return "local"
|
||||
if self.chat_id:
|
||||
return f"{self.platform.value}:{self.chat_id}"
|
||||
return self.platform.value
|
||||
|
||||
|
||||
class DeliveryRouter:
|
||||
"""
|
||||
Routes messages to appropriate destinations.
|
||||
|
||||
Handles the logic of resolving delivery targets and dispatching
|
||||
messages to the right platform adapters.
|
||||
"""
|
||||
|
||||
def __init__(self, config: GatewayConfig, adapters: Dict[Platform, Any] = None):
|
||||
"""
|
||||
Initialize the delivery router.
|
||||
|
||||
Args:
|
||||
config: Gateway configuration
|
||||
adapters: Dict mapping platforms to their adapter instances
|
||||
"""
|
||||
self.config = config
|
||||
self.adapters = adapters or {}
|
||||
self.output_dir = Path.home() / ".hermes" / "cron" / "output"
|
||||
|
||||
def resolve_targets(
|
||||
self,
|
||||
deliver: Union[str, List[str]],
|
||||
origin: Optional[SessionSource] = None
|
||||
) -> List[DeliveryTarget]:
|
||||
"""
|
||||
Resolve delivery specification to concrete targets.
|
||||
|
||||
Args:
|
||||
deliver: Delivery spec - "origin", "telegram", ["local", "discord"], etc.
|
||||
origin: The source where the request originated (for "origin" target)
|
||||
|
||||
Returns:
|
||||
List of resolved delivery targets
|
||||
"""
|
||||
if isinstance(deliver, str):
|
||||
deliver = [deliver]
|
||||
|
||||
targets = []
|
||||
seen_platforms = set()
|
||||
|
||||
for target_str in deliver:
|
||||
target = DeliveryTarget.parse(target_str, origin)
|
||||
|
||||
# Resolve home channel if needed
|
||||
if target.chat_id is None and target.platform != Platform.LOCAL:
|
||||
home = self.config.get_home_channel(target.platform)
|
||||
if home:
|
||||
target.chat_id = home.chat_id
|
||||
else:
|
||||
# No home channel configured, skip this platform
|
||||
continue
|
||||
|
||||
# Deduplicate
|
||||
key = (target.platform, target.chat_id)
|
||||
if key not in seen_platforms:
|
||||
seen_platforms.add(key)
|
||||
targets.append(target)
|
||||
|
||||
# Always include local if configured
|
||||
if self.config.always_log_local:
|
||||
local_key = (Platform.LOCAL, None)
|
||||
if local_key not in seen_platforms:
|
||||
targets.append(DeliveryTarget(platform=Platform.LOCAL))
|
||||
|
||||
return targets
|
||||
|
||||
async def deliver(
|
||||
self,
|
||||
content: str,
|
||||
targets: List[DeliveryTarget],
|
||||
job_id: Optional[str] = None,
|
||||
job_name: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Deliver content to all specified targets.
|
||||
|
||||
Args:
|
||||
content: The message/output to deliver
|
||||
targets: List of delivery targets
|
||||
job_id: Optional job ID (for cron jobs)
|
||||
job_name: Optional job name
|
||||
metadata: Additional metadata to include
|
||||
|
||||
Returns:
|
||||
Dict with delivery results per target
|
||||
"""
|
||||
results = {}
|
||||
|
||||
for target in targets:
|
||||
try:
|
||||
if target.platform == Platform.LOCAL:
|
||||
result = self._deliver_local(content, job_id, job_name, metadata)
|
||||
else:
|
||||
result = await self._deliver_to_platform(target, content, metadata)
|
||||
|
||||
results[target.to_string()] = {
|
||||
"success": True,
|
||||
"result": result
|
||||
}
|
||||
except Exception as e:
|
||||
results[target.to_string()] = {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
def _deliver_local(
|
||||
self,
|
||||
content: str,
|
||||
job_id: Optional[str],
|
||||
job_name: Optional[str],
|
||||
metadata: Optional[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""Save content to local files."""
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
if job_id:
|
||||
output_path = self.output_dir / job_id / f"{timestamp}.md"
|
||||
else:
|
||||
output_path = self.output_dir / "misc" / f"{timestamp}.md"
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Build the output document
|
||||
lines = []
|
||||
if job_name:
|
||||
lines.append(f"# {job_name}")
|
||||
else:
|
||||
lines.append("# Delivery Output")
|
||||
|
||||
lines.append("")
|
||||
lines.append(f"**Timestamp:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
if job_id:
|
||||
lines.append(f"**Job ID:** {job_id}")
|
||||
|
||||
if metadata:
|
||||
for key, value in metadata.items():
|
||||
lines.append(f"**{key}:** {value}")
|
||||
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
lines.append(content)
|
||||
|
||||
output_path.write_text("\n".join(lines))
|
||||
|
||||
return {
|
||||
"path": str(output_path),
|
||||
"timestamp": timestamp
|
||||
}
|
||||
|
||||
async def _deliver_to_platform(
|
||||
self,
|
||||
target: DeliveryTarget,
|
||||
content: str,
|
||||
metadata: Optional[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""Deliver content to a messaging platform."""
|
||||
adapter = self.adapters.get(target.platform)
|
||||
|
||||
if not adapter:
|
||||
raise ValueError(f"No adapter configured for {target.platform.value}")
|
||||
|
||||
if not target.chat_id:
|
||||
raise ValueError(f"No chat ID for {target.platform.value} delivery")
|
||||
|
||||
# Call the adapter's send method
|
||||
# Adapters should implement: async def send(chat_id: str, content: str) -> Dict
|
||||
return await adapter.send(target.chat_id, content, metadata=metadata)
|
||||
|
||||
|
||||
def parse_deliver_spec(
|
||||
deliver: Optional[Union[str, List[str]]],
|
||||
origin: Optional[SessionSource] = None,
|
||||
default: str = "origin"
|
||||
) -> Union[str, List[str]]:
|
||||
"""
|
||||
Normalize a delivery specification.
|
||||
|
||||
If None or empty, returns the default.
|
||||
"""
|
||||
if not deliver:
|
||||
return default
|
||||
return deliver
|
||||
|
||||
|
||||
def build_delivery_context_for_tool(
|
||||
config: GatewayConfig,
|
||||
origin: Optional[SessionSource] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Build context for the schedule_cronjob tool to understand delivery options.
|
||||
|
||||
This is passed to the tool so it can validate and explain delivery targets.
|
||||
"""
|
||||
connected = config.get_connected_platforms()
|
||||
|
||||
options = {
|
||||
"origin": {
|
||||
"description": "Back to where this job was created",
|
||||
"available": origin is not None,
|
||||
},
|
||||
"local": {
|
||||
"description": "Save to local files only",
|
||||
"available": True,
|
||||
}
|
||||
}
|
||||
|
||||
for platform in connected:
|
||||
home = config.get_home_channel(platform)
|
||||
options[platform.value] = {
|
||||
"description": f"{platform.value.title()} home channel",
|
||||
"available": True,
|
||||
"home_channel": home.to_dict() if home else None,
|
||||
}
|
||||
|
||||
return {
|
||||
"origin": origin.to_dict() if origin else None,
|
||||
"options": options,
|
||||
"always_log_local": config.always_log_local,
|
||||
}
|
||||
@@ -1,17 +0,0 @@
|
||||
"""
|
||||
Platform adapters for messaging integrations.
|
||||
|
||||
Each adapter handles:
|
||||
- Receiving messages from a platform
|
||||
- Sending messages/responses back
|
||||
- Platform-specific authentication
|
||||
- Message formatting and media handling
|
||||
"""
|
||||
|
||||
from .base import BasePlatformAdapter, MessageEvent, SendResult
|
||||
|
||||
__all__ = [
|
||||
"BasePlatformAdapter",
|
||||
"MessageEvent",
|
||||
"SendResult",
|
||||
]
|
||||
@@ -1,365 +0,0 @@
|
||||
"""
|
||||
Base platform adapter interface.
|
||||
|
||||
All platform adapters (Telegram, Discord, WhatsApp) inherit from this
|
||||
and implement the required methods.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional, Any, Callable, Awaitable
|
||||
from enum import Enum
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
|
||||
|
||||
from gateway.config import Platform, PlatformConfig
|
||||
from gateway.session import SessionSource
|
||||
|
||||
|
||||
class MessageType(Enum):
|
||||
"""Types of incoming messages."""
|
||||
TEXT = "text"
|
||||
PHOTO = "photo"
|
||||
VIDEO = "video"
|
||||
AUDIO = "audio"
|
||||
VOICE = "voice"
|
||||
DOCUMENT = "document"
|
||||
STICKER = "sticker"
|
||||
COMMAND = "command" # /command style
|
||||
|
||||
|
||||
@dataclass
|
||||
class MessageEvent:
|
||||
"""
|
||||
Incoming message from a platform.
|
||||
|
||||
Normalized representation that all adapters produce.
|
||||
"""
|
||||
# Message content
|
||||
text: str
|
||||
message_type: MessageType = MessageType.TEXT
|
||||
|
||||
# Source information
|
||||
source: SessionSource = None
|
||||
|
||||
# Original platform data
|
||||
raw_message: Any = None
|
||||
message_id: Optional[str] = None
|
||||
|
||||
# Media attachments
|
||||
media_urls: List[str] = field(default_factory=list)
|
||||
media_types: List[str] = field(default_factory=list)
|
||||
|
||||
# Reply context
|
||||
reply_to_message_id: Optional[str] = None
|
||||
|
||||
# Timestamps
|
||||
timestamp: datetime = field(default_factory=datetime.now)
|
||||
|
||||
def is_command(self) -> bool:
|
||||
"""Check if this is a command message (e.g., /new, /reset)."""
|
||||
return self.text.startswith("/")
|
||||
|
||||
def get_command(self) -> Optional[str]:
|
||||
"""Extract command name if this is a command message."""
|
||||
if not self.is_command():
|
||||
return None
|
||||
# Split on space and get first word, strip the /
|
||||
parts = self.text.split(maxsplit=1)
|
||||
return parts[0][1:].lower() if parts else None
|
||||
|
||||
def get_command_args(self) -> str:
|
||||
"""Get the arguments after a command."""
|
||||
if not self.is_command():
|
||||
return self.text
|
||||
parts = self.text.split(maxsplit=1)
|
||||
return parts[1] if len(parts) > 1 else ""
|
||||
|
||||
|
||||
@dataclass
|
||||
class SendResult:
|
||||
"""Result of sending a message."""
|
||||
success: bool
|
||||
message_id: Optional[str] = None
|
||||
error: Optional[str] = None
|
||||
raw_response: Any = None
|
||||
|
||||
|
||||
# Type for message handlers
|
||||
MessageHandler = Callable[[MessageEvent], Awaitable[Optional[str]]]
|
||||
|
||||
|
||||
class BasePlatformAdapter(ABC):
|
||||
"""
|
||||
Base class for platform adapters.
|
||||
|
||||
Subclasses implement platform-specific logic for:
|
||||
- Connecting and authenticating
|
||||
- Receiving messages
|
||||
- Sending messages/responses
|
||||
- Handling media
|
||||
"""
|
||||
|
||||
def __init__(self, config: PlatformConfig, platform: Platform):
|
||||
self.config = config
|
||||
self.platform = platform
|
||||
self._message_handler: Optional[MessageHandler] = None
|
||||
self._running = False
|
||||
|
||||
# Track active message handlers per session for interrupt support
|
||||
# Key: session_key (e.g., chat_id), Value: (event, asyncio.Event for interrupt)
|
||||
self._active_sessions: Dict[str, asyncio.Event] = {}
|
||||
self._pending_messages: Dict[str, MessageEvent] = {}
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
"""Human-readable name for this adapter."""
|
||||
return self.platform.value.title()
|
||||
|
||||
@property
|
||||
def is_connected(self) -> bool:
|
||||
"""Check if adapter is currently connected."""
|
||||
return self._running
|
||||
|
||||
def set_message_handler(self, handler: MessageHandler) -> None:
|
||||
"""
|
||||
Set the handler for incoming messages.
|
||||
|
||||
The handler receives a MessageEvent and should return
|
||||
an optional response string.
|
||||
"""
|
||||
self._message_handler = handler
|
||||
|
||||
@abstractmethod
|
||||
async def connect(self) -> bool:
|
||||
"""
|
||||
Connect to the platform and start receiving messages.
|
||||
|
||||
Returns True if connection was successful.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def disconnect(self) -> None:
|
||||
"""Disconnect from the platform."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def send(
|
||||
self,
|
||||
chat_id: str,
|
||||
content: str,
|
||||
reply_to: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
) -> SendResult:
|
||||
"""
|
||||
Send a message to a chat.
|
||||
|
||||
Args:
|
||||
chat_id: The chat/channel ID to send to
|
||||
content: Message content (may be markdown)
|
||||
reply_to: Optional message ID to reply to
|
||||
metadata: Additional platform-specific options
|
||||
|
||||
Returns:
|
||||
SendResult with success status and message ID
|
||||
"""
|
||||
pass
|
||||
|
||||
async def send_typing(self, chat_id: str) -> None:
|
||||
"""
|
||||
Send a typing indicator.
|
||||
|
||||
Override in subclasses if the platform supports it.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def _keep_typing(self, chat_id: str, interval: float = 2.0) -> None:
|
||||
"""
|
||||
Continuously send typing indicator until cancelled.
|
||||
|
||||
Telegram/Discord typing status expires after ~5 seconds, so we refresh every 2
|
||||
to recover quickly after progress messages interrupt it.
|
||||
"""
|
||||
try:
|
||||
while True:
|
||||
await self.send_typing(chat_id)
|
||||
await asyncio.sleep(interval)
|
||||
except asyncio.CancelledError:
|
||||
pass # Normal cancellation when handler completes
|
||||
|
||||
async def handle_message(self, event: MessageEvent) -> None:
|
||||
"""
|
||||
Process an incoming message.
|
||||
|
||||
This method returns quickly by spawning background tasks.
|
||||
This allows new messages to be processed even while an agent is running,
|
||||
enabling interruption support.
|
||||
"""
|
||||
if not self._message_handler:
|
||||
return
|
||||
|
||||
session_key = event.source.chat_id
|
||||
|
||||
# Check if there's already an active handler for this session
|
||||
if session_key in self._active_sessions:
|
||||
# Store this as a pending message - it will interrupt the running agent
|
||||
print(f"[{self.name}] ⚡ New message while session {session_key} is active - triggering interrupt")
|
||||
self._pending_messages[session_key] = event
|
||||
# Signal the interrupt (the processing task checks this)
|
||||
self._active_sessions[session_key].set()
|
||||
return # Don't process now - will be handled after current task finishes
|
||||
|
||||
# Spawn background task to process this message
|
||||
asyncio.create_task(self._process_message_background(event, session_key))
|
||||
|
||||
async def _process_message_background(self, event: MessageEvent, session_key: str) -> None:
|
||||
"""Background task that actually processes the message."""
|
||||
# Create interrupt event for this session
|
||||
interrupt_event = asyncio.Event()
|
||||
self._active_sessions[session_key] = interrupt_event
|
||||
|
||||
# Start continuous typing indicator (refreshes every 2 seconds)
|
||||
typing_task = asyncio.create_task(self._keep_typing(event.source.chat_id))
|
||||
|
||||
try:
|
||||
# Call the handler (this can take a while with tool calls)
|
||||
response = await self._message_handler(event)
|
||||
|
||||
# Send response if any
|
||||
if response:
|
||||
result = await self.send(
|
||||
chat_id=event.source.chat_id,
|
||||
content=response,
|
||||
reply_to=event.message_id
|
||||
)
|
||||
|
||||
# Log send failures (don't raise - user already saw tool progress)
|
||||
if not result.success:
|
||||
print(f"[{self.name}] Failed to send response: {result.error}")
|
||||
# Try sending without markdown as fallback
|
||||
fallback_result = await self.send(
|
||||
chat_id=event.source.chat_id,
|
||||
content=f"(Response formatting failed, plain text:)\n\n{response[:3500]}",
|
||||
reply_to=event.message_id
|
||||
)
|
||||
if not fallback_result.success:
|
||||
print(f"[{self.name}] Fallback send also failed: {fallback_result.error}")
|
||||
|
||||
# Check if there's a pending message that was queued during our processing
|
||||
if session_key in self._pending_messages:
|
||||
pending_event = self._pending_messages.pop(session_key)
|
||||
print(f"[{self.name}] 📨 Processing queued message from interrupt")
|
||||
# Clean up current session before processing pending
|
||||
if session_key in self._active_sessions:
|
||||
del self._active_sessions[session_key]
|
||||
typing_task.cancel()
|
||||
try:
|
||||
await typing_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
# Process pending message in new background task
|
||||
await self._process_message_background(pending_event, session_key)
|
||||
return # Already cleaned up
|
||||
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Error handling message: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
# Stop typing indicator
|
||||
typing_task.cancel()
|
||||
try:
|
||||
await typing_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
# Clean up session tracking
|
||||
if session_key in self._active_sessions:
|
||||
del self._active_sessions[session_key]
|
||||
|
||||
def has_pending_interrupt(self, session_key: str) -> bool:
|
||||
"""Check if there's a pending interrupt for a session."""
|
||||
return session_key in self._active_sessions and self._active_sessions[session_key].is_set()
|
||||
|
||||
def get_pending_message(self, session_key: str) -> Optional[MessageEvent]:
|
||||
"""Get and clear any pending message for a session."""
|
||||
return self._pending_messages.get(session_key)
|
||||
|
||||
def build_source(
|
||||
self,
|
||||
chat_id: str,
|
||||
chat_name: Optional[str] = None,
|
||||
chat_type: str = "dm",
|
||||
user_id: Optional[str] = None,
|
||||
user_name: Optional[str] = None,
|
||||
thread_id: Optional[str] = None
|
||||
) -> SessionSource:
|
||||
"""Helper to build a SessionSource for this platform."""
|
||||
return SessionSource(
|
||||
platform=self.platform,
|
||||
chat_id=str(chat_id),
|
||||
chat_name=chat_name,
|
||||
chat_type=chat_type,
|
||||
user_id=str(user_id) if user_id else None,
|
||||
user_name=user_name,
|
||||
thread_id=str(thread_id) if thread_id else None,
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Get information about a chat/channel.
|
||||
|
||||
Returns dict with at least:
|
||||
- name: Chat name
|
||||
- type: "dm", "group", "channel"
|
||||
"""
|
||||
pass
|
||||
|
||||
def format_message(self, content: str) -> str:
|
||||
"""
|
||||
Format a message for this platform.
|
||||
|
||||
Override in subclasses to handle platform-specific formatting
|
||||
(e.g., Telegram MarkdownV2, Discord markdown).
|
||||
|
||||
Default implementation returns content as-is.
|
||||
"""
|
||||
return content
|
||||
|
||||
def truncate_message(self, content: str, max_length: int = 4096) -> List[str]:
|
||||
"""
|
||||
Split a long message into chunks.
|
||||
|
||||
Args:
|
||||
content: The full message content
|
||||
max_length: Maximum length per chunk (platform-specific)
|
||||
|
||||
Returns:
|
||||
List of message chunks
|
||||
"""
|
||||
if len(content) <= max_length:
|
||||
return [content]
|
||||
|
||||
chunks = []
|
||||
while content:
|
||||
if len(content) <= max_length:
|
||||
chunks.append(content)
|
||||
break
|
||||
|
||||
# Try to split at a newline
|
||||
split_idx = content.rfind("\n", 0, max_length)
|
||||
if split_idx == -1:
|
||||
# No newline, split at space
|
||||
split_idx = content.rfind(" ", 0, max_length)
|
||||
if split_idx == -1:
|
||||
# No space either, hard split
|
||||
split_idx = max_length
|
||||
|
||||
chunks.append(content[:split_idx])
|
||||
content = content[split_idx:].lstrip()
|
||||
|
||||
return chunks
|
||||
@@ -1,297 +0,0 @@
|
||||
"""
|
||||
Discord platform adapter.
|
||||
|
||||
Uses discord.py library for:
|
||||
- Receiving messages from servers and DMs
|
||||
- Sending responses back
|
||||
- Handling threads and channels
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
try:
|
||||
import discord
|
||||
from discord import Message as DiscordMessage, Intents
|
||||
from discord.ext import commands
|
||||
DISCORD_AVAILABLE = True
|
||||
except ImportError:
|
||||
DISCORD_AVAILABLE = False
|
||||
discord = None
|
||||
DiscordMessage = Any
|
||||
Intents = Any
|
||||
commands = None
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
|
||||
|
||||
from gateway.config import Platform, PlatformConfig
|
||||
from gateway.platforms.base import (
|
||||
BasePlatformAdapter,
|
||||
MessageEvent,
|
||||
MessageType,
|
||||
SendResult,
|
||||
)
|
||||
|
||||
|
||||
def check_discord_requirements() -> bool:
|
||||
"""Check if Discord dependencies are available."""
|
||||
return DISCORD_AVAILABLE
|
||||
|
||||
|
||||
class DiscordAdapter(BasePlatformAdapter):
|
||||
"""
|
||||
Discord bot adapter.
|
||||
|
||||
Handles:
|
||||
- Receiving messages from servers and DMs
|
||||
- Sending responses with Discord markdown
|
||||
- Thread support
|
||||
- Slash commands (future)
|
||||
"""
|
||||
|
||||
# Discord message limits
|
||||
MAX_MESSAGE_LENGTH = 2000
|
||||
|
||||
def __init__(self, config: PlatformConfig):
|
||||
super().__init__(config, Platform.DISCORD)
|
||||
self._client: Optional[commands.Bot] = None
|
||||
self._ready_event = asyncio.Event()
|
||||
|
||||
async def connect(self) -> bool:
|
||||
"""Connect to Discord and start receiving events."""
|
||||
if not DISCORD_AVAILABLE:
|
||||
print(f"[{self.name}] discord.py not installed. Run: pip install discord.py")
|
||||
return False
|
||||
|
||||
if not self.config.token:
|
||||
print(f"[{self.name}] No bot token configured")
|
||||
return False
|
||||
|
||||
try:
|
||||
# Set up intents
|
||||
intents = Intents.default()
|
||||
intents.message_content = True
|
||||
intents.dm_messages = True
|
||||
intents.guild_messages = True
|
||||
|
||||
# Create bot
|
||||
self._client = commands.Bot(
|
||||
command_prefix="!", # Not really used, we handle raw messages
|
||||
intents=intents,
|
||||
)
|
||||
|
||||
# Register event handlers
|
||||
@self._client.event
|
||||
async def on_ready():
|
||||
print(f"[{self.name}] Connected as {self._client.user}")
|
||||
self._ready_event.set()
|
||||
|
||||
@self._client.event
|
||||
async def on_message(message: DiscordMessage):
|
||||
# Ignore bot's own messages
|
||||
if message.author == self._client.user:
|
||||
return
|
||||
await self._handle_message(message)
|
||||
|
||||
# Start the bot in background
|
||||
asyncio.create_task(self._client.start(self.config.token))
|
||||
|
||||
# Wait for ready
|
||||
await asyncio.wait_for(self._ready_event.wait(), timeout=30)
|
||||
|
||||
self._running = True
|
||||
return True
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
print(f"[{self.name}] Timeout waiting for connection")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Failed to connect: {e}")
|
||||
return False
|
||||
|
||||
async def disconnect(self) -> None:
|
||||
"""Disconnect from Discord."""
|
||||
if self._client:
|
||||
try:
|
||||
await self._client.close()
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Error during disconnect: {e}")
|
||||
|
||||
self._running = False
|
||||
self._client = None
|
||||
self._ready_event.clear()
|
||||
print(f"[{self.name}] Disconnected")
|
||||
|
||||
async def send(
|
||||
self,
|
||||
chat_id: str,
|
||||
content: str,
|
||||
reply_to: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
) -> SendResult:
|
||||
"""Send a message to a Discord channel."""
|
||||
if not self._client:
|
||||
return SendResult(success=False, error="Not connected")
|
||||
|
||||
try:
|
||||
# Get the channel
|
||||
channel = self._client.get_channel(int(chat_id))
|
||||
if not channel:
|
||||
channel = await self._client.fetch_channel(int(chat_id))
|
||||
|
||||
if not channel:
|
||||
return SendResult(success=False, error=f"Channel {chat_id} not found")
|
||||
|
||||
# Format and split message if needed
|
||||
formatted = self.format_message(content)
|
||||
chunks = self.truncate_message(formatted, self.MAX_MESSAGE_LENGTH)
|
||||
|
||||
message_ids = []
|
||||
reference = None
|
||||
|
||||
if reply_to:
|
||||
try:
|
||||
ref_msg = await channel.fetch_message(int(reply_to))
|
||||
reference = ref_msg
|
||||
except Exception:
|
||||
pass # Ignore if we can't find the referenced message
|
||||
|
||||
for i, chunk in enumerate(chunks):
|
||||
msg = await channel.send(
|
||||
content=chunk,
|
||||
reference=reference if i == 0 else None,
|
||||
)
|
||||
message_ids.append(str(msg.id))
|
||||
|
||||
return SendResult(
|
||||
success=True,
|
||||
message_id=message_ids[0] if message_ids else None,
|
||||
raw_response={"message_ids": message_ids}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
return SendResult(success=False, error=str(e))
|
||||
|
||||
async def send_typing(self, chat_id: str) -> None:
|
||||
"""Send typing indicator."""
|
||||
if self._client:
|
||||
try:
|
||||
channel = self._client.get_channel(int(chat_id))
|
||||
if channel:
|
||||
await channel.typing()
|
||||
except Exception:
|
||||
pass # Ignore typing indicator failures
|
||||
|
||||
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
|
||||
"""Get information about a Discord channel."""
|
||||
if not self._client:
|
||||
return {"name": "Unknown", "type": "dm"}
|
||||
|
||||
try:
|
||||
channel = self._client.get_channel(int(chat_id))
|
||||
if not channel:
|
||||
channel = await self._client.fetch_channel(int(chat_id))
|
||||
|
||||
if not channel:
|
||||
return {"name": str(chat_id), "type": "dm"}
|
||||
|
||||
# Determine channel type
|
||||
if isinstance(channel, discord.DMChannel):
|
||||
chat_type = "dm"
|
||||
name = channel.recipient.name if channel.recipient else str(chat_id)
|
||||
elif isinstance(channel, discord.Thread):
|
||||
chat_type = "thread"
|
||||
name = channel.name
|
||||
elif isinstance(channel, discord.TextChannel):
|
||||
chat_type = "channel"
|
||||
name = f"#{channel.name}"
|
||||
if channel.guild:
|
||||
name = f"{channel.guild.name} / {name}"
|
||||
else:
|
||||
chat_type = "channel"
|
||||
name = getattr(channel, "name", str(chat_id))
|
||||
|
||||
return {
|
||||
"name": name,
|
||||
"type": chat_type,
|
||||
"guild_id": str(channel.guild.id) if hasattr(channel, "guild") and channel.guild else None,
|
||||
"guild_name": channel.guild.name if hasattr(channel, "guild") and channel.guild else None,
|
||||
}
|
||||
except Exception as e:
|
||||
return {"name": str(chat_id), "type": "dm", "error": str(e)}
|
||||
|
||||
def format_message(self, content: str) -> str:
|
||||
"""
|
||||
Format message for Discord.
|
||||
|
||||
Discord uses its own markdown variant.
|
||||
"""
|
||||
# Discord markdown is fairly standard, no special escaping needed
|
||||
return content
|
||||
|
||||
async def _handle_message(self, message: DiscordMessage) -> None:
|
||||
"""Handle incoming Discord messages."""
|
||||
# Determine message type
|
||||
msg_type = MessageType.TEXT
|
||||
if message.content.startswith("/"):
|
||||
msg_type = MessageType.COMMAND
|
||||
elif message.attachments:
|
||||
# Check attachment types
|
||||
for att in message.attachments:
|
||||
if att.content_type:
|
||||
if att.content_type.startswith("image/"):
|
||||
msg_type = MessageType.PHOTO
|
||||
elif att.content_type.startswith("video/"):
|
||||
msg_type = MessageType.VIDEO
|
||||
elif att.content_type.startswith("audio/"):
|
||||
msg_type = MessageType.AUDIO
|
||||
else:
|
||||
msg_type = MessageType.DOCUMENT
|
||||
break
|
||||
|
||||
# Determine chat type
|
||||
if isinstance(message.channel, discord.DMChannel):
|
||||
chat_type = "dm"
|
||||
chat_name = message.author.name
|
||||
elif isinstance(message.channel, discord.Thread):
|
||||
chat_type = "thread"
|
||||
chat_name = message.channel.name
|
||||
else:
|
||||
chat_type = "group" # Treat server channels as groups
|
||||
chat_name = getattr(message.channel, "name", str(message.channel.id))
|
||||
if hasattr(message.channel, "guild") and message.channel.guild:
|
||||
chat_name = f"{message.channel.guild.name} / #{chat_name}"
|
||||
|
||||
# Get thread ID if in a thread
|
||||
thread_id = None
|
||||
if isinstance(message.channel, discord.Thread):
|
||||
thread_id = str(message.channel.id)
|
||||
|
||||
# Build source
|
||||
source = self.build_source(
|
||||
chat_id=str(message.channel.id),
|
||||
chat_name=chat_name,
|
||||
chat_type=chat_type,
|
||||
user_id=str(message.author.id),
|
||||
user_name=message.author.display_name,
|
||||
thread_id=thread_id,
|
||||
)
|
||||
|
||||
# Build media URLs
|
||||
media_urls = [att.url for att in message.attachments]
|
||||
media_types = [att.content_type or "unknown" for att in message.attachments]
|
||||
|
||||
event = MessageEvent(
|
||||
text=message.content,
|
||||
message_type=msg_type,
|
||||
source=source,
|
||||
raw_message=message,
|
||||
message_id=str(message.id),
|
||||
media_urls=media_urls,
|
||||
media_types=media_types,
|
||||
reply_to_message_id=str(message.reference.message_id) if message.reference else None,
|
||||
timestamp=message.created_at,
|
||||
)
|
||||
|
||||
await self.handle_message(event)
|
||||
@@ -1,298 +0,0 @@
|
||||
"""
|
||||
Telegram platform adapter.
|
||||
|
||||
Uses python-telegram-bot library for:
|
||||
- Receiving messages from users/groups
|
||||
- Sending responses back
|
||||
- Handling media and commands
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
try:
|
||||
from telegram import Update, Bot, Message
|
||||
from telegram.ext import (
|
||||
Application,
|
||||
CommandHandler,
|
||||
MessageHandler as TelegramMessageHandler,
|
||||
ContextTypes,
|
||||
filters,
|
||||
)
|
||||
from telegram.constants import ParseMode, ChatType
|
||||
TELEGRAM_AVAILABLE = True
|
||||
except ImportError:
|
||||
TELEGRAM_AVAILABLE = False
|
||||
Update = Any
|
||||
Bot = Any
|
||||
Message = Any
|
||||
Application = Any
|
||||
ContextTypes = Any
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
|
||||
|
||||
from gateway.config import Platform, PlatformConfig
|
||||
from gateway.platforms.base import (
|
||||
BasePlatformAdapter,
|
||||
MessageEvent,
|
||||
MessageType,
|
||||
SendResult,
|
||||
)
|
||||
|
||||
|
||||
def check_telegram_requirements() -> bool:
|
||||
"""Check if Telegram dependencies are available."""
|
||||
return TELEGRAM_AVAILABLE
|
||||
|
||||
|
||||
class TelegramAdapter(BasePlatformAdapter):
|
||||
"""
|
||||
Telegram bot adapter.
|
||||
|
||||
Handles:
|
||||
- Receiving messages from users and groups
|
||||
- Sending responses with Telegram markdown
|
||||
- Forum topics (thread_id support)
|
||||
- Media messages
|
||||
"""
|
||||
|
||||
# Telegram message limits
|
||||
MAX_MESSAGE_LENGTH = 4096
|
||||
|
||||
def __init__(self, config: PlatformConfig):
|
||||
super().__init__(config, Platform.TELEGRAM)
|
||||
self._app: Optional[Application] = None
|
||||
self._bot: Optional[Bot] = None
|
||||
|
||||
async def connect(self) -> bool:
|
||||
"""Connect to Telegram and start polling for updates."""
|
||||
if not TELEGRAM_AVAILABLE:
|
||||
print(f"[{self.name}] python-telegram-bot not installed. Run: pip install python-telegram-bot")
|
||||
return False
|
||||
|
||||
if not self.config.token:
|
||||
print(f"[{self.name}] No bot token configured")
|
||||
return False
|
||||
|
||||
try:
|
||||
# Build the application
|
||||
self._app = Application.builder().token(self.config.token).build()
|
||||
self._bot = self._app.bot
|
||||
|
||||
# Register handlers
|
||||
self._app.add_handler(TelegramMessageHandler(
|
||||
filters.TEXT & ~filters.COMMAND,
|
||||
self._handle_text_message
|
||||
))
|
||||
self._app.add_handler(TelegramMessageHandler(
|
||||
filters.COMMAND,
|
||||
self._handle_command
|
||||
))
|
||||
self._app.add_handler(TelegramMessageHandler(
|
||||
filters.PHOTO | filters.VIDEO | filters.AUDIO | filters.VOICE | filters.Document.ALL,
|
||||
self._handle_media_message
|
||||
))
|
||||
|
||||
# Start polling in background
|
||||
await self._app.initialize()
|
||||
await self._app.start()
|
||||
await self._app.updater.start_polling(allowed_updates=Update.ALL_TYPES)
|
||||
|
||||
self._running = True
|
||||
print(f"[{self.name}] Connected and polling for updates")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Failed to connect: {e}")
|
||||
return False
|
||||
|
||||
async def disconnect(self) -> None:
|
||||
"""Stop polling and disconnect."""
|
||||
if self._app:
|
||||
try:
|
||||
await self._app.updater.stop()
|
||||
await self._app.stop()
|
||||
await self._app.shutdown()
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Error during disconnect: {e}")
|
||||
|
||||
self._running = False
|
||||
self._app = None
|
||||
self._bot = None
|
||||
print(f"[{self.name}] Disconnected")
|
||||
|
||||
async def send(
|
||||
self,
|
||||
chat_id: str,
|
||||
content: str,
|
||||
reply_to: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
) -> SendResult:
|
||||
"""Send a message to a Telegram chat."""
|
||||
if not self._bot:
|
||||
return SendResult(success=False, error="Not connected")
|
||||
|
||||
try:
|
||||
# Format and split message if needed
|
||||
formatted = self.format_message(content)
|
||||
chunks = self.truncate_message(formatted, self.MAX_MESSAGE_LENGTH)
|
||||
|
||||
message_ids = []
|
||||
thread_id = metadata.get("thread_id") if metadata else None
|
||||
|
||||
for i, chunk in enumerate(chunks):
|
||||
# Try Markdown first, fall back to plain text if it fails
|
||||
try:
|
||||
msg = await self._bot.send_message(
|
||||
chat_id=int(chat_id),
|
||||
text=chunk,
|
||||
parse_mode=ParseMode.MARKDOWN,
|
||||
reply_to_message_id=int(reply_to) if reply_to and i == 0 else None,
|
||||
message_thread_id=int(thread_id) if thread_id else None,
|
||||
)
|
||||
except Exception as md_error:
|
||||
# Markdown parsing failed, try plain text
|
||||
if "parse" in str(md_error).lower() or "markdown" in str(md_error).lower():
|
||||
msg = await self._bot.send_message(
|
||||
chat_id=int(chat_id),
|
||||
text=chunk,
|
||||
parse_mode=None, # Plain text
|
||||
reply_to_message_id=int(reply_to) if reply_to and i == 0 else None,
|
||||
message_thread_id=int(thread_id) if thread_id else None,
|
||||
)
|
||||
else:
|
||||
raise # Re-raise if not a parse error
|
||||
message_ids.append(str(msg.message_id))
|
||||
|
||||
return SendResult(
|
||||
success=True,
|
||||
message_id=message_ids[0] if message_ids else None,
|
||||
raw_response={"message_ids": message_ids}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
return SendResult(success=False, error=str(e))
|
||||
|
||||
async def send_typing(self, chat_id: str) -> None:
|
||||
"""Send typing indicator."""
|
||||
if self._bot:
|
||||
try:
|
||||
await self._bot.send_chat_action(
|
||||
chat_id=int(chat_id),
|
||||
action="typing"
|
||||
)
|
||||
except Exception:
|
||||
pass # Ignore typing indicator failures
|
||||
|
||||
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
|
||||
"""Get information about a Telegram chat."""
|
||||
if not self._bot:
|
||||
return {"name": "Unknown", "type": "dm"}
|
||||
|
||||
try:
|
||||
chat = await self._bot.get_chat(int(chat_id))
|
||||
|
||||
chat_type = "dm"
|
||||
if chat.type == ChatType.GROUP:
|
||||
chat_type = "group"
|
||||
elif chat.type == ChatType.SUPERGROUP:
|
||||
chat_type = "group"
|
||||
if chat.is_forum:
|
||||
chat_type = "forum"
|
||||
elif chat.type == ChatType.CHANNEL:
|
||||
chat_type = "channel"
|
||||
|
||||
return {
|
||||
"name": chat.title or chat.full_name or str(chat_id),
|
||||
"type": chat_type,
|
||||
"username": chat.username,
|
||||
"is_forum": getattr(chat, "is_forum", False),
|
||||
}
|
||||
except Exception as e:
|
||||
return {"name": str(chat_id), "type": "dm", "error": str(e)}
|
||||
|
||||
def format_message(self, content: str) -> str:
|
||||
"""
|
||||
Format message for Telegram.
|
||||
|
||||
Telegram uses a subset of markdown. We'll use the simpler
|
||||
Markdown mode (not MarkdownV2) for compatibility.
|
||||
"""
|
||||
# Basic escaping for Telegram Markdown
|
||||
# In Markdown mode (not V2), only certain characters need escaping
|
||||
return content
|
||||
|
||||
async def _handle_text_message(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
"""Handle incoming text messages."""
|
||||
if not update.message or not update.message.text:
|
||||
return
|
||||
|
||||
event = self._build_message_event(update.message, MessageType.TEXT)
|
||||
await self.handle_message(event)
|
||||
|
||||
async def _handle_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
"""Handle incoming command messages."""
|
||||
if not update.message or not update.message.text:
|
||||
return
|
||||
|
||||
event = self._build_message_event(update.message, MessageType.COMMAND)
|
||||
await self.handle_message(event)
|
||||
|
||||
async def _handle_media_message(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
"""Handle incoming media messages."""
|
||||
if not update.message:
|
||||
return
|
||||
|
||||
msg = update.message
|
||||
|
||||
# Determine media type
|
||||
if msg.photo:
|
||||
msg_type = MessageType.PHOTO
|
||||
elif msg.video:
|
||||
msg_type = MessageType.VIDEO
|
||||
elif msg.audio:
|
||||
msg_type = MessageType.AUDIO
|
||||
elif msg.voice:
|
||||
msg_type = MessageType.VOICE
|
||||
else:
|
||||
msg_type = MessageType.DOCUMENT
|
||||
|
||||
event = self._build_message_event(msg, msg_type)
|
||||
|
||||
# Add caption as text
|
||||
if msg.caption:
|
||||
event.text = msg.caption
|
||||
|
||||
await self.handle_message(event)
|
||||
|
||||
def _build_message_event(self, message: Message, msg_type: MessageType) -> MessageEvent:
|
||||
"""Build a MessageEvent from a Telegram message."""
|
||||
chat = message.chat
|
||||
user = message.from_user
|
||||
|
||||
# Determine chat type
|
||||
chat_type = "dm"
|
||||
if chat.type in (ChatType.GROUP, ChatType.SUPERGROUP):
|
||||
chat_type = "group"
|
||||
elif chat.type == ChatType.CHANNEL:
|
||||
chat_type = "channel"
|
||||
|
||||
# Build source
|
||||
source = self.build_source(
|
||||
chat_id=str(chat.id),
|
||||
chat_name=chat.title or (chat.full_name if hasattr(chat, "full_name") else None),
|
||||
chat_type=chat_type,
|
||||
user_id=str(user.id) if user else None,
|
||||
user_name=user.full_name if user else None,
|
||||
thread_id=str(message.message_thread_id) if message.message_thread_id else None,
|
||||
)
|
||||
|
||||
return MessageEvent(
|
||||
text=message.text or "",
|
||||
message_type=msg_type,
|
||||
source=source,
|
||||
raw_message=message,
|
||||
message_id=str(message.message_id),
|
||||
timestamp=message.date,
|
||||
)
|
||||
@@ -1,327 +0,0 @@
|
||||
"""
|
||||
WhatsApp platform adapter.
|
||||
|
||||
WhatsApp integration is more complex than Telegram/Discord because:
|
||||
- No official bot API for personal accounts
|
||||
- Business API requires Meta Business verification
|
||||
- Most solutions use web-based automation
|
||||
|
||||
This adapter supports multiple backends:
|
||||
1. WhatsApp Business API (requires Meta verification)
|
||||
2. whatsapp-web.js (via Node.js subprocess) - for personal accounts
|
||||
3. Baileys (via Node.js subprocess) - alternative for personal accounts
|
||||
|
||||
For simplicity, we'll implement a generic interface that can work
|
||||
with different backends via a bridge pattern.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
|
||||
|
||||
from gateway.config import Platform, PlatformConfig
|
||||
from gateway.platforms.base import (
|
||||
BasePlatformAdapter,
|
||||
MessageEvent,
|
||||
MessageType,
|
||||
SendResult,
|
||||
)
|
||||
|
||||
|
||||
def check_whatsapp_requirements() -> bool:
|
||||
"""
|
||||
Check if WhatsApp dependencies are available.
|
||||
|
||||
WhatsApp requires a Node.js bridge for most implementations.
|
||||
"""
|
||||
# Check for Node.js
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["node", "--version"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5
|
||||
)
|
||||
return result.returncode == 0
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
class WhatsAppAdapter(BasePlatformAdapter):
|
||||
"""
|
||||
WhatsApp adapter.
|
||||
|
||||
This implementation uses a simple HTTP bridge pattern where:
|
||||
1. A Node.js process runs the WhatsApp Web client
|
||||
2. Messages are forwarded via HTTP/IPC to this Python adapter
|
||||
3. Responses are sent back through the bridge
|
||||
|
||||
The actual Node.js bridge implementation can vary:
|
||||
- whatsapp-web.js based
|
||||
- Baileys based
|
||||
- Business API based
|
||||
|
||||
Configuration:
|
||||
- bridge_script: Path to the Node.js bridge script
|
||||
- bridge_port: Port for HTTP communication (default: 3000)
|
||||
- session_path: Path to store WhatsApp session data
|
||||
"""
|
||||
|
||||
# WhatsApp message limits
|
||||
MAX_MESSAGE_LENGTH = 65536 # WhatsApp allows longer messages
|
||||
|
||||
def __init__(self, config: PlatformConfig):
|
||||
super().__init__(config, Platform.WHATSAPP)
|
||||
self._bridge_process: Optional[subprocess.Popen] = None
|
||||
self._bridge_port: int = config.extra.get("bridge_port", 3000)
|
||||
self._bridge_script: Optional[str] = config.extra.get("bridge_script")
|
||||
self._session_path: Path = Path(config.extra.get(
|
||||
"session_path",
|
||||
Path.home() / ".hermes" / "whatsapp" / "session"
|
||||
))
|
||||
self._message_queue: asyncio.Queue = asyncio.Queue()
|
||||
|
||||
async def connect(self) -> bool:
|
||||
"""
|
||||
Start the WhatsApp bridge.
|
||||
|
||||
This launches the Node.js bridge process and waits for it to be ready.
|
||||
"""
|
||||
if not check_whatsapp_requirements():
|
||||
print(f"[{self.name}] Node.js not found. WhatsApp requires Node.js.")
|
||||
return False
|
||||
|
||||
if not self._bridge_script:
|
||||
print(f"[{self.name}] No bridge script configured.")
|
||||
print(f"[{self.name}] Set 'bridge_script' in whatsapp.extra config.")
|
||||
print(f"[{self.name}] See docs/messaging.md for WhatsApp setup instructions.")
|
||||
return False
|
||||
|
||||
bridge_path = Path(self._bridge_script)
|
||||
if not bridge_path.exists():
|
||||
print(f"[{self.name}] Bridge script not found: {bridge_path}")
|
||||
return False
|
||||
|
||||
try:
|
||||
# Ensure session directory exists
|
||||
self._session_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Start the bridge process
|
||||
self._bridge_process = subprocess.Popen(
|
||||
[
|
||||
"node",
|
||||
str(bridge_path),
|
||||
"--port", str(self._bridge_port),
|
||||
"--session", str(self._session_path),
|
||||
],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
text=True,
|
||||
)
|
||||
|
||||
# Wait for bridge to be ready (look for ready signal)
|
||||
# This is a simplified version - real implementation would
|
||||
# wait for an HTTP health check or specific stdout message
|
||||
await asyncio.sleep(5)
|
||||
|
||||
if self._bridge_process.poll() is not None:
|
||||
stderr = self._bridge_process.stderr.read() if self._bridge_process.stderr else ""
|
||||
print(f"[{self.name}] Bridge process died: {stderr}")
|
||||
return False
|
||||
|
||||
# Start message polling task
|
||||
asyncio.create_task(self._poll_messages())
|
||||
|
||||
self._running = True
|
||||
print(f"[{self.name}] Bridge started on port {self._bridge_port}")
|
||||
print(f"[{self.name}] Scan QR code if prompted (check bridge output)")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Failed to start bridge: {e}")
|
||||
return False
|
||||
|
||||
async def disconnect(self) -> None:
|
||||
"""Stop the WhatsApp bridge."""
|
||||
if self._bridge_process:
|
||||
try:
|
||||
self._bridge_process.terminate()
|
||||
await asyncio.sleep(1)
|
||||
if self._bridge_process.poll() is None:
|
||||
self._bridge_process.kill()
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Error stopping bridge: {e}")
|
||||
|
||||
self._running = False
|
||||
self._bridge_process = None
|
||||
print(f"[{self.name}] Disconnected")
|
||||
|
||||
async def send(
|
||||
self,
|
||||
chat_id: str,
|
||||
content: str,
|
||||
reply_to: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
) -> SendResult:
|
||||
"""Send a message via the WhatsApp bridge."""
|
||||
if not self._running:
|
||||
return SendResult(success=False, error="Not connected")
|
||||
|
||||
try:
|
||||
import aiohttp
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
payload = {
|
||||
"chatId": chat_id,
|
||||
"message": content,
|
||||
}
|
||||
if reply_to:
|
||||
payload["replyTo"] = reply_to
|
||||
|
||||
async with session.post(
|
||||
f"http://localhost:{self._bridge_port}/send",
|
||||
json=payload,
|
||||
timeout=aiohttp.ClientTimeout(total=30)
|
||||
) as resp:
|
||||
if resp.status == 200:
|
||||
data = await resp.json()
|
||||
return SendResult(
|
||||
success=True,
|
||||
message_id=data.get("messageId"),
|
||||
raw_response=data
|
||||
)
|
||||
else:
|
||||
error = await resp.text()
|
||||
return SendResult(success=False, error=error)
|
||||
|
||||
except ImportError:
|
||||
return SendResult(
|
||||
success=False,
|
||||
error="aiohttp not installed. Run: pip install aiohttp"
|
||||
)
|
||||
except Exception as e:
|
||||
return SendResult(success=False, error=str(e))
|
||||
|
||||
async def send_typing(self, chat_id: str) -> None:
|
||||
"""Send typing indicator via bridge."""
|
||||
if not self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
import aiohttp
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
await session.post(
|
||||
f"http://localhost:{self._bridge_port}/typing",
|
||||
json={"chatId": chat_id},
|
||||
timeout=aiohttp.ClientTimeout(total=5)
|
||||
)
|
||||
except Exception:
|
||||
pass # Ignore typing indicator failures
|
||||
|
||||
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
|
||||
"""Get information about a WhatsApp chat."""
|
||||
if not self._running:
|
||||
return {"name": "Unknown", "type": "dm"}
|
||||
|
||||
try:
|
||||
import aiohttp
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(
|
||||
f"http://localhost:{self._bridge_port}/chat/{chat_id}",
|
||||
timeout=aiohttp.ClientTimeout(total=10)
|
||||
) as resp:
|
||||
if resp.status == 200:
|
||||
data = await resp.json()
|
||||
return {
|
||||
"name": data.get("name", chat_id),
|
||||
"type": "group" if data.get("isGroup") else "dm",
|
||||
"participants": data.get("participants", []),
|
||||
}
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return {"name": chat_id, "type": "dm"}
|
||||
|
||||
async def _poll_messages(self) -> None:
|
||||
"""Poll the bridge for incoming messages."""
|
||||
try:
|
||||
import aiohttp
|
||||
except ImportError:
|
||||
print(f"[{self.name}] aiohttp not installed, message polling disabled")
|
||||
return
|
||||
|
||||
while self._running:
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(
|
||||
f"http://localhost:{self._bridge_port}/messages",
|
||||
timeout=aiohttp.ClientTimeout(total=30)
|
||||
) as resp:
|
||||
if resp.status == 200:
|
||||
messages = await resp.json()
|
||||
for msg_data in messages:
|
||||
event = self._build_message_event(msg_data)
|
||||
if event:
|
||||
await self.handle_message(event)
|
||||
except asyncio.CancelledError:
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Poll error: {e}")
|
||||
await asyncio.sleep(5)
|
||||
|
||||
await asyncio.sleep(1) # Poll interval
|
||||
|
||||
def _build_message_event(self, data: Dict[str, Any]) -> Optional[MessageEvent]:
|
||||
"""Build a MessageEvent from bridge message data."""
|
||||
try:
|
||||
# Determine message type
|
||||
msg_type = MessageType.TEXT
|
||||
if data.get("hasMedia"):
|
||||
media_type = data.get("mediaType", "")
|
||||
if "image" in media_type:
|
||||
msg_type = MessageType.PHOTO
|
||||
elif "video" in media_type:
|
||||
msg_type = MessageType.VIDEO
|
||||
elif "audio" in media_type or "ptt" in media_type: # ptt = voice note
|
||||
msg_type = MessageType.VOICE
|
||||
else:
|
||||
msg_type = MessageType.DOCUMENT
|
||||
|
||||
# Determine chat type
|
||||
is_group = data.get("isGroup", False)
|
||||
chat_type = "group" if is_group else "dm"
|
||||
|
||||
# Build source
|
||||
source = self.build_source(
|
||||
chat_id=data.get("chatId", ""),
|
||||
chat_name=data.get("chatName"),
|
||||
chat_type=chat_type,
|
||||
user_id=data.get("senderId"),
|
||||
user_name=data.get("senderName"),
|
||||
)
|
||||
|
||||
return MessageEvent(
|
||||
text=data.get("body", ""),
|
||||
message_type=msg_type,
|
||||
source=source,
|
||||
raw_message=data,
|
||||
message_id=data.get("messageId"),
|
||||
media_urls=data.get("mediaUrls", []),
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[{self.name}] Error building event: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# Note: A reference Node.js bridge script would be provided in scripts/whatsapp-bridge/
|
||||
# It would use whatsapp-web.js or Baileys to:
|
||||
# 1. Handle WhatsApp Web authentication (QR code)
|
||||
# 2. Listen for incoming messages
|
||||
# 3. Expose HTTP endpoints for send/receive/status
|
||||
666
gateway/run.py
666
gateway/run.py
@@ -1,666 +0,0 @@
|
||||
"""
|
||||
Gateway runner - entry point for messaging platform integrations.
|
||||
|
||||
This module provides:
|
||||
- start_gateway(): Start all configured platform adapters
|
||||
- GatewayRunner: Main class managing the gateway lifecycle
|
||||
|
||||
Usage:
|
||||
# Start the gateway
|
||||
python -m gateway.run
|
||||
|
||||
# Or from CLI
|
||||
python cli.py --gateway
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import signal
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from typing import Dict, Optional, Any, List
|
||||
|
||||
# Add parent directory to path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
# Load environment variables from ~/.hermes/.env first
|
||||
from dotenv import load_dotenv
|
||||
_env_path = Path.home() / '.hermes' / '.env'
|
||||
if _env_path.exists():
|
||||
load_dotenv(_env_path)
|
||||
# Also try project .env as fallback
|
||||
load_dotenv()
|
||||
|
||||
# Gateway runs in quiet mode - suppress debug output and use cwd directly (no temp dirs)
|
||||
os.environ["HERMES_QUIET"] = "1"
|
||||
|
||||
# Set terminal working directory for messaging platforms
|
||||
# Uses MESSAGING_CWD if set, otherwise defaults to home directory
|
||||
# This is separate from CLI which uses the directory where `hermes` is run
|
||||
messaging_cwd = os.getenv("MESSAGING_CWD") or str(Path.home())
|
||||
os.environ["TERMINAL_CWD"] = messaging_cwd
|
||||
|
||||
from gateway.config import (
|
||||
Platform,
|
||||
GatewayConfig,
|
||||
load_gateway_config,
|
||||
)
|
||||
from gateway.session import (
|
||||
SessionStore,
|
||||
SessionSource,
|
||||
SessionContext,
|
||||
build_session_context,
|
||||
build_session_context_prompt,
|
||||
)
|
||||
from gateway.delivery import DeliveryRouter, DeliveryTarget
|
||||
from gateway.platforms.base import BasePlatformAdapter, MessageEvent
|
||||
|
||||
|
||||
class GatewayRunner:
|
||||
"""
|
||||
Main gateway controller.
|
||||
|
||||
Manages the lifecycle of all platform adapters and routes
|
||||
messages to/from the agent.
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[GatewayConfig] = None):
|
||||
self.config = config or load_gateway_config()
|
||||
self.adapters: Dict[Platform, BasePlatformAdapter] = {}
|
||||
self.session_store = SessionStore(self.config.sessions_dir, self.config)
|
||||
self.delivery_router = DeliveryRouter(self.config)
|
||||
self._running = False
|
||||
self._shutdown_event = asyncio.Event()
|
||||
|
||||
# Track running agents per session for interrupt support
|
||||
# Key: session_key, Value: AIAgent instance
|
||||
self._running_agents: Dict[str, Any] = {}
|
||||
self._pending_messages: Dict[str, str] = {} # Queued messages during interrupt
|
||||
|
||||
async def start(self) -> bool:
|
||||
"""
|
||||
Start the gateway and all configured platform adapters.
|
||||
|
||||
Returns True if at least one adapter connected successfully.
|
||||
"""
|
||||
print("[gateway] Starting Hermes Gateway...")
|
||||
print(f"[gateway] Session storage: {self.config.sessions_dir}")
|
||||
|
||||
connected_count = 0
|
||||
|
||||
# Initialize and connect each configured platform
|
||||
for platform, platform_config in self.config.platforms.items():
|
||||
if not platform_config.enabled:
|
||||
continue
|
||||
|
||||
adapter = self._create_adapter(platform, platform_config)
|
||||
if not adapter:
|
||||
print(f"[gateway] No adapter available for {platform.value}")
|
||||
continue
|
||||
|
||||
# Set up message handler
|
||||
adapter.set_message_handler(self._handle_message)
|
||||
|
||||
# Try to connect
|
||||
print(f"[gateway] Connecting to {platform.value}...")
|
||||
try:
|
||||
success = await adapter.connect()
|
||||
if success:
|
||||
self.adapters[platform] = adapter
|
||||
connected_count += 1
|
||||
print(f"[gateway] ✓ {platform.value} connected")
|
||||
else:
|
||||
print(f"[gateway] ✗ {platform.value} failed to connect")
|
||||
except Exception as e:
|
||||
print(f"[gateway] ✗ {platform.value} error: {e}")
|
||||
|
||||
if connected_count == 0:
|
||||
print("[gateway] No platforms connected. Check your configuration.")
|
||||
return False
|
||||
|
||||
# Update delivery router with adapters
|
||||
self.delivery_router.adapters = self.adapters
|
||||
|
||||
self._running = True
|
||||
print(f"[gateway] Gateway running with {connected_count} platform(s)")
|
||||
print("[gateway] Press Ctrl+C to stop")
|
||||
|
||||
return True
|
||||
|
||||
async def stop(self) -> None:
|
||||
"""Stop the gateway and disconnect all adapters."""
|
||||
print("[gateway] Stopping gateway...")
|
||||
self._running = False
|
||||
|
||||
for platform, adapter in self.adapters.items():
|
||||
try:
|
||||
await adapter.disconnect()
|
||||
print(f"[gateway] ✓ {platform.value} disconnected")
|
||||
except Exception as e:
|
||||
print(f"[gateway] ✗ {platform.value} disconnect error: {e}")
|
||||
|
||||
self.adapters.clear()
|
||||
self._shutdown_event.set()
|
||||
print("[gateway] Gateway stopped")
|
||||
|
||||
async def wait_for_shutdown(self) -> None:
|
||||
"""Wait for shutdown signal."""
|
||||
await self._shutdown_event.wait()
|
||||
|
||||
def _create_adapter(
|
||||
self,
|
||||
platform: Platform,
|
||||
config: Any
|
||||
) -> Optional[BasePlatformAdapter]:
|
||||
"""Create the appropriate adapter for a platform."""
|
||||
if platform == Platform.TELEGRAM:
|
||||
from gateway.platforms.telegram import TelegramAdapter, check_telegram_requirements
|
||||
if not check_telegram_requirements():
|
||||
print(f"[gateway] Telegram: python-telegram-bot not installed")
|
||||
return None
|
||||
return TelegramAdapter(config)
|
||||
|
||||
elif platform == Platform.DISCORD:
|
||||
from gateway.platforms.discord import DiscordAdapter, check_discord_requirements
|
||||
if not check_discord_requirements():
|
||||
print(f"[gateway] Discord: discord.py not installed")
|
||||
return None
|
||||
return DiscordAdapter(config)
|
||||
|
||||
elif platform == Platform.WHATSAPP:
|
||||
from gateway.platforms.whatsapp import WhatsAppAdapter, check_whatsapp_requirements
|
||||
if not check_whatsapp_requirements():
|
||||
print(f"[gateway] WhatsApp: Node.js not installed or bridge not configured")
|
||||
return None
|
||||
return WhatsAppAdapter(config)
|
||||
|
||||
return None
|
||||
|
||||
def _is_user_authorized(self, source: SessionSource) -> bool:
|
||||
"""
|
||||
Check if a user is authorized to use the bot.
|
||||
|
||||
Authorization is checked via environment variables:
|
||||
- GATEWAY_ALLOWED_USERS: Comma-separated list of user IDs (all platforms)
|
||||
- TELEGRAM_ALLOWED_USERS: Telegram-specific user IDs
|
||||
- DISCORD_ALLOWED_USERS: Discord-specific user IDs
|
||||
|
||||
If no allowlist is configured, all users are allowed (open access).
|
||||
"""
|
||||
user_id = source.user_id
|
||||
if not user_id:
|
||||
return False # Can't verify unknown users
|
||||
|
||||
# Check platform-specific allowlist first
|
||||
platform_env_map = {
|
||||
Platform.TELEGRAM: "TELEGRAM_ALLOWED_USERS",
|
||||
Platform.DISCORD: "DISCORD_ALLOWED_USERS",
|
||||
Platform.WHATSAPP: "WHATSAPP_ALLOWED_USERS",
|
||||
}
|
||||
|
||||
platform_allowlist = os.getenv(platform_env_map.get(source.platform, ""))
|
||||
global_allowlist = os.getenv("GATEWAY_ALLOWED_USERS", "")
|
||||
|
||||
# If no allowlists configured, allow all (backward compatible)
|
||||
if not platform_allowlist and not global_allowlist:
|
||||
return True
|
||||
|
||||
# Check if user is in any allowlist
|
||||
allowed_ids = set()
|
||||
if platform_allowlist:
|
||||
allowed_ids.update(uid.strip() for uid in platform_allowlist.split(","))
|
||||
if global_allowlist:
|
||||
allowed_ids.update(uid.strip() for uid in global_allowlist.split(","))
|
||||
|
||||
return user_id in allowed_ids
|
||||
|
||||
async def _handle_message(self, event: MessageEvent) -> Optional[str]:
|
||||
"""
|
||||
Handle an incoming message from any platform.
|
||||
|
||||
This is the core message processing pipeline:
|
||||
1. Check user authorization
|
||||
2. Check for commands (/new, /reset, etc.)
|
||||
3. Check for running agent and interrupt if needed
|
||||
4. Get or create session
|
||||
5. Build context for agent
|
||||
6. Run agent conversation
|
||||
7. Return response
|
||||
"""
|
||||
source = event.source
|
||||
|
||||
# Check if user is authorized
|
||||
if not self._is_user_authorized(source):
|
||||
print(f"[gateway] Unauthorized user: {source.user_id} ({source.user_name}) on {source.platform.value}")
|
||||
return None # Silently ignore unauthorized users
|
||||
|
||||
# Check for commands
|
||||
command = event.get_command()
|
||||
if command in ["new", "reset"]:
|
||||
return await self._handle_reset_command(event)
|
||||
|
||||
if command == "status":
|
||||
return await self._handle_status_command(event)
|
||||
|
||||
if command == "stop":
|
||||
return await self._handle_stop_command(event)
|
||||
|
||||
# Get or create session
|
||||
session_entry = self.session_store.get_or_create_session(source)
|
||||
session_key = session_entry.session_key
|
||||
|
||||
# Check if there's already a running agent for this session
|
||||
if session_key in self._running_agents:
|
||||
running_agent = self._running_agents[session_key]
|
||||
print(f"[gateway] ⚡ Interrupting running agent for session {session_key[:20]}...")
|
||||
running_agent.interrupt(event.text)
|
||||
# Store the new message to be processed after current agent finishes
|
||||
self._pending_messages[session_key] = event.text
|
||||
return None # Don't respond yet - let the interrupt handle it
|
||||
|
||||
# Build session context
|
||||
context = build_session_context(source, self.config, session_entry)
|
||||
|
||||
# Set environment variables for tools
|
||||
self._set_session_env(context)
|
||||
|
||||
# Build the context prompt to inject
|
||||
context_prompt = build_session_context_prompt(context)
|
||||
|
||||
# Load conversation history from transcript
|
||||
history = self.session_store.load_transcript(session_entry.session_id)
|
||||
|
||||
try:
|
||||
# Run the agent
|
||||
response = await self._run_agent(
|
||||
message=event.text,
|
||||
context_prompt=context_prompt,
|
||||
history=history,
|
||||
source=source,
|
||||
session_id=session_entry.session_id,
|
||||
session_key=session_key
|
||||
)
|
||||
|
||||
# Append to transcript
|
||||
self.session_store.append_to_transcript(
|
||||
session_entry.session_id,
|
||||
{"role": "user", "content": event.text, "timestamp": datetime.now().isoformat()}
|
||||
)
|
||||
self.session_store.append_to_transcript(
|
||||
session_entry.session_id,
|
||||
{"role": "assistant", "content": response, "timestamp": datetime.now().isoformat()}
|
||||
)
|
||||
|
||||
# Update session
|
||||
self.session_store.update_session(session_entry.session_key)
|
||||
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
print(f"[gateway] Agent error: {e}")
|
||||
return f"Sorry, I encountered an error: {str(e)}"
|
||||
finally:
|
||||
# Clear session env
|
||||
self._clear_session_env()
|
||||
|
||||
async def _handle_reset_command(self, event: MessageEvent) -> str:
|
||||
"""Handle /new or /reset command."""
|
||||
source = event.source
|
||||
|
||||
# Get existing session key
|
||||
session_key = f"agent:main:{source.platform.value}:" + \
|
||||
(f"dm" if source.chat_type == "dm" else f"{source.chat_type}:{source.chat_id}")
|
||||
|
||||
# Reset the session
|
||||
new_entry = self.session_store.reset_session(session_key)
|
||||
|
||||
if new_entry:
|
||||
return "✨ Session reset! I've started fresh with no memory of our previous conversation."
|
||||
else:
|
||||
# No existing session, just create one
|
||||
self.session_store.get_or_create_session(source, force_new=True)
|
||||
return "✨ New session started!"
|
||||
|
||||
async def _handle_status_command(self, event: MessageEvent) -> str:
|
||||
"""Handle /status command."""
|
||||
source = event.source
|
||||
session_entry = self.session_store.get_or_create_session(source)
|
||||
|
||||
connected_platforms = [p.value for p in self.adapters.keys()]
|
||||
|
||||
# Check if there's an active agent
|
||||
session_key = session_entry.session_key
|
||||
is_running = session_key in self._running_agents
|
||||
|
||||
lines = [
|
||||
"📊 **Hermes Gateway Status**",
|
||||
"",
|
||||
f"**Session ID:** `{session_entry.session_id[:12]}...`",
|
||||
f"**Created:** {session_entry.created_at.strftime('%Y-%m-%d %H:%M')}",
|
||||
f"**Last Activity:** {session_entry.updated_at.strftime('%Y-%m-%d %H:%M')}",
|
||||
f"**Tokens:** {session_entry.total_tokens:,}",
|
||||
f"**Agent Running:** {'Yes ⚡' if is_running else 'No'}",
|
||||
"",
|
||||
f"**Connected Platforms:** {', '.join(connected_platforms)}",
|
||||
]
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
async def _handle_stop_command(self, event: MessageEvent) -> str:
|
||||
"""Handle /stop command - interrupt a running agent."""
|
||||
source = event.source
|
||||
session_entry = self.session_store.get_or_create_session(source)
|
||||
session_key = session_entry.session_key
|
||||
|
||||
if session_key in self._running_agents:
|
||||
agent = self._running_agents[session_key]
|
||||
agent.interrupt()
|
||||
return "⚡ Stopping the current task... The agent will finish its current step and respond."
|
||||
else:
|
||||
return "No active task to stop."
|
||||
|
||||
def _set_session_env(self, context: SessionContext) -> None:
|
||||
"""Set environment variables for the current session."""
|
||||
os.environ["HERMES_SESSION_PLATFORM"] = context.source.platform.value
|
||||
os.environ["HERMES_SESSION_CHAT_ID"] = context.source.chat_id
|
||||
if context.source.chat_name:
|
||||
os.environ["HERMES_SESSION_CHAT_NAME"] = context.source.chat_name
|
||||
|
||||
def _clear_session_env(self) -> None:
|
||||
"""Clear session environment variables."""
|
||||
for var in ["HERMES_SESSION_PLATFORM", "HERMES_SESSION_CHAT_ID", "HERMES_SESSION_CHAT_NAME"]:
|
||||
if var in os.environ:
|
||||
del os.environ[var]
|
||||
|
||||
async def _run_agent(
|
||||
self,
|
||||
message: str,
|
||||
context_prompt: str,
|
||||
history: List[Dict[str, Any]],
|
||||
source: SessionSource,
|
||||
session_id: str,
|
||||
session_key: str = None
|
||||
) -> str:
|
||||
"""
|
||||
Run the agent with the given message and context.
|
||||
|
||||
This is run in a thread pool to not block the event loop.
|
||||
Supports interruption via new messages.
|
||||
"""
|
||||
from run_agent import AIAgent
|
||||
import queue
|
||||
|
||||
# Determine toolset based on platform
|
||||
toolset_map = {
|
||||
Platform.LOCAL: "hermes-cli",
|
||||
Platform.TELEGRAM: "hermes-telegram",
|
||||
Platform.DISCORD: "hermes-discord",
|
||||
Platform.WHATSAPP: "hermes-whatsapp",
|
||||
}
|
||||
toolset = toolset_map.get(source.platform, "hermes-telegram")
|
||||
|
||||
# Check if tool progress notifications are enabled
|
||||
tool_progress_enabled = os.getenv("HERMES_TOOL_PROGRESS", "").lower() in ("1", "true", "yes")
|
||||
progress_mode = os.getenv("HERMES_TOOL_PROGRESS_MODE", "new") # "all" or "new" (only new tools)
|
||||
|
||||
# Queue for progress messages (thread-safe)
|
||||
progress_queue = queue.Queue() if tool_progress_enabled else None
|
||||
last_tool = [None] # Mutable container for tracking in closure
|
||||
|
||||
def progress_callback(tool_name: str, preview: str = None):
|
||||
"""Callback invoked by agent when a tool is called."""
|
||||
if not progress_queue:
|
||||
return
|
||||
|
||||
# "new" mode: only report when tool changes
|
||||
if progress_mode == "new" and tool_name == last_tool[0]:
|
||||
return
|
||||
last_tool[0] = tool_name
|
||||
|
||||
# Build progress message
|
||||
tool_emojis = {
|
||||
"terminal": "💻",
|
||||
"web_search": "🔍",
|
||||
"web_extract": "📄",
|
||||
"read_file": "📖",
|
||||
"write_file": "✍️",
|
||||
"list_directory": "📂",
|
||||
"image_generate": "🎨",
|
||||
"browser_navigate": "🌐",
|
||||
"browser_click": "👆",
|
||||
"moa_query": "🧠",
|
||||
}
|
||||
emoji = tool_emojis.get(tool_name, "⚙️")
|
||||
|
||||
if tool_name == "terminal" and preview:
|
||||
msg = f"{emoji} `{preview}`..."
|
||||
else:
|
||||
msg = f"{emoji} {tool_name}..."
|
||||
|
||||
progress_queue.put(msg)
|
||||
|
||||
# Background task to send progress messages
|
||||
async def send_progress_messages():
|
||||
if not progress_queue:
|
||||
return
|
||||
|
||||
adapter = self.adapters.get(source.platform)
|
||||
if not adapter:
|
||||
return
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Non-blocking check with small timeout
|
||||
msg = progress_queue.get_nowait()
|
||||
await adapter.send(chat_id=source.chat_id, content=msg)
|
||||
# Restore typing indicator after sending progress message
|
||||
await asyncio.sleep(0.3)
|
||||
await adapter.send_typing(source.chat_id)
|
||||
except queue.Empty:
|
||||
await asyncio.sleep(0.3) # Check again soon
|
||||
except asyncio.CancelledError:
|
||||
# Drain remaining messages
|
||||
while not progress_queue.empty():
|
||||
try:
|
||||
msg = progress_queue.get_nowait()
|
||||
await adapter.send(chat_id=source.chat_id, content=msg)
|
||||
except:
|
||||
break
|
||||
return
|
||||
except Exception as e:
|
||||
print(f"[Gateway] Progress message error: {e}")
|
||||
await asyncio.sleep(1)
|
||||
|
||||
# We need to share the agent instance for interrupt support
|
||||
agent_holder = [None] # Mutable container for the agent instance
|
||||
result_holder = [None] # Mutable container for the result
|
||||
|
||||
def run_sync():
|
||||
# Read from env var or use default (same as CLI)
|
||||
max_iterations = int(os.getenv("HERMES_MAX_ITERATIONS", "60"))
|
||||
|
||||
agent = AIAgent(
|
||||
model=os.getenv("HERMES_MODEL", "anthropic/claude-sonnet-4"),
|
||||
max_iterations=max_iterations,
|
||||
quiet_mode=True,
|
||||
enabled_toolsets=[toolset],
|
||||
ephemeral_system_prompt=context_prompt,
|
||||
session_id=session_id,
|
||||
tool_progress_callback=progress_callback if tool_progress_enabled else None,
|
||||
)
|
||||
|
||||
# Store agent reference for interrupt support
|
||||
agent_holder[0] = agent
|
||||
|
||||
# Convert transcript history to agent format
|
||||
# Transcript has timestamps; agent expects {"role": ..., "content": ...}
|
||||
agent_history = []
|
||||
for msg in history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content")
|
||||
if role and content:
|
||||
agent_history.append({"role": role, "content": content})
|
||||
|
||||
result = agent.run_conversation(message, conversation_history=agent_history)
|
||||
result_holder[0] = result
|
||||
|
||||
# Return final response, or a message if something went wrong
|
||||
final_response = result.get("final_response")
|
||||
if final_response:
|
||||
return final_response
|
||||
elif result.get("error"):
|
||||
# Agent couldn't recover - show the error
|
||||
return f"⚠️ {result['error']}"
|
||||
else:
|
||||
return "(No response generated)"
|
||||
|
||||
# Start progress message sender if enabled
|
||||
progress_task = None
|
||||
if tool_progress_enabled:
|
||||
progress_task = asyncio.create_task(send_progress_messages())
|
||||
|
||||
# Track this agent as running for this session (for interrupt support)
|
||||
# We do this in a callback after the agent is created
|
||||
async def track_agent():
|
||||
# Wait for agent to be created
|
||||
while agent_holder[0] is None:
|
||||
await asyncio.sleep(0.05)
|
||||
if session_key:
|
||||
self._running_agents[session_key] = agent_holder[0]
|
||||
|
||||
tracking_task = asyncio.create_task(track_agent())
|
||||
|
||||
# Monitor for interrupts from the adapter (new messages arriving)
|
||||
async def monitor_for_interrupt():
|
||||
adapter = self.adapters.get(source.platform)
|
||||
if not adapter:
|
||||
return
|
||||
|
||||
chat_id = source.chat_id
|
||||
while True:
|
||||
await asyncio.sleep(0.2) # Check every 200ms
|
||||
# Check if adapter has a pending interrupt for this session
|
||||
if hasattr(adapter, 'has_pending_interrupt') and adapter.has_pending_interrupt(chat_id):
|
||||
agent = agent_holder[0]
|
||||
if agent:
|
||||
pending_event = adapter.get_pending_message(chat_id)
|
||||
pending_text = pending_event.text if pending_event else None
|
||||
print(f"[gateway] ⚡ Interrupt detected from adapter, signaling agent...")
|
||||
agent.interrupt(pending_text)
|
||||
break
|
||||
|
||||
interrupt_monitor = asyncio.create_task(monitor_for_interrupt())
|
||||
|
||||
try:
|
||||
# Run in thread pool to not block
|
||||
loop = asyncio.get_event_loop()
|
||||
response = await loop.run_in_executor(None, run_sync)
|
||||
|
||||
# Check if we were interrupted and have a pending message
|
||||
result = result_holder[0]
|
||||
adapter = self.adapters.get(source.platform)
|
||||
|
||||
# Get pending message from adapter if interrupted
|
||||
pending = None
|
||||
if result and result.get("interrupted") and adapter:
|
||||
pending_event = adapter.get_pending_message(source.chat_id)
|
||||
if pending_event:
|
||||
pending = pending_event.text
|
||||
elif result.get("interrupt_message"):
|
||||
pending = result.get("interrupt_message")
|
||||
|
||||
if pending:
|
||||
print(f"[gateway] 📨 Processing interrupted message: '{pending[:40]}...'")
|
||||
# Add an indicator to the response
|
||||
if response:
|
||||
response = response + "\n\n---\n_[Interrupted - processing your new message]_"
|
||||
|
||||
# Send the interrupted response first
|
||||
if adapter and response:
|
||||
await adapter.send(chat_id=source.chat_id, content=response)
|
||||
|
||||
# Now process the pending message with updated history
|
||||
updated_history = result.get("messages", history)
|
||||
return await self._run_agent(
|
||||
message=pending,
|
||||
context_prompt=context_prompt,
|
||||
history=updated_history,
|
||||
source=source,
|
||||
session_id=session_id,
|
||||
session_key=session_key
|
||||
)
|
||||
finally:
|
||||
# Stop progress sender and interrupt monitor
|
||||
if progress_task:
|
||||
progress_task.cancel()
|
||||
interrupt_monitor.cancel()
|
||||
|
||||
# Clean up tracking
|
||||
tracking_task.cancel()
|
||||
if session_key and session_key in self._running_agents:
|
||||
del self._running_agents[session_key]
|
||||
|
||||
# Wait for cancelled tasks
|
||||
for task in [progress_task, interrupt_monitor, tracking_task]:
|
||||
if task:
|
||||
try:
|
||||
await task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
return response
|
||||
|
||||
|
||||
async def start_gateway(config: Optional[GatewayConfig] = None) -> None:
|
||||
"""
|
||||
Start the gateway and run until interrupted.
|
||||
|
||||
This is the main entry point for running the gateway.
|
||||
"""
|
||||
runner = GatewayRunner(config)
|
||||
|
||||
# Set up signal handlers
|
||||
def signal_handler():
|
||||
asyncio.create_task(runner.stop())
|
||||
|
||||
loop = asyncio.get_event_loop()
|
||||
for sig in (signal.SIGINT, signal.SIGTERM):
|
||||
try:
|
||||
loop.add_signal_handler(sig, signal_handler)
|
||||
except NotImplementedError:
|
||||
# Windows doesn't support add_signal_handler
|
||||
pass
|
||||
|
||||
# Start the gateway
|
||||
success = await runner.start()
|
||||
if not success:
|
||||
return
|
||||
|
||||
# Wait for shutdown
|
||||
await runner.wait_for_shutdown()
|
||||
|
||||
|
||||
def main():
|
||||
"""CLI entry point for the gateway."""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="Hermes Gateway - Multi-platform messaging")
|
||||
parser.add_argument("--config", "-c", help="Path to gateway config file")
|
||||
parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
config = None
|
||||
if args.config:
|
||||
import json
|
||||
with open(args.config) as f:
|
||||
data = json.load(f)
|
||||
config = GatewayConfig.from_dict(data)
|
||||
|
||||
# Run the gateway
|
||||
asyncio.run(start_gateway(config))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,522 +0,0 @@
|
||||
"""
|
||||
Session management for the gateway.
|
||||
|
||||
Handles:
|
||||
- Session context tracking (where messages come from)
|
||||
- Session storage (conversations persisted to disk)
|
||||
- Reset policy evaluation (when to start fresh)
|
||||
- Dynamic system prompt injection (agent knows its context)
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
from .config import (
|
||||
Platform,
|
||||
GatewayConfig,
|
||||
SessionResetPolicy,
|
||||
HomeChannel,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionSource:
|
||||
"""
|
||||
Describes where a message originated from.
|
||||
|
||||
This information is used to:
|
||||
1. Route responses back to the right place
|
||||
2. Inject context into the system prompt
|
||||
3. Track origin for cron job delivery
|
||||
"""
|
||||
platform: Platform
|
||||
chat_id: str
|
||||
chat_name: Optional[str] = None
|
||||
chat_type: str = "dm" # "dm", "group", "channel", "thread"
|
||||
user_id: Optional[str] = None
|
||||
user_name: Optional[str] = None
|
||||
thread_id: Optional[str] = None # For forum topics, Discord threads, etc.
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
"""Human-readable description of the source."""
|
||||
if self.platform == Platform.LOCAL:
|
||||
return "CLI terminal"
|
||||
|
||||
parts = []
|
||||
if self.chat_type == "dm":
|
||||
parts.append(f"DM with {self.user_name or self.user_id or 'user'}")
|
||||
elif self.chat_type == "group":
|
||||
parts.append(f"group: {self.chat_name or self.chat_id}")
|
||||
elif self.chat_type == "channel":
|
||||
parts.append(f"channel: {self.chat_name or self.chat_id}")
|
||||
else:
|
||||
parts.append(self.chat_name or self.chat_id)
|
||||
|
||||
if self.thread_id:
|
||||
parts.append(f"thread: {self.thread_id}")
|
||||
|
||||
return ", ".join(parts)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"platform": self.platform.value,
|
||||
"chat_id": self.chat_id,
|
||||
"chat_name": self.chat_name,
|
||||
"chat_type": self.chat_type,
|
||||
"user_id": self.user_id,
|
||||
"user_name": self.user_name,
|
||||
"thread_id": self.thread_id,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "SessionSource":
|
||||
return cls(
|
||||
platform=Platform(data["platform"]),
|
||||
chat_id=str(data["chat_id"]),
|
||||
chat_name=data.get("chat_name"),
|
||||
chat_type=data.get("chat_type", "dm"),
|
||||
user_id=data.get("user_id"),
|
||||
user_name=data.get("user_name"),
|
||||
thread_id=data.get("thread_id"),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def local_cli(cls) -> "SessionSource":
|
||||
"""Create a source representing the local CLI."""
|
||||
return cls(
|
||||
platform=Platform.LOCAL,
|
||||
chat_id="cli",
|
||||
chat_name="CLI terminal",
|
||||
chat_type="dm",
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionContext:
|
||||
"""
|
||||
Full context for a session, used for dynamic system prompt injection.
|
||||
|
||||
The agent receives this information to understand:
|
||||
- Where messages are coming from
|
||||
- What platforms are available
|
||||
- Where it can deliver scheduled task outputs
|
||||
"""
|
||||
source: SessionSource
|
||||
connected_platforms: List[Platform]
|
||||
home_channels: Dict[Platform, HomeChannel]
|
||||
|
||||
# Session metadata
|
||||
session_key: str = ""
|
||||
session_id: str = ""
|
||||
created_at: Optional[datetime] = None
|
||||
updated_at: Optional[datetime] = None
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"source": self.source.to_dict(),
|
||||
"connected_platforms": [p.value for p in self.connected_platforms],
|
||||
"home_channels": {
|
||||
p.value: hc.to_dict() for p, hc in self.home_channels.items()
|
||||
},
|
||||
"session_key": self.session_key,
|
||||
"session_id": self.session_id,
|
||||
"created_at": self.created_at.isoformat() if self.created_at else None,
|
||||
"updated_at": self.updated_at.isoformat() if self.updated_at else None,
|
||||
}
|
||||
|
||||
|
||||
def build_session_context_prompt(context: SessionContext) -> str:
|
||||
"""
|
||||
Build the dynamic system prompt section that tells the agent about its context.
|
||||
|
||||
This is injected into the system prompt so the agent knows:
|
||||
- Where messages are coming from
|
||||
- What platforms are connected
|
||||
- Where it can deliver scheduled task outputs
|
||||
"""
|
||||
lines = [
|
||||
"## Current Session Context",
|
||||
"",
|
||||
]
|
||||
|
||||
# Source info
|
||||
platform_name = context.source.platform.value.title()
|
||||
if context.source.platform == Platform.LOCAL:
|
||||
lines.append(f"**Source:** {platform_name} (the machine running this agent)")
|
||||
else:
|
||||
lines.append(f"**Source:** {platform_name} ({context.source.description})")
|
||||
|
||||
# Connected platforms
|
||||
platforms_list = ["local (files on this machine)"]
|
||||
for p in context.connected_platforms:
|
||||
if p != Platform.LOCAL:
|
||||
platforms_list.append(f"{p.value}: Connected ✓")
|
||||
|
||||
lines.append(f"**Connected Platforms:** {', '.join(platforms_list)}")
|
||||
|
||||
# Home channels
|
||||
if context.home_channels:
|
||||
lines.append("")
|
||||
lines.append("**Home Channels (default destinations):**")
|
||||
for platform, home in context.home_channels.items():
|
||||
lines.append(f" - {platform.value}: {home.name} (ID: {home.chat_id})")
|
||||
|
||||
# Delivery options for scheduled tasks
|
||||
lines.append("")
|
||||
lines.append("**Delivery options for scheduled tasks:**")
|
||||
|
||||
# Origin delivery
|
||||
if context.source.platform == Platform.LOCAL:
|
||||
lines.append("- `\"origin\"` → Local output (saved to files)")
|
||||
else:
|
||||
lines.append(f"- `\"origin\"` → Back to this chat ({context.source.chat_name or context.source.chat_id})")
|
||||
|
||||
# Local always available
|
||||
lines.append("- `\"local\"` → Save to local files only (~/.hermes/cron/output/)")
|
||||
|
||||
# Platform home channels
|
||||
for platform, home in context.home_channels.items():
|
||||
lines.append(f"- `\"{platform.value}\"` → Home channel ({home.name})")
|
||||
|
||||
# Note about explicit targeting
|
||||
lines.append("")
|
||||
lines.append("*For explicit targeting, use `\"platform:chat_id\"` format if the user provides a specific chat ID.*")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionEntry:
|
||||
"""
|
||||
Entry in the session store.
|
||||
|
||||
Maps a session key to its current session ID and metadata.
|
||||
"""
|
||||
session_key: str
|
||||
session_id: str
|
||||
created_at: datetime
|
||||
updated_at: datetime
|
||||
|
||||
# Origin metadata for delivery routing
|
||||
origin: Optional[SessionSource] = None
|
||||
|
||||
# Display metadata
|
||||
display_name: Optional[str] = None
|
||||
platform: Optional[Platform] = None
|
||||
chat_type: str = "dm"
|
||||
|
||||
# Token tracking
|
||||
input_tokens: int = 0
|
||||
output_tokens: int = 0
|
||||
total_tokens: int = 0
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
result = {
|
||||
"session_key": self.session_key,
|
||||
"session_id": self.session_id,
|
||||
"created_at": self.created_at.isoformat(),
|
||||
"updated_at": self.updated_at.isoformat(),
|
||||
"display_name": self.display_name,
|
||||
"platform": self.platform.value if self.platform else None,
|
||||
"chat_type": self.chat_type,
|
||||
"input_tokens": self.input_tokens,
|
||||
"output_tokens": self.output_tokens,
|
||||
"total_tokens": self.total_tokens,
|
||||
}
|
||||
if self.origin:
|
||||
result["origin"] = self.origin.to_dict()
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "SessionEntry":
|
||||
origin = None
|
||||
if "origin" in data and data["origin"]:
|
||||
origin = SessionSource.from_dict(data["origin"])
|
||||
|
||||
platform = None
|
||||
if data.get("platform"):
|
||||
try:
|
||||
platform = Platform(data["platform"])
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
return cls(
|
||||
session_key=data["session_key"],
|
||||
session_id=data["session_id"],
|
||||
created_at=datetime.fromisoformat(data["created_at"]),
|
||||
updated_at=datetime.fromisoformat(data["updated_at"]),
|
||||
origin=origin,
|
||||
display_name=data.get("display_name"),
|
||||
platform=platform,
|
||||
chat_type=data.get("chat_type", "dm"),
|
||||
input_tokens=data.get("input_tokens", 0),
|
||||
output_tokens=data.get("output_tokens", 0),
|
||||
total_tokens=data.get("total_tokens", 0),
|
||||
)
|
||||
|
||||
|
||||
class SessionStore:
|
||||
"""
|
||||
Manages session storage and retrieval.
|
||||
|
||||
Sessions are stored in:
|
||||
- sessions.json: Index mapping session keys to session IDs
|
||||
- {session_id}.jsonl: Conversation transcripts
|
||||
"""
|
||||
|
||||
def __init__(self, sessions_dir: Path, config: GatewayConfig):
|
||||
self.sessions_dir = sessions_dir
|
||||
self.config = config
|
||||
self._entries: Dict[str, SessionEntry] = {}
|
||||
self._loaded = False
|
||||
|
||||
def _ensure_loaded(self) -> None:
|
||||
"""Load sessions from disk if not already loaded."""
|
||||
if self._loaded:
|
||||
return
|
||||
|
||||
self.sessions_dir.mkdir(parents=True, exist_ok=True)
|
||||
sessions_file = self.sessions_dir / "sessions.json"
|
||||
|
||||
if sessions_file.exists():
|
||||
try:
|
||||
with open(sessions_file, "r") as f:
|
||||
data = json.load(f)
|
||||
for key, entry_data in data.items():
|
||||
self._entries[key] = SessionEntry.from_dict(entry_data)
|
||||
except Exception as e:
|
||||
print(f"[gateway] Warning: Failed to load sessions: {e}")
|
||||
|
||||
self._loaded = True
|
||||
|
||||
def _save(self) -> None:
|
||||
"""Save sessions index to disk."""
|
||||
self.sessions_dir.mkdir(parents=True, exist_ok=True)
|
||||
sessions_file = self.sessions_dir / "sessions.json"
|
||||
|
||||
data = {key: entry.to_dict() for key, entry in self._entries.items()}
|
||||
with open(sessions_file, "w") as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
def _generate_session_key(self, source: SessionSource) -> str:
|
||||
"""Generate a session key from a source."""
|
||||
platform = source.platform.value
|
||||
|
||||
if source.chat_type == "dm":
|
||||
# DMs share the main session per platform
|
||||
return f"agent:main:{platform}:dm"
|
||||
else:
|
||||
# Groups/channels get their own keys
|
||||
return f"agent:main:{platform}:{source.chat_type}:{source.chat_id}"
|
||||
|
||||
def _should_reset(self, entry: SessionEntry, source: SessionSource) -> bool:
|
||||
"""
|
||||
Check if a session should be reset based on policy.
|
||||
|
||||
Returns True if the session is stale and should start fresh.
|
||||
"""
|
||||
policy = self.config.get_reset_policy(
|
||||
platform=source.platform,
|
||||
session_type=source.chat_type
|
||||
)
|
||||
|
||||
now = datetime.now()
|
||||
|
||||
# Check idle timeout
|
||||
if policy.mode in ("idle", "both"):
|
||||
idle_deadline = entry.updated_at + timedelta(minutes=policy.idle_minutes)
|
||||
if now > idle_deadline:
|
||||
return True
|
||||
|
||||
# Check daily reset
|
||||
if policy.mode in ("daily", "both"):
|
||||
# Find the most recent reset boundary
|
||||
today_reset = now.replace(
|
||||
hour=policy.at_hour,
|
||||
minute=0,
|
||||
second=0,
|
||||
microsecond=0
|
||||
)
|
||||
if now.hour < policy.at_hour:
|
||||
# Reset boundary was yesterday
|
||||
today_reset -= timedelta(days=1)
|
||||
|
||||
if entry.updated_at < today_reset:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def get_or_create_session(
|
||||
self,
|
||||
source: SessionSource,
|
||||
force_new: bool = False
|
||||
) -> SessionEntry:
|
||||
"""
|
||||
Get an existing session or create a new one.
|
||||
|
||||
Evaluates reset policy to determine if the existing session is stale.
|
||||
"""
|
||||
self._ensure_loaded()
|
||||
|
||||
session_key = self._generate_session_key(source)
|
||||
now = datetime.now()
|
||||
|
||||
# Check for existing session
|
||||
if session_key in self._entries and not force_new:
|
||||
entry = self._entries[session_key]
|
||||
|
||||
# Check if session should be reset
|
||||
if not self._should_reset(entry, source):
|
||||
# Update timestamp and return existing
|
||||
entry.updated_at = now
|
||||
self._save()
|
||||
return entry
|
||||
|
||||
# Create new session
|
||||
session_id = f"{now.strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
|
||||
|
||||
entry = SessionEntry(
|
||||
session_key=session_key,
|
||||
session_id=session_id,
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
origin=source,
|
||||
display_name=source.chat_name,
|
||||
platform=source.platform,
|
||||
chat_type=source.chat_type,
|
||||
)
|
||||
|
||||
self._entries[session_key] = entry
|
||||
self._save()
|
||||
|
||||
return entry
|
||||
|
||||
def update_session(
|
||||
self,
|
||||
session_key: str,
|
||||
input_tokens: int = 0,
|
||||
output_tokens: int = 0
|
||||
) -> None:
|
||||
"""Update a session's metadata after an interaction."""
|
||||
self._ensure_loaded()
|
||||
|
||||
if session_key in self._entries:
|
||||
entry = self._entries[session_key]
|
||||
entry.updated_at = datetime.now()
|
||||
entry.input_tokens += input_tokens
|
||||
entry.output_tokens += output_tokens
|
||||
entry.total_tokens = entry.input_tokens + entry.output_tokens
|
||||
self._save()
|
||||
|
||||
def reset_session(self, session_key: str) -> Optional[SessionEntry]:
|
||||
"""Force reset a session, creating a new session ID."""
|
||||
self._ensure_loaded()
|
||||
|
||||
if session_key not in self._entries:
|
||||
return None
|
||||
|
||||
old_entry = self._entries[session_key]
|
||||
now = datetime.now()
|
||||
session_id = f"{now.strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
|
||||
|
||||
new_entry = SessionEntry(
|
||||
session_key=session_key,
|
||||
session_id=session_id,
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
origin=old_entry.origin,
|
||||
display_name=old_entry.display_name,
|
||||
platform=old_entry.platform,
|
||||
chat_type=old_entry.chat_type,
|
||||
)
|
||||
|
||||
self._entries[session_key] = new_entry
|
||||
self._save()
|
||||
|
||||
return new_entry
|
||||
|
||||
def list_sessions(self, active_minutes: Optional[int] = None) -> List[SessionEntry]:
|
||||
"""
|
||||
List all sessions, optionally filtered by activity.
|
||||
|
||||
Args:
|
||||
active_minutes: If provided, only return sessions updated within this many minutes
|
||||
"""
|
||||
self._ensure_loaded()
|
||||
|
||||
entries = list(self._entries.values())
|
||||
|
||||
if active_minutes is not None:
|
||||
cutoff = datetime.now() - timedelta(minutes=active_minutes)
|
||||
entries = [e for e in entries if e.updated_at >= cutoff]
|
||||
|
||||
# Sort by most recently updated
|
||||
entries.sort(key=lambda e: e.updated_at, reverse=True)
|
||||
|
||||
return entries
|
||||
|
||||
def get_transcript_path(self, session_id: str) -> Path:
|
||||
"""Get the path to a session's transcript file."""
|
||||
return self.sessions_dir / f"{session_id}.jsonl"
|
||||
|
||||
def append_to_transcript(self, session_id: str, message: Dict[str, Any]) -> None:
|
||||
"""Append a message to a session's transcript."""
|
||||
transcript_path = self.get_transcript_path(session_id)
|
||||
|
||||
with open(transcript_path, "a") as f:
|
||||
f.write(json.dumps(message, ensure_ascii=False) + "\n")
|
||||
|
||||
def load_transcript(self, session_id: str) -> List[Dict[str, Any]]:
|
||||
"""Load all messages from a session's transcript."""
|
||||
transcript_path = self.get_transcript_path(session_id)
|
||||
|
||||
if not transcript_path.exists():
|
||||
return []
|
||||
|
||||
messages = []
|
||||
with open(transcript_path, "r") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
messages.append(json.loads(line))
|
||||
|
||||
return messages
|
||||
|
||||
|
||||
def build_session_context(
|
||||
source: SessionSource,
|
||||
config: GatewayConfig,
|
||||
session_entry: Optional[SessionEntry] = None
|
||||
) -> SessionContext:
|
||||
"""
|
||||
Build a full session context from a source and config.
|
||||
|
||||
This is used to inject context into the agent's system prompt.
|
||||
"""
|
||||
connected = config.get_connected_platforms()
|
||||
|
||||
home_channels = {}
|
||||
for platform in connected:
|
||||
home = config.get_home_channel(platform)
|
||||
if home:
|
||||
home_channels[platform] = home
|
||||
|
||||
context = SessionContext(
|
||||
source=source,
|
||||
connected_platforms=connected,
|
||||
home_channels=home_channels,
|
||||
)
|
||||
|
||||
if session_entry:
|
||||
context.session_key = session_entry.session_key
|
||||
context.session_id = session_entry.session_id
|
||||
context.created_at = session_entry.created_at
|
||||
context.updated_at = session_entry.updated_at
|
||||
|
||||
return context
|
||||
12
hermes
12
hermes
@@ -1,12 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Hermes Agent CLI Launcher
|
||||
|
||||
This is a convenience wrapper to launch the Hermes CLI.
|
||||
Usage: ./hermes [options]
|
||||
"""
|
||||
|
||||
if __name__ == "__main__":
|
||||
from cli import main
|
||||
import fire
|
||||
fire.Fire(main)
|
||||
@@ -1,14 +0,0 @@
|
||||
"""
|
||||
Hermes CLI - Unified command-line interface for Hermes Agent.
|
||||
|
||||
Provides subcommands for:
|
||||
- hermes chat - Interactive chat (same as ./hermes)
|
||||
- hermes gateway - Run gateway in foreground
|
||||
- hermes gateway start - Start gateway service
|
||||
- hermes gateway stop - Stop gateway service
|
||||
- hermes setup - Interactive setup wizard
|
||||
- hermes status - Show status of all components
|
||||
- hermes cron - Manage cron jobs
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
@@ -1,785 +0,0 @@
|
||||
"""
|
||||
Configuration management for Hermes Agent.
|
||||
|
||||
Config files are stored in ~/.hermes/ for easy access:
|
||||
- ~/.hermes/config.yaml - All settings (model, toolsets, terminal, etc.)
|
||||
- ~/.hermes/.env - API keys and secrets
|
||||
|
||||
This module provides:
|
||||
- hermes config - Show current configuration
|
||||
- hermes config edit - Open config in editor
|
||||
- hermes config set - Set a specific value
|
||||
- hermes config wizard - Re-run setup wizard
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, Optional, List, Tuple
|
||||
|
||||
import yaml
|
||||
|
||||
# ANSI colors
|
||||
class Colors:
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
BLUE = "\033[34m"
|
||||
MAGENTA = "\033[35m"
|
||||
CYAN = "\033[36m"
|
||||
|
||||
def color(text: str, *codes) -> str:
|
||||
if not sys.stdout.isatty():
|
||||
return text
|
||||
return "".join(codes) + text + Colors.RESET
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Config paths
|
||||
# =============================================================================
|
||||
|
||||
def get_hermes_home() -> Path:
|
||||
"""Get the Hermes home directory (~/.hermes)."""
|
||||
return Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
|
||||
|
||||
def get_config_path() -> Path:
|
||||
"""Get the main config file path."""
|
||||
return get_hermes_home() / "config.yaml"
|
||||
|
||||
def get_env_path() -> Path:
|
||||
"""Get the .env file path (for API keys)."""
|
||||
return get_hermes_home() / ".env"
|
||||
|
||||
def get_project_root() -> Path:
|
||||
"""Get the project installation directory."""
|
||||
return Path(__file__).parent.parent.resolve()
|
||||
|
||||
def ensure_hermes_home():
|
||||
"""Ensure ~/.hermes directory structure exists."""
|
||||
home = get_hermes_home()
|
||||
(home / "cron").mkdir(parents=True, exist_ok=True)
|
||||
(home / "sessions").mkdir(parents=True, exist_ok=True)
|
||||
(home / "logs").mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Config loading/saving
|
||||
# =============================================================================
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"model": "anthropic/claude-sonnet-4.5",
|
||||
"toolsets": ["hermes-cli"],
|
||||
"max_turns": 100,
|
||||
|
||||
"terminal": {
|
||||
"backend": "local",
|
||||
"cwd": ".", # Use current directory
|
||||
"timeout": 180,
|
||||
"docker_image": "nikolaik/python-nodejs:python3.11-nodejs20",
|
||||
"singularity_image": "docker://nikolaik/python-nodejs:python3.11-nodejs20",
|
||||
"modal_image": "nikolaik/python-nodejs:python3.11-nodejs20",
|
||||
},
|
||||
|
||||
"browser": {
|
||||
"inactivity_timeout": 120,
|
||||
},
|
||||
|
||||
"compression": {
|
||||
"enabled": True,
|
||||
"threshold": 0.85,
|
||||
"summary_model": "google/gemini-2.0-flash-001",
|
||||
},
|
||||
|
||||
"display": {
|
||||
"compact": False,
|
||||
"personality": "kawaii",
|
||||
},
|
||||
|
||||
# Permanently allowed dangerous command patterns (added via "always" approval)
|
||||
"command_allowlist": [],
|
||||
|
||||
# Config schema version - bump this when adding new required fields
|
||||
"_config_version": 1,
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# Config Migration System
|
||||
# =============================================================================
|
||||
|
||||
# Required environment variables with metadata for migration prompts
|
||||
REQUIRED_ENV_VARS = {
|
||||
"OPENROUTER_API_KEY": {
|
||||
"description": "OpenRouter API key (required for vision, web scraping, and tools)",
|
||||
"prompt": "OpenRouter API key",
|
||||
"url": "https://openrouter.ai/keys",
|
||||
"required": True,
|
||||
"password": True,
|
||||
},
|
||||
}
|
||||
|
||||
# Optional environment variables that enhance functionality
|
||||
OPTIONAL_ENV_VARS = {
|
||||
"FIRECRAWL_API_KEY": {
|
||||
"description": "Firecrawl API key for web search and scraping",
|
||||
"prompt": "Firecrawl API key",
|
||||
"url": "https://firecrawl.dev/",
|
||||
"tools": ["web_search", "web_extract"],
|
||||
"password": True,
|
||||
},
|
||||
"BROWSERBASE_API_KEY": {
|
||||
"description": "Browserbase API key for browser automation",
|
||||
"prompt": "Browserbase API key",
|
||||
"url": "https://browserbase.com/",
|
||||
"tools": ["browser_navigate", "browser_click", "etc."],
|
||||
"password": True,
|
||||
},
|
||||
"BROWSERBASE_PROJECT_ID": {
|
||||
"description": "Browserbase project ID",
|
||||
"prompt": "Browserbase project ID",
|
||||
"url": "https://browserbase.com/",
|
||||
"tools": ["browser_navigate", "browser_click", "etc."],
|
||||
"password": False,
|
||||
},
|
||||
"FAL_KEY": {
|
||||
"description": "FAL API key for image generation",
|
||||
"prompt": "FAL API key",
|
||||
"url": "https://fal.ai/",
|
||||
"tools": ["image_generate"],
|
||||
"password": True,
|
||||
},
|
||||
"TINKER_API_KEY": {
|
||||
"description": "Tinker API key for RL training",
|
||||
"prompt": "Tinker API key",
|
||||
"url": "https://tinker-console.thinkingmachines.ai/keys",
|
||||
"tools": ["rl_start_training", "rl_check_status", "rl_stop_training"],
|
||||
"password": True,
|
||||
},
|
||||
"WANDB_API_KEY": {
|
||||
"description": "Weights & Biases API key for experiment tracking",
|
||||
"prompt": "WandB API key",
|
||||
"url": "https://wandb.ai/authorize",
|
||||
"tools": ["rl_get_results", "rl_check_status"],
|
||||
"password": True,
|
||||
},
|
||||
"OPENAI_BASE_URL": {
|
||||
"description": "Custom OpenAI-compatible API endpoint URL",
|
||||
"prompt": "API base URL (e.g., https://api.example.com/v1)",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
"OPENAI_API_KEY": {
|
||||
"description": "API key for custom OpenAI-compatible endpoint",
|
||||
"prompt": "API key for custom endpoint",
|
||||
"url": None,
|
||||
"password": True,
|
||||
},
|
||||
# Messaging platform tokens
|
||||
"TELEGRAM_BOT_TOKEN": {
|
||||
"description": "Telegram bot token from @BotFather",
|
||||
"prompt": "Telegram bot token",
|
||||
"url": "https://t.me/BotFather",
|
||||
"password": True,
|
||||
},
|
||||
"TELEGRAM_ALLOWED_USERS": {
|
||||
"description": "Comma-separated Telegram user IDs allowed to use the bot (get ID from @userinfobot)",
|
||||
"prompt": "Allowed Telegram user IDs (comma-separated)",
|
||||
"url": "https://t.me/userinfobot",
|
||||
"password": False,
|
||||
},
|
||||
"DISCORD_BOT_TOKEN": {
|
||||
"description": "Discord bot token from Developer Portal",
|
||||
"prompt": "Discord bot token",
|
||||
"url": "https://discord.com/developers/applications",
|
||||
"password": True,
|
||||
},
|
||||
"DISCORD_ALLOWED_USERS": {
|
||||
"description": "Comma-separated Discord user IDs allowed to use the bot",
|
||||
"prompt": "Allowed Discord user IDs (comma-separated)",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
# Terminal configuration
|
||||
"MESSAGING_CWD": {
|
||||
"description": "Working directory for terminal commands via messaging (Telegram/Discord/etc). CLI always uses current directory.",
|
||||
"prompt": "Messaging working directory (default: home)",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
"SUDO_PASSWORD": {
|
||||
"description": "Sudo password for terminal commands requiring root access",
|
||||
"prompt": "Sudo password",
|
||||
"url": None,
|
||||
"password": True,
|
||||
},
|
||||
# Agent configuration
|
||||
"HERMES_MAX_ITERATIONS": {
|
||||
"description": "Maximum tool-calling iterations per conversation (default: 60)",
|
||||
"prompt": "Max iterations",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
"HERMES_TOOL_PROGRESS": {
|
||||
"description": "Send tool progress messages in messaging channels (true/false)",
|
||||
"prompt": "Enable tool progress messages",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
"HERMES_TOOL_PROGRESS_MODE": {
|
||||
"description": "Progress mode: 'all' (every tool) or 'new' (only when tool changes)",
|
||||
"prompt": "Progress mode (all/new)",
|
||||
"url": None,
|
||||
"password": False,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_missing_env_vars(required_only: bool = False) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Check which environment variables are missing.
|
||||
|
||||
Returns list of dicts with var info for missing variables.
|
||||
"""
|
||||
missing = []
|
||||
|
||||
# Check required vars
|
||||
for var_name, info in REQUIRED_ENV_VARS.items():
|
||||
if not get_env_value(var_name):
|
||||
missing.append({"name": var_name, **info, "is_required": True})
|
||||
|
||||
# Check optional vars (if not required_only)
|
||||
if not required_only:
|
||||
for var_name, info in OPTIONAL_ENV_VARS.items():
|
||||
if not get_env_value(var_name):
|
||||
missing.append({"name": var_name, **info, "is_required": False})
|
||||
|
||||
return missing
|
||||
|
||||
|
||||
def get_missing_config_fields() -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Check which config fields are missing or outdated.
|
||||
|
||||
Returns list of missing/outdated fields.
|
||||
"""
|
||||
config = load_config()
|
||||
missing = []
|
||||
|
||||
# Check for new top-level keys in DEFAULT_CONFIG
|
||||
for key, default_value in DEFAULT_CONFIG.items():
|
||||
if key.startswith('_'):
|
||||
continue # Skip internal keys
|
||||
if key not in config:
|
||||
missing.append({
|
||||
"key": key,
|
||||
"default": default_value,
|
||||
"description": f"New config section: {key}",
|
||||
})
|
||||
elif isinstance(default_value, dict):
|
||||
# Check nested keys
|
||||
for subkey, subvalue in default_value.items():
|
||||
if subkey not in config.get(key, {}):
|
||||
missing.append({
|
||||
"key": f"{key}.{subkey}",
|
||||
"default": subvalue,
|
||||
"description": f"New config option: {key}.{subkey}",
|
||||
})
|
||||
|
||||
return missing
|
||||
|
||||
|
||||
def check_config_version() -> Tuple[int, int]:
|
||||
"""
|
||||
Check config version.
|
||||
|
||||
Returns (current_version, latest_version).
|
||||
"""
|
||||
config = load_config()
|
||||
current = config.get("_config_version", 0)
|
||||
latest = DEFAULT_CONFIG.get("_config_version", 1)
|
||||
return current, latest
|
||||
|
||||
|
||||
def migrate_config(interactive: bool = True, quiet: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Migrate config to latest version, prompting for new required fields.
|
||||
|
||||
Args:
|
||||
interactive: If True, prompt user for missing values
|
||||
quiet: If True, suppress output
|
||||
|
||||
Returns:
|
||||
Dict with migration results: {"env_added": [...], "config_added": [...], "warnings": [...]}
|
||||
"""
|
||||
results = {"env_added": [], "config_added": [], "warnings": []}
|
||||
|
||||
# Check config version
|
||||
current_ver, latest_ver = check_config_version()
|
||||
|
||||
if current_ver < latest_ver and not quiet:
|
||||
print(f"Config version: {current_ver} → {latest_ver}")
|
||||
|
||||
# Check for missing required env vars
|
||||
missing_env = get_missing_env_vars(required_only=True)
|
||||
|
||||
if missing_env and not quiet:
|
||||
print("\n⚠️ Missing required environment variables:")
|
||||
for var in missing_env:
|
||||
print(f" • {var['name']}: {var['description']}")
|
||||
|
||||
if interactive and missing_env:
|
||||
print("\nLet's configure them now:\n")
|
||||
for var in missing_env:
|
||||
if var.get("url"):
|
||||
print(f" Get your key at: {var['url']}")
|
||||
|
||||
if var.get("password"):
|
||||
import getpass
|
||||
value = getpass.getpass(f" {var['prompt']}: ")
|
||||
else:
|
||||
value = input(f" {var['prompt']}: ").strip()
|
||||
|
||||
if value:
|
||||
save_env_value(var["name"], value)
|
||||
results["env_added"].append(var["name"])
|
||||
print(f" ✓ Saved {var['name']}")
|
||||
else:
|
||||
results["warnings"].append(f"Skipped {var['name']} - some features may not work")
|
||||
print()
|
||||
|
||||
# Check for missing config fields
|
||||
missing_config = get_missing_config_fields()
|
||||
|
||||
if missing_config:
|
||||
config = load_config()
|
||||
|
||||
for field in missing_config:
|
||||
key = field["key"]
|
||||
default = field["default"]
|
||||
|
||||
# Add with default value
|
||||
if "." in key:
|
||||
# Nested key
|
||||
parent, child = key.split(".", 1)
|
||||
if parent not in config:
|
||||
config[parent] = {}
|
||||
config[parent][child] = default
|
||||
else:
|
||||
config[key] = default
|
||||
|
||||
results["config_added"].append(key)
|
||||
if not quiet:
|
||||
print(f" ✓ Added {key} = {default}")
|
||||
|
||||
# Update version and save
|
||||
config["_config_version"] = latest_ver
|
||||
save_config(config)
|
||||
elif current_ver < latest_ver:
|
||||
# Just update version
|
||||
config = load_config()
|
||||
config["_config_version"] = latest_ver
|
||||
save_config(config)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def load_config() -> Dict[str, Any]:
|
||||
"""Load configuration from ~/.hermes/config.yaml."""
|
||||
config_path = get_config_path()
|
||||
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
|
||||
if config_path.exists():
|
||||
try:
|
||||
with open(config_path) as f:
|
||||
user_config = yaml.safe_load(f) or {}
|
||||
|
||||
# Deep merge
|
||||
for key, value in user_config.items():
|
||||
if isinstance(value, dict) and key in config and isinstance(config[key], dict):
|
||||
config[key].update(value)
|
||||
else:
|
||||
config[key] = value
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load config: {e}")
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def save_config(config: Dict[str, Any]):
|
||||
"""Save configuration to ~/.hermes/config.yaml."""
|
||||
ensure_hermes_home()
|
||||
config_path = get_config_path()
|
||||
|
||||
with open(config_path, 'w') as f:
|
||||
yaml.dump(config, f, default_flow_style=False, sort_keys=False)
|
||||
|
||||
|
||||
def load_env() -> Dict[str, str]:
|
||||
"""Load environment variables from ~/.hermes/.env."""
|
||||
env_path = get_env_path()
|
||||
env_vars = {}
|
||||
|
||||
if env_path.exists():
|
||||
with open(env_path) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line and not line.startswith('#') and '=' in line:
|
||||
key, _, value = line.partition('=')
|
||||
env_vars[key.strip()] = value.strip().strip('"\'')
|
||||
|
||||
return env_vars
|
||||
|
||||
|
||||
def save_env_value(key: str, value: str):
|
||||
"""Save or update a value in ~/.hermes/.env."""
|
||||
ensure_hermes_home()
|
||||
env_path = get_env_path()
|
||||
|
||||
# Load existing
|
||||
lines = []
|
||||
if env_path.exists():
|
||||
with open(env_path) as f:
|
||||
lines = f.readlines()
|
||||
|
||||
# Find and update or append
|
||||
found = False
|
||||
for i, line in enumerate(lines):
|
||||
if line.strip().startswith(f"{key}="):
|
||||
lines[i] = f"{key}={value}\n"
|
||||
found = True
|
||||
break
|
||||
|
||||
if not found:
|
||||
lines.append(f"{key}={value}\n")
|
||||
|
||||
with open(env_path, 'w') as f:
|
||||
f.writelines(lines)
|
||||
|
||||
|
||||
def get_env_value(key: str) -> Optional[str]:
|
||||
"""Get a value from ~/.hermes/.env or environment."""
|
||||
# Check environment first
|
||||
if key in os.environ:
|
||||
return os.environ[key]
|
||||
|
||||
# Then check .env file
|
||||
env_vars = load_env()
|
||||
return env_vars.get(key)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Config display
|
||||
# =============================================================================
|
||||
|
||||
def redact_key(key: str) -> str:
|
||||
"""Redact an API key for display."""
|
||||
if not key:
|
||||
return color("(not set)", Colors.DIM)
|
||||
if len(key) < 12:
|
||||
return "***"
|
||||
return key[:4] + "..." + key[-4:]
|
||||
|
||||
|
||||
def show_config():
|
||||
"""Display current configuration."""
|
||||
config = load_config()
|
||||
env_vars = load_env()
|
||||
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.CYAN))
|
||||
print(color("│ 🦋 Hermes Configuration │", Colors.CYAN))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.CYAN))
|
||||
|
||||
# Paths
|
||||
print()
|
||||
print(color("◆ Paths", Colors.CYAN, Colors.BOLD))
|
||||
print(f" Config: {get_config_path()}")
|
||||
print(f" Secrets: {get_env_path()}")
|
||||
print(f" Install: {get_project_root()}")
|
||||
|
||||
# API Keys
|
||||
print()
|
||||
print(color("◆ API Keys", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
keys = [
|
||||
("OPENROUTER_API_KEY", "OpenRouter"),
|
||||
("ANTHROPIC_API_KEY", "Anthropic"),
|
||||
("OPENAI_API_KEY", "OpenAI"),
|
||||
("FIRECRAWL_API_KEY", "Firecrawl"),
|
||||
("BROWSERBASE_API_KEY", "Browserbase"),
|
||||
("FAL_KEY", "FAL"),
|
||||
]
|
||||
|
||||
for env_key, name in keys:
|
||||
value = get_env_value(env_key)
|
||||
print(f" {name:<14} {redact_key(value)}")
|
||||
|
||||
# Model settings
|
||||
print()
|
||||
print(color("◆ Model", Colors.CYAN, Colors.BOLD))
|
||||
print(f" Model: {config.get('model', 'not set')}")
|
||||
print(f" Max turns: {config.get('max_turns', 100)}")
|
||||
print(f" Toolsets: {', '.join(config.get('toolsets', ['all']))}")
|
||||
|
||||
# Terminal
|
||||
print()
|
||||
print(color("◆ Terminal", Colors.CYAN, Colors.BOLD))
|
||||
terminal = config.get('terminal', {})
|
||||
print(f" Backend: {terminal.get('backend', 'local')}")
|
||||
print(f" Working dir: {terminal.get('cwd', '.')}")
|
||||
print(f" Timeout: {terminal.get('timeout', 60)}s")
|
||||
|
||||
if terminal.get('backend') == 'docker':
|
||||
print(f" Docker image: {terminal.get('docker_image', 'python:3.11-slim')}")
|
||||
elif terminal.get('backend') == 'singularity':
|
||||
print(f" Image: {terminal.get('singularity_image', 'docker://python:3.11')}")
|
||||
elif terminal.get('backend') == 'modal':
|
||||
print(f" Modal image: {terminal.get('modal_image', 'python:3.11')}")
|
||||
modal_token = get_env_value('MODAL_TOKEN_ID')
|
||||
print(f" Modal token: {'configured' if modal_token else '(not set)'}")
|
||||
elif terminal.get('backend') == 'ssh':
|
||||
ssh_host = get_env_value('TERMINAL_SSH_HOST')
|
||||
ssh_user = get_env_value('TERMINAL_SSH_USER')
|
||||
print(f" SSH host: {ssh_host or '(not set)'}")
|
||||
print(f" SSH user: {ssh_user or '(not set)'}")
|
||||
|
||||
# Compression
|
||||
print()
|
||||
print(color("◆ Context Compression", Colors.CYAN, Colors.BOLD))
|
||||
compression = config.get('compression', {})
|
||||
enabled = compression.get('enabled', True)
|
||||
print(f" Enabled: {'yes' if enabled else 'no'}")
|
||||
if enabled:
|
||||
print(f" Threshold: {compression.get('threshold', 0.85) * 100:.0f}%")
|
||||
print(f" Model: {compression.get('summary_model', 'google/gemini-2.0-flash-001')}")
|
||||
|
||||
# Messaging
|
||||
print()
|
||||
print(color("◆ Messaging Platforms", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
telegram_token = get_env_value('TELEGRAM_BOT_TOKEN')
|
||||
discord_token = get_env_value('DISCORD_BOT_TOKEN')
|
||||
|
||||
print(f" Telegram: {'configured' if telegram_token else color('not configured', Colors.DIM)}")
|
||||
print(f" Discord: {'configured' if discord_token else color('not configured', Colors.DIM)}")
|
||||
|
||||
print()
|
||||
print(color("─" * 60, Colors.DIM))
|
||||
print(color(" hermes config edit # Edit config file", Colors.DIM))
|
||||
print(color(" hermes config set KEY VALUE", Colors.DIM))
|
||||
print(color(" hermes setup # Run setup wizard", Colors.DIM))
|
||||
print()
|
||||
|
||||
|
||||
def edit_config():
|
||||
"""Open config file in user's editor."""
|
||||
config_path = get_config_path()
|
||||
|
||||
# Ensure config exists
|
||||
if not config_path.exists():
|
||||
save_config(DEFAULT_CONFIG)
|
||||
print(f"Created {config_path}")
|
||||
|
||||
# Find editor
|
||||
editor = os.getenv('EDITOR') or os.getenv('VISUAL')
|
||||
|
||||
if not editor:
|
||||
# Try common editors
|
||||
for cmd in ['nano', 'vim', 'vi', 'code', 'notepad']:
|
||||
import shutil
|
||||
if shutil.which(cmd):
|
||||
editor = cmd
|
||||
break
|
||||
|
||||
if not editor:
|
||||
print(f"No editor found. Config file is at:")
|
||||
print(f" {config_path}")
|
||||
return
|
||||
|
||||
print(f"Opening {config_path} in {editor}...")
|
||||
subprocess.run([editor, str(config_path)])
|
||||
|
||||
|
||||
def set_config_value(key: str, value: str):
|
||||
"""Set a configuration value."""
|
||||
# Check if it's an API key (goes to .env)
|
||||
api_keys = [
|
||||
'OPENROUTER_API_KEY', 'ANTHROPIC_API_KEY', 'OPENAI_API_KEY',
|
||||
'FIRECRAWL_API_KEY', 'BROWSERBASE_API_KEY', 'BROWSERBASE_PROJECT_ID',
|
||||
'FAL_KEY', 'TELEGRAM_BOT_TOKEN', 'DISCORD_BOT_TOKEN',
|
||||
'TERMINAL_SSH_HOST', 'TERMINAL_SSH_USER', 'TERMINAL_SSH_KEY',
|
||||
'SUDO_PASSWORD'
|
||||
]
|
||||
|
||||
if key.upper() in api_keys or key.upper().startswith('TERMINAL_SSH'):
|
||||
save_env_value(key.upper(), value)
|
||||
print(f"✓ Set {key} in {get_env_path()}")
|
||||
return
|
||||
|
||||
# Otherwise it goes to config.yaml
|
||||
config = load_config()
|
||||
|
||||
# Handle nested keys (e.g., "terminal.backend")
|
||||
parts = key.split('.')
|
||||
current = config
|
||||
|
||||
for part in parts[:-1]:
|
||||
if part not in current:
|
||||
current[part] = {}
|
||||
current = current[part]
|
||||
|
||||
# Convert value to appropriate type
|
||||
if value.lower() in ('true', 'yes', 'on'):
|
||||
value = True
|
||||
elif value.lower() in ('false', 'no', 'off'):
|
||||
value = False
|
||||
elif value.isdigit():
|
||||
value = int(value)
|
||||
elif value.replace('.', '', 1).isdigit():
|
||||
value = float(value)
|
||||
|
||||
current[parts[-1]] = value
|
||||
save_config(config)
|
||||
print(f"✓ Set {key} = {value} in {get_config_path()}")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Command handler
|
||||
# =============================================================================
|
||||
|
||||
def config_command(args):
|
||||
"""Handle config subcommands."""
|
||||
subcmd = getattr(args, 'config_command', None)
|
||||
|
||||
if subcmd is None or subcmd == "show":
|
||||
show_config()
|
||||
|
||||
elif subcmd == "edit":
|
||||
edit_config()
|
||||
|
||||
elif subcmd == "set":
|
||||
key = getattr(args, 'key', None)
|
||||
value = getattr(args, 'value', None)
|
||||
if not key or not value:
|
||||
print("Usage: hermes config set KEY VALUE")
|
||||
print()
|
||||
print("Examples:")
|
||||
print(" hermes config set model anthropic/claude-sonnet-4")
|
||||
print(" hermes config set terminal.backend docker")
|
||||
print(" hermes config set OPENROUTER_API_KEY sk-or-...")
|
||||
sys.exit(1)
|
||||
set_config_value(key, value)
|
||||
|
||||
elif subcmd == "path":
|
||||
print(get_config_path())
|
||||
|
||||
elif subcmd == "env-path":
|
||||
print(get_env_path())
|
||||
|
||||
elif subcmd == "migrate":
|
||||
print()
|
||||
print(color("🔄 Checking configuration for updates...", Colors.CYAN, Colors.BOLD))
|
||||
print()
|
||||
|
||||
# Check what's missing
|
||||
missing_env = get_missing_env_vars(required_only=False)
|
||||
missing_config = get_missing_config_fields()
|
||||
current_ver, latest_ver = check_config_version()
|
||||
|
||||
if not missing_env and not missing_config and current_ver >= latest_ver:
|
||||
print(color("✓ Configuration is up to date!", Colors.GREEN))
|
||||
print()
|
||||
return
|
||||
|
||||
# Show what needs to be updated
|
||||
if current_ver < latest_ver:
|
||||
print(f" Config version: {current_ver} → {latest_ver}")
|
||||
|
||||
if missing_config:
|
||||
print(f"\n {len(missing_config)} new config option(s) will be added with defaults")
|
||||
|
||||
required_missing = [v for v in missing_env if v.get("is_required")]
|
||||
optional_missing = [v for v in missing_env if not v.get("is_required")]
|
||||
|
||||
if required_missing:
|
||||
print(f"\n ⚠️ {len(required_missing)} required API key(s) missing:")
|
||||
for var in required_missing:
|
||||
print(f" • {var['name']}")
|
||||
|
||||
if optional_missing:
|
||||
print(f"\n ℹ️ {len(optional_missing)} optional API key(s) not configured:")
|
||||
for var in optional_missing:
|
||||
tools = var.get("tools", [])
|
||||
tools_str = f" (enables: {', '.join(tools[:2])})" if tools else ""
|
||||
print(f" • {var['name']}{tools_str}")
|
||||
|
||||
print()
|
||||
|
||||
# Run migration
|
||||
results = migrate_config(interactive=True, quiet=False)
|
||||
|
||||
print()
|
||||
if results["env_added"] or results["config_added"]:
|
||||
print(color("✓ Configuration updated!", Colors.GREEN))
|
||||
|
||||
if results["warnings"]:
|
||||
print()
|
||||
for warning in results["warnings"]:
|
||||
print(color(f" ⚠️ {warning}", Colors.YELLOW))
|
||||
|
||||
print()
|
||||
|
||||
elif subcmd == "check":
|
||||
# Non-interactive check for what's missing
|
||||
print()
|
||||
print(color("📋 Configuration Status", Colors.CYAN, Colors.BOLD))
|
||||
print()
|
||||
|
||||
current_ver, latest_ver = check_config_version()
|
||||
if current_ver >= latest_ver:
|
||||
print(f" Config version: {current_ver} ✓")
|
||||
else:
|
||||
print(color(f" Config version: {current_ver} → {latest_ver} (update available)", Colors.YELLOW))
|
||||
|
||||
print()
|
||||
print(color(" Required:", Colors.BOLD))
|
||||
for var_name in REQUIRED_ENV_VARS:
|
||||
if get_env_value(var_name):
|
||||
print(f" ✓ {var_name}")
|
||||
else:
|
||||
print(color(f" ✗ {var_name} (missing)", Colors.RED))
|
||||
|
||||
print()
|
||||
print(color(" Optional:", Colors.BOLD))
|
||||
for var_name, info in OPTIONAL_ENV_VARS.items():
|
||||
if get_env_value(var_name):
|
||||
print(f" ✓ {var_name}")
|
||||
else:
|
||||
tools = info.get("tools", [])
|
||||
tools_str = f" → {', '.join(tools[:2])}" if tools else ""
|
||||
print(color(f" ○ {var_name}{tools_str}", Colors.DIM))
|
||||
|
||||
missing_config = get_missing_config_fields()
|
||||
if missing_config:
|
||||
print()
|
||||
print(color(f" {len(missing_config)} new config option(s) available", Colors.YELLOW))
|
||||
print(f" Run 'hermes config migrate' to add them")
|
||||
|
||||
print()
|
||||
|
||||
else:
|
||||
print(f"Unknown config command: {subcmd}")
|
||||
print()
|
||||
print("Available commands:")
|
||||
print(" hermes config Show current configuration")
|
||||
print(" hermes config edit Open config in editor")
|
||||
print(" hermes config set K V Set a config value")
|
||||
print(" hermes config check Check for missing/outdated config")
|
||||
print(" hermes config migrate Update config with new options")
|
||||
print(" hermes config path Show config file path")
|
||||
print(" hermes config env-path Show .env file path")
|
||||
sys.exit(1)
|
||||
@@ -1,131 +0,0 @@
|
||||
"""
|
||||
Cron subcommand for hermes CLI.
|
||||
|
||||
Handles: hermes cron [list|daemon|tick]
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
# ANSI colors
|
||||
class Colors:
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
CYAN = "\033[36m"
|
||||
|
||||
def color(text: str, *codes) -> str:
|
||||
if not sys.stdout.isatty():
|
||||
return text
|
||||
return "".join(codes) + text + Colors.RESET
|
||||
|
||||
|
||||
def cron_list(show_all: bool = False):
|
||||
"""List all scheduled jobs."""
|
||||
from cron.jobs import list_jobs
|
||||
|
||||
jobs = list_jobs(include_disabled=show_all)
|
||||
|
||||
if not jobs:
|
||||
print(color("No scheduled jobs.", Colors.DIM))
|
||||
print(color("Create one with: hermes cron add <schedule> <prompt>", Colors.DIM))
|
||||
return
|
||||
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────────────────────┐", Colors.CYAN))
|
||||
print(color("│ Scheduled Jobs │", Colors.CYAN))
|
||||
print(color("└─────────────────────────────────────────────────────────────────────────┘", Colors.CYAN))
|
||||
print()
|
||||
|
||||
for job in jobs:
|
||||
job_id = job.get("id", "?")[:8]
|
||||
name = job.get("name", "(unnamed)")
|
||||
schedule = job.get("schedule_display", job.get("schedule", {}).get("value", "?"))
|
||||
enabled = job.get("enabled", True)
|
||||
next_run = job.get("next_run_at", "?")
|
||||
|
||||
# Repeat info
|
||||
repeat_info = job.get("repeat", {})
|
||||
repeat_times = repeat_info.get("times")
|
||||
repeat_completed = repeat_info.get("completed", 0)
|
||||
|
||||
if repeat_times:
|
||||
repeat_str = f"{repeat_completed}/{repeat_times}"
|
||||
else:
|
||||
repeat_str = "∞"
|
||||
|
||||
# Delivery targets
|
||||
deliver = job.get("deliver", ["local"])
|
||||
if isinstance(deliver, str):
|
||||
deliver = [deliver]
|
||||
deliver_str = ", ".join(deliver)
|
||||
|
||||
# Status indicator
|
||||
if not enabled:
|
||||
status = color("[disabled]", Colors.RED)
|
||||
else:
|
||||
status = color("[active]", Colors.GREEN)
|
||||
|
||||
print(f" {color(job_id, Colors.YELLOW)} {status}")
|
||||
print(f" Name: {name}")
|
||||
print(f" Schedule: {schedule}")
|
||||
print(f" Repeat: {repeat_str}")
|
||||
print(f" Next run: {next_run}")
|
||||
print(f" Deliver: {deliver_str}")
|
||||
print()
|
||||
|
||||
|
||||
def cron_daemon(interval: int = 60):
|
||||
"""Run the cron daemon."""
|
||||
from cron.scheduler import start_daemon
|
||||
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.CYAN))
|
||||
print(color("│ 🦋 Hermes Cron Daemon │", Colors.CYAN))
|
||||
print(color("├─────────────────────────────────────────────────────────┤", Colors.CYAN))
|
||||
print(color("│ Press Ctrl+C to stop │", Colors.CYAN))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.CYAN))
|
||||
print()
|
||||
|
||||
try:
|
||||
start_daemon(interval=interval)
|
||||
except KeyboardInterrupt:
|
||||
print()
|
||||
print(color("Cron daemon stopped.", Colors.YELLOW))
|
||||
|
||||
|
||||
def cron_tick():
|
||||
"""Run due jobs once (for system cron integration)."""
|
||||
from cron.scheduler import tick
|
||||
|
||||
print(f"[{datetime.now().isoformat()}] Running cron tick...")
|
||||
tick()
|
||||
|
||||
|
||||
def cron_command(args):
|
||||
"""Handle cron subcommands."""
|
||||
subcmd = getattr(args, 'cron_command', None)
|
||||
|
||||
if subcmd is None or subcmd == "list":
|
||||
show_all = getattr(args, 'all', False)
|
||||
cron_list(show_all)
|
||||
|
||||
elif subcmd == "daemon":
|
||||
interval = getattr(args, 'interval', 60)
|
||||
cron_daemon(interval)
|
||||
|
||||
elif subcmd == "tick":
|
||||
cron_tick()
|
||||
|
||||
else:
|
||||
print(f"Unknown cron command: {subcmd}")
|
||||
print("Usage: hermes cron [list|daemon|tick]")
|
||||
sys.exit(1)
|
||||
@@ -1,352 +0,0 @@
|
||||
"""
|
||||
Doctor command for hermes CLI.
|
||||
|
||||
Diagnoses issues with Hermes Agent setup.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
|
||||
|
||||
# ANSI colors
|
||||
class Colors:
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
CYAN = "\033[36m"
|
||||
|
||||
def color(text: str, *codes) -> str:
|
||||
if not sys.stdout.isatty():
|
||||
return text
|
||||
return "".join(codes) + text + Colors.RESET
|
||||
|
||||
def check_ok(text: str, detail: str = ""):
|
||||
print(f" {color('✓', Colors.GREEN)} {text}" + (f" {color(detail, Colors.DIM)}" if detail else ""))
|
||||
|
||||
def check_warn(text: str, detail: str = ""):
|
||||
print(f" {color('⚠', Colors.YELLOW)} {text}" + (f" {color(detail, Colors.DIM)}" if detail else ""))
|
||||
|
||||
def check_fail(text: str, detail: str = ""):
|
||||
print(f" {color('✗', Colors.RED)} {text}" + (f" {color(detail, Colors.DIM)}" if detail else ""))
|
||||
|
||||
def check_info(text: str):
|
||||
print(f" {color('→', Colors.CYAN)} {text}")
|
||||
|
||||
|
||||
def run_doctor(args):
|
||||
"""Run diagnostic checks."""
|
||||
should_fix = getattr(args, 'fix', False)
|
||||
|
||||
issues = []
|
||||
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.CYAN))
|
||||
print(color("│ 🩺 Hermes Doctor │", Colors.CYAN))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.CYAN))
|
||||
|
||||
# =========================================================================
|
||||
# Check: Python version
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Python Environment", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
py_version = sys.version_info
|
||||
if py_version >= (3, 11):
|
||||
check_ok(f"Python {py_version.major}.{py_version.minor}.{py_version.micro}")
|
||||
elif py_version >= (3, 10):
|
||||
check_ok(f"Python {py_version.major}.{py_version.minor}.{py_version.micro}")
|
||||
check_warn("Python 3.11+ recommended for RL Training tools (tinker requires >= 3.11)")
|
||||
elif py_version >= (3, 8):
|
||||
check_warn(f"Python {py_version.major}.{py_version.minor}.{py_version.micro}", "(3.10+ recommended)")
|
||||
else:
|
||||
check_fail(f"Python {py_version.major}.{py_version.minor}.{py_version.micro}", "(3.10+ required)")
|
||||
issues.append("Upgrade Python to 3.10+")
|
||||
|
||||
# Check if in virtual environment
|
||||
in_venv = sys.prefix != sys.base_prefix
|
||||
if in_venv:
|
||||
check_ok("Virtual environment active")
|
||||
else:
|
||||
check_warn("Not in virtual environment", "(recommended)")
|
||||
|
||||
# =========================================================================
|
||||
# Check: Required packages
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Required Packages", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
required_packages = [
|
||||
("openai", "OpenAI SDK"),
|
||||
("rich", "Rich (terminal UI)"),
|
||||
("dotenv", "python-dotenv"),
|
||||
("yaml", "PyYAML"),
|
||||
("httpx", "HTTPX"),
|
||||
]
|
||||
|
||||
optional_packages = [
|
||||
("croniter", "Croniter (cron expressions)"),
|
||||
("browserbase", "Browserbase SDK"),
|
||||
("telegram", "python-telegram-bot"),
|
||||
("discord", "discord.py"),
|
||||
]
|
||||
|
||||
for module, name in required_packages:
|
||||
try:
|
||||
__import__(module)
|
||||
check_ok(name)
|
||||
except ImportError:
|
||||
check_fail(name, "(missing)")
|
||||
issues.append(f"Install {name}: uv pip install {module}")
|
||||
|
||||
for module, name in optional_packages:
|
||||
try:
|
||||
__import__(module)
|
||||
check_ok(name, "(optional)")
|
||||
except ImportError:
|
||||
check_warn(name, "(optional, not installed)")
|
||||
|
||||
# =========================================================================
|
||||
# Check: Configuration files
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Configuration Files", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
env_path = PROJECT_ROOT / '.env'
|
||||
if env_path.exists():
|
||||
check_ok(".env file exists")
|
||||
|
||||
# Check for common issues
|
||||
content = env_path.read_text()
|
||||
if "OPENROUTER_API_KEY" in content or "ANTHROPIC_API_KEY" in content:
|
||||
check_ok("API key configured")
|
||||
else:
|
||||
check_warn("No API key found in .env")
|
||||
issues.append("Run 'hermes setup' to configure API keys")
|
||||
else:
|
||||
check_fail(".env file missing")
|
||||
check_info("Run 'hermes setup' to create one")
|
||||
issues.append("Run 'hermes setup' to create .env")
|
||||
|
||||
config_path = PROJECT_ROOT / 'cli-config.yaml'
|
||||
if config_path.exists():
|
||||
check_ok("cli-config.yaml exists")
|
||||
else:
|
||||
check_warn("cli-config.yaml not found", "(using defaults)")
|
||||
|
||||
# =========================================================================
|
||||
# Check: Directory structure
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Directory Structure", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
hermes_home = Path.home() / ".hermes"
|
||||
if hermes_home.exists():
|
||||
check_ok("~/.hermes directory exists")
|
||||
else:
|
||||
check_warn("~/.hermes not found", "(will be created on first use)")
|
||||
|
||||
logs_dir = PROJECT_ROOT / "logs"
|
||||
if logs_dir.exists():
|
||||
check_ok("logs/ directory exists")
|
||||
else:
|
||||
check_warn("logs/ not found", "(will be created on first use)")
|
||||
|
||||
# =========================================================================
|
||||
# Check: External tools
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ External Tools", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
# Git
|
||||
if shutil.which("git"):
|
||||
check_ok("git")
|
||||
else:
|
||||
check_warn("git not found", "(optional)")
|
||||
|
||||
# ripgrep (optional, for faster file search)
|
||||
if shutil.which("rg"):
|
||||
check_ok("ripgrep (rg)", "(faster file search)")
|
||||
else:
|
||||
check_warn("ripgrep (rg) not found", "(file search uses grep fallback)")
|
||||
check_info("Install for faster search: sudo apt install ripgrep")
|
||||
|
||||
# Docker (optional)
|
||||
terminal_env = os.getenv("TERMINAL_ENV", "local")
|
||||
if terminal_env == "docker":
|
||||
if shutil.which("docker"):
|
||||
# Check if docker daemon is running
|
||||
result = subprocess.run(["docker", "info"], capture_output=True)
|
||||
if result.returncode == 0:
|
||||
check_ok("docker", "(daemon running)")
|
||||
else:
|
||||
check_fail("docker daemon not running")
|
||||
issues.append("Start Docker daemon")
|
||||
else:
|
||||
check_fail("docker not found", "(required for TERMINAL_ENV=docker)")
|
||||
issues.append("Install Docker or change TERMINAL_ENV")
|
||||
else:
|
||||
if shutil.which("docker"):
|
||||
check_ok("docker", "(optional)")
|
||||
else:
|
||||
check_warn("docker not found", "(optional)")
|
||||
|
||||
# SSH (if using ssh backend)
|
||||
if terminal_env == "ssh":
|
||||
ssh_host = os.getenv("TERMINAL_SSH_HOST")
|
||||
if ssh_host:
|
||||
# Try to connect
|
||||
result = subprocess.run(
|
||||
["ssh", "-o", "ConnectTimeout=5", "-o", "BatchMode=yes", ssh_host, "echo ok"],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
if result.returncode == 0:
|
||||
check_ok(f"SSH connection to {ssh_host}")
|
||||
else:
|
||||
check_fail(f"SSH connection to {ssh_host}")
|
||||
issues.append(f"Check SSH configuration for {ssh_host}")
|
||||
else:
|
||||
check_fail("TERMINAL_SSH_HOST not set", "(required for TERMINAL_ENV=ssh)")
|
||||
issues.append("Set TERMINAL_SSH_HOST in .env")
|
||||
|
||||
# =========================================================================
|
||||
# Check: API connectivity
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ API Connectivity", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
openrouter_key = os.getenv("OPENROUTER_API_KEY")
|
||||
if openrouter_key:
|
||||
try:
|
||||
import httpx
|
||||
response = httpx.get(
|
||||
"https://openrouter.ai/api/v1/models",
|
||||
headers={"Authorization": f"Bearer {openrouter_key}"},
|
||||
timeout=10
|
||||
)
|
||||
if response.status_code == 200:
|
||||
check_ok("OpenRouter API")
|
||||
elif response.status_code == 401:
|
||||
check_fail("OpenRouter API", "(invalid API key)")
|
||||
issues.append("Check OPENROUTER_API_KEY in .env")
|
||||
else:
|
||||
check_fail("OpenRouter API", f"(HTTP {response.status_code})")
|
||||
except Exception as e:
|
||||
check_fail("OpenRouter API", f"({e})")
|
||||
issues.append("Check network connectivity")
|
||||
else:
|
||||
check_warn("OpenRouter API", "(not configured)")
|
||||
|
||||
anthropic_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
if anthropic_key:
|
||||
try:
|
||||
import httpx
|
||||
response = httpx.get(
|
||||
"https://api.anthropic.com/v1/models",
|
||||
headers={
|
||||
"x-api-key": anthropic_key,
|
||||
"anthropic-version": "2023-06-01"
|
||||
},
|
||||
timeout=10
|
||||
)
|
||||
if response.status_code == 200:
|
||||
check_ok("Anthropic API")
|
||||
elif response.status_code == 401:
|
||||
check_fail("Anthropic API", "(invalid API key)")
|
||||
else:
|
||||
# Note: Anthropic may not have /models endpoint
|
||||
check_warn("Anthropic API", "(couldn't verify)")
|
||||
except Exception as e:
|
||||
check_warn("Anthropic API", f"({e})")
|
||||
|
||||
# =========================================================================
|
||||
# Check: Submodules
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Submodules", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
# mini-swe-agent (terminal tool backend)
|
||||
mini_swe_dir = PROJECT_ROOT / "mini-swe-agent"
|
||||
if mini_swe_dir.exists() and (mini_swe_dir / "pyproject.toml").exists():
|
||||
try:
|
||||
__import__("minisweagent")
|
||||
check_ok("mini-swe-agent", "(terminal backend)")
|
||||
except ImportError:
|
||||
check_warn("mini-swe-agent found but not installed", "(run: uv pip install -e ./mini-swe-agent)")
|
||||
issues.append("Install mini-swe-agent: uv pip install -e ./mini-swe-agent")
|
||||
else:
|
||||
check_warn("mini-swe-agent not found", "(run: git submodule update --init --recursive)")
|
||||
|
||||
# tinker-atropos (RL training backend)
|
||||
tinker_dir = PROJECT_ROOT / "tinker-atropos"
|
||||
if tinker_dir.exists() and (tinker_dir / "pyproject.toml").exists():
|
||||
if py_version >= (3, 11):
|
||||
try:
|
||||
__import__("tinker_atropos")
|
||||
check_ok("tinker-atropos", "(RL training backend)")
|
||||
except ImportError:
|
||||
check_warn("tinker-atropos found but not installed", "(run: uv pip install -e ./tinker-atropos)")
|
||||
issues.append("Install tinker-atropos: uv pip install -e ./tinker-atropos")
|
||||
else:
|
||||
check_warn("tinker-atropos requires Python 3.11+", f"(current: {py_version.major}.{py_version.minor})")
|
||||
else:
|
||||
check_warn("tinker-atropos not found", "(run: git submodule update --init --recursive)")
|
||||
|
||||
# =========================================================================
|
||||
# Check: Tool Availability
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Tool Availability", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
try:
|
||||
# Add project root to path for imports
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
from model_tools import check_tool_availability, TOOLSET_REQUIREMENTS
|
||||
|
||||
available, unavailable = check_tool_availability()
|
||||
|
||||
for tid in available:
|
||||
info = TOOLSET_REQUIREMENTS.get(tid, {})
|
||||
check_ok(info.get("name", tid))
|
||||
|
||||
for item in unavailable:
|
||||
if item["missing_vars"]:
|
||||
vars_str = ", ".join(item["missing_vars"])
|
||||
check_warn(item["name"], f"(missing {vars_str})")
|
||||
else:
|
||||
check_warn(item["name"], "(system dependency not met)")
|
||||
|
||||
# Count disabled tools with API key requirements
|
||||
api_disabled = [u for u in unavailable if u["missing_vars"]]
|
||||
if api_disabled:
|
||||
issues.append("Run 'hermes setup' to configure missing API keys for full tool access")
|
||||
except Exception as e:
|
||||
check_warn("Could not check tool availability", f"({e})")
|
||||
|
||||
# =========================================================================
|
||||
# Summary
|
||||
# =========================================================================
|
||||
print()
|
||||
if issues:
|
||||
print(color("─" * 60, Colors.YELLOW))
|
||||
print(color(f" Found {len(issues)} issue(s) to address:", Colors.YELLOW, Colors.BOLD))
|
||||
print()
|
||||
for i, issue in enumerate(issues, 1):
|
||||
print(f" {i}. {issue}")
|
||||
print()
|
||||
|
||||
if should_fix:
|
||||
print(color(" Attempting auto-fix is not yet implemented.", Colors.DIM))
|
||||
print(color(" Please resolve issues manually.", Colors.DIM))
|
||||
else:
|
||||
print(color("─" * 60, Colors.GREEN))
|
||||
print(color(" All checks passed! 🎉", Colors.GREEN, Colors.BOLD))
|
||||
|
||||
print()
|
||||
@@ -1,487 +0,0 @@
|
||||
"""
|
||||
Gateway subcommand for hermes CLI.
|
||||
|
||||
Handles: hermes gateway [run|start|stop|restart|status|install|uninstall]
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import signal
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Process Management (for manual gateway runs)
|
||||
# =============================================================================
|
||||
|
||||
def find_gateway_pids() -> list:
|
||||
"""Find PIDs of running gateway processes."""
|
||||
pids = []
|
||||
try:
|
||||
# Look for gateway processes with multiple patterns
|
||||
patterns = [
|
||||
"hermes_cli.main gateway",
|
||||
"hermes gateway",
|
||||
"gateway/run.py",
|
||||
]
|
||||
|
||||
result = subprocess.run(
|
||||
["ps", "aux"],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
|
||||
for line in result.stdout.split('\n'):
|
||||
# Skip grep and current process
|
||||
if 'grep' in line or str(os.getpid()) in line:
|
||||
continue
|
||||
|
||||
for pattern in patterns:
|
||||
if pattern in line:
|
||||
parts = line.split()
|
||||
if len(parts) > 1:
|
||||
try:
|
||||
pid = int(parts[1])
|
||||
if pid not in pids:
|
||||
pids.append(pid)
|
||||
except ValueError:
|
||||
continue
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return pids
|
||||
|
||||
|
||||
def kill_gateway_processes(force: bool = False) -> int:
|
||||
"""Kill any running gateway processes. Returns count killed."""
|
||||
pids = find_gateway_pids()
|
||||
killed = 0
|
||||
|
||||
for pid in pids:
|
||||
try:
|
||||
if force:
|
||||
os.kill(pid, signal.SIGKILL)
|
||||
else:
|
||||
os.kill(pid, signal.SIGTERM)
|
||||
killed += 1
|
||||
except ProcessLookupError:
|
||||
# Process already gone
|
||||
pass
|
||||
except PermissionError:
|
||||
print(f"⚠ Permission denied to kill PID {pid}")
|
||||
|
||||
return killed
|
||||
|
||||
|
||||
def is_linux() -> bool:
|
||||
return sys.platform.startswith('linux')
|
||||
|
||||
def is_macos() -> bool:
|
||||
return sys.platform == 'darwin'
|
||||
|
||||
def is_windows() -> bool:
|
||||
return sys.platform == 'win32'
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Service Configuration
|
||||
# =============================================================================
|
||||
|
||||
SERVICE_NAME = "hermes-gateway"
|
||||
SERVICE_DESCRIPTION = "Hermes Agent Gateway - Messaging Platform Integration"
|
||||
|
||||
def get_systemd_unit_path() -> Path:
|
||||
return Path.home() / ".config" / "systemd" / "user" / f"{SERVICE_NAME}.service"
|
||||
|
||||
def get_launchd_plist_path() -> Path:
|
||||
return Path.home() / "Library" / "LaunchAgents" / "ai.hermes.gateway.plist"
|
||||
|
||||
def get_python_path() -> str:
|
||||
venv_python = PROJECT_ROOT / "venv" / "bin" / "python"
|
||||
if venv_python.exists():
|
||||
return str(venv_python)
|
||||
return sys.executable
|
||||
|
||||
def get_hermes_cli_path() -> str:
|
||||
"""Get the path to the hermes CLI."""
|
||||
# Check if installed via pip
|
||||
import shutil
|
||||
hermes_bin = shutil.which("hermes")
|
||||
if hermes_bin:
|
||||
return hermes_bin
|
||||
|
||||
# Fallback to direct module execution
|
||||
return f"{get_python_path()} -m hermes_cli.main"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Systemd (Linux)
|
||||
# =============================================================================
|
||||
|
||||
def generate_systemd_unit() -> str:
|
||||
python_path = get_python_path()
|
||||
working_dir = str(PROJECT_ROOT)
|
||||
|
||||
return f"""[Unit]
|
||||
Description={SERVICE_DESCRIPTION}
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
ExecStart={python_path} -m hermes_cli.main gateway run
|
||||
WorkingDirectory={working_dir}
|
||||
Restart=on-failure
|
||||
RestartSec=10
|
||||
StandardOutput=journal
|
||||
StandardError=journal
|
||||
|
||||
[Install]
|
||||
WantedBy=default.target
|
||||
"""
|
||||
|
||||
def systemd_install(force: bool = False):
|
||||
unit_path = get_systemd_unit_path()
|
||||
|
||||
if unit_path.exists() and not force:
|
||||
print(f"Service already installed at: {unit_path}")
|
||||
print("Use --force to reinstall")
|
||||
return
|
||||
|
||||
unit_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
print(f"Installing systemd service to: {unit_path}")
|
||||
unit_path.write_text(generate_systemd_unit())
|
||||
|
||||
subprocess.run(["systemctl", "--user", "daemon-reload"], check=True)
|
||||
subprocess.run(["systemctl", "--user", "enable", SERVICE_NAME], check=True)
|
||||
|
||||
print()
|
||||
print("✓ Service installed and enabled!")
|
||||
print()
|
||||
print("Next steps:")
|
||||
print(f" hermes gateway start # Start the service")
|
||||
print(f" hermes gateway status # Check status")
|
||||
print(f" journalctl --user -u {SERVICE_NAME} -f # View logs")
|
||||
print()
|
||||
print("To enable lingering (keeps running after logout):")
|
||||
print(" sudo loginctl enable-linger $USER")
|
||||
|
||||
def systemd_uninstall():
|
||||
subprocess.run(["systemctl", "--user", "stop", SERVICE_NAME], check=False)
|
||||
subprocess.run(["systemctl", "--user", "disable", SERVICE_NAME], check=False)
|
||||
|
||||
unit_path = get_systemd_unit_path()
|
||||
if unit_path.exists():
|
||||
unit_path.unlink()
|
||||
print(f"✓ Removed {unit_path}")
|
||||
|
||||
subprocess.run(["systemctl", "--user", "daemon-reload"], check=True)
|
||||
print("✓ Service uninstalled")
|
||||
|
||||
def systemd_start():
|
||||
subprocess.run(["systemctl", "--user", "start", SERVICE_NAME], check=True)
|
||||
print("✓ Service started")
|
||||
|
||||
def systemd_stop():
|
||||
subprocess.run(["systemctl", "--user", "stop", SERVICE_NAME], check=True)
|
||||
print("✓ Service stopped")
|
||||
|
||||
def systemd_restart():
|
||||
subprocess.run(["systemctl", "--user", "restart", SERVICE_NAME], check=True)
|
||||
print("✓ Service restarted")
|
||||
|
||||
def systemd_status(deep: bool = False):
|
||||
# Check if service unit file exists
|
||||
unit_path = get_systemd_unit_path()
|
||||
if not unit_path.exists():
|
||||
print("✗ Gateway service is not installed")
|
||||
print(" Run: hermes gateway install")
|
||||
return
|
||||
|
||||
# Show detailed status first
|
||||
subprocess.run(
|
||||
["systemctl", "--user", "status", SERVICE_NAME, "--no-pager"],
|
||||
capture_output=False
|
||||
)
|
||||
|
||||
# Check if service is active
|
||||
result = subprocess.run(
|
||||
["systemctl", "--user", "is-active", SERVICE_NAME],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
|
||||
status = result.stdout.strip()
|
||||
|
||||
if status == "active":
|
||||
print("✓ Gateway service is running")
|
||||
else:
|
||||
print("✗ Gateway service is stopped")
|
||||
print(" Run: hermes gateway start")
|
||||
|
||||
if deep:
|
||||
print()
|
||||
print("Recent logs:")
|
||||
subprocess.run([
|
||||
"journalctl", "--user", "-u", SERVICE_NAME,
|
||||
"-n", "20", "--no-pager"
|
||||
])
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Launchd (macOS)
|
||||
# =============================================================================
|
||||
|
||||
def generate_launchd_plist() -> str:
|
||||
python_path = get_python_path()
|
||||
working_dir = str(PROJECT_ROOT)
|
||||
log_dir = Path.home() / ".hermes" / "logs"
|
||||
log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
return f"""<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>Label</key>
|
||||
<string>ai.hermes.gateway</string>
|
||||
|
||||
<key>ProgramArguments</key>
|
||||
<array>
|
||||
<string>{python_path}</string>
|
||||
<string>-m</string>
|
||||
<string>hermes_cli.main</string>
|
||||
<string>gateway</string>
|
||||
<string>run</string>
|
||||
</array>
|
||||
|
||||
<key>WorkingDirectory</key>
|
||||
<string>{working_dir}</string>
|
||||
|
||||
<key>RunAtLoad</key>
|
||||
<true/>
|
||||
|
||||
<key>KeepAlive</key>
|
||||
<dict>
|
||||
<key>SuccessfulExit</key>
|
||||
<false/>
|
||||
</dict>
|
||||
|
||||
<key>StandardOutPath</key>
|
||||
<string>{log_dir}/gateway.log</string>
|
||||
|
||||
<key>StandardErrorPath</key>
|
||||
<string>{log_dir}/gateway.error.log</string>
|
||||
</dict>
|
||||
</plist>
|
||||
"""
|
||||
|
||||
def launchd_install(force: bool = False):
|
||||
plist_path = get_launchd_plist_path()
|
||||
|
||||
if plist_path.exists() and not force:
|
||||
print(f"Service already installed at: {plist_path}")
|
||||
print("Use --force to reinstall")
|
||||
return
|
||||
|
||||
plist_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
print(f"Installing launchd service to: {plist_path}")
|
||||
plist_path.write_text(generate_launchd_plist())
|
||||
|
||||
subprocess.run(["launchctl", "load", str(plist_path)], check=True)
|
||||
|
||||
print()
|
||||
print("✓ Service installed and loaded!")
|
||||
print()
|
||||
print("Next steps:")
|
||||
print(" hermes gateway status # Check status")
|
||||
print(" tail -f ~/.hermes/logs/gateway.log # View logs")
|
||||
|
||||
def launchd_uninstall():
|
||||
plist_path = get_launchd_plist_path()
|
||||
subprocess.run(["launchctl", "unload", str(plist_path)], check=False)
|
||||
|
||||
if plist_path.exists():
|
||||
plist_path.unlink()
|
||||
print(f"✓ Removed {plist_path}")
|
||||
|
||||
print("✓ Service uninstalled")
|
||||
|
||||
def launchd_start():
|
||||
subprocess.run(["launchctl", "start", "ai.hermes.gateway"], check=True)
|
||||
print("✓ Service started")
|
||||
|
||||
def launchd_stop():
|
||||
subprocess.run(["launchctl", "stop", "ai.hermes.gateway"], check=True)
|
||||
print("✓ Service stopped")
|
||||
|
||||
def launchd_restart():
|
||||
launchd_stop()
|
||||
launchd_start()
|
||||
|
||||
def launchd_status(deep: bool = False):
|
||||
result = subprocess.run(
|
||||
["launchctl", "list", "ai.hermes.gateway"],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
|
||||
if result.returncode == 0:
|
||||
print("✓ Gateway service is loaded")
|
||||
print(result.stdout)
|
||||
else:
|
||||
print("✗ Gateway service is not loaded")
|
||||
|
||||
if deep:
|
||||
log_file = Path.home() / ".hermes" / "logs" / "gateway.log"
|
||||
if log_file.exists():
|
||||
print()
|
||||
print("Recent logs:")
|
||||
subprocess.run(["tail", "-20", str(log_file)])
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Gateway Runner
|
||||
# =============================================================================
|
||||
|
||||
def run_gateway(verbose: bool = False):
|
||||
"""Run the gateway in foreground."""
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
from gateway.run import start_gateway
|
||||
|
||||
print("┌─────────────────────────────────────────────────────────┐")
|
||||
print("│ 🦋 Hermes Gateway Starting... │")
|
||||
print("├─────────────────────────────────────────────────────────┤")
|
||||
print("│ Press Ctrl+C to stop │")
|
||||
print("└─────────────────────────────────────────────────────────┘")
|
||||
print()
|
||||
|
||||
asyncio.run(start_gateway())
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Main Command Handler
|
||||
# =============================================================================
|
||||
|
||||
def gateway_command(args):
|
||||
"""Handle gateway subcommands."""
|
||||
subcmd = getattr(args, 'gateway_command', None)
|
||||
|
||||
# Default to run if no subcommand
|
||||
if subcmd is None or subcmd == "run":
|
||||
verbose = getattr(args, 'verbose', False)
|
||||
run_gateway(verbose)
|
||||
return
|
||||
|
||||
# Service management commands
|
||||
if subcmd == "install":
|
||||
force = getattr(args, 'force', False)
|
||||
if is_linux():
|
||||
systemd_install(force)
|
||||
elif is_macos():
|
||||
launchd_install(force)
|
||||
else:
|
||||
print("Service installation not supported on this platform.")
|
||||
print("Run manually: hermes gateway run")
|
||||
sys.exit(1)
|
||||
|
||||
elif subcmd == "uninstall":
|
||||
if is_linux():
|
||||
systemd_uninstall()
|
||||
elif is_macos():
|
||||
launchd_uninstall()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif subcmd == "start":
|
||||
if is_linux():
|
||||
systemd_start()
|
||||
elif is_macos():
|
||||
launchd_start()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif subcmd == "stop":
|
||||
# Try service first, fall back to killing processes directly
|
||||
service_available = False
|
||||
|
||||
if is_linux() and get_systemd_unit_path().exists():
|
||||
try:
|
||||
systemd_stop()
|
||||
service_available = True
|
||||
except subprocess.CalledProcessError:
|
||||
pass # Fall through to process kill
|
||||
elif is_macos() and get_launchd_plist_path().exists():
|
||||
try:
|
||||
launchd_stop()
|
||||
service_available = True
|
||||
except subprocess.CalledProcessError:
|
||||
pass
|
||||
|
||||
if not service_available:
|
||||
# Kill gateway processes directly
|
||||
killed = kill_gateway_processes()
|
||||
if killed:
|
||||
print(f"✓ Stopped {killed} gateway process(es)")
|
||||
else:
|
||||
print("✗ No gateway processes found")
|
||||
|
||||
elif subcmd == "restart":
|
||||
# Try service first, fall back to killing and restarting
|
||||
service_available = False
|
||||
|
||||
if is_linux() and get_systemd_unit_path().exists():
|
||||
try:
|
||||
systemd_restart()
|
||||
service_available = True
|
||||
except subprocess.CalledProcessError:
|
||||
pass
|
||||
elif is_macos() and get_launchd_plist_path().exists():
|
||||
try:
|
||||
launchd_restart()
|
||||
service_available = True
|
||||
except subprocess.CalledProcessError:
|
||||
pass
|
||||
|
||||
if not service_available:
|
||||
# Manual restart: kill existing processes
|
||||
killed = kill_gateway_processes()
|
||||
if killed:
|
||||
print(f"✓ Stopped {killed} gateway process(es)")
|
||||
|
||||
import time
|
||||
time.sleep(2)
|
||||
|
||||
# Start fresh
|
||||
print("Starting gateway...")
|
||||
run_gateway(verbose=False)
|
||||
|
||||
elif subcmd == "status":
|
||||
deep = getattr(args, 'deep', False)
|
||||
|
||||
# Check for service first
|
||||
if is_linux() and get_systemd_unit_path().exists():
|
||||
systemd_status(deep)
|
||||
elif is_macos() and get_launchd_plist_path().exists():
|
||||
launchd_status(deep)
|
||||
else:
|
||||
# Check for manually running processes
|
||||
pids = find_gateway_pids()
|
||||
if pids:
|
||||
print(f"✓ Gateway is running (PID: {', '.join(map(str, pids))})")
|
||||
print(" (Running manually, not as a system service)")
|
||||
print()
|
||||
print("To install as a service:")
|
||||
print(" hermes gateway install")
|
||||
else:
|
||||
print("✗ Gateway is not running")
|
||||
print()
|
||||
print("To start:")
|
||||
print(" hermes gateway # Run in foreground")
|
||||
print(" hermes gateway install # Install as service")
|
||||
@@ -1,516 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Hermes CLI - Main entry point.
|
||||
|
||||
Usage:
|
||||
hermes # Interactive chat (default)
|
||||
hermes chat # Interactive chat
|
||||
hermes gateway # Run gateway in foreground
|
||||
hermes gateway start # Start gateway as service
|
||||
hermes gateway stop # Stop gateway service
|
||||
hermes gateway status # Show gateway status
|
||||
hermes gateway install # Install gateway service
|
||||
hermes gateway uninstall # Uninstall gateway service
|
||||
hermes setup # Interactive setup wizard
|
||||
hermes status # Show status of all components
|
||||
hermes cron # Manage cron jobs
|
||||
hermes cron list # List cron jobs
|
||||
hermes cron daemon # Run cron daemon
|
||||
hermes doctor # Check configuration and dependencies
|
||||
hermes version # Show version
|
||||
hermes update # Update to latest version
|
||||
hermes uninstall # Uninstall Hermes Agent
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add project root to path
|
||||
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
# Load .env file
|
||||
from dotenv import load_dotenv
|
||||
env_path = PROJECT_ROOT / '.env'
|
||||
if env_path.exists():
|
||||
load_dotenv(dotenv_path=env_path)
|
||||
|
||||
from hermes_cli import __version__
|
||||
|
||||
|
||||
def cmd_chat(args):
|
||||
"""Run interactive chat CLI."""
|
||||
# Import and run the CLI
|
||||
from cli import main as cli_main
|
||||
|
||||
# Build kwargs from args
|
||||
kwargs = {
|
||||
"model": args.model,
|
||||
"toolsets": args.toolsets,
|
||||
"verbose": args.verbose,
|
||||
"query": args.query,
|
||||
}
|
||||
# Filter out None values
|
||||
kwargs = {k: v for k, v in kwargs.items() if v is not None}
|
||||
|
||||
cli_main(**kwargs)
|
||||
|
||||
|
||||
def cmd_gateway(args):
|
||||
"""Gateway management commands."""
|
||||
from hermes_cli.gateway import gateway_command
|
||||
gateway_command(args)
|
||||
|
||||
|
||||
def cmd_setup(args):
|
||||
"""Interactive setup wizard."""
|
||||
from hermes_cli.setup import run_setup_wizard
|
||||
run_setup_wizard(args)
|
||||
|
||||
|
||||
def cmd_status(args):
|
||||
"""Show status of all components."""
|
||||
from hermes_cli.status import show_status
|
||||
show_status(args)
|
||||
|
||||
|
||||
def cmd_cron(args):
|
||||
"""Cron job management."""
|
||||
from hermes_cli.cron import cron_command
|
||||
cron_command(args)
|
||||
|
||||
|
||||
def cmd_doctor(args):
|
||||
"""Check configuration and dependencies."""
|
||||
from hermes_cli.doctor import run_doctor
|
||||
run_doctor(args)
|
||||
|
||||
|
||||
def cmd_config(args):
|
||||
"""Configuration management."""
|
||||
from hermes_cli.config import config_command
|
||||
config_command(args)
|
||||
|
||||
|
||||
def cmd_version(args):
|
||||
"""Show version."""
|
||||
print(f"Hermes Agent v{__version__}")
|
||||
print(f"Project: {PROJECT_ROOT}")
|
||||
|
||||
# Show Python version
|
||||
print(f"Python: {sys.version.split()[0]}")
|
||||
|
||||
# Check for key dependencies
|
||||
try:
|
||||
import openai
|
||||
print(f"OpenAI SDK: {openai.__version__}")
|
||||
except ImportError:
|
||||
print("OpenAI SDK: Not installed")
|
||||
|
||||
|
||||
def cmd_uninstall(args):
|
||||
"""Uninstall Hermes Agent."""
|
||||
from hermes_cli.uninstall import run_uninstall
|
||||
run_uninstall(args)
|
||||
|
||||
|
||||
def cmd_update(args):
|
||||
"""Update Hermes Agent to the latest version."""
|
||||
import subprocess
|
||||
import shutil
|
||||
|
||||
print("🦋 Updating Hermes Agent...")
|
||||
print()
|
||||
|
||||
# Check if we're in a git repo
|
||||
git_dir = PROJECT_ROOT / '.git'
|
||||
if not git_dir.exists():
|
||||
print("✗ Not a git repository. Please reinstall:")
|
||||
print(" curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash")
|
||||
sys.exit(1)
|
||||
|
||||
# Fetch and pull
|
||||
try:
|
||||
print("→ Fetching updates...")
|
||||
subprocess.run(["git", "fetch", "origin"], cwd=PROJECT_ROOT, check=True)
|
||||
|
||||
# Get current branch
|
||||
result = subprocess.run(
|
||||
["git", "rev-parse", "--abbrev-ref", "HEAD"],
|
||||
cwd=PROJECT_ROOT,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
check=True
|
||||
)
|
||||
branch = result.stdout.strip()
|
||||
|
||||
# Check if there are updates
|
||||
result = subprocess.run(
|
||||
["git", "rev-list", f"HEAD..origin/{branch}", "--count"],
|
||||
cwd=PROJECT_ROOT,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
check=True
|
||||
)
|
||||
commit_count = int(result.stdout.strip())
|
||||
|
||||
if commit_count == 0:
|
||||
print("✓ Already up to date!")
|
||||
return
|
||||
|
||||
print(f"→ Found {commit_count} new commit(s)")
|
||||
print("→ Pulling updates...")
|
||||
subprocess.run(["git", "pull", "origin", branch], cwd=PROJECT_ROOT, check=True)
|
||||
|
||||
# Reinstall Python dependencies (prefer uv for speed, fall back to pip)
|
||||
print("→ Updating Python dependencies...")
|
||||
uv_bin = shutil.which("uv")
|
||||
if uv_bin:
|
||||
subprocess.run(
|
||||
[uv_bin, "pip", "install", "-e", ".", "--quiet"],
|
||||
cwd=PROJECT_ROOT, check=True,
|
||||
env={**os.environ, "VIRTUAL_ENV": str(PROJECT_ROOT / "venv")}
|
||||
)
|
||||
else:
|
||||
venv_pip = PROJECT_ROOT / "venv" / "bin" / "pip"
|
||||
if venv_pip.exists():
|
||||
subprocess.run([str(venv_pip), "install", "-e", ".", "--quiet"], cwd=PROJECT_ROOT, check=True)
|
||||
else:
|
||||
subprocess.run(["pip", "install", "-e", ".", "--quiet"], cwd=PROJECT_ROOT, check=True)
|
||||
|
||||
# Check for Node.js deps
|
||||
if (PROJECT_ROOT / "package.json").exists():
|
||||
import shutil
|
||||
if shutil.which("npm"):
|
||||
print("→ Updating Node.js dependencies...")
|
||||
subprocess.run(["npm", "install", "--silent"], cwd=PROJECT_ROOT, check=False)
|
||||
|
||||
print()
|
||||
print("✓ Code updated!")
|
||||
|
||||
# Check for config migrations
|
||||
print()
|
||||
print("→ Checking configuration for new options...")
|
||||
|
||||
from hermes_cli.config import (
|
||||
get_missing_env_vars, get_missing_config_fields,
|
||||
check_config_version, migrate_config
|
||||
)
|
||||
|
||||
missing_env = get_missing_env_vars(required_only=True)
|
||||
missing_config = get_missing_config_fields()
|
||||
current_ver, latest_ver = check_config_version()
|
||||
|
||||
needs_migration = missing_env or missing_config or current_ver < latest_ver
|
||||
|
||||
if needs_migration:
|
||||
print()
|
||||
if missing_env:
|
||||
print(f" ⚠️ {len(missing_env)} new required setting(s) need configuration")
|
||||
if missing_config:
|
||||
print(f" ℹ️ {len(missing_config)} new config option(s) available")
|
||||
|
||||
print()
|
||||
response = input("Would you like to configure them now? [Y/n]: ").strip().lower()
|
||||
|
||||
if response in ('', 'y', 'yes'):
|
||||
print()
|
||||
results = migrate_config(interactive=True, quiet=False)
|
||||
|
||||
if results["env_added"] or results["config_added"]:
|
||||
print()
|
||||
print("✓ Configuration updated!")
|
||||
else:
|
||||
print()
|
||||
print("Skipped. Run 'hermes config migrate' later to configure.")
|
||||
else:
|
||||
print(" ✓ Configuration is up to date")
|
||||
|
||||
print()
|
||||
print("✓ Update complete!")
|
||||
print()
|
||||
print("Note: If you have the gateway service running, restart it:")
|
||||
print(" hermes gateway restart")
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"✗ Update failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point for hermes CLI."""
|
||||
parser = argparse.ArgumentParser(
|
||||
prog="hermes",
|
||||
description="Hermes Agent - AI assistant with tool-calling capabilities",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
hermes Start interactive chat
|
||||
hermes chat -q "Hello" Single query mode
|
||||
hermes setup Run setup wizard
|
||||
hermes config View configuration
|
||||
hermes config edit Edit config in $EDITOR
|
||||
hermes config set model gpt-4 Set a config value
|
||||
hermes gateway Run messaging gateway
|
||||
hermes gateway install Install as system service
|
||||
hermes update Update to latest version
|
||||
|
||||
For more help on a command:
|
||||
hermes <command> --help
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--version", "-V",
|
||||
action="store_true",
|
||||
help="Show version and exit"
|
||||
)
|
||||
|
||||
subparsers = parser.add_subparsers(dest="command", help="Command to run")
|
||||
|
||||
# =========================================================================
|
||||
# chat command
|
||||
# =========================================================================
|
||||
chat_parser = subparsers.add_parser(
|
||||
"chat",
|
||||
help="Interactive chat with the agent",
|
||||
description="Start an interactive chat session with Hermes Agent"
|
||||
)
|
||||
chat_parser.add_argument(
|
||||
"-q", "--query",
|
||||
help="Single query (non-interactive mode)"
|
||||
)
|
||||
chat_parser.add_argument(
|
||||
"-m", "--model",
|
||||
help="Model to use (e.g., anthropic/claude-sonnet-4)"
|
||||
)
|
||||
chat_parser.add_argument(
|
||||
"-t", "--toolsets",
|
||||
help="Comma-separated toolsets to enable"
|
||||
)
|
||||
chat_parser.add_argument(
|
||||
"-v", "--verbose",
|
||||
action="store_true",
|
||||
help="Verbose output"
|
||||
)
|
||||
chat_parser.set_defaults(func=cmd_chat)
|
||||
|
||||
# =========================================================================
|
||||
# gateway command
|
||||
# =========================================================================
|
||||
gateway_parser = subparsers.add_parser(
|
||||
"gateway",
|
||||
help="Messaging gateway management",
|
||||
description="Manage the messaging gateway (Telegram, Discord, WhatsApp)"
|
||||
)
|
||||
gateway_subparsers = gateway_parser.add_subparsers(dest="gateway_command")
|
||||
|
||||
# gateway run (default)
|
||||
gateway_run = gateway_subparsers.add_parser("run", help="Run gateway in foreground")
|
||||
gateway_run.add_argument("-v", "--verbose", action="store_true")
|
||||
|
||||
# gateway start
|
||||
gateway_start = gateway_subparsers.add_parser("start", help="Start gateway service")
|
||||
|
||||
# gateway stop
|
||||
gateway_stop = gateway_subparsers.add_parser("stop", help="Stop gateway service")
|
||||
|
||||
# gateway restart
|
||||
gateway_restart = gateway_subparsers.add_parser("restart", help="Restart gateway service")
|
||||
|
||||
# gateway status
|
||||
gateway_status = gateway_subparsers.add_parser("status", help="Show gateway status")
|
||||
gateway_status.add_argument("--deep", action="store_true", help="Deep status check")
|
||||
|
||||
# gateway install
|
||||
gateway_install = gateway_subparsers.add_parser("install", help="Install gateway as service")
|
||||
gateway_install.add_argument("--force", action="store_true", help="Force reinstall")
|
||||
|
||||
# gateway uninstall
|
||||
gateway_uninstall = gateway_subparsers.add_parser("uninstall", help="Uninstall gateway service")
|
||||
|
||||
gateway_parser.set_defaults(func=cmd_gateway)
|
||||
|
||||
# =========================================================================
|
||||
# setup command
|
||||
# =========================================================================
|
||||
setup_parser = subparsers.add_parser(
|
||||
"setup",
|
||||
help="Interactive setup wizard",
|
||||
description="Configure Hermes Agent with an interactive wizard"
|
||||
)
|
||||
setup_parser.add_argument(
|
||||
"--non-interactive",
|
||||
action="store_true",
|
||||
help="Non-interactive mode (use defaults/env vars)"
|
||||
)
|
||||
setup_parser.add_argument(
|
||||
"--reset",
|
||||
action="store_true",
|
||||
help="Reset configuration to defaults"
|
||||
)
|
||||
setup_parser.set_defaults(func=cmd_setup)
|
||||
|
||||
# =========================================================================
|
||||
# status command
|
||||
# =========================================================================
|
||||
status_parser = subparsers.add_parser(
|
||||
"status",
|
||||
help="Show status of all components",
|
||||
description="Display status of Hermes Agent components"
|
||||
)
|
||||
status_parser.add_argument(
|
||||
"--all",
|
||||
action="store_true",
|
||||
help="Show all details (redacted for sharing)"
|
||||
)
|
||||
status_parser.add_argument(
|
||||
"--deep",
|
||||
action="store_true",
|
||||
help="Run deep checks (may take longer)"
|
||||
)
|
||||
status_parser.set_defaults(func=cmd_status)
|
||||
|
||||
# =========================================================================
|
||||
# cron command
|
||||
# =========================================================================
|
||||
cron_parser = subparsers.add_parser(
|
||||
"cron",
|
||||
help="Cron job management",
|
||||
description="Manage scheduled tasks"
|
||||
)
|
||||
cron_subparsers = cron_parser.add_subparsers(dest="cron_command")
|
||||
|
||||
# cron list
|
||||
cron_list = cron_subparsers.add_parser("list", help="List scheduled jobs")
|
||||
cron_list.add_argument("--all", action="store_true", help="Include disabled jobs")
|
||||
|
||||
# cron daemon
|
||||
cron_daemon = cron_subparsers.add_parser("daemon", help="Run cron daemon")
|
||||
cron_daemon.add_argument("--interval", type=int, default=60, help="Check interval in seconds")
|
||||
|
||||
# cron tick
|
||||
cron_tick = cron_subparsers.add_parser("tick", help="Run due jobs once (for system cron)")
|
||||
|
||||
cron_parser.set_defaults(func=cmd_cron)
|
||||
|
||||
# =========================================================================
|
||||
# doctor command
|
||||
# =========================================================================
|
||||
doctor_parser = subparsers.add_parser(
|
||||
"doctor",
|
||||
help="Check configuration and dependencies",
|
||||
description="Diagnose issues with Hermes Agent setup"
|
||||
)
|
||||
doctor_parser.add_argument(
|
||||
"--fix",
|
||||
action="store_true",
|
||||
help="Attempt to fix issues automatically"
|
||||
)
|
||||
doctor_parser.set_defaults(func=cmd_doctor)
|
||||
|
||||
# =========================================================================
|
||||
# config command
|
||||
# =========================================================================
|
||||
config_parser = subparsers.add_parser(
|
||||
"config",
|
||||
help="View and edit configuration",
|
||||
description="Manage Hermes Agent configuration"
|
||||
)
|
||||
config_subparsers = config_parser.add_subparsers(dest="config_command")
|
||||
|
||||
# config show (default)
|
||||
config_show = config_subparsers.add_parser("show", help="Show current configuration")
|
||||
|
||||
# config edit
|
||||
config_edit = config_subparsers.add_parser("edit", help="Open config file in editor")
|
||||
|
||||
# config set
|
||||
config_set = config_subparsers.add_parser("set", help="Set a configuration value")
|
||||
config_set.add_argument("key", nargs="?", help="Configuration key (e.g., model, terminal.backend)")
|
||||
config_set.add_argument("value", nargs="?", help="Value to set")
|
||||
|
||||
# config path
|
||||
config_path = config_subparsers.add_parser("path", help="Print config file path")
|
||||
|
||||
# config env-path
|
||||
config_env = config_subparsers.add_parser("env-path", help="Print .env file path")
|
||||
|
||||
# config check
|
||||
config_check = config_subparsers.add_parser("check", help="Check for missing/outdated config")
|
||||
|
||||
# config migrate
|
||||
config_migrate = config_subparsers.add_parser("migrate", help="Update config with new options")
|
||||
|
||||
config_parser.set_defaults(func=cmd_config)
|
||||
|
||||
# =========================================================================
|
||||
# version command
|
||||
# =========================================================================
|
||||
version_parser = subparsers.add_parser(
|
||||
"version",
|
||||
help="Show version information"
|
||||
)
|
||||
version_parser.set_defaults(func=cmd_version)
|
||||
|
||||
# =========================================================================
|
||||
# update command
|
||||
# =========================================================================
|
||||
update_parser = subparsers.add_parser(
|
||||
"update",
|
||||
help="Update Hermes Agent to the latest version",
|
||||
description="Pull the latest changes from git and reinstall dependencies"
|
||||
)
|
||||
update_parser.set_defaults(func=cmd_update)
|
||||
|
||||
# =========================================================================
|
||||
# uninstall command
|
||||
# =========================================================================
|
||||
uninstall_parser = subparsers.add_parser(
|
||||
"uninstall",
|
||||
help="Uninstall Hermes Agent",
|
||||
description="Remove Hermes Agent from your system. Can keep configs/data for reinstall."
|
||||
)
|
||||
uninstall_parser.add_argument(
|
||||
"--full",
|
||||
action="store_true",
|
||||
help="Full uninstall - remove everything including configs and data"
|
||||
)
|
||||
uninstall_parser.add_argument(
|
||||
"--yes", "-y",
|
||||
action="store_true",
|
||||
help="Skip confirmation prompts"
|
||||
)
|
||||
uninstall_parser.set_defaults(func=cmd_uninstall)
|
||||
|
||||
# =========================================================================
|
||||
# Parse and execute
|
||||
# =========================================================================
|
||||
args = parser.parse_args()
|
||||
|
||||
# Handle --version flag
|
||||
if args.version:
|
||||
cmd_version(args)
|
||||
return
|
||||
|
||||
# Default to chat if no command specified
|
||||
if args.command is None:
|
||||
# No command = run chat
|
||||
args.query = None
|
||||
args.model = None
|
||||
args.toolsets = None
|
||||
args.verbose = False
|
||||
cmd_chat(args)
|
||||
return
|
||||
|
||||
# Execute the command
|
||||
if hasattr(args, 'func'):
|
||||
args.func(args)
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1075
hermes_cli/setup.py
1075
hermes_cli/setup.py
File diff suppressed because it is too large
Load Diff
@@ -1,241 +0,0 @@
|
||||
"""
|
||||
Status command for hermes CLI.
|
||||
|
||||
Shows the status of all Hermes Agent components.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
|
||||
|
||||
# ANSI colors
|
||||
class Colors:
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
CYAN = "\033[36m"
|
||||
|
||||
def color(text: str, *codes) -> str:
|
||||
if not sys.stdout.isatty():
|
||||
return text
|
||||
return "".join(codes) + text + Colors.RESET
|
||||
|
||||
def check_mark(ok: bool) -> str:
|
||||
if ok:
|
||||
return color("✓", Colors.GREEN)
|
||||
return color("✗", Colors.RED)
|
||||
|
||||
def redact_key(key: str) -> str:
|
||||
"""Redact an API key for display."""
|
||||
if not key:
|
||||
return "(not set)"
|
||||
if len(key) < 12:
|
||||
return "***"
|
||||
return key[:4] + "..." + key[-4:]
|
||||
|
||||
|
||||
def show_status(args):
|
||||
"""Show status of all Hermes Agent components."""
|
||||
show_all = getattr(args, 'all', False)
|
||||
deep = getattr(args, 'deep', False)
|
||||
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.CYAN))
|
||||
print(color("│ 🦋 Hermes Agent Status │", Colors.CYAN))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.CYAN))
|
||||
|
||||
# =========================================================================
|
||||
# Environment
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Environment", Colors.CYAN, Colors.BOLD))
|
||||
print(f" Project: {PROJECT_ROOT}")
|
||||
print(f" Python: {sys.version.split()[0]}")
|
||||
|
||||
env_path = PROJECT_ROOT / '.env'
|
||||
print(f" .env file: {check_mark(env_path.exists())} {'exists' if env_path.exists() else 'not found'}")
|
||||
|
||||
# =========================================================================
|
||||
# API Keys
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ API Keys", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
keys = {
|
||||
"OpenRouter": "OPENROUTER_API_KEY",
|
||||
"Anthropic": "ANTHROPIC_API_KEY",
|
||||
"OpenAI": "OPENAI_API_KEY",
|
||||
"Firecrawl": "FIRECRAWL_API_KEY",
|
||||
"Browserbase": "BROWSERBASE_API_KEY",
|
||||
"FAL": "FAL_KEY",
|
||||
"Tinker": "TINKER_API_KEY",
|
||||
"WandB": "WANDB_API_KEY",
|
||||
}
|
||||
|
||||
for name, env_var in keys.items():
|
||||
value = os.getenv(env_var, "")
|
||||
has_key = bool(value)
|
||||
display = redact_key(value) if not show_all else value
|
||||
print(f" {name:<12} {check_mark(has_key)} {display}")
|
||||
|
||||
# =========================================================================
|
||||
# Terminal Configuration
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Terminal Backend", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
terminal_env = os.getenv("TERMINAL_ENV", "local")
|
||||
print(f" Backend: {terminal_env}")
|
||||
|
||||
if terminal_env == "ssh":
|
||||
ssh_host = os.getenv("TERMINAL_SSH_HOST", "")
|
||||
ssh_user = os.getenv("TERMINAL_SSH_USER", "")
|
||||
print(f" SSH Host: {ssh_host or '(not set)'}")
|
||||
print(f" SSH User: {ssh_user or '(not set)'}")
|
||||
elif terminal_env == "docker":
|
||||
docker_image = os.getenv("TERMINAL_DOCKER_IMAGE", "python:3.11-slim")
|
||||
print(f" Docker Image: {docker_image}")
|
||||
|
||||
sudo_password = os.getenv("SUDO_PASSWORD", "")
|
||||
print(f" Sudo: {check_mark(bool(sudo_password))} {'enabled' if sudo_password else 'disabled'}")
|
||||
|
||||
# =========================================================================
|
||||
# Messaging Platforms
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Messaging Platforms", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
platforms = {
|
||||
"Telegram": ("TELEGRAM_BOT_TOKEN", "TELEGRAM_HOME_CHANNEL"),
|
||||
"Discord": ("DISCORD_BOT_TOKEN", "DISCORD_HOME_CHANNEL"),
|
||||
"WhatsApp": ("WHATSAPP_ENABLED", None),
|
||||
}
|
||||
|
||||
for name, (token_var, home_var) in platforms.items():
|
||||
token = os.getenv(token_var, "")
|
||||
has_token = bool(token)
|
||||
|
||||
home_channel = ""
|
||||
if home_var:
|
||||
home_channel = os.getenv(home_var, "")
|
||||
|
||||
status = "configured" if has_token else "not configured"
|
||||
if home_channel:
|
||||
status += f" (home: {home_channel})"
|
||||
|
||||
print(f" {name:<12} {check_mark(has_token)} {status}")
|
||||
|
||||
# =========================================================================
|
||||
# Gateway Status
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Gateway Service", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
if sys.platform.startswith('linux'):
|
||||
result = subprocess.run(
|
||||
["systemctl", "--user", "is-active", "hermes-gateway"],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
is_active = result.stdout.strip() == "active"
|
||||
print(f" Status: {check_mark(is_active)} {'running' if is_active else 'stopped'}")
|
||||
print(f" Manager: systemd (user)")
|
||||
|
||||
elif sys.platform == 'darwin':
|
||||
result = subprocess.run(
|
||||
["launchctl", "list", "ai.hermes.gateway"],
|
||||
capture_output=True,
|
||||
text=True
|
||||
)
|
||||
is_loaded = result.returncode == 0
|
||||
print(f" Status: {check_mark(is_loaded)} {'loaded' if is_loaded else 'not loaded'}")
|
||||
print(f" Manager: launchd")
|
||||
else:
|
||||
print(f" Status: {color('N/A', Colors.DIM)}")
|
||||
print(f" Manager: (not supported on this platform)")
|
||||
|
||||
# =========================================================================
|
||||
# Cron Jobs
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Scheduled Jobs", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
jobs_file = Path.home() / ".hermes" / "cron" / "jobs.json"
|
||||
if jobs_file.exists():
|
||||
import json
|
||||
try:
|
||||
with open(jobs_file) as f:
|
||||
data = json.load(f)
|
||||
jobs = data.get("jobs", [])
|
||||
enabled_jobs = [j for j in jobs if j.get("enabled", True)]
|
||||
print(f" Jobs: {len(enabled_jobs)} active, {len(jobs)} total")
|
||||
except:
|
||||
print(f" Jobs: (error reading jobs file)")
|
||||
else:
|
||||
print(f" Jobs: 0")
|
||||
|
||||
# =========================================================================
|
||||
# Sessions
|
||||
# =========================================================================
|
||||
print()
|
||||
print(color("◆ Sessions", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
sessions_file = Path.home() / ".hermes" / "sessions" / "sessions.json"
|
||||
if sessions_file.exists():
|
||||
import json
|
||||
try:
|
||||
with open(sessions_file) as f:
|
||||
data = json.load(f)
|
||||
print(f" Active: {len(data)} session(s)")
|
||||
except:
|
||||
print(f" Active: (error reading sessions file)")
|
||||
else:
|
||||
print(f" Active: 0")
|
||||
|
||||
# =========================================================================
|
||||
# Deep checks
|
||||
# =========================================================================
|
||||
if deep:
|
||||
print()
|
||||
print(color("◆ Deep Checks", Colors.CYAN, Colors.BOLD))
|
||||
|
||||
# Check OpenRouter connectivity
|
||||
openrouter_key = os.getenv("OPENROUTER_API_KEY", "")
|
||||
if openrouter_key:
|
||||
try:
|
||||
import httpx
|
||||
response = httpx.get(
|
||||
"https://openrouter.ai/api/v1/models",
|
||||
headers={"Authorization": f"Bearer {openrouter_key}"},
|
||||
timeout=10
|
||||
)
|
||||
ok = response.status_code == 200
|
||||
print(f" OpenRouter: {check_mark(ok)} {'reachable' if ok else f'error ({response.status_code})'}")
|
||||
except Exception as e:
|
||||
print(f" OpenRouter: {check_mark(False)} error: {e}")
|
||||
|
||||
# Check gateway port
|
||||
try:
|
||||
import socket
|
||||
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
sock.settimeout(1)
|
||||
result = sock.connect_ex(('127.0.0.1', 18789))
|
||||
sock.close()
|
||||
# Port in use = gateway likely running
|
||||
port_in_use = result == 0
|
||||
# This is informational, not necessarily bad
|
||||
print(f" Port 18789: {'in use' if port_in_use else 'available'}")
|
||||
except:
|
||||
pass
|
||||
|
||||
print()
|
||||
print(color("─" * 60, Colors.DIM))
|
||||
print(color(" Run 'hermes doctor' for detailed diagnostics", Colors.DIM))
|
||||
print(color(" Run 'hermes setup' to configure", Colors.DIM))
|
||||
print()
|
||||
@@ -1,341 +0,0 @@
|
||||
"""
|
||||
Hermes Agent Uninstaller.
|
||||
|
||||
Provides options for:
|
||||
- Full uninstall: Remove everything including configs and data
|
||||
- Keep data: Remove code but keep ~/.hermes/ (configs, sessions, logs)
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import shutil
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
# ANSI colors
|
||||
class Colors:
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
BLUE = "\033[34m"
|
||||
MAGENTA = "\033[35m"
|
||||
CYAN = "\033[36m"
|
||||
|
||||
def color(text: str, *codes) -> str:
|
||||
"""Apply color codes to text (only in TTY)."""
|
||||
if not sys.stdout.isatty():
|
||||
return text
|
||||
return "".join(codes) + text + Colors.RESET
|
||||
|
||||
def log_info(msg: str):
|
||||
print(f"{color('→', Colors.CYAN)} {msg}")
|
||||
|
||||
def log_success(msg: str):
|
||||
print(f"{color('✓', Colors.GREEN)} {msg}")
|
||||
|
||||
def log_warn(msg: str):
|
||||
print(f"{color('⚠', Colors.YELLOW)} {msg}")
|
||||
|
||||
def log_error(msg: str):
|
||||
print(f"{color('✗', Colors.RED)} {msg}")
|
||||
|
||||
|
||||
def get_project_root() -> Path:
|
||||
"""Get the project installation directory."""
|
||||
return Path(__file__).parent.parent.resolve()
|
||||
|
||||
|
||||
def get_hermes_home() -> Path:
|
||||
"""Get the Hermes home directory (~/.hermes)."""
|
||||
return Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
|
||||
|
||||
|
||||
def find_shell_configs() -> list:
|
||||
"""Find shell configuration files that might have PATH entries."""
|
||||
home = Path.home()
|
||||
configs = []
|
||||
|
||||
candidates = [
|
||||
home / ".bashrc",
|
||||
home / ".bash_profile",
|
||||
home / ".profile",
|
||||
home / ".zshrc",
|
||||
home / ".zprofile",
|
||||
]
|
||||
|
||||
for config in candidates:
|
||||
if config.exists():
|
||||
configs.append(config)
|
||||
|
||||
return configs
|
||||
|
||||
|
||||
def remove_path_from_shell_configs():
|
||||
"""Remove Hermes PATH entries from shell configuration files."""
|
||||
configs = find_shell_configs()
|
||||
removed_from = []
|
||||
|
||||
for config_path in configs:
|
||||
try:
|
||||
content = config_path.read_text()
|
||||
original_content = content
|
||||
|
||||
# Remove lines containing hermes-agent or hermes PATH entries
|
||||
new_lines = []
|
||||
skip_next = False
|
||||
|
||||
for line in content.split('\n'):
|
||||
# Skip the "# Hermes Agent" comment and following line
|
||||
if '# Hermes Agent' in line or '# hermes-agent' in line:
|
||||
skip_next = True
|
||||
continue
|
||||
if skip_next and ('hermes' in line.lower() and 'PATH' in line):
|
||||
skip_next = False
|
||||
continue
|
||||
skip_next = False
|
||||
|
||||
# Remove any PATH line containing hermes
|
||||
if 'hermes' in line.lower() and ('PATH=' in line or 'path=' in line.lower()):
|
||||
continue
|
||||
|
||||
new_lines.append(line)
|
||||
|
||||
new_content = '\n'.join(new_lines)
|
||||
|
||||
# Clean up multiple blank lines
|
||||
while '\n\n\n' in new_content:
|
||||
new_content = new_content.replace('\n\n\n', '\n\n')
|
||||
|
||||
if new_content != original_content:
|
||||
config_path.write_text(new_content)
|
||||
removed_from.append(config_path)
|
||||
|
||||
except Exception as e:
|
||||
log_warn(f"Could not update {config_path}: {e}")
|
||||
|
||||
return removed_from
|
||||
|
||||
|
||||
def remove_wrapper_script():
|
||||
"""Remove the hermes wrapper script if it exists."""
|
||||
wrapper_paths = [
|
||||
Path.home() / ".local" / "bin" / "hermes",
|
||||
Path("/usr/local/bin/hermes"),
|
||||
]
|
||||
|
||||
removed = []
|
||||
for wrapper in wrapper_paths:
|
||||
if wrapper.exists():
|
||||
try:
|
||||
# Check if it's our wrapper (contains hermes_cli reference)
|
||||
content = wrapper.read_text()
|
||||
if 'hermes_cli' in content or 'hermes-agent' in content:
|
||||
wrapper.unlink()
|
||||
removed.append(wrapper)
|
||||
except Exception as e:
|
||||
log_warn(f"Could not remove {wrapper}: {e}")
|
||||
|
||||
return removed
|
||||
|
||||
|
||||
def uninstall_gateway_service():
|
||||
"""Stop and uninstall the gateway service if running."""
|
||||
import platform
|
||||
|
||||
if platform.system() != "Linux":
|
||||
return False
|
||||
|
||||
service_file = Path.home() / ".config" / "systemd" / "user" / "hermes-gateway.service"
|
||||
|
||||
if not service_file.exists():
|
||||
return False
|
||||
|
||||
try:
|
||||
# Stop the service
|
||||
subprocess.run(
|
||||
["systemctl", "--user", "stop", "hermes-gateway"],
|
||||
capture_output=True,
|
||||
check=False
|
||||
)
|
||||
|
||||
# Disable the service
|
||||
subprocess.run(
|
||||
["systemctl", "--user", "disable", "hermes-gateway"],
|
||||
capture_output=True,
|
||||
check=False
|
||||
)
|
||||
|
||||
# Remove service file
|
||||
service_file.unlink()
|
||||
|
||||
# Reload systemd
|
||||
subprocess.run(
|
||||
["systemctl", "--user", "daemon-reload"],
|
||||
capture_output=True,
|
||||
check=False
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
log_warn(f"Could not fully remove gateway service: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def run_uninstall(args):
|
||||
"""
|
||||
Run the uninstall process.
|
||||
|
||||
Options:
|
||||
- Full uninstall: removes code + ~/.hermes/ (configs, data, logs)
|
||||
- Keep data: removes code but keeps ~/.hermes/ for future reinstall
|
||||
"""
|
||||
project_root = get_project_root()
|
||||
hermes_home = get_hermes_home()
|
||||
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.MAGENTA, Colors.BOLD))
|
||||
print(color("│ 🦋 Hermes Agent Uninstaller │", Colors.MAGENTA, Colors.BOLD))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.MAGENTA, Colors.BOLD))
|
||||
print()
|
||||
|
||||
# Show what will be affected
|
||||
print(color("Current Installation:", Colors.CYAN, Colors.BOLD))
|
||||
print(f" Code: {project_root}")
|
||||
print(f" Config: {hermes_home / 'config.yaml'}")
|
||||
print(f" Secrets: {hermes_home / '.env'}")
|
||||
print(f" Data: {hermes_home / 'cron/'}, {hermes_home / 'sessions/'}, {hermes_home / 'logs/'}")
|
||||
print()
|
||||
|
||||
# Ask for confirmation
|
||||
print(color("Uninstall Options:", Colors.YELLOW, Colors.BOLD))
|
||||
print()
|
||||
print(" 1) " + color("Keep data", Colors.GREEN) + " - Remove code only, keep configs/sessions/logs")
|
||||
print(" (Recommended - you can reinstall later with your settings intact)")
|
||||
print()
|
||||
print(" 2) " + color("Full uninstall", Colors.RED) + " - Remove everything including all data")
|
||||
print(" (Warning: This deletes all configs, sessions, and logs permanently)")
|
||||
print()
|
||||
print(" 3) " + color("Cancel", Colors.CYAN) + " - Don't uninstall")
|
||||
print()
|
||||
|
||||
try:
|
||||
choice = input(color("Select option [1/2/3]: ", Colors.BOLD)).strip()
|
||||
except (KeyboardInterrupt, EOFError):
|
||||
print()
|
||||
print("Cancelled.")
|
||||
return
|
||||
|
||||
if choice == "3" or choice.lower() in ("c", "cancel", "q", "quit", "n", "no"):
|
||||
print()
|
||||
print("Uninstall cancelled.")
|
||||
return
|
||||
|
||||
full_uninstall = (choice == "2")
|
||||
|
||||
# Final confirmation
|
||||
print()
|
||||
if full_uninstall:
|
||||
print(color("⚠️ WARNING: This will permanently delete ALL Hermes data!", Colors.RED, Colors.BOLD))
|
||||
print(color(" Including: configs, API keys, sessions, scheduled jobs, logs", Colors.RED))
|
||||
else:
|
||||
print("This will remove the Hermes code but keep your configuration and data.")
|
||||
|
||||
print()
|
||||
try:
|
||||
confirm = input(f"Type '{color('yes', Colors.YELLOW)}' to confirm: ").strip().lower()
|
||||
except (KeyboardInterrupt, EOFError):
|
||||
print()
|
||||
print("Cancelled.")
|
||||
return
|
||||
|
||||
if confirm != "yes":
|
||||
print()
|
||||
print("Uninstall cancelled.")
|
||||
return
|
||||
|
||||
print()
|
||||
print(color("Uninstalling...", Colors.CYAN, Colors.BOLD))
|
||||
print()
|
||||
|
||||
# 1. Stop and uninstall gateway service
|
||||
log_info("Checking for gateway service...")
|
||||
if uninstall_gateway_service():
|
||||
log_success("Gateway service stopped and removed")
|
||||
else:
|
||||
log_info("No gateway service found")
|
||||
|
||||
# 2. Remove PATH entries from shell configs
|
||||
log_info("Removing PATH entries from shell configs...")
|
||||
removed_configs = remove_path_from_shell_configs()
|
||||
if removed_configs:
|
||||
for config in removed_configs:
|
||||
log_success(f"Updated {config}")
|
||||
else:
|
||||
log_info("No PATH entries found to remove")
|
||||
|
||||
# 3. Remove wrapper script
|
||||
log_info("Removing hermes command...")
|
||||
removed_wrappers = remove_wrapper_script()
|
||||
if removed_wrappers:
|
||||
for wrapper in removed_wrappers:
|
||||
log_success(f"Removed {wrapper}")
|
||||
else:
|
||||
log_info("No wrapper script found")
|
||||
|
||||
# 4. Remove installation directory (code)
|
||||
log_info(f"Removing installation directory...")
|
||||
|
||||
# Check if we're running from within the install dir
|
||||
# We need to be careful here
|
||||
try:
|
||||
if project_root.exists():
|
||||
# If the install is inside ~/.hermes/, just remove the hermes-agent subdir
|
||||
if hermes_home in project_root.parents or project_root.parent == hermes_home:
|
||||
shutil.rmtree(project_root)
|
||||
log_success(f"Removed {project_root}")
|
||||
else:
|
||||
# Installation is somewhere else entirely
|
||||
shutil.rmtree(project_root)
|
||||
log_success(f"Removed {project_root}")
|
||||
except Exception as e:
|
||||
log_warn(f"Could not fully remove {project_root}: {e}")
|
||||
log_info("You may need to manually remove it")
|
||||
|
||||
# 5. Optionally remove ~/.hermes/ data directory
|
||||
if full_uninstall:
|
||||
log_info("Removing configuration and data...")
|
||||
try:
|
||||
if hermes_home.exists():
|
||||
shutil.rmtree(hermes_home)
|
||||
log_success(f"Removed {hermes_home}")
|
||||
except Exception as e:
|
||||
log_warn(f"Could not fully remove {hermes_home}: {e}")
|
||||
log_info("You may need to manually remove it")
|
||||
else:
|
||||
log_info(f"Keeping configuration and data in {hermes_home}")
|
||||
|
||||
# Done
|
||||
print()
|
||||
print(color("┌─────────────────────────────────────────────────────────┐", Colors.GREEN, Colors.BOLD))
|
||||
print(color("│ ✓ Uninstall Complete! │", Colors.GREEN, Colors.BOLD))
|
||||
print(color("└─────────────────────────────────────────────────────────┘", Colors.GREEN, Colors.BOLD))
|
||||
print()
|
||||
|
||||
if not full_uninstall:
|
||||
print(color("Your configuration and data have been preserved:", Colors.CYAN))
|
||||
print(f" {hermes_home}/")
|
||||
print()
|
||||
print("To reinstall later with your existing settings:")
|
||||
print(color(" curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash", Colors.DIM))
|
||||
print()
|
||||
|
||||
print(color("Reload your shell to complete the process:", Colors.YELLOW))
|
||||
print(" source ~/.bashrc # or ~/.zshrc")
|
||||
print()
|
||||
print("Thank you for using Hermes Agent! 🦋")
|
||||
print()
|
||||
@@ -2,14 +2,14 @@
|
||||
"""
|
||||
Image Generation Tools Module
|
||||
|
||||
This module provides image generation tools using FAL.ai's FLUX 2 Pro model with
|
||||
This module provides image generation tools using FAL.ai's FLUX.1 Krea model with
|
||||
automatic upscaling via FAL.ai's Clarity Upscaler for enhanced image quality.
|
||||
|
||||
Available tools:
|
||||
- image_generate_tool: Generate images from text prompts with automatic upscaling
|
||||
|
||||
Features:
|
||||
- High-quality image generation using FLUX 2 Pro model
|
||||
- High-quality image generation using FLUX.1 Krea model
|
||||
- Automatic 2x upscaling using Clarity Upscaler for enhanced quality
|
||||
- Comprehensive parameter control (size, steps, guidance, etc.)
|
||||
- Proper error handling and validation with fallback to original images
|
||||
@@ -38,25 +38,13 @@ from typing import Dict, Any, Optional, Union
|
||||
import fal_client
|
||||
|
||||
# Configuration for image generation
|
||||
DEFAULT_MODEL = "fal-ai/flux-2-pro"
|
||||
DEFAULT_ASPECT_RATIO = "landscape"
|
||||
DEFAULT_MODEL = "fal-ai/flux/krea"
|
||||
DEFAULT_IMAGE_SIZE = "landscape_4_3"
|
||||
DEFAULT_NUM_INFERENCE_STEPS = 50
|
||||
DEFAULT_GUIDANCE_SCALE = 4.5
|
||||
DEFAULT_NUM_IMAGES = 1
|
||||
DEFAULT_OUTPUT_FORMAT = "png"
|
||||
|
||||
# Safety settings
|
||||
ENABLE_SAFETY_CHECKER = False
|
||||
SAFETY_TOLERANCE = "5" # Maximum tolerance (1-5, where 5 is most permissive)
|
||||
|
||||
# Aspect ratio mapping - simplified choices for model to select
|
||||
ASPECT_RATIO_MAP = {
|
||||
"landscape": "landscape_16_9",
|
||||
"square": "square_hd",
|
||||
"portrait": "portrait_16_9"
|
||||
}
|
||||
VALID_ASPECT_RATIOS = list(ASPECT_RATIO_MAP.keys())
|
||||
|
||||
# Configuration for automatic upscaling
|
||||
UPSCALER_MODEL = "fal-ai/clarity-upscaler"
|
||||
UPSCALER_FACTOR = 2
|
||||
@@ -68,7 +56,7 @@ UPSCALER_RESEMBLANCE = 0.6
|
||||
UPSCALER_GUIDANCE_SCALE = 4
|
||||
UPSCALER_NUM_INFERENCE_STEPS = 18
|
||||
|
||||
# Valid parameter values for validation based on FLUX 2 Pro documentation
|
||||
# Valid parameter values for validation based on FLUX Krea documentation
|
||||
VALID_IMAGE_SIZES = [
|
||||
"square_hd", "square", "portrait_4_3", "portrait_16_9", "landscape_4_3", "landscape_16_9"
|
||||
]
|
||||
@@ -145,7 +133,7 @@ def _validate_parameters(
|
||||
acceleration: str = "none"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Validate and normalize image generation parameters for FLUX 2 Pro model.
|
||||
Validate and normalize image generation parameters for FLUX Krea model.
|
||||
|
||||
Args:
|
||||
image_size: Either a preset string or custom size dict
|
||||
@@ -186,7 +174,7 @@ def _validate_parameters(
|
||||
raise ValueError("num_inference_steps must be an integer between 1 and 100")
|
||||
validated["num_inference_steps"] = num_inference_steps
|
||||
|
||||
# Validate guidance_scale (FLUX 2 Pro default is 4.5)
|
||||
# Validate guidance_scale (FLUX Krea default is 4.5)
|
||||
if not isinstance(guidance_scale, (int, float)) or guidance_scale < 0.1 or guidance_scale > 20.0:
|
||||
raise ValueError("guidance_scale must be a number between 0.1 and 20.0")
|
||||
validated["guidance_scale"] = float(guidance_scale)
|
||||
@@ -266,28 +254,34 @@ async def _upscale_image(image_url: str, original_prompt: str) -> Dict[str, Any]
|
||||
|
||||
async def image_generate_tool(
|
||||
prompt: str,
|
||||
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
|
||||
image_size: Union[str, Dict[str, int]] = DEFAULT_IMAGE_SIZE,
|
||||
num_inference_steps: int = DEFAULT_NUM_INFERENCE_STEPS,
|
||||
guidance_scale: float = DEFAULT_GUIDANCE_SCALE,
|
||||
num_images: int = DEFAULT_NUM_IMAGES,
|
||||
enable_safety_checker: bool = True,
|
||||
output_format: str = DEFAULT_OUTPUT_FORMAT,
|
||||
acceleration: str = "none",
|
||||
allow_nsfw_images: bool = True,
|
||||
seed: Optional[int] = None
|
||||
) -> str:
|
||||
"""
|
||||
Generate images from text prompts using FAL.ai's FLUX 2 Pro model with automatic upscaling.
|
||||
Generate images from text prompts using FAL.ai's FLUX.1 Krea model with automatic upscaling.
|
||||
|
||||
This tool uses FAL.ai's FLUX 2 Pro model for high-quality text-to-image generation
|
||||
This tool uses FAL.ai's FLUX.1 Krea model for high-quality text-to-image generation
|
||||
with extensive customization options. Generated images are automatically upscaled 2x
|
||||
using FAL.ai's Clarity Upscaler for enhanced quality. The final upscaled images are
|
||||
returned as URLs that can be displayed using <img src="{URL}"></img> tags.
|
||||
|
||||
Args:
|
||||
prompt (str): The text prompt describing the desired image
|
||||
aspect_ratio (str): Image aspect ratio - "landscape", "square", or "portrait" (default: "landscape")
|
||||
num_inference_steps (int): Number of denoising steps (1-50, default: 50)
|
||||
image_size (Union[str, Dict[str, int]]): Preset size or custom {"width": int, "height": int}
|
||||
num_inference_steps (int): Number of denoising steps (1-50, default: 28)
|
||||
guidance_scale (float): How closely to follow prompt (0.1-20.0, default: 4.5)
|
||||
num_images (int): Number of images to generate (1-4, default: 1)
|
||||
enable_safety_checker (bool): Enable content safety filtering (default: True)
|
||||
output_format (str): Image format "jpeg" or "png" (default: "png")
|
||||
acceleration (str): Generation speed "none", "regular", or "high" (default: "none")
|
||||
allow_nsfw_images (bool): Allow generation of NSFW content (default: True)
|
||||
seed (Optional[int]): Random seed for reproducible results (optional)
|
||||
|
||||
Returns:
|
||||
@@ -297,22 +291,17 @@ async def image_generate_tool(
|
||||
"image": str or None # URL of the upscaled image, or None if failed
|
||||
}
|
||||
"""
|
||||
# Validate and map aspect_ratio to actual image_size
|
||||
aspect_ratio_lower = aspect_ratio.lower().strip() if aspect_ratio else DEFAULT_ASPECT_RATIO
|
||||
if aspect_ratio_lower not in ASPECT_RATIO_MAP:
|
||||
print(f"⚠️ Invalid aspect_ratio '{aspect_ratio}', defaulting to '{DEFAULT_ASPECT_RATIO}'")
|
||||
aspect_ratio_lower = DEFAULT_ASPECT_RATIO
|
||||
image_size = ASPECT_RATIO_MAP[aspect_ratio_lower]
|
||||
|
||||
debug_call_data = {
|
||||
"parameters": {
|
||||
"prompt": prompt,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"image_size": image_size,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"guidance_scale": guidance_scale,
|
||||
"num_images": num_images,
|
||||
"enable_safety_checker": enable_safety_checker,
|
||||
"output_format": output_format,
|
||||
"acceleration": acceleration,
|
||||
"allow_nsfw_images": allow_nsfw_images,
|
||||
"seed": seed
|
||||
},
|
||||
"error": None,
|
||||
@@ -324,31 +313,35 @@ async def image_generate_tool(
|
||||
start_time = datetime.datetime.now()
|
||||
|
||||
try:
|
||||
print(f"🎨 Generating {num_images} image(s) with FLUX 2 Pro: {prompt[:80]}{'...' if len(prompt) > 80 else ''}")
|
||||
print(f"🎨 Generating {num_images} image(s) with FLUX Krea: {prompt[:80]}{'...' if len(prompt) > 80 else ''}")
|
||||
|
||||
# Validate prompt
|
||||
if not prompt or not isinstance(prompt, str) or len(prompt.strip()) == 0:
|
||||
raise ValueError("Prompt is required and must be a non-empty string")
|
||||
|
||||
if len(prompt) > 1000:
|
||||
raise ValueError("Prompt must be 1000 characters or less")
|
||||
|
||||
# Check API key availability
|
||||
if not os.getenv("FAL_KEY"):
|
||||
raise ValueError("FAL_KEY environment variable not set")
|
||||
|
||||
# Validate other parameters
|
||||
# Validate parameters
|
||||
validated_params = _validate_parameters(
|
||||
image_size, num_inference_steps, guidance_scale, num_images, output_format, "none"
|
||||
image_size, num_inference_steps, guidance_scale, num_images, output_format, acceleration
|
||||
)
|
||||
|
||||
# Prepare arguments for FAL.ai FLUX 2 Pro API
|
||||
# Prepare arguments for FAL.ai FLUX Krea API
|
||||
arguments = {
|
||||
"prompt": prompt.strip(),
|
||||
"image_size": validated_params["image_size"],
|
||||
"num_inference_steps": validated_params["num_inference_steps"],
|
||||
"guidance_scale": validated_params["guidance_scale"],
|
||||
"num_images": validated_params["num_images"],
|
||||
"enable_safety_checker": enable_safety_checker,
|
||||
"output_format": validated_params["output_format"],
|
||||
"enable_safety_checker": ENABLE_SAFETY_CHECKER,
|
||||
"safety_tolerance": SAFETY_TOLERANCE,
|
||||
"acceleration": validated_params["acceleration"],
|
||||
"allow_nsfw_images": allow_nsfw_images,
|
||||
"sync_mode": True # Use sync mode for immediate results
|
||||
}
|
||||
|
||||
@@ -356,11 +349,12 @@ async def image_generate_tool(
|
||||
if seed is not None and isinstance(seed, int):
|
||||
arguments["seed"] = seed
|
||||
|
||||
print(f"🚀 Submitting generation request to FAL.ai FLUX 2 Pro...")
|
||||
print(f"🚀 Submitting generation request to FAL.ai FLUX Krea...")
|
||||
print(f" Model: {DEFAULT_MODEL}")
|
||||
print(f" Aspect Ratio: {aspect_ratio_lower} → {image_size}")
|
||||
print(f" Size: {validated_params['image_size']}")
|
||||
print(f" Steps: {validated_params['num_inference_steps']}")
|
||||
print(f" Guidance: {validated_params['guidance_scale']}")
|
||||
print(f" Acceleration: {validated_params['acceleration']}")
|
||||
|
||||
# Submit request to FAL.ai
|
||||
handler = await fal_client.submit_async(
|
||||
@@ -423,7 +417,7 @@ async def image_generate_tool(
|
||||
_log_debug_call("image_generate_tool", debug_call_data)
|
||||
_save_debug_log()
|
||||
|
||||
return json.dumps(response_data, indent=2, ensure_ascii=False)
|
||||
return json.dumps(response_data, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
generation_time = (datetime.datetime.now() - start_time).total_seconds()
|
||||
@@ -441,7 +435,7 @@ async def image_generate_tool(
|
||||
_log_debug_call("image_generate_tool", debug_call_data)
|
||||
_save_debug_log()
|
||||
|
||||
return json.dumps(response_data, indent=2, ensure_ascii=False)
|
||||
return json.dumps(response_data, indent=2)
|
||||
|
||||
|
||||
def check_fal_api_key() -> bool:
|
||||
@@ -501,7 +495,7 @@ if __name__ == "__main__":
|
||||
"""
|
||||
Simple test/demo when run directly
|
||||
"""
|
||||
print("🎨 Image Generation Tools Module - FLUX 2 Pro + Auto Upscaling")
|
||||
print("🎨 Image Generation Tools Module - FLUX.1 Krea + Auto Upscaling")
|
||||
print("=" * 60)
|
||||
|
||||
# Check if API key is available
|
||||
Submodule mini-swe-agent deleted from 07aa6a7385
@@ -1,708 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Mini-SWE-Agent Runner with Hermes Trajectory Format
|
||||
|
||||
This module provides a runner that uses mini-swe-agent's execution environments
|
||||
(local, docker, modal) but outputs trajectories in the Hermes-Agent format
|
||||
compatible with batch_runner.py and trajectory_compressor.py.
|
||||
|
||||
Features:
|
||||
- Uses mini-swe-agent's Docker, Modal, or Local environments for command execution
|
||||
- Outputs trajectories in Hermes format (from/value pairs with <tool_call>/<tool_response> XML)
|
||||
- Compatible with the trajectory compression pipeline
|
||||
- Supports batch processing from JSONL prompt files
|
||||
|
||||
Usage:
|
||||
# Run a single task with local environment
|
||||
python mini_swe_runner.py --task "Create a hello world Python script" --env local
|
||||
|
||||
# Run with Docker
|
||||
python mini_swe_runner.py --task "List files in /tmp" --env docker --image python:3.11-slim
|
||||
|
||||
# Run with Modal (cloud)
|
||||
python mini_swe_runner.py --task "Install numpy and test it" --env modal --image python:3.11-slim
|
||||
|
||||
# Batch mode from JSONL file
|
||||
python mini_swe_runner.py --prompts_file prompts.jsonl --output_file trajectories.jsonl --env docker
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional, Literal
|
||||
|
||||
import fire
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Add mini-swe-agent to path if not installed
|
||||
mini_swe_path = Path(__file__).parent / "mini-swe-agent" / "src"
|
||||
if mini_swe_path.exists():
|
||||
sys.path.insert(0, str(mini_swe_path))
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Terminal Tool Definition (matches Hermes-Agent format)
|
||||
# ============================================================================
|
||||
|
||||
TERMINAL_TOOL_DEFINITION = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "terminal",
|
||||
"description": """Execute bash commands in a sandboxed environment.
|
||||
|
||||
**Environment:**
|
||||
- Isolated execution environment (local, Docker, or Modal cloud)
|
||||
- Filesystem persists between tool calls within the same task
|
||||
- Internet access available
|
||||
|
||||
**Command Execution:**
|
||||
- Provide the command to execute via the 'command' parameter
|
||||
- Optional 'timeout' parameter in seconds (default: 60)
|
||||
|
||||
**Examples:**
|
||||
- Run command: `{"command": "ls -la"}`
|
||||
- With timeout: `{"command": "long_task.sh", "timeout": 300}`
|
||||
|
||||
**Best Practices:**
|
||||
- Use non-interactive commands (avoid vim, nano, interactive python)
|
||||
- Pipe to cat if output might be large
|
||||
- Install tools with apt-get or pip as needed
|
||||
|
||||
**Completion:**
|
||||
- When task is complete, output: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by your result
|
||||
""",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"command": {
|
||||
"type": "string",
|
||||
"description": "The bash command to execute"
|
||||
},
|
||||
"timeout": {
|
||||
"type": "integer",
|
||||
"description": "Command timeout in seconds (default: 60)"
|
||||
}
|
||||
},
|
||||
"required": ["command"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Environment Factory
|
||||
# ============================================================================
|
||||
|
||||
def create_environment(
|
||||
env_type: str = "local",
|
||||
image: str = "python:3.11-slim",
|
||||
cwd: str = "/tmp",
|
||||
timeout: int = 60,
|
||||
**kwargs
|
||||
):
|
||||
"""
|
||||
Create an execution environment from mini-swe-agent.
|
||||
|
||||
Args:
|
||||
env_type: One of "local", "docker", "modal"
|
||||
image: Docker/Modal image name (ignored for local)
|
||||
cwd: Working directory
|
||||
timeout: Default command timeout
|
||||
**kwargs: Additional environment-specific options
|
||||
|
||||
Returns:
|
||||
Environment instance with execute() method
|
||||
"""
|
||||
if env_type == "local":
|
||||
from minisweagent.environments.local import LocalEnvironment
|
||||
return LocalEnvironment(cwd=cwd, timeout=timeout)
|
||||
|
||||
elif env_type == "docker":
|
||||
from minisweagent.environments.docker import DockerEnvironment
|
||||
return DockerEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
|
||||
|
||||
elif env_type == "modal":
|
||||
from minisweagent.environments.extra.swerex_modal import SwerexModalEnvironment
|
||||
return SwerexModalEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown environment type: {env_type}. Use 'local', 'docker', or 'modal'")
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Mini-SWE Runner with Hermes Trajectory Format
|
||||
# ============================================================================
|
||||
|
||||
class MiniSWERunner:
|
||||
"""
|
||||
Agent runner that uses mini-swe-agent environments but outputs
|
||||
trajectories in Hermes-Agent format.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: str = "anthropic/claude-sonnet-4-20250514",
|
||||
base_url: str = None,
|
||||
api_key: str = None,
|
||||
env_type: str = "local",
|
||||
image: str = "python:3.11-slim",
|
||||
cwd: str = "/tmp",
|
||||
max_iterations: int = 15,
|
||||
command_timeout: int = 60,
|
||||
verbose: bool = False,
|
||||
):
|
||||
"""
|
||||
Initialize the Mini-SWE Runner.
|
||||
|
||||
Args:
|
||||
model: Model name for OpenAI-compatible API
|
||||
base_url: API base URL (optional, uses env vars if not provided)
|
||||
api_key: API key (optional, uses env vars if not provided)
|
||||
env_type: Environment type - "local", "docker", or "modal"
|
||||
image: Docker/Modal image (ignored for local)
|
||||
cwd: Working directory for commands
|
||||
max_iterations: Maximum tool-calling iterations
|
||||
command_timeout: Default timeout for commands
|
||||
verbose: Enable verbose logging
|
||||
"""
|
||||
self.model = model
|
||||
self.max_iterations = max_iterations
|
||||
self.command_timeout = command_timeout
|
||||
self.verbose = verbose
|
||||
self.env_type = env_type
|
||||
self.image = image
|
||||
self.cwd = cwd
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG if verbose else logging.INFO,
|
||||
format='%(asctime)s - %(levelname)s - %(message)s',
|
||||
datefmt='%H:%M:%S'
|
||||
)
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
# Initialize OpenAI client - defaults to OpenRouter
|
||||
from openai import OpenAI
|
||||
|
||||
client_kwargs = {}
|
||||
|
||||
# Default to OpenRouter if no base_url provided
|
||||
if base_url:
|
||||
client_kwargs["base_url"] = base_url
|
||||
else:
|
||||
client_kwargs["base_url"] = "https://openrouter.ai/api/v1"
|
||||
|
||||
# Handle API key - OpenRouter is the primary provider
|
||||
if api_key:
|
||||
client_kwargs["api_key"] = api_key
|
||||
else:
|
||||
client_kwargs["api_key"] = os.getenv(
|
||||
"OPENROUTER_API_KEY",
|
||||
os.getenv("ANTHROPIC_API_KEY", os.getenv("OPENAI_API_KEY", ""))
|
||||
)
|
||||
|
||||
self.client = OpenAI(**client_kwargs)
|
||||
|
||||
# Environment will be created per-task
|
||||
self.env = None
|
||||
|
||||
# Tool definition
|
||||
self.tools = [TERMINAL_TOOL_DEFINITION]
|
||||
|
||||
print(f"🤖 Mini-SWE Runner initialized")
|
||||
print(f" Model: {self.model}")
|
||||
print(f" Environment: {self.env_type}")
|
||||
if self.env_type != "local":
|
||||
print(f" Image: {self.image}")
|
||||
print(f" Max iterations: {self.max_iterations}")
|
||||
|
||||
def _create_env(self):
|
||||
"""Create the execution environment."""
|
||||
print(f"🔧 Creating {self.env_type} environment...")
|
||||
self.env = create_environment(
|
||||
env_type=self.env_type,
|
||||
image=self.image,
|
||||
cwd=self.cwd,
|
||||
timeout=self.command_timeout
|
||||
)
|
||||
print(f"✅ Environment ready")
|
||||
|
||||
def _cleanup_env(self):
|
||||
"""Cleanup the execution environment."""
|
||||
if self.env is not None:
|
||||
if hasattr(self.env, 'cleanup'):
|
||||
self.env.cleanup()
|
||||
elif hasattr(self.env, 'stop'):
|
||||
self.env.stop()
|
||||
self.env = None
|
||||
|
||||
def _execute_command(self, command: str, timeout: int = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Execute a command in the environment.
|
||||
|
||||
Args:
|
||||
command: Bash command to execute
|
||||
timeout: Optional timeout override
|
||||
|
||||
Returns:
|
||||
Dict with 'output' and 'returncode'
|
||||
"""
|
||||
if self.env is None:
|
||||
self._create_env()
|
||||
|
||||
try:
|
||||
result = self.env.execute(command, timeout=timeout or self.command_timeout)
|
||||
return {
|
||||
"output": result.get("output", ""),
|
||||
"exit_code": result.get("returncode", 0),
|
||||
"error": None
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
"output": "",
|
||||
"exit_code": -1,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def _format_tools_for_system_message(self) -> str:
|
||||
"""Format tool definitions for the system message."""
|
||||
formatted_tools = []
|
||||
for tool in self.tools:
|
||||
func = tool["function"]
|
||||
formatted_tools.append({
|
||||
"name": func["name"],
|
||||
"description": func.get("description", ""),
|
||||
"parameters": func.get("parameters", {}),
|
||||
"required": None
|
||||
})
|
||||
return json.dumps(formatted_tools, ensure_ascii=False)
|
||||
|
||||
def _convert_to_hermes_format(
|
||||
self,
|
||||
messages: List[Dict[str, Any]],
|
||||
user_query: str,
|
||||
completed: bool
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Convert internal message format to Hermes trajectory format.
|
||||
|
||||
This produces the exact format used by batch_runner.py.
|
||||
"""
|
||||
trajectory = []
|
||||
|
||||
# System message with tool definitions
|
||||
system_msg = (
|
||||
"You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. "
|
||||
"You may call one or more functions to assist with the user query. If available tools are not relevant in assisting "
|
||||
"with user query, just respond in natural conversational language. Don't make assumptions about what values to plug "
|
||||
"into functions. After calling & executing the functions, you will be provided with function results within "
|
||||
"<tool_response> </tool_response> XML tags. Here are the available tools:\n"
|
||||
f"<tools>\n{self._format_tools_for_system_message()}\n</tools>\n"
|
||||
"For each function call return a JSON object, with the following pydantic model json schema for each:\n"
|
||||
"{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, "
|
||||
"'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n"
|
||||
"Each function call should be enclosed within <tool_call> </tool_call> XML tags.\n"
|
||||
"Example:\n<tool_call>\n{'name': <function-name>,'arguments': <args-dict>}\n</tool_call>"
|
||||
)
|
||||
|
||||
trajectory.append({"from": "system", "value": system_msg})
|
||||
trajectory.append({"from": "human", "value": user_query})
|
||||
|
||||
# Process messages (skip first user message as we already added it)
|
||||
i = 1
|
||||
while i < len(messages):
|
||||
msg = messages[i]
|
||||
|
||||
if msg["role"] == "assistant":
|
||||
if "tool_calls" in msg and msg["tool_calls"]:
|
||||
# Assistant message with tool calls
|
||||
content = ""
|
||||
|
||||
# Add reasoning if present
|
||||
if msg.get("reasoning"):
|
||||
content = f"<think>{msg['reasoning']}</think>"
|
||||
|
||||
if msg.get("content"):
|
||||
content += msg["content"] + "\n"
|
||||
|
||||
# Add tool calls in XML format
|
||||
for tool_call in msg["tool_calls"]:
|
||||
try:
|
||||
arguments = json.loads(tool_call["function"]["arguments"]) \
|
||||
if isinstance(tool_call["function"]["arguments"], str) \
|
||||
else tool_call["function"]["arguments"]
|
||||
except json.JSONDecodeError:
|
||||
arguments = {}
|
||||
|
||||
tool_call_json = {
|
||||
"name": tool_call["function"]["name"],
|
||||
"arguments": arguments
|
||||
}
|
||||
content += f"<tool_call>\n{json.dumps(tool_call_json, ensure_ascii=False)}\n</tool_call>\n"
|
||||
|
||||
trajectory.append({"from": "gpt", "value": content.rstrip()})
|
||||
|
||||
# Collect subsequent tool responses
|
||||
tool_responses = []
|
||||
j = i + 1
|
||||
while j < len(messages) and messages[j]["role"] == "tool":
|
||||
tool_msg = messages[j]
|
||||
tool_content = tool_msg["content"]
|
||||
|
||||
# Try to parse as JSON
|
||||
try:
|
||||
if tool_content.strip().startswith(("{", "[")):
|
||||
tool_content = json.loads(tool_content)
|
||||
except (json.JSONDecodeError, AttributeError):
|
||||
pass
|
||||
|
||||
tool_response = f"<tool_response>\n"
|
||||
tool_response += json.dumps({
|
||||
"tool_call_id": tool_msg.get("tool_call_id", ""),
|
||||
"name": msg["tool_calls"][len(tool_responses)]["function"]["name"] \
|
||||
if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
|
||||
"content": tool_content
|
||||
}, ensure_ascii=False)
|
||||
tool_response += "\n</tool_response>"
|
||||
tool_responses.append(tool_response)
|
||||
j += 1
|
||||
|
||||
if tool_responses:
|
||||
trajectory.append({"from": "tool", "value": "\n".join(tool_responses)})
|
||||
i = j - 1
|
||||
|
||||
else:
|
||||
# Regular assistant message (no tool calls)
|
||||
content = ""
|
||||
if msg.get("reasoning"):
|
||||
content = f"<think>{msg['reasoning']}</think>"
|
||||
content += msg.get("content") or ""
|
||||
trajectory.append({"from": "gpt", "value": content})
|
||||
|
||||
elif msg["role"] == "user":
|
||||
trajectory.append({"from": "human", "value": msg["content"]})
|
||||
|
||||
i += 1
|
||||
|
||||
return trajectory
|
||||
|
||||
def run_task(self, task: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Run a single task and return the result with trajectory.
|
||||
|
||||
Args:
|
||||
task: The task/prompt to execute
|
||||
|
||||
Returns:
|
||||
Dict with trajectory, completion status, and metadata
|
||||
"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"📝 Task: {task[:80]}{'...' if len(task) > 80 else ''}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# Initialize environment
|
||||
self._create_env()
|
||||
|
||||
# Message history
|
||||
messages = [{"role": "user", "content": task}]
|
||||
|
||||
# System prompt for the LLM (ephemeral - not saved to trajectory)
|
||||
system_prompt = """You are an AI agent that can execute bash commands to complete tasks.
|
||||
|
||||
When you need to run commands, use the 'terminal' tool with your bash command.
|
||||
|
||||
**Important:**
|
||||
- When you have completed the task successfully, run: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by a summary
|
||||
- Be concise and efficient in your approach
|
||||
- Install any needed tools with apt-get or pip
|
||||
- Avoid interactive commands (no vim, nano, less, etc.)
|
||||
|
||||
Complete the user's task step by step."""
|
||||
|
||||
api_call_count = 0
|
||||
completed = False
|
||||
final_response = None
|
||||
|
||||
try:
|
||||
while api_call_count < self.max_iterations:
|
||||
api_call_count += 1
|
||||
print(f"\n🔄 API call #{api_call_count}/{self.max_iterations}")
|
||||
|
||||
# Prepare API messages
|
||||
api_messages = [{"role": "system", "content": system_prompt}] + messages
|
||||
|
||||
# Make API call
|
||||
try:
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=api_messages,
|
||||
tools=self.tools,
|
||||
timeout=300.0
|
||||
)
|
||||
except Exception as e:
|
||||
self.logger.error(f"API call failed: {e}")
|
||||
break
|
||||
|
||||
assistant_message = response.choices[0].message
|
||||
|
||||
# Log assistant response
|
||||
if assistant_message.content:
|
||||
print(f"🤖 Assistant: {assistant_message.content[:100]}...")
|
||||
|
||||
# Check for tool calls
|
||||
if assistant_message.tool_calls:
|
||||
print(f"🔧 Tool calls: {len(assistant_message.tool_calls)}")
|
||||
|
||||
# Add assistant message with tool calls
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": assistant_message.content,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tc.id,
|
||||
"type": tc.type,
|
||||
"function": {
|
||||
"name": tc.function.name,
|
||||
"arguments": tc.function.arguments
|
||||
}
|
||||
}
|
||||
for tc in assistant_message.tool_calls
|
||||
]
|
||||
})
|
||||
|
||||
# Execute each tool call
|
||||
for tc in assistant_message.tool_calls:
|
||||
try:
|
||||
args = json.loads(tc.function.arguments)
|
||||
except json.JSONDecodeError:
|
||||
args = {}
|
||||
|
||||
command = args.get("command", "echo 'No command provided'")
|
||||
timeout = args.get("timeout", self.command_timeout)
|
||||
|
||||
print(f" 📞 terminal: {command[:60]}...")
|
||||
|
||||
# Execute command
|
||||
result = self._execute_command(command, timeout)
|
||||
|
||||
# Format result
|
||||
result_json = json.dumps({
|
||||
"content": {
|
||||
"output": result["output"],
|
||||
"exit_code": result["exit_code"],
|
||||
"error": result["error"]
|
||||
}
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# Check for task completion signal
|
||||
if "MINI_SWE_AGENT_FINAL_OUTPUT" in result["output"]:
|
||||
print(f" ✅ Task completion signal detected!")
|
||||
completed = True
|
||||
|
||||
# Add tool response
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"content": result_json,
|
||||
"tool_call_id": tc.id
|
||||
})
|
||||
|
||||
print(f" ✅ exit_code={result['exit_code']}, output={len(result['output'])} chars")
|
||||
|
||||
# If task completed, we can stop
|
||||
if completed:
|
||||
final_response = assistant_message.content
|
||||
break
|
||||
|
||||
else:
|
||||
# No tool calls - final response
|
||||
final_response = assistant_message.content or ""
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": final_response
|
||||
})
|
||||
completed = True
|
||||
print(f"🎉 Agent finished (no more tool calls)")
|
||||
break
|
||||
|
||||
if api_call_count >= self.max_iterations:
|
||||
print(f"⚠️ Reached max iterations ({self.max_iterations})")
|
||||
|
||||
finally:
|
||||
# Cleanup environment
|
||||
self._cleanup_env()
|
||||
|
||||
# Convert to Hermes trajectory format
|
||||
trajectory = self._convert_to_hermes_format(messages, task, completed)
|
||||
|
||||
return {
|
||||
"conversations": trajectory,
|
||||
"completed": completed,
|
||||
"api_calls": api_call_count,
|
||||
"metadata": {
|
||||
"model": self.model,
|
||||
"env_type": self.env_type,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
}
|
||||
|
||||
def run_batch(
|
||||
self,
|
||||
prompts: List[str],
|
||||
output_file: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Run multiple tasks and save trajectories to a JSONL file.
|
||||
|
||||
Args:
|
||||
prompts: List of task prompts
|
||||
output_file: Output JSONL file path
|
||||
|
||||
Returns:
|
||||
List of results
|
||||
"""
|
||||
results = []
|
||||
|
||||
print(f"\n📦 Running batch of {len(prompts)} tasks")
|
||||
print(f"📁 Output: {output_file}")
|
||||
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
for i, prompt in enumerate(prompts, 1):
|
||||
print(f"\n{'='*60}")
|
||||
print(f"📋 Task {i}/{len(prompts)}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
try:
|
||||
result = self.run_task(prompt)
|
||||
results.append(result)
|
||||
|
||||
# Write to file immediately
|
||||
f.write(json.dumps(result, ensure_ascii=False) + "\n")
|
||||
f.flush()
|
||||
|
||||
print(f"✅ Task {i} completed (api_calls={result['api_calls']})")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error on task {i}: {e}")
|
||||
error_result = {
|
||||
"conversations": [],
|
||||
"completed": False,
|
||||
"api_calls": 0,
|
||||
"error": str(e),
|
||||
"metadata": {"timestamp": datetime.now().isoformat()}
|
||||
}
|
||||
results.append(error_result)
|
||||
f.write(json.dumps(error_result, ensure_ascii=False) + "\n")
|
||||
f.flush()
|
||||
|
||||
print(f"\n✅ Batch complete! {len(results)} trajectories saved to {output_file}")
|
||||
return results
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# CLI Interface
|
||||
# ============================================================================
|
||||
|
||||
def main(
|
||||
task: str = None,
|
||||
prompts_file: str = None,
|
||||
output_file: str = "mini-swe-agent-test1.jsonl",
|
||||
model: str = "claude-sonnet-4-20250514",
|
||||
base_url: str = None,
|
||||
api_key: str = None,
|
||||
env: str = "local",
|
||||
image: str = "python:3.11-slim",
|
||||
cwd: str = "/tmp",
|
||||
max_iterations: int = 15,
|
||||
timeout: int = 60,
|
||||
verbose: bool = False,
|
||||
):
|
||||
"""
|
||||
Run mini-swe-agent tasks with Hermes trajectory format output.
|
||||
|
||||
Args:
|
||||
task: Single task to run (use this OR prompts_file)
|
||||
prompts_file: JSONL file with prompts (each line: {"prompt": "..."})
|
||||
output_file: Output JSONL file for trajectories
|
||||
model: Model name (default: claude-sonnet-4-20250514)
|
||||
base_url: API base URL (optional)
|
||||
api_key: API key (optional, uses env vars)
|
||||
env: Environment type - "local", "docker", or "modal"
|
||||
image: Docker/Modal image (default: python:3.11-slim)
|
||||
cwd: Working directory (default: /tmp)
|
||||
max_iterations: Maximum tool-calling iterations (default: 15)
|
||||
timeout: Command timeout in seconds (default: 60)
|
||||
verbose: Enable verbose logging
|
||||
|
||||
Examples:
|
||||
# Single task with local environment
|
||||
python mini_swe_runner.py --task "Create hello.py that prints Hello World"
|
||||
|
||||
# Single task with Docker
|
||||
python mini_swe_runner.py --task "List files" --env docker
|
||||
|
||||
# Batch from file
|
||||
python mini_swe_runner.py --prompts_file tasks.jsonl --output_file results.jsonl
|
||||
"""
|
||||
print("🚀 Mini-SWE Runner with Hermes Trajectory Format")
|
||||
print("=" * 60)
|
||||
|
||||
# Initialize runner
|
||||
runner = MiniSWERunner(
|
||||
model=model,
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
env_type=env,
|
||||
image=image,
|
||||
cwd=cwd,
|
||||
max_iterations=max_iterations,
|
||||
command_timeout=timeout,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
||||
if task:
|
||||
# Single task mode
|
||||
result = runner.run_task(task)
|
||||
|
||||
# Save to file
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
f.write(json.dumps(result, ensure_ascii=False) + "\n")
|
||||
|
||||
print(f"\n📁 Trajectory saved to: {output_file}")
|
||||
print(f"✅ Completed: {result['completed']}")
|
||||
print(f"📞 API calls: {result['api_calls']}")
|
||||
print(f"💬 Turns: {len(result['conversations'])}")
|
||||
|
||||
elif prompts_file:
|
||||
# Batch mode
|
||||
prompts = []
|
||||
with open(prompts_file, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
try:
|
||||
entry = json.loads(line)
|
||||
prompts.append(entry.get("prompt", entry.get("task", "")))
|
||||
except json.JSONDecodeError:
|
||||
prompts.append(line)
|
||||
|
||||
if not prompts:
|
||||
print(f"❌ No prompts found in {prompts_file}")
|
||||
return
|
||||
|
||||
runner.run_batch(prompts, output_file)
|
||||
|
||||
else:
|
||||
print("❌ Please provide either --task or --prompts_file")
|
||||
print(" Example: python mini_swe_runner.py --task 'Create a hello world script'")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
File diff suppressed because it is too large
Load Diff
243
mock_web_tools.py
Normal file
243
mock_web_tools.py
Normal file
@@ -0,0 +1,243 @@
|
||||
"""
|
||||
Mock Web Tools for Testing WebSocket Reconnection
|
||||
|
||||
This module provides mock implementations of web_search and web_extract
|
||||
that simulate long-running operations without making real API calls.
|
||||
|
||||
Perfect for testing WebSocket timeout/reconnection behavior without:
|
||||
- Wasting API credits
|
||||
- Waiting for real web crawling
|
||||
- Network dependencies
|
||||
"""
|
||||
|
||||
import time
|
||||
import json
|
||||
from typing import List
|
||||
|
||||
|
||||
def mock_web_search(query: str, delay: int = 2) -> str:
|
||||
"""
|
||||
Mock web search that returns fake results after a delay.
|
||||
|
||||
Args:
|
||||
query: Search query (ignored, just for API compatibility)
|
||||
delay: Seconds to sleep (default: 2s)
|
||||
|
||||
Returns:
|
||||
JSON string with fake search results
|
||||
"""
|
||||
print(f"🔍 [MOCK] Searching for: '{query}' (will take {delay}s)...")
|
||||
time.sleep(delay)
|
||||
|
||||
result = {
|
||||
"success": True,
|
||||
"data": {
|
||||
"web": [
|
||||
{
|
||||
"url": "https://example.com/article1",
|
||||
"title": "Mock Article 1 - Water Utilities",
|
||||
"description": "This is a mock search result for testing purposes. Real data would appear here.",
|
||||
"category": None
|
||||
},
|
||||
{
|
||||
"url": "https://example.com/article2",
|
||||
"title": "Mock Article 2 - AI Data Centers",
|
||||
"description": "Another mock result. This simulates web_search without making real API calls.",
|
||||
"category": None
|
||||
},
|
||||
{
|
||||
"url": "https://example.com/article3",
|
||||
"title": "Mock Article 3 - Investment Opportunities",
|
||||
"description": "Third mock result for testing. Query was: " + query,
|
||||
"category": None
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
return json.dumps(result, indent=2)
|
||||
|
||||
|
||||
def mock_web_extract(urls: List[str], delay: int = 60) -> str:
|
||||
"""
|
||||
Mock web extraction that simulates long-running crawl.
|
||||
|
||||
This is perfect for testing WebSocket timeout/reconnection because:
|
||||
- Default 60s delay triggers the ~30s WebSocket timeout
|
||||
- No actual web requests made
|
||||
- No API credits consumed
|
||||
- Predictable, reproducible behavior
|
||||
|
||||
Args:
|
||||
urls: List of URLs to "extract" (ignored)
|
||||
delay: Seconds to sleep (default: 60s to trigger timeout)
|
||||
|
||||
Returns:
|
||||
JSON string with fake extraction results
|
||||
"""
|
||||
print(f"🌐 [MOCK] Extracting {len(urls)} URLs (will take {delay}s)...")
|
||||
print(f"📊 [MOCK] This will test WebSocket reconnection (timeout at ~30s)")
|
||||
|
||||
# Simulate long-running operation
|
||||
# Show progress so user knows it's working
|
||||
for i in range(delay):
|
||||
if i % 10 == 0 and i > 0:
|
||||
print(f" ⏱️ [MOCK] {i}/{delay}s elapsed...")
|
||||
time.sleep(1)
|
||||
|
||||
# Generate fake but realistic-looking content
|
||||
result = {
|
||||
"success": True,
|
||||
"data": []
|
||||
}
|
||||
|
||||
for idx, url in enumerate(urls, 1):
|
||||
result["data"].append({
|
||||
"url": url,
|
||||
"title": f"Mock Extracted Content {idx}",
|
||||
"content": f"# Mock Content from {url}\n\n"
|
||||
f"This is simulated extracted content for testing purposes. "
|
||||
f"In a real scenario, this would contain the full text from the webpage. "
|
||||
f"\n\n## Key Points\n"
|
||||
f"- Mock point 1 about water utilities\n"
|
||||
f"- Mock point 2 about AI data centers\n"
|
||||
f"- Mock point 3 about investment opportunities\n"
|
||||
f"\n\nThis content took {delay} seconds to 'extract', which is long enough "
|
||||
f"to trigger WebSocket timeout and test reconnection logic."
|
||||
* 10, # Make it longer to simulate real extraction
|
||||
"extracted_at": "2025-10-10T14:00:00Z"
|
||||
})
|
||||
|
||||
json_result = json.dumps(result, indent=2)
|
||||
size_kb = len(json_result) / 1024
|
||||
|
||||
print(f"✅ [MOCK] Extraction completed: {len(urls)} URLs, {size_kb:.1f} KB")
|
||||
return json_result
|
||||
|
||||
|
||||
def mock_web_crawl(start_url: str, max_pages: int = 10, delay: int = 30) -> str:
|
||||
"""
|
||||
Mock web crawling that simulates multi-page crawl.
|
||||
|
||||
Args:
|
||||
start_url: Starting URL (ignored)
|
||||
max_pages: Max pages to crawl (just affects result count)
|
||||
delay: Seconds to sleep (default: 30s)
|
||||
|
||||
Returns:
|
||||
JSON string with fake crawl results
|
||||
"""
|
||||
print(f"🕷️ [MOCK] Crawling from: {start_url} (max {max_pages} pages, {delay}s)...")
|
||||
time.sleep(delay)
|
||||
|
||||
result = {
|
||||
"success": True,
|
||||
"data": {
|
||||
"start_url": start_url,
|
||||
"pages_crawled": min(max_pages, 5),
|
||||
"pages": []
|
||||
}
|
||||
}
|
||||
|
||||
for i in range(min(max_pages, 5)):
|
||||
result["data"]["pages"].append({
|
||||
"url": f"{start_url}/page{i+1}",
|
||||
"title": f"Mock Page {i+1}",
|
||||
"content": f"Mock content from page {i+1}. " * 50
|
||||
})
|
||||
|
||||
print(f"✅ [MOCK] Crawl completed: {len(result['data']['pages'])} pages")
|
||||
return json.dumps(result, indent=2)
|
||||
|
||||
|
||||
# Tool definitions for the agent (same format as real tools)
|
||||
MOCK_WEB_TOOLS = [
|
||||
{
|
||||
"name": "web_search",
|
||||
"description": "[MOCK] Search the web for information. Returns fake results after 2s delay. Perfect for quick tests.",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "The search query"
|
||||
},
|
||||
"delay": {
|
||||
"type": "integer",
|
||||
"description": "Seconds to delay (default: 2)",
|
||||
"default": 2
|
||||
}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "web_extract",
|
||||
"description": "[MOCK] Extract content from URLs. Simulates 60s delay to test WebSocket timeout/reconnection. Returns fake content without making real requests. PERFECT FOR TESTING!",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"urls": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"description": "List of URLs to extract"
|
||||
},
|
||||
"delay": {
|
||||
"type": "integer",
|
||||
"description": "Seconds to delay (default: 60 to trigger timeout)",
|
||||
"default": 60
|
||||
}
|
||||
},
|
||||
"required": ["urls"]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "web_crawl",
|
||||
"description": "[MOCK] Crawl website starting from URL. Returns fake results after 30s delay.",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"start_url": {
|
||||
"type": "string",
|
||||
"description": "Starting URL for crawl"
|
||||
},
|
||||
"max_pages": {
|
||||
"type": "integer",
|
||||
"description": "Max pages to crawl (default: 10)",
|
||||
"default": 10
|
||||
},
|
||||
"delay": {
|
||||
"type": "integer",
|
||||
"description": "Seconds to delay (default: 30)",
|
||||
"default": 30
|
||||
}
|
||||
},
|
||||
"required": ["start_url"]
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
# Map function names to implementations
|
||||
MOCK_TOOL_FUNCTIONS = {
|
||||
"web_search": mock_web_search,
|
||||
"web_extract": mock_web_extract,
|
||||
"web_crawl": mock_web_crawl
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Demo/test the mock tools
|
||||
print("Testing Mock Web Tools")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n1. Mock web_search (2s delay):")
|
||||
result = mock_web_search("test query", delay=2)
|
||||
print(f"Result length: {len(result)} chars\n")
|
||||
|
||||
print("\n2. Mock web_extract (5s delay for demo - normally 60s):")
|
||||
result = mock_web_extract(["https://example.com"], delay=5)
|
||||
print(f"Result length: {len(result)} chars\n")
|
||||
|
||||
print("\n✅ All mock tools working!")
|
||||
|
||||
1329
model_tools.py
1329
model_tools.py
File diff suppressed because it is too large
Load Diff
527
output.txt
Normal file
527
output.txt
Normal file
File diff suppressed because one or more lines are too long
77
package-lock.json
generated
77
package-lock.json
generated
@@ -1,77 +0,0 @@
|
||||
{
|
||||
"name": "hermes-agent",
|
||||
"version": "1.0.0",
|
||||
"lockfileVersion": 3,
|
||||
"requires": true,
|
||||
"packages": {
|
||||
"": {
|
||||
"name": "hermes-agent",
|
||||
"version": "1.0.0",
|
||||
"hasInstallScript": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"agent-browser": "^0.7.6"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=18.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/agent-browser": {
|
||||
"version": "0.7.6",
|
||||
"resolved": "https://registry.npmjs.org/agent-browser/-/agent-browser-0.7.6.tgz",
|
||||
"integrity": "sha512-BDmzFlTM0siqn5P8LSBxgOBUNGv02Vo7RYztvXXjNOwQ+8rFJILWfBPxmw+57l/PcMst61AscjIe8uZ5sWrRZQ==",
|
||||
"hasInstallScript": true,
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"playwright-core": "^1.57.0",
|
||||
"ws": "^8.19.0",
|
||||
"zod": "^3.22.4"
|
||||
},
|
||||
"bin": {
|
||||
"agent-browser": "bin/agent-browser"
|
||||
}
|
||||
},
|
||||
"node_modules/playwright-core": {
|
||||
"version": "1.58.0",
|
||||
"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.58.0.tgz",
|
||||
"integrity": "sha512-aaoB1RWrdNi3//rOeKuMiS65UCcgOVljU46At6eFcOFPFHWtd2weHRRow6z/n+Lec0Lvu0k9ZPKJSjPugikirw==",
|
||||
"license": "Apache-2.0",
|
||||
"bin": {
|
||||
"playwright-core": "cli.js"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=18"
|
||||
}
|
||||
},
|
||||
"node_modules/ws": {
|
||||
"version": "8.19.0",
|
||||
"resolved": "https://registry.npmjs.org/ws/-/ws-8.19.0.tgz",
|
||||
"integrity": "sha512-blAT2mjOEIi0ZzruJfIhb3nps74PRWTCz1IjglWEEpQl5XS/UNama6u2/rjFkDDouqr4L67ry+1aGIALViWjDg==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=10.0.0"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"bufferutil": "^4.0.1",
|
||||
"utf-8-validate": ">=5.0.2"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"bufferutil": {
|
||||
"optional": true
|
||||
},
|
||||
"utf-8-validate": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/zod": {
|
||||
"version": "3.25.76",
|
||||
"resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
|
||||
"integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==",
|
||||
"license": "MIT",
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/colinhacks"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
24
package.json
24
package.json
@@ -1,24 +0,0 @@
|
||||
{
|
||||
"name": "hermes-agent",
|
||||
"version": "1.0.0",
|
||||
"description": "An AI agent with advanced tool-calling capabilities, featuring a flexible toolsets system for organizing and managing tools.",
|
||||
"private": true,
|
||||
"scripts": {
|
||||
"postinstall": "echo '✅ Browser tools ready. Run: python run_agent.py --help'"
|
||||
},
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "git+https://github.com/NousResearch/Hermes-Agent.git"
|
||||
},
|
||||
"license": "MIT",
|
||||
"bugs": {
|
||||
"url": "https://github.com/NousResearch/Hermes-Agent/issues"
|
||||
},
|
||||
"homepage": "https://github.com/NousResearch/Hermes-Agent#readme",
|
||||
"dependencies": {
|
||||
"agent-browser": "^0.7.6"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=18.0.0"
|
||||
}
|
||||
}
|
||||
@@ -1,58 +0,0 @@
|
||||
[build-system]
|
||||
requires = ["setuptools>=61.0"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "hermes-agent"
|
||||
version = "0.1.0"
|
||||
description = "AI agent with advanced tool-calling and toolsets"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
authors = [{ name = "Nous Research" }]
|
||||
license = { text = "MIT" }
|
||||
dependencies = [
|
||||
# Core
|
||||
"openai",
|
||||
"python-dotenv",
|
||||
"fire",
|
||||
"httpx",
|
||||
"rich",
|
||||
"tenacity",
|
||||
"pyyaml",
|
||||
"requests",
|
||||
"jinja2",
|
||||
"pydantic>=2.0",
|
||||
# Interactive CLI (prompt_toolkit is used directly by cli.py)
|
||||
"prompt_toolkit",
|
||||
# Tools
|
||||
"firecrawl-py",
|
||||
"fal-client",
|
||||
# mini-swe-agent deps (terminal tool)
|
||||
"litellm>=1.75.5",
|
||||
"typer",
|
||||
"platformdirs",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
modal = ["swe-rex[modal]>=1.4.0"]
|
||||
dev = ["pytest", "pytest-asyncio"]
|
||||
messaging = ["python-telegram-bot>=20.0", "discord.py>=2.0", "aiohttp>=3.9.0"]
|
||||
cron = ["croniter"]
|
||||
cli = ["simple-term-menu"]
|
||||
all = [
|
||||
"hermes-agent[modal]",
|
||||
"hermes-agent[messaging]",
|
||||
"hermes-agent[cron]",
|
||||
"hermes-agent[cli]",
|
||||
"hermes-agent[dev]",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
hermes = "hermes_cli.main:main"
|
||||
hermes-agent = "run_agent:main"
|
||||
|
||||
[tool.setuptools]
|
||||
py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajectory_compressor", "toolset_distributions", "cli"]
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
include = ["tools", "hermes_cli", "gateway", "cron"]
|
||||
@@ -1,43 +1,14 @@
|
||||
# Core dependencies
|
||||
firecrawl-py
|
||||
openai
|
||||
fal-client
|
||||
python-dotenv
|
||||
fire
|
||||
httpx
|
||||
rich
|
||||
tenacity
|
||||
prompt_toolkit
|
||||
pyyaml
|
||||
requests
|
||||
jinja2
|
||||
pydantic>=2.0
|
||||
|
||||
# Web tools
|
||||
firecrawl-py
|
||||
|
||||
# Image generation
|
||||
fal-client
|
||||
|
||||
# mini-swe-agent dependencies (for terminal tool)
|
||||
# Note: Install mini-swe-agent itself with: pip install -e ./mini-swe-agent
|
||||
litellm>=1.75.5
|
||||
typer
|
||||
platformdirs
|
||||
|
||||
# Optional: For Docker backend (recommended)
|
||||
# Requires Docker installed and user in 'docker' group
|
||||
|
||||
# Optional: For Modal backend (cloud execution)
|
||||
# swe-rex[modal]>=1.4.0 # Includes modal + boto3 + swe-rex runtime
|
||||
|
||||
# Optional: For cron expression parsing (cronjob scheduling)
|
||||
croniter
|
||||
|
||||
# Optional: For messaging platform integrations (gateway)
|
||||
# Telegram
|
||||
python-telegram-bot>=20.0
|
||||
|
||||
# Discord
|
||||
discord.py>=2.0
|
||||
|
||||
# WhatsApp bridge communication + general async HTTP (used by gateway)
|
||||
aiohttp>=3.9.0
|
||||
yt-dlp
|
||||
streamlit
|
||||
fastapi
|
||||
uvicorn
|
||||
websockets
|
||||
PySide6>=6.6.0
|
||||
websocket-client>=1.7.0
|
||||
requests>=2.31.0
|
||||
|
||||
448
rl_cli.py
448
rl_cli.py
@@ -1,448 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
RL Training CLI Runner
|
||||
|
||||
Dedicated CLI runner for RL training workflows with:
|
||||
- Extended timeouts for long-running training
|
||||
- RL-focused system prompts
|
||||
- Full toolset including RL training tools
|
||||
- Special handling for 30-minute check intervals
|
||||
|
||||
Usage:
|
||||
python rl_cli.py "Train a model on GSM8k for math reasoning"
|
||||
python rl_cli.py --interactive
|
||||
python rl_cli.py --list-environments
|
||||
|
||||
Environment Variables:
|
||||
TINKER_API_KEY: API key for Tinker service (required)
|
||||
WANDB_API_KEY: API key for WandB metrics (required)
|
||||
OPENROUTER_API_KEY: API key for OpenRouter (required for agent)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import fire
|
||||
import yaml
|
||||
|
||||
# Load environment variables from .env file
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load from ~/.hermes/.env first, then local .env
|
||||
hermes_env_path = Path.home() / '.hermes' / '.env'
|
||||
local_env_path = Path(__file__).parent / '.env'
|
||||
|
||||
if hermes_env_path.exists():
|
||||
load_dotenv(dotenv_path=hermes_env_path)
|
||||
print(f"✅ Loaded environment variables from {hermes_env_path}")
|
||||
elif local_env_path.exists():
|
||||
load_dotenv(dotenv_path=local_env_path)
|
||||
print(f"✅ Loaded environment variables from {local_env_path}")
|
||||
|
||||
# Set terminal working directory to tinker-atropos submodule
|
||||
# This ensures terminal commands run in the right context for RL work
|
||||
tinker_atropos_dir = Path(__file__).parent / 'tinker-atropos'
|
||||
if tinker_atropos_dir.exists():
|
||||
os.environ['TERMINAL_CWD'] = str(tinker_atropos_dir)
|
||||
os.environ['HERMES_QUIET'] = '1' # Disable temp subdirectory creation
|
||||
print(f"📂 Terminal working directory: {tinker_atropos_dir}")
|
||||
else:
|
||||
# Fall back to hermes-agent directory if submodule not found
|
||||
os.environ['TERMINAL_CWD'] = str(Path(__file__).parent)
|
||||
os.environ['HERMES_QUIET'] = '1'
|
||||
print(f"⚠️ tinker-atropos submodule not found, using: {Path(__file__).parent}")
|
||||
|
||||
# Import agent and tools
|
||||
from run_agent import AIAgent
|
||||
from model_tools import get_tool_definitions, check_toolset_requirements
|
||||
from tools.rl_training_tool import check_rl_api_keys, get_missing_keys
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Config Loading
|
||||
# ============================================================================
|
||||
|
||||
DEFAULT_MODEL = "anthropic/claude-opus-4.5"
|
||||
DEFAULT_BASE_URL = "https://openrouter.ai/api/v1"
|
||||
|
||||
|
||||
def load_hermes_config() -> dict:
|
||||
"""
|
||||
Load configuration from ~/.hermes/config.yaml.
|
||||
|
||||
Returns:
|
||||
dict: Configuration with model, base_url, etc.
|
||||
"""
|
||||
config_path = Path.home() / '.hermes' / 'config.yaml'
|
||||
|
||||
config = {
|
||||
"model": DEFAULT_MODEL,
|
||||
"base_url": DEFAULT_BASE_URL,
|
||||
}
|
||||
|
||||
if config_path.exists():
|
||||
try:
|
||||
with open(config_path, "r") as f:
|
||||
file_config = yaml.safe_load(f) or {}
|
||||
|
||||
# Get model from config
|
||||
if "model" in file_config:
|
||||
if isinstance(file_config["model"], str):
|
||||
config["model"] = file_config["model"]
|
||||
elif isinstance(file_config["model"], dict):
|
||||
config["model"] = file_config["model"].get("default", DEFAULT_MODEL)
|
||||
|
||||
# Get base_url if specified
|
||||
if "base_url" in file_config:
|
||||
config["base_url"] = file_config["base_url"]
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Warning: Failed to load config.yaml: {e}")
|
||||
|
||||
return config
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# RL-Specific Configuration
|
||||
# ============================================================================
|
||||
|
||||
# Extended timeouts for long-running RL operations
|
||||
RL_MAX_ITERATIONS = 200 # Allow many more iterations for long workflows
|
||||
|
||||
# RL-focused system prompt
|
||||
RL_SYSTEM_PROMPT = """You are an automated post-training engineer specializing in reinforcement learning for language models.
|
||||
|
||||
## Your Capabilities
|
||||
|
||||
You have access to RL training tools for running reinforcement learning on models through Tinker-Atropos:
|
||||
|
||||
1. **DISCOVER**: Use `rl_list_environments` to see available RL environments
|
||||
2. **INSPECT**: Read environment files to understand how they work (verifiers, data loading, rewards)
|
||||
3. **INSPECT DATA**: Use terminal to explore HuggingFace datasets and understand their format
|
||||
4. **CREATE**: Copy existing environments as templates, modify for your needs
|
||||
5. **CONFIGURE**: Use `rl_select_environment` and `rl_edit_config` to set up training
|
||||
6. **TEST**: Always use `rl_test_inference` before full training to validate your setup
|
||||
7. **TRAIN**: Use `rl_start_training` to begin, `rl_check_status` to monitor
|
||||
8. **EVALUATE**: Use `rl_get_results` and analyze WandB metrics to assess performance
|
||||
|
||||
## Environment Files
|
||||
|
||||
Environment files are located in: `tinker-atropos/tinker_atropos/environments/`
|
||||
|
||||
Study existing environments to learn patterns. Look for:
|
||||
- `load_dataset()` calls - how data is loaded
|
||||
- `score_answer()` / `score()` - verification logic
|
||||
- `get_next_item()` - prompt formatting
|
||||
- `system_prompt` - instruction format
|
||||
- `config_init()` - default configuration
|
||||
|
||||
## Creating New Environments
|
||||
|
||||
To create a new environment:
|
||||
1. Read an existing environment file (e.g., gsm8k_tinker.py)
|
||||
2. Use terminal to explore the target dataset format
|
||||
3. Copy the environment file as a template
|
||||
4. Modify the dataset loading, prompt formatting, and verifier logic
|
||||
5. Test with `rl_test_inference` before training
|
||||
|
||||
## Important Guidelines
|
||||
|
||||
- **Always test before training**: Training runs take hours - verify everything works first
|
||||
- **Monitor metrics**: Check WandB for reward/mean and percent_correct
|
||||
- **Status check intervals**: Wait at least 30 minutes between status checks
|
||||
- **Early stopping**: Stop training early if metrics look bad or stagnant
|
||||
- **Iterate quickly**: Start with small total_steps to validate, then scale up
|
||||
|
||||
## Available Toolsets
|
||||
|
||||
You have access to:
|
||||
- **RL tools**: Environment discovery, config management, training, testing
|
||||
- **Terminal**: Run commands, inspect files, explore datasets
|
||||
- **Web**: Search for information, documentation, papers
|
||||
- **File tools**: Read and modify code files
|
||||
|
||||
When asked to train a model, follow this workflow:
|
||||
1. List available environments
|
||||
2. Select and configure the appropriate environment
|
||||
3. Test with sample prompts
|
||||
4. Start training with conservative settings
|
||||
5. Monitor progress and adjust as needed
|
||||
"""
|
||||
|
||||
# Toolsets to enable for RL workflows
|
||||
RL_TOOLSETS = ["terminal", "web", "rl"]
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Helper Functions
|
||||
# ============================================================================
|
||||
|
||||
def check_requirements():
|
||||
"""Check that all required environment variables and services are available."""
|
||||
errors = []
|
||||
|
||||
# Check API keys
|
||||
if not os.getenv("OPENROUTER_API_KEY"):
|
||||
errors.append("OPENROUTER_API_KEY not set - required for agent")
|
||||
|
||||
missing_rl_keys = get_missing_keys()
|
||||
if missing_rl_keys:
|
||||
errors.append(f"Missing RL API keys: {', '.join(missing_rl_keys)}")
|
||||
|
||||
if errors:
|
||||
print("❌ Missing requirements:")
|
||||
for error in errors:
|
||||
print(f" - {error}")
|
||||
print("\nPlease set these environment variables in your .env file or shell.")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def check_tinker_atropos():
|
||||
"""Check if tinker-atropos submodule is properly set up."""
|
||||
tinker_path = Path(__file__).parent / "tinker-atropos"
|
||||
|
||||
if not tinker_path.exists():
|
||||
return False, "tinker-atropos submodule not found. Run: git submodule update --init"
|
||||
|
||||
envs_path = tinker_path / "tinker_atropos" / "environments"
|
||||
if not envs_path.exists():
|
||||
return False, f"environments directory not found at {envs_path}"
|
||||
|
||||
env_files = list(envs_path.glob("*.py"))
|
||||
env_files = [f for f in env_files if not f.name.startswith("_")]
|
||||
|
||||
return True, {"path": str(tinker_path), "environments_count": len(env_files)}
|
||||
|
||||
|
||||
def list_environments_sync():
|
||||
"""List available environments (synchronous wrapper)."""
|
||||
from tools.rl_training_tool import rl_list_environments
|
||||
import json
|
||||
|
||||
async def _list():
|
||||
result = await rl_list_environments()
|
||||
return json.loads(result)
|
||||
|
||||
return asyncio.run(_list())
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Main CLI
|
||||
# ============================================================================
|
||||
|
||||
def main(
|
||||
task: str = None,
|
||||
model: str = None,
|
||||
api_key: str = None,
|
||||
base_url: str = None,
|
||||
max_iterations: int = RL_MAX_ITERATIONS,
|
||||
interactive: bool = False,
|
||||
list_environments: bool = False,
|
||||
check_server: bool = False,
|
||||
verbose: bool = False,
|
||||
save_trajectories: bool = True,
|
||||
):
|
||||
"""
|
||||
RL Training CLI - Dedicated runner for RL training workflows.
|
||||
|
||||
Args:
|
||||
task: The training task/goal (e.g., "Train a model on GSM8k for math")
|
||||
model: Model to use for the agent (reads from ~/.hermes/config.yaml if not provided)
|
||||
api_key: OpenRouter API key (uses OPENROUTER_API_KEY env var if not provided)
|
||||
base_url: API base URL (reads from config or defaults to OpenRouter)
|
||||
max_iterations: Maximum agent iterations (default: 200 for long workflows)
|
||||
interactive: Run in interactive mode (multiple conversations)
|
||||
list_environments: Just list available RL environments and exit
|
||||
check_server: Check if RL API server is running and exit
|
||||
verbose: Enable verbose logging
|
||||
save_trajectories: Save conversation trajectories (default: True for RL)
|
||||
|
||||
Examples:
|
||||
# Train on a specific environment
|
||||
python rl_cli.py "Train a model on GSM8k math problems"
|
||||
|
||||
# Interactive mode
|
||||
python rl_cli.py --interactive
|
||||
|
||||
# List available environments
|
||||
python rl_cli.py --list-environments
|
||||
|
||||
# Check server status
|
||||
python rl_cli.py --check-server
|
||||
"""
|
||||
# Load config from ~/.hermes/config.yaml
|
||||
config = load_hermes_config()
|
||||
|
||||
# Use config values if not explicitly provided
|
||||
if model is None:
|
||||
model = config["model"]
|
||||
if base_url is None:
|
||||
base_url = config["base_url"]
|
||||
|
||||
print("🎯 RL Training Agent")
|
||||
print("=" * 60)
|
||||
|
||||
# Handle setup check
|
||||
if check_server:
|
||||
print("\n🔍 Checking tinker-atropos setup...")
|
||||
ok, result = check_tinker_atropos()
|
||||
if ok:
|
||||
print("✅ tinker-atropos submodule found")
|
||||
print(f" Path: {result.get('path')}")
|
||||
print(f" Environments found: {result.get('environments_count', 0)}")
|
||||
|
||||
# Also check API keys
|
||||
missing = get_missing_keys()
|
||||
if missing:
|
||||
print(f"\n⚠️ Missing API keys: {', '.join(missing)}")
|
||||
print(" Add them to ~/.hermes/.env")
|
||||
else:
|
||||
print("✅ API keys configured")
|
||||
else:
|
||||
print(f"❌ tinker-atropos not set up: {result}")
|
||||
print("\nTo set up:")
|
||||
print(" git submodule update --init")
|
||||
print(" pip install -e ./tinker-atropos")
|
||||
return
|
||||
|
||||
# Handle environment listing
|
||||
if list_environments:
|
||||
print("\n📋 Available RL Environments:")
|
||||
print("-" * 40)
|
||||
try:
|
||||
data = list_environments_sync()
|
||||
if "error" in data:
|
||||
print(f"❌ Error: {data['error']}")
|
||||
return
|
||||
|
||||
envs = data.get("environments", [])
|
||||
if not envs:
|
||||
print("No environments found.")
|
||||
print("\nMake sure tinker-atropos is set up:")
|
||||
print(" git submodule update --init")
|
||||
return
|
||||
|
||||
for env in envs:
|
||||
print(f"\n 📦 {env['name']}")
|
||||
print(f" Class: {env['class_name']}")
|
||||
print(f" Path: {env['file_path']}")
|
||||
if env.get('description'):
|
||||
desc = env['description'][:100] + "..." if len(env.get('description', '')) > 100 else env.get('description', '')
|
||||
print(f" Description: {desc}")
|
||||
|
||||
print(f"\n📊 Total: {len(envs)} environments")
|
||||
print("\nUse `rl_select_environment(name)` to select an environment for training.")
|
||||
except Exception as e:
|
||||
print(f"❌ Error listing environments: {e}")
|
||||
print("\nMake sure tinker-atropos is set up:")
|
||||
print(" git submodule update --init")
|
||||
print(" pip install -e ./tinker-atropos")
|
||||
return
|
||||
|
||||
# Check requirements
|
||||
if not check_requirements():
|
||||
sys.exit(1)
|
||||
|
||||
# Set default task if none provided
|
||||
if not task and not interactive:
|
||||
print("\n⚠️ No task provided. Use --interactive for interactive mode or provide a task.")
|
||||
print("\nExamples:")
|
||||
print(' python rl_cli.py "Train a model on GSM8k math problems"')
|
||||
print(' python rl_cli.py "Create an RL environment for code generation"')
|
||||
print(' python rl_cli.py --interactive')
|
||||
return
|
||||
|
||||
# Get API key
|
||||
api_key = api_key or os.getenv("OPENROUTER_API_KEY")
|
||||
if not api_key:
|
||||
print("❌ No API key provided. Set OPENROUTER_API_KEY or pass --api-key")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"\n🤖 Model: {model}")
|
||||
print(f"🔧 Max iterations: {max_iterations}")
|
||||
print(f"📁 Toolsets: {', '.join(RL_TOOLSETS)}")
|
||||
print("=" * 60)
|
||||
|
||||
# Create agent with RL configuration
|
||||
agent = AIAgent(
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
model=model,
|
||||
max_iterations=max_iterations,
|
||||
enabled_toolsets=RL_TOOLSETS,
|
||||
save_trajectories=save_trajectories,
|
||||
verbose_logging=verbose,
|
||||
quiet_mode=False,
|
||||
ephemeral_system_prompt=RL_SYSTEM_PROMPT,
|
||||
)
|
||||
|
||||
if interactive:
|
||||
# Interactive mode - multiple conversations
|
||||
print("\n🔄 Interactive RL Training Mode")
|
||||
print("Type 'quit' or 'exit' to end the session.")
|
||||
print("Type 'status' to check active training runs.")
|
||||
print("-" * 40)
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\n🎯 RL Task> ").strip()
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() in ('quit', 'exit', 'q'):
|
||||
print("\n👋 Goodbye!")
|
||||
break
|
||||
|
||||
if user_input.lower() == 'status':
|
||||
# Quick status check
|
||||
from tools.rl_training_tool import rl_list_runs
|
||||
import json
|
||||
result = asyncio.run(rl_list_runs())
|
||||
runs = json.loads(result)
|
||||
if isinstance(runs, list) and runs:
|
||||
print("\n📊 Active Runs:")
|
||||
for run in runs:
|
||||
print(f" - {run['run_id']}: {run['environment']} ({run['status']})")
|
||||
else:
|
||||
print("\nNo active runs.")
|
||||
continue
|
||||
|
||||
# Run the agent
|
||||
print("\n" + "=" * 60)
|
||||
response = agent.run_conversation(user_input)
|
||||
print("\n" + "=" * 60)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n\n👋 Interrupted. Goodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
if verbose:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
else:
|
||||
# Single task mode
|
||||
print(f"\n📝 Task: {task}")
|
||||
print("-" * 40)
|
||||
|
||||
try:
|
||||
response = agent.run_conversation(task)
|
||||
print("\n" + "=" * 60)
|
||||
print("✅ Task completed")
|
||||
except KeyboardInterrupt:
|
||||
print("\n\n⚠️ Interrupted by user")
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
if verbose:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
2080
run_agent.py
2080
run_agent.py
File diff suppressed because it is too large
Load Diff
@@ -1,414 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Hermes Gateway - Standalone messaging platform integration.
|
||||
|
||||
This is the proper entry point for running the gateway as a service.
|
||||
NOT tied to the CLI - runs independently.
|
||||
|
||||
Usage:
|
||||
# Run in foreground (for testing)
|
||||
./scripts/hermes-gateway
|
||||
|
||||
# Install as systemd service
|
||||
./scripts/hermes-gateway install
|
||||
|
||||
# Manage the service
|
||||
./scripts/hermes-gateway start
|
||||
./scripts/hermes-gateway stop
|
||||
./scripts/hermes-gateway restart
|
||||
./scripts/hermes-gateway status
|
||||
|
||||
# Uninstall
|
||||
./scripts/hermes-gateway uninstall
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add parent directory to path
|
||||
SCRIPT_DIR = Path(__file__).parent.resolve()
|
||||
PROJECT_DIR = SCRIPT_DIR.parent
|
||||
sys.path.insert(0, str(PROJECT_DIR))
|
||||
|
||||
# Load .env file
|
||||
from dotenv import load_dotenv
|
||||
env_path = PROJECT_DIR / '.env'
|
||||
if env_path.exists():
|
||||
load_dotenv(dotenv_path=env_path)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Service Configuration
|
||||
# =============================================================================
|
||||
|
||||
SERVICE_NAME = "hermes-gateway"
|
||||
SERVICE_DESCRIPTION = "Hermes Agent Gateway - Messaging Platform Integration"
|
||||
|
||||
def get_systemd_unit_path() -> Path:
|
||||
"""Get the path for the systemd user service file."""
|
||||
return Path.home() / ".config" / "systemd" / "user" / f"{SERVICE_NAME}.service"
|
||||
|
||||
def get_launchd_plist_path() -> Path:
|
||||
"""Get the path for the launchd plist file (macOS)."""
|
||||
return Path.home() / "Library" / "LaunchAgents" / f"ai.hermes.gateway.plist"
|
||||
|
||||
def get_python_path() -> str:
|
||||
"""Get the path to the Python interpreter."""
|
||||
# Prefer the venv if it exists
|
||||
venv_python = PROJECT_DIR / "venv" / "bin" / "python"
|
||||
if venv_python.exists():
|
||||
return str(venv_python)
|
||||
return sys.executable
|
||||
|
||||
def get_gateway_script_path() -> str:
|
||||
"""Get the path to this script."""
|
||||
return str(Path(__file__).resolve())
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Systemd Service (Linux)
|
||||
# =============================================================================
|
||||
|
||||
def generate_systemd_unit() -> str:
|
||||
"""Generate the systemd unit file content."""
|
||||
python_path = get_python_path()
|
||||
script_path = get_gateway_script_path()
|
||||
working_dir = str(PROJECT_DIR)
|
||||
|
||||
return f"""[Unit]
|
||||
Description={SERVICE_DESCRIPTION}
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
ExecStart={python_path} {script_path} run
|
||||
WorkingDirectory={working_dir}
|
||||
Restart=on-failure
|
||||
RestartSec=10
|
||||
StandardOutput=journal
|
||||
StandardError=journal
|
||||
|
||||
# Environment (optional - can also use .env file)
|
||||
# Environment="TELEGRAM_BOT_TOKEN=your_token"
|
||||
# Environment="DISCORD_BOT_TOKEN=your_token"
|
||||
|
||||
[Install]
|
||||
WantedBy=default.target
|
||||
"""
|
||||
|
||||
def install_systemd():
|
||||
"""Install the systemd user service."""
|
||||
unit_path = get_systemd_unit_path()
|
||||
unit_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(f"Installing systemd service to: {unit_path}")
|
||||
unit_path.write_text(generate_systemd_unit())
|
||||
|
||||
# Reload systemd
|
||||
subprocess.run(["systemctl", "--user", "daemon-reload"], check=True)
|
||||
|
||||
# Enable the service (start on boot)
|
||||
subprocess.run(["systemctl", "--user", "enable", SERVICE_NAME], check=True)
|
||||
|
||||
print(f"✓ Service installed and enabled")
|
||||
print(f"")
|
||||
print(f"To start the service:")
|
||||
print(f" systemctl --user start {SERVICE_NAME}")
|
||||
print(f"")
|
||||
print(f"To view logs:")
|
||||
print(f" journalctl --user -u {SERVICE_NAME} -f")
|
||||
print(f"")
|
||||
print(f"To enable lingering (keeps service running after logout):")
|
||||
print(f" sudo loginctl enable-linger $USER")
|
||||
|
||||
def uninstall_systemd():
|
||||
"""Uninstall the systemd user service."""
|
||||
unit_path = get_systemd_unit_path()
|
||||
|
||||
# Stop and disable first
|
||||
subprocess.run(["systemctl", "--user", "stop", SERVICE_NAME], check=False)
|
||||
subprocess.run(["systemctl", "--user", "disable", SERVICE_NAME], check=False)
|
||||
|
||||
# Remove the unit file
|
||||
if unit_path.exists():
|
||||
unit_path.unlink()
|
||||
print(f"✓ Removed {unit_path}")
|
||||
|
||||
# Reload systemd
|
||||
subprocess.run(["systemctl", "--user", "daemon-reload"], check=True)
|
||||
print(f"✓ Service uninstalled")
|
||||
|
||||
def systemd_status():
|
||||
"""Show systemd service status."""
|
||||
subprocess.run(["systemctl", "--user", "status", SERVICE_NAME])
|
||||
|
||||
def systemd_start():
|
||||
"""Start the systemd service."""
|
||||
subprocess.run(["systemctl", "--user", "start", SERVICE_NAME], check=True)
|
||||
print(f"✓ Service started")
|
||||
|
||||
def systemd_stop():
|
||||
"""Stop the systemd service."""
|
||||
subprocess.run(["systemctl", "--user", "stop", SERVICE_NAME], check=True)
|
||||
print(f"✓ Service stopped")
|
||||
|
||||
def systemd_restart():
|
||||
"""Restart the systemd service."""
|
||||
subprocess.run(["systemctl", "--user", "restart", SERVICE_NAME], check=True)
|
||||
print(f"✓ Service restarted")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Launchd Service (macOS)
|
||||
# =============================================================================
|
||||
|
||||
def generate_launchd_plist() -> str:
|
||||
"""Generate the launchd plist file content."""
|
||||
python_path = get_python_path()
|
||||
script_path = get_gateway_script_path()
|
||||
working_dir = str(PROJECT_DIR)
|
||||
log_dir = Path.home() / ".hermes" / "logs"
|
||||
|
||||
return f"""<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>Label</key>
|
||||
<string>ai.hermes.gateway</string>
|
||||
|
||||
<key>ProgramArguments</key>
|
||||
<array>
|
||||
<string>{python_path}</string>
|
||||
<string>{script_path}</string>
|
||||
<string>run</string>
|
||||
</array>
|
||||
|
||||
<key>WorkingDirectory</key>
|
||||
<string>{working_dir}</string>
|
||||
|
||||
<key>RunAtLoad</key>
|
||||
<true/>
|
||||
|
||||
<key>KeepAlive</key>
|
||||
<dict>
|
||||
<key>SuccessfulExit</key>
|
||||
<false/>
|
||||
</dict>
|
||||
|
||||
<key>StandardOutPath</key>
|
||||
<string>{log_dir}/gateway.log</string>
|
||||
|
||||
<key>StandardErrorPath</key>
|
||||
<string>{log_dir}/gateway.error.log</string>
|
||||
|
||||
<key>EnvironmentVariables</key>
|
||||
<dict>
|
||||
<key>PATH</key>
|
||||
<string>/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
|
||||
</dict>
|
||||
</dict>
|
||||
</plist>
|
||||
"""
|
||||
|
||||
def install_launchd():
|
||||
"""Install the launchd service (macOS)."""
|
||||
plist_path = get_launchd_plist_path()
|
||||
plist_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Ensure log directory exists
|
||||
log_dir = Path.home() / ".hermes" / "logs"
|
||||
log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(f"Installing launchd service to: {plist_path}")
|
||||
plist_path.write_text(generate_launchd_plist())
|
||||
|
||||
# Load the service
|
||||
subprocess.run(["launchctl", "load", str(plist_path)], check=True)
|
||||
|
||||
print(f"✓ Service installed and loaded")
|
||||
print(f"")
|
||||
print(f"To view logs:")
|
||||
print(f" tail -f ~/.hermes/logs/gateway.log")
|
||||
print(f"")
|
||||
print(f"To manage the service:")
|
||||
print(f" launchctl start ai.hermes.gateway")
|
||||
print(f" launchctl stop ai.hermes.gateway")
|
||||
|
||||
def uninstall_launchd():
|
||||
"""Uninstall the launchd service (macOS)."""
|
||||
plist_path = get_launchd_plist_path()
|
||||
|
||||
# Unload first
|
||||
subprocess.run(["launchctl", "unload", str(plist_path)], check=False)
|
||||
|
||||
# Remove the plist file
|
||||
if plist_path.exists():
|
||||
plist_path.unlink()
|
||||
print(f"✓ Removed {plist_path}")
|
||||
|
||||
print(f"✓ Service uninstalled")
|
||||
|
||||
def launchd_status():
|
||||
"""Show launchd service status."""
|
||||
subprocess.run(["launchctl", "list", "ai.hermes.gateway"])
|
||||
|
||||
def launchd_start():
|
||||
"""Start the launchd service."""
|
||||
subprocess.run(["launchctl", "start", "ai.hermes.gateway"], check=True)
|
||||
print(f"✓ Service started")
|
||||
|
||||
def launchd_stop():
|
||||
"""Stop the launchd service."""
|
||||
subprocess.run(["launchctl", "stop", "ai.hermes.gateway"], check=True)
|
||||
print(f"✓ Service stopped")
|
||||
|
||||
def launchd_restart():
|
||||
"""Restart the launchd service."""
|
||||
launchd_stop()
|
||||
launchd_start()
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Platform Detection
|
||||
# =============================================================================
|
||||
|
||||
def is_linux() -> bool:
|
||||
return sys.platform.startswith('linux')
|
||||
|
||||
def is_macos() -> bool:
|
||||
return sys.platform == 'darwin'
|
||||
|
||||
def is_windows() -> bool:
|
||||
return sys.platform == 'win32'
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Gateway Runner
|
||||
# =============================================================================
|
||||
|
||||
def run_gateway():
|
||||
"""Run the gateway in foreground."""
|
||||
from gateway.run import start_gateway
|
||||
print("Starting Hermes Gateway...")
|
||||
print("Press Ctrl+C to stop.")
|
||||
print()
|
||||
asyncio.run(start_gateway())
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Main CLI
|
||||
# =============================================================================
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Hermes Gateway - Messaging Platform Integration",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Run in foreground (for testing)
|
||||
./scripts/hermes-gateway run
|
||||
|
||||
# Install as system service
|
||||
./scripts/hermes-gateway install
|
||||
|
||||
# Manage the service
|
||||
./scripts/hermes-gateway start
|
||||
./scripts/hermes-gateway stop
|
||||
./scripts/hermes-gateway restart
|
||||
./scripts/hermes-gateway status
|
||||
|
||||
# Uninstall
|
||||
./scripts/hermes-gateway uninstall
|
||||
|
||||
Configuration:
|
||||
Set environment variables in .env file or system environment:
|
||||
- TELEGRAM_BOT_TOKEN
|
||||
- DISCORD_BOT_TOKEN
|
||||
- WHATSAPP_ENABLED
|
||||
|
||||
Or create ~/.hermes/gateway.json for advanced configuration.
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"command",
|
||||
choices=["run", "install", "uninstall", "start", "stop", "restart", "status"],
|
||||
nargs="?",
|
||||
default="run",
|
||||
help="Command to execute (default: run)"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--verbose", "-v",
|
||||
action="store_true",
|
||||
help="Verbose output"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Detect platform and dispatch command
|
||||
if args.command == "run":
|
||||
run_gateway()
|
||||
|
||||
elif args.command == "install":
|
||||
if is_linux():
|
||||
install_systemd()
|
||||
elif is_macos():
|
||||
install_launchd()
|
||||
else:
|
||||
print("Service installation not supported on this platform.")
|
||||
print("Please run manually: ./scripts/hermes-gateway run")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.command == "uninstall":
|
||||
if is_linux():
|
||||
uninstall_systemd()
|
||||
elif is_macos():
|
||||
uninstall_launchd()
|
||||
else:
|
||||
print("Service uninstallation not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.command == "start":
|
||||
if is_linux():
|
||||
systemd_start()
|
||||
elif is_macos():
|
||||
launchd_start()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.command == "stop":
|
||||
if is_linux():
|
||||
systemd_stop()
|
||||
elif is_macos():
|
||||
launchd_stop()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.command == "restart":
|
||||
if is_linux():
|
||||
systemd_restart()
|
||||
elif is_macos():
|
||||
launchd_restart()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.command == "status":
|
||||
if is_linux():
|
||||
systemd_status()
|
||||
elif is_macos():
|
||||
launchd_status()
|
||||
else:
|
||||
print("Not supported on this platform.")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,582 +0,0 @@
|
||||
# ============================================================================
|
||||
# Hermes Agent Installer for Windows
|
||||
# ============================================================================
|
||||
# Installation script for Windows (PowerShell).
|
||||
# Uses uv for fast Python provisioning and package management.
|
||||
#
|
||||
# Usage:
|
||||
# irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex
|
||||
#
|
||||
# Or download and run with options:
|
||||
# .\install.ps1 -NoVenv -SkipSetup
|
||||
#
|
||||
# ============================================================================
|
||||
|
||||
param(
|
||||
[switch]$NoVenv,
|
||||
[switch]$SkipSetup,
|
||||
[string]$Branch = "main",
|
||||
[string]$HermesHome = "$env:USERPROFILE\.hermes",
|
||||
[string]$InstallDir = "$env:USERPROFILE\.hermes\hermes-agent"
|
||||
)
|
||||
|
||||
$ErrorActionPreference = "Stop"
|
||||
|
||||
# ============================================================================
|
||||
# Configuration
|
||||
# ============================================================================
|
||||
|
||||
$RepoUrlSsh = "git@github.com:NousResearch/hermes-agent.git"
|
||||
$RepoUrlHttps = "https://github.com/NousResearch/hermes-agent.git"
|
||||
$PythonVersion = "3.11"
|
||||
|
||||
# ============================================================================
|
||||
# Helper functions
|
||||
# ============================================================================
|
||||
|
||||
function Write-Banner {
|
||||
Write-Host ""
|
||||
Write-Host "┌─────────────────────────────────────────────────────────┐" -ForegroundColor Magenta
|
||||
Write-Host "│ 🦋 Hermes Agent Installer │" -ForegroundColor Magenta
|
||||
Write-Host "├─────────────────────────────────────────────────────────┤" -ForegroundColor Magenta
|
||||
Write-Host "│ I'm just a butterfly with a lot of tools. │" -ForegroundColor Magenta
|
||||
Write-Host "└─────────────────────────────────────────────────────────┘" -ForegroundColor Magenta
|
||||
Write-Host ""
|
||||
}
|
||||
|
||||
function Write-Info {
|
||||
param([string]$Message)
|
||||
Write-Host "→ $Message" -ForegroundColor Cyan
|
||||
}
|
||||
|
||||
function Write-Success {
|
||||
param([string]$Message)
|
||||
Write-Host "✓ $Message" -ForegroundColor Green
|
||||
}
|
||||
|
||||
function Write-Warn {
|
||||
param([string]$Message)
|
||||
Write-Host "⚠ $Message" -ForegroundColor Yellow
|
||||
}
|
||||
|
||||
function Write-Err {
|
||||
param([string]$Message)
|
||||
Write-Host "✗ $Message" -ForegroundColor Red
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Dependency checks
|
||||
# ============================================================================
|
||||
|
||||
function Install-Uv {
|
||||
Write-Info "Checking for uv package manager..."
|
||||
|
||||
# Check if uv is already available
|
||||
if (Get-Command uv -ErrorAction SilentlyContinue) {
|
||||
$version = uv --version
|
||||
$script:UvCmd = "uv"
|
||||
Write-Success "uv found ($version)"
|
||||
return $true
|
||||
}
|
||||
|
||||
# Check common install locations
|
||||
$uvPaths = @(
|
||||
"$env:USERPROFILE\.local\bin\uv.exe",
|
||||
"$env:USERPROFILE\.cargo\bin\uv.exe"
|
||||
)
|
||||
foreach ($uvPath in $uvPaths) {
|
||||
if (Test-Path $uvPath) {
|
||||
$script:UvCmd = $uvPath
|
||||
$version = & $uvPath --version
|
||||
Write-Success "uv found at $uvPath ($version)"
|
||||
return $true
|
||||
}
|
||||
}
|
||||
|
||||
# Install uv
|
||||
Write-Info "Installing uv (fast Python package manager)..."
|
||||
try {
|
||||
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" 2>&1 | Out-Null
|
||||
|
||||
# Find the installed binary
|
||||
$uvExe = "$env:USERPROFILE\.local\bin\uv.exe"
|
||||
if (-not (Test-Path $uvExe)) {
|
||||
$uvExe = "$env:USERPROFILE\.cargo\bin\uv.exe"
|
||||
}
|
||||
if (-not (Test-Path $uvExe)) {
|
||||
# Refresh PATH and try again
|
||||
$env:Path = [Environment]::GetEnvironmentVariable("Path", "User") + ";" + [Environment]::GetEnvironmentVariable("Path", "Machine")
|
||||
if (Get-Command uv -ErrorAction SilentlyContinue) {
|
||||
$uvExe = (Get-Command uv).Source
|
||||
}
|
||||
}
|
||||
|
||||
if (Test-Path $uvExe) {
|
||||
$script:UvCmd = $uvExe
|
||||
$version = & $uvExe --version
|
||||
Write-Success "uv installed ($version)"
|
||||
return $true
|
||||
}
|
||||
|
||||
Write-Err "uv installed but not found on PATH"
|
||||
Write-Info "Try restarting your terminal and re-running"
|
||||
return $false
|
||||
} catch {
|
||||
Write-Err "Failed to install uv"
|
||||
Write-Info "Install manually: https://docs.astral.sh/uv/getting-started/installation/"
|
||||
return $false
|
||||
}
|
||||
}
|
||||
|
||||
function Test-Python {
|
||||
Write-Info "Checking Python $PythonVersion..."
|
||||
|
||||
# Let uv find or install Python
|
||||
try {
|
||||
$pythonPath = & $UvCmd python find $PythonVersion 2>$null
|
||||
if ($pythonPath) {
|
||||
$ver = & $pythonPath --version 2>$null
|
||||
Write-Success "Python found: $ver"
|
||||
return $true
|
||||
}
|
||||
} catch { }
|
||||
|
||||
# Python not found — use uv to install it (no admin needed!)
|
||||
Write-Info "Python $PythonVersion not found, installing via uv..."
|
||||
try {
|
||||
& $UvCmd python install $PythonVersion 2>&1 | Out-Null
|
||||
$pythonPath = & $UvCmd python find $PythonVersion 2>$null
|
||||
if ($pythonPath) {
|
||||
$ver = & $pythonPath --version 2>$null
|
||||
Write-Success "Python installed: $ver"
|
||||
return $true
|
||||
}
|
||||
} catch { }
|
||||
|
||||
Write-Err "Failed to install Python $PythonVersion"
|
||||
Write-Info "Install Python $PythonVersion manually, then re-run this script"
|
||||
return $false
|
||||
}
|
||||
|
||||
function Test-Git {
|
||||
Write-Info "Checking Git..."
|
||||
|
||||
if (Get-Command git -ErrorAction SilentlyContinue) {
|
||||
$version = git --version
|
||||
Write-Success "Git found ($version)"
|
||||
return $true
|
||||
}
|
||||
|
||||
Write-Err "Git not found"
|
||||
Write-Info "Please install Git from:"
|
||||
Write-Info " https://git-scm.com/download/win"
|
||||
return $false
|
||||
}
|
||||
|
||||
function Test-Node {
|
||||
Write-Info "Checking Node.js (optional, for browser tools)..."
|
||||
|
||||
if (Get-Command node -ErrorAction SilentlyContinue) {
|
||||
$version = node --version
|
||||
Write-Success "Node.js $version found"
|
||||
$script:HasNode = $true
|
||||
return $true
|
||||
}
|
||||
|
||||
Write-Warn "Node.js not found (browser tools will be limited)"
|
||||
Write-Info "To install Node.js (optional):"
|
||||
Write-Info " https://nodejs.org/en/download/"
|
||||
$script:HasNode = $false
|
||||
return $true # Don't fail - Node is optional
|
||||
}
|
||||
|
||||
function Test-Ripgrep {
|
||||
Write-Info "Checking ripgrep (optional, for faster file search)..."
|
||||
|
||||
if (Get-Command rg -ErrorAction SilentlyContinue) {
|
||||
$version = rg --version | Select-Object -First 1
|
||||
Write-Success "$version found"
|
||||
$script:HasRipgrep = $true
|
||||
return $true
|
||||
}
|
||||
|
||||
Write-Warn "ripgrep not found (file search will use findstr fallback)"
|
||||
|
||||
# Check what package managers are available
|
||||
$hasWinget = Get-Command winget -ErrorAction SilentlyContinue
|
||||
$hasChoco = Get-Command choco -ErrorAction SilentlyContinue
|
||||
$hasScoop = Get-Command scoop -ErrorAction SilentlyContinue
|
||||
|
||||
# Offer to install
|
||||
Write-Host ""
|
||||
$response = Read-Host "Would you like to install ripgrep? (faster search, recommended) [Y/n]"
|
||||
|
||||
if ($response -eq "" -or $response -match "^[Yy]") {
|
||||
Write-Info "Installing ripgrep..."
|
||||
|
||||
if ($hasWinget) {
|
||||
try {
|
||||
winget install BurntSushi.ripgrep.MSVC --silent 2>&1 | Out-Null
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Success "ripgrep installed via winget"
|
||||
$script:HasRipgrep = $true
|
||||
return $true
|
||||
}
|
||||
} catch { }
|
||||
}
|
||||
|
||||
if ($hasChoco) {
|
||||
try {
|
||||
choco install ripgrep -y 2>&1 | Out-Null
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Success "ripgrep installed via chocolatey"
|
||||
$script:HasRipgrep = $true
|
||||
return $true
|
||||
}
|
||||
} catch { }
|
||||
}
|
||||
|
||||
if ($hasScoop) {
|
||||
try {
|
||||
scoop install ripgrep 2>&1 | Out-Null
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Success "ripgrep installed via scoop"
|
||||
$script:HasRipgrep = $true
|
||||
return $true
|
||||
}
|
||||
} catch { }
|
||||
}
|
||||
|
||||
Write-Warn "Auto-install failed. You can install manually:"
|
||||
} else {
|
||||
Write-Info "Skipping ripgrep installation. To install manually:"
|
||||
}
|
||||
|
||||
# Show manual install instructions
|
||||
Write-Info " winget install BurntSushi.ripgrep.MSVC"
|
||||
Write-Info " Or: choco install ripgrep"
|
||||
Write-Info " Or: scoop install ripgrep"
|
||||
Write-Info " Or download from: https://github.com/BurntSushi/ripgrep/releases"
|
||||
|
||||
$script:HasRipgrep = $false
|
||||
return $true # Don't fail - ripgrep is optional
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Installation
|
||||
# ============================================================================
|
||||
|
||||
function Install-Repository {
|
||||
Write-Info "Installing to $InstallDir..."
|
||||
|
||||
if (Test-Path $InstallDir) {
|
||||
if (Test-Path "$InstallDir\.git") {
|
||||
Write-Info "Existing installation found, updating..."
|
||||
Push-Location $InstallDir
|
||||
git fetch origin
|
||||
git checkout $Branch
|
||||
git pull origin $Branch
|
||||
Pop-Location
|
||||
} else {
|
||||
Write-Err "Directory exists but is not a git repository: $InstallDir"
|
||||
Write-Info "Remove it or choose a different directory with -InstallDir"
|
||||
exit 1
|
||||
}
|
||||
} else {
|
||||
# Try SSH first (for private repo access), fall back to HTTPS
|
||||
Write-Info "Trying SSH clone..."
|
||||
$sshResult = git clone --branch $Branch --recurse-submodules $RepoUrlSsh $InstallDir 2>&1
|
||||
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Success "Cloned via SSH"
|
||||
} else {
|
||||
Write-Info "SSH failed, trying HTTPS..."
|
||||
$httpsResult = git clone --branch $Branch --recurse-submodules $RepoUrlHttps $InstallDir 2>&1
|
||||
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Success "Cloned via HTTPS"
|
||||
} else {
|
||||
Write-Err "Failed to clone repository"
|
||||
Write-Info "For private repo access, ensure your SSH key is added to GitHub:"
|
||||
Write-Info " ssh-add ~/.ssh/id_rsa"
|
||||
Write-Info " ssh -T git@github.com # Test connection"
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Ensure submodules are initialized and updated
|
||||
Write-Info "Initializing submodules (mini-swe-agent, tinker-atropos)..."
|
||||
Push-Location $InstallDir
|
||||
git submodule update --init --recursive
|
||||
Pop-Location
|
||||
Write-Success "Submodules ready"
|
||||
|
||||
Write-Success "Repository ready"
|
||||
}
|
||||
|
||||
function Install-Venv {
|
||||
if ($NoVenv) {
|
||||
Write-Info "Skipping virtual environment (-NoVenv)"
|
||||
return
|
||||
}
|
||||
|
||||
Write-Info "Creating virtual environment with Python $PythonVersion..."
|
||||
|
||||
Push-Location $InstallDir
|
||||
|
||||
if (Test-Path "venv") {
|
||||
Write-Info "Virtual environment already exists, recreating..."
|
||||
Remove-Item -Recurse -Force "venv"
|
||||
}
|
||||
|
||||
# uv creates the venv and pins the Python version in one step
|
||||
& $UvCmd venv venv --python $PythonVersion
|
||||
|
||||
Pop-Location
|
||||
|
||||
Write-Success "Virtual environment ready (Python $PythonVersion)"
|
||||
}
|
||||
|
||||
function Install-Dependencies {
|
||||
Write-Info "Installing dependencies..."
|
||||
|
||||
Push-Location $InstallDir
|
||||
|
||||
if (-not $NoVenv) {
|
||||
# Tell uv to install into our venv (no activation needed)
|
||||
$env:VIRTUAL_ENV = "$InstallDir\venv"
|
||||
}
|
||||
|
||||
# Install main package with all extras
|
||||
try {
|
||||
& $UvCmd pip install -e ".[all]" 2>&1 | Out-Null
|
||||
} catch {
|
||||
& $UvCmd pip install -e "." | Out-Null
|
||||
}
|
||||
|
||||
Write-Success "Main package installed"
|
||||
|
||||
# Install submodules
|
||||
Write-Info "Installing mini-swe-agent (terminal tool backend)..."
|
||||
if (Test-Path "mini-swe-agent\pyproject.toml") {
|
||||
try {
|
||||
& $UvCmd pip install -e ".\mini-swe-agent" 2>&1 | Out-Null
|
||||
Write-Success "mini-swe-agent installed"
|
||||
} catch {
|
||||
Write-Warn "mini-swe-agent install failed (terminal tools may not work)"
|
||||
}
|
||||
} else {
|
||||
Write-Warn "mini-swe-agent not found (run: git submodule update --init)"
|
||||
}
|
||||
|
||||
Write-Info "Installing tinker-atropos (RL training backend)..."
|
||||
if (Test-Path "tinker-atropos\pyproject.toml") {
|
||||
try {
|
||||
& $UvCmd pip install -e ".\tinker-atropos" 2>&1 | Out-Null
|
||||
Write-Success "tinker-atropos installed"
|
||||
} catch {
|
||||
Write-Warn "tinker-atropos install failed (RL tools may not work)"
|
||||
}
|
||||
} else {
|
||||
Write-Warn "tinker-atropos not found (run: git submodule update --init)"
|
||||
}
|
||||
|
||||
Pop-Location
|
||||
|
||||
Write-Success "All dependencies installed"
|
||||
}
|
||||
|
||||
function Set-PathVariable {
|
||||
Write-Info "Setting up hermes command..."
|
||||
|
||||
if ($NoVenv) {
|
||||
$hermesBin = "$InstallDir"
|
||||
} else {
|
||||
$hermesBin = "$InstallDir\venv\Scripts"
|
||||
}
|
||||
|
||||
# Add the venv Scripts dir to user PATH so hermes is globally available
|
||||
# On Windows, the hermes.exe in venv\Scripts\ has the venv Python baked in
|
||||
$currentPath = [Environment]::GetEnvironmentVariable("Path", "User")
|
||||
|
||||
if ($currentPath -notlike "*$hermesBin*") {
|
||||
[Environment]::SetEnvironmentVariable(
|
||||
"Path",
|
||||
"$hermesBin;$currentPath",
|
||||
"User"
|
||||
)
|
||||
Write-Success "Added to user PATH: $hermesBin"
|
||||
} else {
|
||||
Write-Info "PATH already configured"
|
||||
}
|
||||
|
||||
# Update current session
|
||||
$env:Path = "$hermesBin;$env:Path"
|
||||
|
||||
Write-Success "hermes command ready"
|
||||
}
|
||||
|
||||
function Copy-ConfigTemplates {
|
||||
Write-Info "Setting up configuration files..."
|
||||
|
||||
# Create ~/.hermes directory structure
|
||||
New-Item -ItemType Directory -Force -Path "$HermesHome\cron" | Out-Null
|
||||
New-Item -ItemType Directory -Force -Path "$HermesHome\sessions" | Out-Null
|
||||
New-Item -ItemType Directory -Force -Path "$HermesHome\logs" | Out-Null
|
||||
|
||||
# Create .env
|
||||
$envPath = "$HermesHome\.env"
|
||||
if (-not (Test-Path $envPath)) {
|
||||
$examplePath = "$InstallDir\.env.example"
|
||||
if (Test-Path $examplePath) {
|
||||
Copy-Item $examplePath $envPath
|
||||
Write-Success "Created ~/.hermes/.env from template"
|
||||
} else {
|
||||
New-Item -ItemType File -Force -Path $envPath | Out-Null
|
||||
Write-Success "Created ~/.hermes/.env"
|
||||
}
|
||||
} else {
|
||||
Write-Info "~/.hermes/.env already exists, keeping it"
|
||||
}
|
||||
|
||||
# Create config.yaml
|
||||
$configPath = "$HermesHome\config.yaml"
|
||||
if (-not (Test-Path $configPath)) {
|
||||
$examplePath = "$InstallDir\cli-config.yaml.example"
|
||||
if (Test-Path $examplePath) {
|
||||
Copy-Item $examplePath $configPath
|
||||
Write-Success "Created ~/.hermes/config.yaml from template"
|
||||
}
|
||||
} else {
|
||||
Write-Info "~/.hermes/config.yaml already exists, keeping it"
|
||||
}
|
||||
|
||||
Write-Success "Configuration directory ready: ~/.hermes/"
|
||||
}
|
||||
|
||||
function Install-NodeDeps {
|
||||
if (-not $HasNode) {
|
||||
Write-Info "Skipping Node.js dependencies (Node not installed)"
|
||||
return
|
||||
}
|
||||
|
||||
Push-Location $InstallDir
|
||||
|
||||
if (Test-Path "package.json") {
|
||||
Write-Info "Installing Node.js dependencies..."
|
||||
try {
|
||||
npm install --silent 2>&1 | Out-Null
|
||||
Write-Success "Node.js dependencies installed"
|
||||
} catch {
|
||||
Write-Warn "npm install failed (browser tools may not work)"
|
||||
}
|
||||
}
|
||||
|
||||
Pop-Location
|
||||
}
|
||||
|
||||
function Invoke-SetupWizard {
|
||||
if ($SkipSetup) {
|
||||
Write-Info "Skipping setup wizard (-SkipSetup)"
|
||||
return
|
||||
}
|
||||
|
||||
Write-Host ""
|
||||
Write-Info "Starting setup wizard..."
|
||||
Write-Host ""
|
||||
|
||||
Push-Location $InstallDir
|
||||
|
||||
# Run hermes setup using the venv Python directly (no activation needed)
|
||||
if (-not $NoVenv) {
|
||||
& ".\venv\Scripts\python.exe" -m hermes_cli.main setup
|
||||
} else {
|
||||
python -m hermes_cli.main setup
|
||||
}
|
||||
|
||||
Pop-Location
|
||||
}
|
||||
|
||||
function Write-Completion {
|
||||
Write-Host ""
|
||||
Write-Host "┌─────────────────────────────────────────────────────────┐" -ForegroundColor Green
|
||||
Write-Host "│ ✓ Installation Complete! │" -ForegroundColor Green
|
||||
Write-Host "└─────────────────────────────────────────────────────────┘" -ForegroundColor Green
|
||||
Write-Host ""
|
||||
|
||||
# Show file locations
|
||||
Write-Host "📁 Your files (all in ~/.hermes/):" -ForegroundColor Cyan
|
||||
Write-Host ""
|
||||
Write-Host " Config: " -NoNewline -ForegroundColor Yellow
|
||||
Write-Host "$HermesHome\config.yaml"
|
||||
Write-Host " API Keys: " -NoNewline -ForegroundColor Yellow
|
||||
Write-Host "$HermesHome\.env"
|
||||
Write-Host " Data: " -NoNewline -ForegroundColor Yellow
|
||||
Write-Host "$HermesHome\cron\, sessions\, logs\"
|
||||
Write-Host " Code: " -NoNewline -ForegroundColor Yellow
|
||||
Write-Host "$HermesHome\hermes-agent\"
|
||||
Write-Host ""
|
||||
|
||||
Write-Host "─────────────────────────────────────────────────────────" -ForegroundColor Cyan
|
||||
Write-Host ""
|
||||
Write-Host "🚀 Commands:" -ForegroundColor Cyan
|
||||
Write-Host ""
|
||||
Write-Host " hermes " -NoNewline -ForegroundColor Green
|
||||
Write-Host "Start chatting"
|
||||
Write-Host " hermes setup " -NoNewline -ForegroundColor Green
|
||||
Write-Host "Configure API keys & settings"
|
||||
Write-Host " hermes config " -NoNewline -ForegroundColor Green
|
||||
Write-Host "View/edit configuration"
|
||||
Write-Host " hermes config edit " -NoNewline -ForegroundColor Green
|
||||
Write-Host "Open config in editor"
|
||||
Write-Host " hermes gateway " -NoNewline -ForegroundColor Green
|
||||
Write-Host "Run messaging gateway"
|
||||
Write-Host " hermes update " -NoNewline -ForegroundColor Green
|
||||
Write-Host "Update to latest version"
|
||||
Write-Host ""
|
||||
|
||||
Write-Host "─────────────────────────────────────────────────────────" -ForegroundColor Cyan
|
||||
Write-Host ""
|
||||
Write-Host "⚡ Restart your terminal for PATH changes to take effect" -ForegroundColor Yellow
|
||||
Write-Host ""
|
||||
|
||||
if (-not $HasNode) {
|
||||
Write-Host "Note: Node.js was not found. Browser automation tools" -ForegroundColor Yellow
|
||||
Write-Host "will have limited functionality." -ForegroundColor Yellow
|
||||
Write-Host ""
|
||||
}
|
||||
|
||||
if (-not $HasRipgrep) {
|
||||
Write-Host "Note: ripgrep (rg) was not found. File search will use" -ForegroundColor Yellow
|
||||
Write-Host "findstr as a fallback. For faster search:" -ForegroundColor Yellow
|
||||
Write-Host " winget install BurntSushi.ripgrep.MSVC" -ForegroundColor Yellow
|
||||
Write-Host ""
|
||||
}
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Main
|
||||
# ============================================================================
|
||||
|
||||
function Main {
|
||||
Write-Banner
|
||||
|
||||
if (-not (Install-Uv)) { exit 1 }
|
||||
if (-not (Test-Python)) { exit 1 }
|
||||
if (-not (Test-Git)) { exit 1 }
|
||||
Test-Node # Optional, doesn't fail
|
||||
Test-Ripgrep # Optional, doesn't fail
|
||||
|
||||
Install-Repository
|
||||
Install-Venv
|
||||
Install-Dependencies
|
||||
Install-NodeDeps
|
||||
Set-PathVariable
|
||||
Copy-ConfigTemplates
|
||||
Invoke-SetupWizard
|
||||
|
||||
Write-Completion
|
||||
}
|
||||
|
||||
Main
|
||||
@@ -1,722 +0,0 @@
|
||||
#!/bin/bash
|
||||
# ============================================================================
|
||||
# Hermes Agent Installer
|
||||
# ============================================================================
|
||||
# Installation script for Linux and macOS.
|
||||
# Uses uv for fast Python provisioning and package management.
|
||||
#
|
||||
# Usage:
|
||||
# curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
|
||||
#
|
||||
# Or with options:
|
||||
# curl -fsSL ... | bash -s -- --no-venv --skip-setup
|
||||
#
|
||||
# ============================================================================
|
||||
|
||||
set -e
|
||||
|
||||
# Colors
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[0;33m'
|
||||
BLUE='\033[0;34m'
|
||||
MAGENTA='\033[0;35m'
|
||||
CYAN='\033[0;36m'
|
||||
NC='\033[0m' # No Color
|
||||
BOLD='\033[1m'
|
||||
|
||||
# Configuration
|
||||
REPO_URL_SSH="git@github.com:NousResearch/hermes-agent.git"
|
||||
REPO_URL_HTTPS="https://github.com/NousResearch/hermes-agent.git"
|
||||
HERMES_HOME="$HOME/.hermes"
|
||||
INSTALL_DIR="${HERMES_INSTALL_DIR:-$HERMES_HOME/hermes-agent}"
|
||||
PYTHON_VERSION="3.11"
|
||||
|
||||
# Options
|
||||
USE_VENV=true
|
||||
RUN_SETUP=true
|
||||
BRANCH="main"
|
||||
|
||||
# Parse arguments
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case $1 in
|
||||
--no-venv)
|
||||
USE_VENV=false
|
||||
shift
|
||||
;;
|
||||
--skip-setup)
|
||||
RUN_SETUP=false
|
||||
shift
|
||||
;;
|
||||
--branch)
|
||||
BRANCH="$2"
|
||||
shift 2
|
||||
;;
|
||||
--dir)
|
||||
INSTALL_DIR="$2"
|
||||
shift 2
|
||||
;;
|
||||
-h|--help)
|
||||
echo "Hermes Agent Installer"
|
||||
echo ""
|
||||
echo "Usage: install.sh [OPTIONS]"
|
||||
echo ""
|
||||
echo "Options:"
|
||||
echo " --no-venv Don't create virtual environment"
|
||||
echo " --skip-setup Skip interactive setup wizard"
|
||||
echo " --branch NAME Git branch to install (default: main)"
|
||||
echo " --dir PATH Installation directory (default: ~/.hermes/hermes-agent)"
|
||||
echo " -h, --help Show this help"
|
||||
exit 0
|
||||
;;
|
||||
*)
|
||||
echo "Unknown option: $1"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
# ============================================================================
|
||||
# Helper functions
|
||||
# ============================================================================
|
||||
|
||||
print_banner() {
|
||||
echo ""
|
||||
echo -e "${MAGENTA}${BOLD}"
|
||||
echo "┌─────────────────────────────────────────────────────────┐"
|
||||
echo "│ 🦋 Hermes Agent Installer │"
|
||||
echo "├─────────────────────────────────────────────────────────┤"
|
||||
echo "│ I'm just a butterfly with a lot of tools. │"
|
||||
echo "└─────────────────────────────────────────────────────────┘"
|
||||
echo -e "${NC}"
|
||||
}
|
||||
|
||||
log_info() {
|
||||
echo -e "${CYAN}→${NC} $1"
|
||||
}
|
||||
|
||||
log_success() {
|
||||
echo -e "${GREEN}✓${NC} $1"
|
||||
}
|
||||
|
||||
log_warn() {
|
||||
echo -e "${YELLOW}⚠${NC} $1"
|
||||
}
|
||||
|
||||
log_error() {
|
||||
echo -e "${RED}✗${NC} $1"
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# System detection
|
||||
# ============================================================================
|
||||
|
||||
detect_os() {
|
||||
case "$(uname -s)" in
|
||||
Linux*)
|
||||
OS="linux"
|
||||
if [ -f /etc/os-release ]; then
|
||||
. /etc/os-release
|
||||
DISTRO="$ID"
|
||||
else
|
||||
DISTRO="unknown"
|
||||
fi
|
||||
;;
|
||||
Darwin*)
|
||||
OS="macos"
|
||||
DISTRO="macos"
|
||||
;;
|
||||
CYGWIN*|MINGW*|MSYS*)
|
||||
OS="windows"
|
||||
DISTRO="windows"
|
||||
log_error "Windows detected. Please use the PowerShell installer:"
|
||||
log_info " irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex"
|
||||
exit 1
|
||||
;;
|
||||
*)
|
||||
OS="unknown"
|
||||
DISTRO="unknown"
|
||||
log_warn "Unknown operating system"
|
||||
;;
|
||||
esac
|
||||
|
||||
log_success "Detected: $OS ($DISTRO)"
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Dependency checks
|
||||
# ============================================================================
|
||||
|
||||
install_uv() {
|
||||
log_info "Checking for uv package manager..."
|
||||
|
||||
# Check common locations for uv
|
||||
if command -v uv &> /dev/null; then
|
||||
UV_CMD="uv"
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
log_success "uv found ($UV_VERSION)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Check ~/.local/bin (default uv install location) even if not on PATH yet
|
||||
if [ -x "$HOME/.local/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.local/bin/uv"
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
log_success "uv found at ~/.local/bin ($UV_VERSION)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Check ~/.cargo/bin (alternative uv install location)
|
||||
if [ -x "$HOME/.cargo/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.cargo/bin/uv"
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
log_success "uv found at ~/.cargo/bin ($UV_VERSION)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Install uv
|
||||
log_info "Installing uv (fast Python package manager)..."
|
||||
if curl -LsSf https://astral.sh/uv/install.sh | sh 2>/dev/null; then
|
||||
# uv installs to ~/.local/bin by default
|
||||
if [ -x "$HOME/.local/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.local/bin/uv"
|
||||
elif [ -x "$HOME/.cargo/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.cargo/bin/uv"
|
||||
elif command -v uv &> /dev/null; then
|
||||
UV_CMD="uv"
|
||||
else
|
||||
log_error "uv installed but not found on PATH"
|
||||
log_info "Try adding ~/.local/bin to your PATH and re-running"
|
||||
exit 1
|
||||
fi
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
log_success "uv installed ($UV_VERSION)"
|
||||
else
|
||||
log_error "Failed to install uv"
|
||||
log_info "Install manually: https://docs.astral.sh/uv/getting-started/installation/"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
check_python() {
|
||||
log_info "Checking Python $PYTHON_VERSION..."
|
||||
|
||||
# Let uv handle Python — it can download and manage Python versions
|
||||
# First check if a suitable Python is already available
|
||||
if $UV_CMD python find "$PYTHON_VERSION" &> /dev/null; then
|
||||
PYTHON_PATH=$($UV_CMD python find "$PYTHON_VERSION")
|
||||
PYTHON_FOUND_VERSION=$($PYTHON_PATH --version 2>/dev/null)
|
||||
log_success "Python found: $PYTHON_FOUND_VERSION"
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Python not found — use uv to install it (no sudo needed!)
|
||||
log_info "Python $PYTHON_VERSION not found, installing via uv..."
|
||||
if $UV_CMD python install "$PYTHON_VERSION"; then
|
||||
PYTHON_PATH=$($UV_CMD python find "$PYTHON_VERSION")
|
||||
PYTHON_FOUND_VERSION=$($PYTHON_PATH --version 2>/dev/null)
|
||||
log_success "Python installed: $PYTHON_FOUND_VERSION"
|
||||
else
|
||||
log_error "Failed to install Python $PYTHON_VERSION"
|
||||
log_info "Install Python $PYTHON_VERSION manually, then re-run this script"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
check_git() {
|
||||
log_info "Checking Git..."
|
||||
|
||||
if command -v git &> /dev/null; then
|
||||
GIT_VERSION=$(git --version | awk '{print $3}')
|
||||
log_success "Git $GIT_VERSION found"
|
||||
return 0
|
||||
fi
|
||||
|
||||
log_error "Git not found"
|
||||
log_info "Please install Git:"
|
||||
|
||||
case "$OS" in
|
||||
linux)
|
||||
case "$DISTRO" in
|
||||
ubuntu|debian)
|
||||
log_info " sudo apt update && sudo apt install git"
|
||||
;;
|
||||
fedora)
|
||||
log_info " sudo dnf install git"
|
||||
;;
|
||||
arch)
|
||||
log_info " sudo pacman -S git"
|
||||
;;
|
||||
*)
|
||||
log_info " Use your package manager to install git"
|
||||
;;
|
||||
esac
|
||||
;;
|
||||
macos)
|
||||
log_info " xcode-select --install"
|
||||
log_info " Or: brew install git"
|
||||
;;
|
||||
esac
|
||||
|
||||
exit 1
|
||||
}
|
||||
|
||||
check_node() {
|
||||
log_info "Checking Node.js (optional, for browser tools)..."
|
||||
|
||||
if command -v node &> /dev/null; then
|
||||
NODE_VERSION=$(node --version)
|
||||
log_success "Node.js $NODE_VERSION found"
|
||||
HAS_NODE=true
|
||||
return 0
|
||||
fi
|
||||
|
||||
log_warn "Node.js not found (browser tools will be limited)"
|
||||
log_info "To install Node.js (optional):"
|
||||
|
||||
case "$OS" in
|
||||
linux)
|
||||
case "$DISTRO" in
|
||||
ubuntu|debian)
|
||||
log_info " curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -"
|
||||
log_info " sudo apt install -y nodejs"
|
||||
;;
|
||||
fedora)
|
||||
log_info " sudo dnf install nodejs"
|
||||
;;
|
||||
arch)
|
||||
log_info " sudo pacman -S nodejs npm"
|
||||
;;
|
||||
*)
|
||||
log_info " https://nodejs.org/en/download/"
|
||||
;;
|
||||
esac
|
||||
;;
|
||||
macos)
|
||||
log_info " brew install node"
|
||||
log_info " Or: https://nodejs.org/en/download/"
|
||||
;;
|
||||
esac
|
||||
|
||||
HAS_NODE=false
|
||||
# Don't exit - Node is optional
|
||||
}
|
||||
|
||||
check_ripgrep() {
|
||||
log_info "Checking ripgrep (optional, for faster file search)..."
|
||||
|
||||
if command -v rg &> /dev/null; then
|
||||
RG_VERSION=$(rg --version | head -1)
|
||||
log_success "$RG_VERSION found"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
|
||||
log_warn "ripgrep not found (file search will use grep fallback)"
|
||||
|
||||
# Offer to install
|
||||
echo ""
|
||||
read -p "Would you like to install ripgrep? (faster search, recommended) [Y/n] " -n 1 -r
|
||||
echo
|
||||
|
||||
if [[ $REPLY =~ ^[Yy]$ ]] || [[ -z $REPLY ]]; then
|
||||
log_info "Installing ripgrep..."
|
||||
|
||||
# Check if we can use sudo
|
||||
CAN_SUDO=false
|
||||
if command -v sudo &> /dev/null; then
|
||||
if sudo -n true 2>/dev/null || sudo -v 2>/dev/null; then
|
||||
CAN_SUDO=true
|
||||
fi
|
||||
fi
|
||||
|
||||
case "$OS" in
|
||||
linux)
|
||||
if [ "$CAN_SUDO" = true ]; then
|
||||
case "$DISTRO" in
|
||||
ubuntu|debian)
|
||||
if sudo apt install -y ripgrep 2>/dev/null; then
|
||||
log_success "ripgrep installed"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
;;
|
||||
fedora)
|
||||
if sudo dnf install -y ripgrep 2>/dev/null; then
|
||||
log_success "ripgrep installed"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
;;
|
||||
arch)
|
||||
if sudo pacman -S --noconfirm ripgrep 2>/dev/null; then
|
||||
log_success "ripgrep installed"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
;;
|
||||
esac
|
||||
else
|
||||
log_warn "sudo not available - cannot auto-install system packages"
|
||||
if command -v cargo &> /dev/null; then
|
||||
log_info "Trying cargo install (no sudo required)..."
|
||||
if cargo install ripgrep 2>/dev/null; then
|
||||
log_success "ripgrep installed via cargo"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
;;
|
||||
macos)
|
||||
if command -v brew &> /dev/null; then
|
||||
if brew install ripgrep 2>/dev/null; then
|
||||
log_success "ripgrep installed"
|
||||
HAS_RIPGREP=true
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
;;
|
||||
esac
|
||||
log_warn "Auto-install failed. You can install manually later:"
|
||||
else
|
||||
log_info "Skipping ripgrep installation. To install manually:"
|
||||
fi
|
||||
|
||||
# Show manual install instructions
|
||||
case "$OS" in
|
||||
linux)
|
||||
case "$DISTRO" in
|
||||
ubuntu|debian)
|
||||
log_info " sudo apt install ripgrep"
|
||||
;;
|
||||
fedora)
|
||||
log_info " sudo dnf install ripgrep"
|
||||
;;
|
||||
arch)
|
||||
log_info " sudo pacman -S ripgrep"
|
||||
;;
|
||||
*)
|
||||
log_info " https://github.com/BurntSushi/ripgrep#installation"
|
||||
;;
|
||||
esac
|
||||
if command -v cargo &> /dev/null; then
|
||||
log_info " Or without sudo: cargo install ripgrep"
|
||||
fi
|
||||
;;
|
||||
macos)
|
||||
log_info " brew install ripgrep"
|
||||
;;
|
||||
esac
|
||||
|
||||
HAS_RIPGREP=false
|
||||
# Don't exit - ripgrep is optional (grep fallback exists)
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Installation
|
||||
# ============================================================================
|
||||
|
||||
clone_repo() {
|
||||
log_info "Installing to $INSTALL_DIR..."
|
||||
|
||||
if [ -d "$INSTALL_DIR" ]; then
|
||||
if [ -d "$INSTALL_DIR/.git" ]; then
|
||||
log_info "Existing installation found, updating..."
|
||||
cd "$INSTALL_DIR"
|
||||
git fetch origin
|
||||
git checkout "$BRANCH"
|
||||
git pull origin "$BRANCH"
|
||||
else
|
||||
log_error "Directory exists but is not a git repository: $INSTALL_DIR"
|
||||
log_info "Remove it or choose a different directory with --dir"
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
# Try SSH first (for private repo access), fall back to HTTPS
|
||||
# Use --recurse-submodules to also clone mini-swe-agent and tinker-atropos
|
||||
log_info "Trying SSH clone..."
|
||||
if git clone --branch "$BRANCH" --recurse-submodules "$REPO_URL_SSH" "$INSTALL_DIR" 2>/dev/null; then
|
||||
log_success "Cloned via SSH"
|
||||
else
|
||||
log_info "SSH failed, trying HTTPS..."
|
||||
if git clone --branch "$BRANCH" --recurse-submodules "$REPO_URL_HTTPS" "$INSTALL_DIR"; then
|
||||
log_success "Cloned via HTTPS"
|
||||
else
|
||||
log_error "Failed to clone repository"
|
||||
log_info "For private repo access, ensure your SSH key is added to GitHub:"
|
||||
log_info " ssh-add ~/.ssh/id_rsa"
|
||||
log_info " ssh -T git@github.com # Test connection"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
cd "$INSTALL_DIR"
|
||||
|
||||
# Ensure submodules are initialized and updated (for existing installs or if --recurse failed)
|
||||
log_info "Initializing submodules (mini-swe-agent, tinker-atropos)..."
|
||||
git submodule update --init --recursive
|
||||
log_success "Submodules ready"
|
||||
|
||||
log_success "Repository ready"
|
||||
}
|
||||
|
||||
setup_venv() {
|
||||
if [ "$USE_VENV" = false ]; then
|
||||
log_info "Skipping virtual environment (--no-venv)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
log_info "Creating virtual environment with Python $PYTHON_VERSION..."
|
||||
|
||||
if [ -d "venv" ]; then
|
||||
log_info "Virtual environment already exists, recreating..."
|
||||
rm -rf venv
|
||||
fi
|
||||
|
||||
# uv creates the venv and pins the Python version in one step
|
||||
$UV_CMD venv venv --python "$PYTHON_VERSION"
|
||||
|
||||
log_success "Virtual environment ready (Python $PYTHON_VERSION)"
|
||||
}
|
||||
|
||||
install_deps() {
|
||||
log_info "Installing dependencies..."
|
||||
|
||||
if [ "$USE_VENV" = true ]; then
|
||||
# Tell uv to install into our venv (no need to activate)
|
||||
export VIRTUAL_ENV="$INSTALL_DIR/venv"
|
||||
fi
|
||||
|
||||
# Install the main package in editable mode with all extras
|
||||
$UV_CMD pip install -e ".[all]" || $UV_CMD pip install -e "."
|
||||
|
||||
log_success "Main package installed"
|
||||
|
||||
# Install submodules
|
||||
log_info "Installing mini-swe-agent (terminal tool backend)..."
|
||||
if [ -d "mini-swe-agent" ] && [ -f "mini-swe-agent/pyproject.toml" ]; then
|
||||
$UV_CMD pip install -e "./mini-swe-agent" || log_warn "mini-swe-agent install failed (terminal tools may not work)"
|
||||
log_success "mini-swe-agent installed"
|
||||
else
|
||||
log_warn "mini-swe-agent not found (run: git submodule update --init)"
|
||||
fi
|
||||
|
||||
log_info "Installing tinker-atropos (RL training backend)..."
|
||||
if [ -d "tinker-atropos" ] && [ -f "tinker-atropos/pyproject.toml" ]; then
|
||||
$UV_CMD pip install -e "./tinker-atropos" || log_warn "tinker-atropos install failed (RL tools may not work)"
|
||||
log_success "tinker-atropos installed"
|
||||
else
|
||||
log_warn "tinker-atropos not found (run: git submodule update --init)"
|
||||
fi
|
||||
|
||||
log_success "All dependencies installed"
|
||||
}
|
||||
|
||||
setup_path() {
|
||||
log_info "Setting up hermes command..."
|
||||
|
||||
if [ "$USE_VENV" = true ]; then
|
||||
HERMES_BIN="$INSTALL_DIR/venv/bin/hermes"
|
||||
else
|
||||
HERMES_BIN="$(which hermes 2>/dev/null || echo "")"
|
||||
if [ -z "$HERMES_BIN" ]; then
|
||||
log_warn "hermes not found on PATH after install"
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
|
||||
# Create symlink in ~/.local/bin (standard user binary location, usually on PATH)
|
||||
mkdir -p "$HOME/.local/bin"
|
||||
ln -sf "$HERMES_BIN" "$HOME/.local/bin/hermes"
|
||||
log_success "Symlinked hermes → ~/.local/bin/hermes"
|
||||
|
||||
# Check if ~/.local/bin is on PATH; if not, add it to shell config
|
||||
if ! echo "$PATH" | tr ':' '\n' | grep -q "^$HOME/.local/bin$"; then
|
||||
SHELL_CONFIG=""
|
||||
if [ -n "$BASH_VERSION" ]; then
|
||||
if [ -f "$HOME/.bashrc" ]; then
|
||||
SHELL_CONFIG="$HOME/.bashrc"
|
||||
elif [ -f "$HOME/.bash_profile" ]; then
|
||||
SHELL_CONFIG="$HOME/.bash_profile"
|
||||
fi
|
||||
elif [ -n "$ZSH_VERSION" ] || [ -f "$HOME/.zshrc" ]; then
|
||||
SHELL_CONFIG="$HOME/.zshrc"
|
||||
fi
|
||||
|
||||
PATH_LINE='export PATH="$HOME/.local/bin:$PATH"'
|
||||
|
||||
if [ -n "$SHELL_CONFIG" ]; then
|
||||
if ! grep -q '\.local/bin' "$SHELL_CONFIG" 2>/dev/null; then
|
||||
echo "" >> "$SHELL_CONFIG"
|
||||
echo "# Hermes Agent — ensure ~/.local/bin is on PATH" >> "$SHELL_CONFIG"
|
||||
echo "$PATH_LINE" >> "$SHELL_CONFIG"
|
||||
log_success "Added ~/.local/bin to PATH in $SHELL_CONFIG"
|
||||
else
|
||||
log_info "~/.local/bin already referenced in $SHELL_CONFIG"
|
||||
fi
|
||||
fi
|
||||
else
|
||||
log_info "~/.local/bin already on PATH"
|
||||
fi
|
||||
|
||||
# Export for current session so hermes works immediately
|
||||
export PATH="$HOME/.local/bin:$PATH"
|
||||
|
||||
log_success "hermes command ready"
|
||||
}
|
||||
|
||||
copy_config_templates() {
|
||||
log_info "Setting up configuration files..."
|
||||
|
||||
# Create ~/.hermes directory structure (config at top level, code in subdir)
|
||||
mkdir -p "$HERMES_HOME/cron"
|
||||
mkdir -p "$HERMES_HOME/sessions"
|
||||
mkdir -p "$HERMES_HOME/logs"
|
||||
|
||||
# Create .env at ~/.hermes/.env (top level, easy to find)
|
||||
if [ ! -f "$HERMES_HOME/.env" ]; then
|
||||
if [ -f "$INSTALL_DIR/.env.example" ]; then
|
||||
cp "$INSTALL_DIR/.env.example" "$HERMES_HOME/.env"
|
||||
log_success "Created ~/.hermes/.env from template"
|
||||
else
|
||||
touch "$HERMES_HOME/.env"
|
||||
log_success "Created ~/.hermes/.env"
|
||||
fi
|
||||
else
|
||||
log_info "~/.hermes/.env already exists, keeping it"
|
||||
fi
|
||||
|
||||
# Create config.yaml at ~/.hermes/config.yaml (top level, easy to find)
|
||||
if [ ! -f "$HERMES_HOME/config.yaml" ]; then
|
||||
if [ -f "$INSTALL_DIR/cli-config.yaml.example" ]; then
|
||||
cp "$INSTALL_DIR/cli-config.yaml.example" "$HERMES_HOME/config.yaml"
|
||||
log_success "Created ~/.hermes/config.yaml from template"
|
||||
fi
|
||||
else
|
||||
log_info "~/.hermes/config.yaml already exists, keeping it"
|
||||
fi
|
||||
|
||||
log_success "Configuration directory ready: ~/.hermes/"
|
||||
}
|
||||
|
||||
install_node_deps() {
|
||||
if [ "$HAS_NODE" = false ]; then
|
||||
log_info "Skipping Node.js dependencies (Node not installed)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
if [ -f "$INSTALL_DIR/package.json" ]; then
|
||||
log_info "Installing Node.js dependencies..."
|
||||
cd "$INSTALL_DIR"
|
||||
npm install --silent 2>/dev/null || {
|
||||
log_warn "npm install failed (browser tools may not work)"
|
||||
return 0
|
||||
}
|
||||
log_success "Node.js dependencies installed"
|
||||
fi
|
||||
}
|
||||
|
||||
run_setup_wizard() {
|
||||
if [ "$RUN_SETUP" = false ]; then
|
||||
log_info "Skipping setup wizard (--skip-setup)"
|
||||
return 0
|
||||
fi
|
||||
|
||||
echo ""
|
||||
log_info "Starting setup wizard..."
|
||||
echo ""
|
||||
|
||||
cd "$INSTALL_DIR"
|
||||
|
||||
# Run hermes setup using the venv Python directly (no activation needed)
|
||||
if [ "$USE_VENV" = true ]; then
|
||||
"$INSTALL_DIR/venv/bin/python" -m hermes_cli.main setup
|
||||
else
|
||||
python -m hermes_cli.main setup
|
||||
fi
|
||||
}
|
||||
|
||||
print_success() {
|
||||
echo ""
|
||||
echo -e "${GREEN}${BOLD}"
|
||||
echo "┌─────────────────────────────────────────────────────────┐"
|
||||
echo "│ ✓ Installation Complete! │"
|
||||
echo "└─────────────────────────────────────────────────────────┘"
|
||||
echo -e "${NC}"
|
||||
echo ""
|
||||
|
||||
# Show file locations
|
||||
echo -e "${CYAN}${BOLD}📁 Your files (all in ~/.hermes/):${NC}"
|
||||
echo ""
|
||||
echo -e " ${YELLOW}Config:${NC} ~/.hermes/config.yaml"
|
||||
echo -e " ${YELLOW}API Keys:${NC} ~/.hermes/.env"
|
||||
echo -e " ${YELLOW}Data:${NC} ~/.hermes/cron/, sessions/, logs/"
|
||||
echo -e " ${YELLOW}Code:${NC} ~/.hermes/hermes-agent/"
|
||||
echo ""
|
||||
|
||||
echo -e "${CYAN}─────────────────────────────────────────────────────────${NC}"
|
||||
echo ""
|
||||
echo -e "${CYAN}${BOLD}🚀 Commands:${NC}"
|
||||
echo ""
|
||||
echo -e " ${GREEN}hermes${NC} Start chatting"
|
||||
echo -e " ${GREEN}hermes setup${NC} Configure API keys & settings"
|
||||
echo -e " ${GREEN}hermes config${NC} View/edit configuration"
|
||||
echo -e " ${GREEN}hermes config edit${NC} Open config in editor"
|
||||
echo -e " ${GREEN}hermes gateway${NC} Run messaging gateway"
|
||||
echo -e " ${GREEN}hermes update${NC} Update to latest version"
|
||||
echo ""
|
||||
|
||||
echo -e "${CYAN}─────────────────────────────────────────────────────────${NC}"
|
||||
echo ""
|
||||
echo -e "${YELLOW}⚡ Reload your shell to use 'hermes' command:${NC}"
|
||||
echo ""
|
||||
echo " source ~/.bashrc # or ~/.zshrc"
|
||||
echo ""
|
||||
|
||||
# Show Node.js warning if not installed
|
||||
if [ "$HAS_NODE" = false ]; then
|
||||
echo -e "${YELLOW}"
|
||||
echo "Note: Node.js was not found. Browser automation tools"
|
||||
echo "will have limited functionality. Install Node.js later"
|
||||
echo "if you need full browser support."
|
||||
echo -e "${NC}"
|
||||
fi
|
||||
|
||||
# Show ripgrep note if not installed
|
||||
if [ "$HAS_RIPGREP" = false ]; then
|
||||
echo -e "${YELLOW}"
|
||||
echo "Note: ripgrep (rg) was not found. File search will use"
|
||||
echo "grep as a fallback. For faster search in large codebases,"
|
||||
echo "install ripgrep: sudo apt install ripgrep (or brew install ripgrep)"
|
||||
echo -e "${NC}"
|
||||
fi
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Main
|
||||
# ============================================================================
|
||||
|
||||
main() {
|
||||
print_banner
|
||||
|
||||
detect_os
|
||||
install_uv
|
||||
check_python
|
||||
check_git
|
||||
check_node
|
||||
check_ripgrep
|
||||
|
||||
clone_repo
|
||||
setup_venv
|
||||
install_deps
|
||||
install_node_deps
|
||||
setup_path
|
||||
copy_config_templates
|
||||
run_setup_wizard
|
||||
|
||||
print_success
|
||||
}
|
||||
|
||||
main
|
||||
@@ -1,411 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Sample and Compress HuggingFace Datasets
|
||||
|
||||
Downloads trajectories from multiple HuggingFace datasets, randomly samples them,
|
||||
and runs trajectory compression to fit within a target token budget.
|
||||
|
||||
Usage:
|
||||
python scripts/sample_and_compress.py
|
||||
|
||||
# Custom sample size
|
||||
python scripts/sample_and_compress.py --total_samples=5000
|
||||
|
||||
# Custom output name
|
||||
python scripts/sample_and_compress.py --output_name=compressed_16k
|
||||
"""
|
||||
|
||||
import json
|
||||
import random
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Tuple
|
||||
import fire
|
||||
|
||||
# Load environment variables
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
|
||||
# Default datasets to sample from
|
||||
DEFAULT_DATASETS = [
|
||||
"NousResearch/swe-terminus-agent-glm-kimi-minimax",
|
||||
"NousResearch/hermes-agent-megascience-sft1",
|
||||
"NousResearch/Hermes-Agent-Thinking-GLM-4.7-SFT2",
|
||||
"NousResearch/Hermes-Agent-Thinking-GLM-4.7-SFT1",
|
||||
"NousResearch/terminal-tasks-glm-hermes-agent"
|
||||
]
|
||||
|
||||
|
||||
def load_dataset_from_hf(dataset_name: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Load a dataset from HuggingFace.
|
||||
|
||||
Args:
|
||||
dataset_name: HuggingFace dataset name (e.g., "NousResearch/dataset-name")
|
||||
|
||||
Returns:
|
||||
List of trajectory entries
|
||||
"""
|
||||
from datasets import load_dataset
|
||||
|
||||
print(f" Loading {dataset_name}...")
|
||||
|
||||
try:
|
||||
# Try loading with default config
|
||||
ds = load_dataset(dataset_name, split="train")
|
||||
except Exception as e:
|
||||
print(f" ⚠️ Error loading {dataset_name}: {e}")
|
||||
return []
|
||||
|
||||
# Convert to list of dicts
|
||||
entries = []
|
||||
for item in ds:
|
||||
# Handle different possible formats
|
||||
if "conversations" in item:
|
||||
entries.append({"conversations": item["conversations"]})
|
||||
elif "messages" in item:
|
||||
# Convert messages format to conversations format if needed
|
||||
entries.append({"conversations": item["messages"]})
|
||||
else:
|
||||
# Assume the whole item is the entry
|
||||
entries.append(dict(item))
|
||||
|
||||
print(f" ✅ Loaded {len(entries):,} entries from {dataset_name}")
|
||||
return entries
|
||||
|
||||
|
||||
# Global tokenizer for multiprocessing (set in worker init)
|
||||
_TOKENIZER = None
|
||||
|
||||
|
||||
def _init_tokenizer_worker(tokenizer_name: str):
|
||||
"""Initialize tokenizer in worker process."""
|
||||
global _TOKENIZER
|
||||
from transformers import AutoTokenizer
|
||||
_TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True)
|
||||
|
||||
|
||||
def _count_tokens_for_entry(entry: Dict) -> Tuple[Dict, int]:
|
||||
"""
|
||||
Count tokens for a single entry (used in parallel processing).
|
||||
|
||||
Args:
|
||||
entry: Trajectory entry with 'conversations' field
|
||||
|
||||
Returns:
|
||||
Tuple of (entry, token_count)
|
||||
"""
|
||||
global _TOKENIZER
|
||||
|
||||
conversations = entry.get("conversations", [])
|
||||
if not conversations:
|
||||
return entry, 0
|
||||
|
||||
total = 0
|
||||
for turn in conversations:
|
||||
value = turn.get("value", "")
|
||||
if value:
|
||||
try:
|
||||
total += len(_TOKENIZER.encode(value))
|
||||
except:
|
||||
# Fallback to character estimate
|
||||
total += len(value) // 4
|
||||
|
||||
return entry, total
|
||||
|
||||
|
||||
def sample_from_datasets(
|
||||
datasets: List[str],
|
||||
total_samples: int,
|
||||
min_tokens: int = 16000,
|
||||
tokenizer_name: str = "moonshotai/Kimi-K2-Thinking",
|
||||
seed: int = 42,
|
||||
num_proc: int = 8
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Load all datasets, filter by token count, then randomly sample from combined pool.
|
||||
|
||||
Args:
|
||||
datasets: List of HuggingFace dataset names
|
||||
total_samples: Total number of samples to collect
|
||||
min_tokens: Minimum token count to include (only sample trajectories >= this)
|
||||
tokenizer_name: HuggingFace tokenizer for counting tokens
|
||||
seed: Random seed for reproducibility
|
||||
num_proc: Number of parallel processes for tokenization
|
||||
|
||||
Returns:
|
||||
List of sampled trajectory entries
|
||||
"""
|
||||
from multiprocessing import Pool
|
||||
from functools import partial
|
||||
|
||||
random.seed(seed)
|
||||
|
||||
print(f"\n📥 Loading {len(datasets)} datasets...")
|
||||
print(f" Minimum tokens: {min_tokens:,} (filtering smaller trajectories)")
|
||||
print(f" Parallel workers: {num_proc}")
|
||||
print()
|
||||
|
||||
# Load ALL entries from all datasets into one pool
|
||||
all_entries = []
|
||||
|
||||
for dataset_name in datasets:
|
||||
entries = load_dataset_from_hf(dataset_name)
|
||||
|
||||
if not entries:
|
||||
print(f" ⚠️ Skipping {dataset_name} (no entries loaded)")
|
||||
continue
|
||||
|
||||
# Add source metadata to each entry
|
||||
for entry in entries:
|
||||
entry["_source_dataset"] = dataset_name
|
||||
|
||||
all_entries.extend(entries)
|
||||
|
||||
print(f"\n📊 Total entries loaded: {len(all_entries):,}")
|
||||
|
||||
# Filter by token count using parallel processing
|
||||
print(f"\n🔍 Filtering trajectories with >= {min_tokens:,} tokens (using {num_proc} workers)...")
|
||||
|
||||
filtered_entries = []
|
||||
token_counts = []
|
||||
|
||||
# Use multiprocessing for token counting
|
||||
with Pool(
|
||||
processes=num_proc,
|
||||
initializer=_init_tokenizer_worker,
|
||||
initargs=(tokenizer_name,)
|
||||
) as pool:
|
||||
# Process in chunks and show progress
|
||||
chunk_size = 1000
|
||||
processed = 0
|
||||
|
||||
for result in pool.imap_unordered(_count_tokens_for_entry, all_entries, chunksize=100):
|
||||
entry, token_count = result
|
||||
processed += 1
|
||||
|
||||
if processed % chunk_size == 0:
|
||||
print(f" Processed {processed:,}/{len(all_entries):,}...", end="\r")
|
||||
|
||||
if token_count >= min_tokens:
|
||||
entry["_original_tokens"] = token_count
|
||||
filtered_entries.append(entry)
|
||||
token_counts.append(token_count)
|
||||
|
||||
print(f"\n ✅ Found {len(filtered_entries):,} trajectories >= {min_tokens:,} tokens")
|
||||
|
||||
if token_counts:
|
||||
avg_tokens = sum(token_counts) / len(token_counts)
|
||||
print(f" 📈 Token stats: min={min(token_counts):,}, max={max(token_counts):,}, avg={avg_tokens:,.0f}")
|
||||
|
||||
# Random sample from the filtered pool
|
||||
if len(filtered_entries) <= total_samples:
|
||||
print(f"\n⚠️ Only {len(filtered_entries):,} trajectories available, using all of them")
|
||||
sampled = filtered_entries
|
||||
else:
|
||||
sampled = random.sample(filtered_entries, total_samples)
|
||||
print(f"\n✅ Randomly sampled {len(sampled):,} trajectories from pool of {len(filtered_entries):,}")
|
||||
|
||||
# Show source distribution
|
||||
source_counts = {}
|
||||
for entry in sampled:
|
||||
source = entry.get("_source_dataset", "unknown").split("/")[-1]
|
||||
source_counts[source] = source_counts.get(source, 0) + 1
|
||||
|
||||
print(f"\n📌 Sample distribution by source:")
|
||||
for source, count in sorted(source_counts.items()):
|
||||
print(f" {source}: {count:,}")
|
||||
|
||||
# Shuffle
|
||||
random.shuffle(sampled)
|
||||
|
||||
return sampled
|
||||
|
||||
|
||||
def save_samples_for_compression(
|
||||
samples: List[Dict[str, Any]],
|
||||
output_dir: Path,
|
||||
batch_size: int = 100
|
||||
):
|
||||
"""
|
||||
Save samples to JSONL files for trajectory compression.
|
||||
|
||||
Args:
|
||||
samples: List of trajectory entries
|
||||
output_dir: Directory to save JSONL files
|
||||
batch_size: Number of entries per file
|
||||
"""
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Split into batches
|
||||
num_batches = (len(samples) + batch_size - 1) // batch_size
|
||||
|
||||
print(f"\n💾 Saving {len(samples)} samples to {output_dir}")
|
||||
print(f" Batch size: {batch_size}, Total batches: {num_batches}")
|
||||
|
||||
for i in range(num_batches):
|
||||
start_idx = i * batch_size
|
||||
end_idx = min((i + 1) * batch_size, len(samples))
|
||||
batch = samples[start_idx:end_idx]
|
||||
|
||||
output_file = output_dir / f"batch_{i}.jsonl"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
for entry in batch:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
print(f" ✅ Saved {num_batches} batch files")
|
||||
|
||||
|
||||
def run_compression(input_dir: Path, output_dir: Path, config_path: str):
|
||||
"""
|
||||
Run trajectory compression on the sampled data.
|
||||
|
||||
Args:
|
||||
input_dir: Directory containing JSONL files to compress
|
||||
output_dir: Directory for compressed output
|
||||
config_path: Path to compression config YAML
|
||||
"""
|
||||
# Import the compressor
|
||||
import sys
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
from trajectory_compressor import TrajectoryCompressor, CompressionConfig
|
||||
|
||||
print(f"\n🗜️ Running trajectory compression...")
|
||||
print(f" Input: {input_dir}")
|
||||
print(f" Output: {output_dir}")
|
||||
print(f" Config: {config_path}")
|
||||
|
||||
# Load config
|
||||
config = CompressionConfig.from_yaml(config_path)
|
||||
|
||||
# Initialize compressor
|
||||
compressor = TrajectoryCompressor(config)
|
||||
|
||||
# Run compression
|
||||
compressor.process_directory(input_dir, output_dir)
|
||||
|
||||
|
||||
def merge_output_to_single_jsonl(input_dir: Path, output_file: Path):
|
||||
"""
|
||||
Merge all JSONL files in a directory into a single JSONL file.
|
||||
|
||||
Args:
|
||||
input_dir: Directory containing JSONL files
|
||||
output_file: Output JSONL file path
|
||||
"""
|
||||
print(f"\n📦 Merging output files into {output_file.name}...")
|
||||
|
||||
all_entries = []
|
||||
for jsonl_file in sorted(input_dir.glob("*.jsonl")):
|
||||
if jsonl_file.name == output_file.name:
|
||||
continue
|
||||
with open(jsonl_file, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
all_entries.append(json.loads(line))
|
||||
|
||||
# Write merged file
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
for entry in all_entries:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
print(f" ✅ Merged {len(all_entries):,} entries into {output_file.name}")
|
||||
return output_file
|
||||
|
||||
|
||||
def main(
|
||||
total_samples: int = 2500,
|
||||
output_name: str = "compressed_agentic",
|
||||
datasets: str = None,
|
||||
config: str = "configs/trajectory_compression.yaml",
|
||||
seed: int = 42,
|
||||
batch_size: int = 100,
|
||||
min_tokens: int = 16000,
|
||||
num_proc: int = 8,
|
||||
skip_download: bool = False,
|
||||
):
|
||||
"""
|
||||
Sample trajectories from HuggingFace datasets and run compression.
|
||||
|
||||
Args:
|
||||
total_samples: Total number of samples to collect (default: 2500)
|
||||
output_name: Name for output directory/file (default: "compressed_agentic")
|
||||
datasets: Comma-separated list of dataset names (uses defaults if not provided)
|
||||
config: Path to compression config YAML
|
||||
seed: Random seed for reproducibility
|
||||
batch_size: Number of entries per JSONL file during processing
|
||||
min_tokens: Minimum token count to filter trajectories (default: 16000)
|
||||
num_proc: Number of parallel workers for tokenization (default: 8)
|
||||
skip_download: Skip download and use existing sampled data
|
||||
"""
|
||||
print("=" * 70)
|
||||
print("📊 TRAJECTORY SAMPLING AND COMPRESSION")
|
||||
print("=" * 70)
|
||||
|
||||
# Parse datasets
|
||||
if datasets:
|
||||
dataset_list = [d.strip() for d in datasets.split(",")]
|
||||
else:
|
||||
dataset_list = DEFAULT_DATASETS
|
||||
|
||||
print(f"\n📋 Configuration:")
|
||||
print(f" Total samples: {total_samples:,}")
|
||||
print(f" Min tokens filter: {min_tokens:,}")
|
||||
print(f" Parallel workers: {num_proc}")
|
||||
print(f" Datasets: {len(dataset_list)}")
|
||||
for ds in dataset_list:
|
||||
print(f" - {ds}")
|
||||
print(f" Output name: {output_name}")
|
||||
print(f" Config: {config}")
|
||||
print(f" Seed: {seed}")
|
||||
|
||||
# Setup paths
|
||||
base_dir = Path(__file__).parent.parent
|
||||
sampled_dir = base_dir / "data" / f"{output_name}_raw"
|
||||
compressed_dir = base_dir / "data" / f"{output_name}_batches"
|
||||
final_output = base_dir / "data" / f"{output_name}.jsonl"
|
||||
|
||||
if not skip_download:
|
||||
# Step 1: Download, filter by token count, and sample from combined pool
|
||||
samples = sample_from_datasets(
|
||||
dataset_list,
|
||||
total_samples,
|
||||
min_tokens=min_tokens,
|
||||
seed=seed,
|
||||
num_proc=num_proc
|
||||
)
|
||||
|
||||
if not samples:
|
||||
print("❌ No samples collected. Exiting.")
|
||||
return
|
||||
|
||||
# Step 2: Save to JSONL files
|
||||
save_samples_for_compression(samples, sampled_dir, batch_size)
|
||||
else:
|
||||
print(f"\n⏭️ Skipping download, using existing data in {sampled_dir}")
|
||||
|
||||
# Step 3: Run compression
|
||||
config_path = base_dir / config
|
||||
if not config_path.exists():
|
||||
print(f"❌ Config not found: {config_path}")
|
||||
return
|
||||
|
||||
run_compression(sampled_dir, compressed_dir, str(config_path))
|
||||
|
||||
# Step 4: Merge into single JSONL file
|
||||
merge_output_to_single_jsonl(compressed_dir, final_output)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("✅ COMPLETE!")
|
||||
print("=" * 70)
|
||||
print(f"\n📁 Raw samples: {sampled_dir}")
|
||||
print(f"📁 Compressed batches: {compressed_dir}")
|
||||
print(f"📁 Final output: {final_output}")
|
||||
print(f"\nTo upload to HuggingFace:")
|
||||
print(f" huggingface-cli upload NousResearch/{output_name} {final_output}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
275
setup-hermes.sh
275
setup-hermes.sh
@@ -1,275 +0,0 @@
|
||||
#!/bin/bash
|
||||
# ============================================================================
|
||||
# Hermes Agent Setup Script
|
||||
# ============================================================================
|
||||
# Quick setup for developers who cloned the repo manually.
|
||||
# Uses uv for fast Python provisioning and package management.
|
||||
#
|
||||
# Usage:
|
||||
# ./setup-hermes.sh
|
||||
#
|
||||
# This script:
|
||||
# 1. Installs uv if not present
|
||||
# 2. Creates a virtual environment with Python 3.11 via uv
|
||||
# 3. Installs all dependencies (main package + submodules)
|
||||
# 4. Creates .env from template (if not exists)
|
||||
# 5. Symlinks the 'hermes' CLI command into ~/.local/bin
|
||||
# 6. Runs the setup wizard (optional)
|
||||
# ============================================================================
|
||||
|
||||
set -e
|
||||
|
||||
# Colors
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[0;33m'
|
||||
CYAN='\033[0;36m'
|
||||
RED='\033[0;31m'
|
||||
NC='\033[0m'
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
cd "$SCRIPT_DIR"
|
||||
|
||||
PYTHON_VERSION="3.11"
|
||||
|
||||
echo ""
|
||||
echo -e "${CYAN}🦋 Hermes Agent Setup${NC}"
|
||||
echo ""
|
||||
|
||||
# ============================================================================
|
||||
# Install / locate uv
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Checking for uv..."
|
||||
|
||||
UV_CMD=""
|
||||
if command -v uv &> /dev/null; then
|
||||
UV_CMD="uv"
|
||||
elif [ -x "$HOME/.local/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.local/bin/uv"
|
||||
elif [ -x "$HOME/.cargo/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.cargo/bin/uv"
|
||||
fi
|
||||
|
||||
if [ -n "$UV_CMD" ]; then
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
echo -e "${GREEN}✓${NC} uv found ($UV_VERSION)"
|
||||
else
|
||||
echo -e "${CYAN}→${NC} Installing uv..."
|
||||
if curl -LsSf https://astral.sh/uv/install.sh | sh 2>/dev/null; then
|
||||
if [ -x "$HOME/.local/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.local/bin/uv"
|
||||
elif [ -x "$HOME/.cargo/bin/uv" ]; then
|
||||
UV_CMD="$HOME/.cargo/bin/uv"
|
||||
fi
|
||||
|
||||
if [ -n "$UV_CMD" ]; then
|
||||
UV_VERSION=$($UV_CMD --version 2>/dev/null)
|
||||
echo -e "${GREEN}✓${NC} uv installed ($UV_VERSION)"
|
||||
else
|
||||
echo -e "${RED}✗${NC} uv installed but not found. Add ~/.local/bin to PATH and retry."
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
echo -e "${RED}✗${NC} Failed to install uv. Visit https://docs.astral.sh/uv/"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# Python check (uv can provision it automatically)
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Checking Python $PYTHON_VERSION..."
|
||||
|
||||
if $UV_CMD python find "$PYTHON_VERSION" &> /dev/null; then
|
||||
PYTHON_PATH=$($UV_CMD python find "$PYTHON_VERSION")
|
||||
PYTHON_FOUND_VERSION=$($PYTHON_PATH --version 2>/dev/null)
|
||||
echo -e "${GREEN}✓${NC} $PYTHON_FOUND_VERSION found"
|
||||
else
|
||||
echo -e "${CYAN}→${NC} Python $PYTHON_VERSION not found, installing via uv..."
|
||||
$UV_CMD python install "$PYTHON_VERSION"
|
||||
PYTHON_PATH=$($UV_CMD python find "$PYTHON_VERSION")
|
||||
PYTHON_FOUND_VERSION=$($PYTHON_PATH --version 2>/dev/null)
|
||||
echo -e "${GREEN}✓${NC} $PYTHON_FOUND_VERSION installed"
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# Virtual environment
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Setting up virtual environment..."
|
||||
|
||||
if [ -d "venv" ]; then
|
||||
echo -e "${CYAN}→${NC} Removing old venv..."
|
||||
rm -rf venv
|
||||
fi
|
||||
|
||||
$UV_CMD venv venv --python "$PYTHON_VERSION"
|
||||
echo -e "${GREEN}✓${NC} venv created (Python $PYTHON_VERSION)"
|
||||
|
||||
# Tell uv to install into this venv (no activation needed for uv)
|
||||
export VIRTUAL_ENV="$SCRIPT_DIR/venv"
|
||||
|
||||
# ============================================================================
|
||||
# Dependencies
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Installing dependencies..."
|
||||
|
||||
$UV_CMD pip install -e ".[all]" || $UV_CMD pip install -e "."
|
||||
|
||||
echo -e "${GREEN}✓${NC} Dependencies installed"
|
||||
|
||||
# ============================================================================
|
||||
# Submodules (terminal backend + RL training)
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Installing submodules..."
|
||||
|
||||
# mini-swe-agent (terminal tool backend)
|
||||
if [ -d "mini-swe-agent" ] && [ -f "mini-swe-agent/pyproject.toml" ]; then
|
||||
$UV_CMD pip install -e "./mini-swe-agent" && \
|
||||
echo -e "${GREEN}✓${NC} mini-swe-agent installed" || \
|
||||
echo -e "${YELLOW}⚠${NC} mini-swe-agent install failed (terminal tools may not work)"
|
||||
else
|
||||
echo -e "${YELLOW}⚠${NC} mini-swe-agent not found (run: git submodule update --init --recursive)"
|
||||
fi
|
||||
|
||||
# tinker-atropos (RL training backend)
|
||||
if [ -d "tinker-atropos" ] && [ -f "tinker-atropos/pyproject.toml" ]; then
|
||||
$UV_CMD pip install -e "./tinker-atropos" && \
|
||||
echo -e "${GREEN}✓${NC} tinker-atropos installed" || \
|
||||
echo -e "${YELLOW}⚠${NC} tinker-atropos install failed (RL tools may not work)"
|
||||
else
|
||||
echo -e "${YELLOW}⚠${NC} tinker-atropos not found (run: git submodule update --init --recursive)"
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# Optional: ripgrep (for faster file search)
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Checking ripgrep (optional, for faster search)..."
|
||||
|
||||
if command -v rg &> /dev/null; then
|
||||
echo -e "${GREEN}✓${NC} ripgrep found"
|
||||
else
|
||||
echo -e "${YELLOW}⚠${NC} ripgrep not found (file search will use grep fallback)"
|
||||
read -p "Install ripgrep for faster search? [Y/n] " -n 1 -r
|
||||
echo
|
||||
if [[ $REPLY =~ ^[Yy]$ ]] || [[ -z $REPLY ]]; then
|
||||
INSTALLED=false
|
||||
|
||||
# Check if sudo is available
|
||||
if command -v sudo &> /dev/null && sudo -n true 2>/dev/null; then
|
||||
if command -v apt &> /dev/null; then
|
||||
sudo apt install -y ripgrep && INSTALLED=true
|
||||
elif command -v dnf &> /dev/null; then
|
||||
sudo dnf install -y ripgrep && INSTALLED=true
|
||||
fi
|
||||
fi
|
||||
|
||||
# Try brew (no sudo needed)
|
||||
if [ "$INSTALLED" = false ] && command -v brew &> /dev/null; then
|
||||
brew install ripgrep && INSTALLED=true
|
||||
fi
|
||||
|
||||
# Try cargo (no sudo needed)
|
||||
if [ "$INSTALLED" = false ] && command -v cargo &> /dev/null; then
|
||||
echo -e "${CYAN}→${NC} Trying cargo install (no sudo required)..."
|
||||
cargo install ripgrep && INSTALLED=true
|
||||
fi
|
||||
|
||||
if [ "$INSTALLED" = true ]; then
|
||||
echo -e "${GREEN}✓${NC} ripgrep installed"
|
||||
else
|
||||
echo -e "${YELLOW}⚠${NC} Auto-install failed. Install options:"
|
||||
echo " sudo apt install ripgrep # Debian/Ubuntu"
|
||||
echo " brew install ripgrep # macOS"
|
||||
echo " cargo install ripgrep # With Rust (no sudo)"
|
||||
echo " https://github.com/BurntSushi/ripgrep#installation"
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# Environment file
|
||||
# ============================================================================
|
||||
|
||||
if [ ! -f ".env" ]; then
|
||||
if [ -f ".env.example" ]; then
|
||||
cp .env.example .env
|
||||
echo -e "${GREEN}✓${NC} Created .env from template"
|
||||
fi
|
||||
else
|
||||
echo -e "${GREEN}✓${NC} .env exists"
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# PATH setup — symlink hermes into ~/.local/bin
|
||||
# ============================================================================
|
||||
|
||||
echo -e "${CYAN}→${NC} Setting up hermes command..."
|
||||
|
||||
HERMES_BIN="$SCRIPT_DIR/venv/bin/hermes"
|
||||
mkdir -p "$HOME/.local/bin"
|
||||
ln -sf "$HERMES_BIN" "$HOME/.local/bin/hermes"
|
||||
echo -e "${GREEN}✓${NC} Symlinked hermes → ~/.local/bin/hermes"
|
||||
|
||||
# Ensure ~/.local/bin is on PATH in shell config
|
||||
SHELL_CONFIG=""
|
||||
if [ -f "$HOME/.zshrc" ]; then
|
||||
SHELL_CONFIG="$HOME/.zshrc"
|
||||
elif [ -f "$HOME/.bashrc" ]; then
|
||||
SHELL_CONFIG="$HOME/.bashrc"
|
||||
elif [ -f "$HOME/.bash_profile" ]; then
|
||||
SHELL_CONFIG="$HOME/.bash_profile"
|
||||
fi
|
||||
|
||||
if [ -n "$SHELL_CONFIG" ]; then
|
||||
if ! echo "$PATH" | tr ':' '\n' | grep -q "^$HOME/.local/bin$"; then
|
||||
if ! grep -q '\.local/bin' "$SHELL_CONFIG" 2>/dev/null; then
|
||||
echo "" >> "$SHELL_CONFIG"
|
||||
echo "# Hermes Agent — ensure ~/.local/bin is on PATH" >> "$SHELL_CONFIG"
|
||||
echo 'export PATH="$HOME/.local/bin:$PATH"' >> "$SHELL_CONFIG"
|
||||
echo -e "${GREEN}✓${NC} Added ~/.local/bin to PATH in $SHELL_CONFIG"
|
||||
else
|
||||
echo -e "${GREEN}✓${NC} ~/.local/bin already in $SHELL_CONFIG"
|
||||
fi
|
||||
else
|
||||
echo -e "${GREEN}✓${NC} ~/.local/bin already on PATH"
|
||||
fi
|
||||
fi
|
||||
|
||||
# ============================================================================
|
||||
# Done
|
||||
# ============================================================================
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}✓ Setup complete!${NC}"
|
||||
echo ""
|
||||
echo "Next steps:"
|
||||
echo ""
|
||||
echo " 1. Reload your shell:"
|
||||
echo " source $SHELL_CONFIG"
|
||||
echo ""
|
||||
echo " 2. Run the setup wizard to configure API keys:"
|
||||
echo " hermes setup"
|
||||
echo ""
|
||||
echo " 3. Start chatting:"
|
||||
echo " hermes"
|
||||
echo ""
|
||||
echo "Other commands:"
|
||||
echo " hermes status # Check configuration"
|
||||
echo " hermes gateway # Start messaging gateway"
|
||||
echo " hermes cron daemon # Run cron daemon"
|
||||
echo " hermes doctor # Diagnose issues"
|
||||
echo ""
|
||||
|
||||
# Ask if they want to run setup wizard now
|
||||
read -p "Would you like to run the setup wizard now? [Y/n] " -n 1 -r
|
||||
echo
|
||||
if [[ $REPLY =~ ^[Yy]$ ]] || [[ -z $REPLY ]]; then
|
||||
echo ""
|
||||
# Run directly with venv Python (no activation needed)
|
||||
"$SCRIPT_DIR/venv/bin/python" -m hermes_cli.main setup
|
||||
fi
|
||||
@@ -1,3 +0,0 @@
|
||||
---
|
||||
description: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
|
||||
---
|
||||
@@ -1,332 +0,0 @@
|
||||
---
|
||||
name: huggingface-accelerate
|
||||
description: Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
|
||||
version: 1.0.0
|
||||
author: Orchestra Research
|
||||
license: MIT
|
||||
tags: [Distributed Training, HuggingFace, Accelerate, DeepSpeed, FSDP, Mixed Precision, PyTorch, DDP, Unified API, Simple]
|
||||
dependencies: [accelerate, torch, transformers]
|
||||
---
|
||||
|
||||
# HuggingFace Accelerate - Unified Distributed Training
|
||||
|
||||
## Quick start
|
||||
|
||||
Accelerate simplifies distributed training to 4 lines of code.
|
||||
|
||||
**Installation**:
|
||||
```bash
|
||||
pip install accelerate
|
||||
```
|
||||
|
||||
**Convert PyTorch script** (4 lines):
|
||||
```python
|
||||
import torch
|
||||
+ from accelerate import Accelerator
|
||||
|
||||
+ accelerator = Accelerator()
|
||||
|
||||
model = torch.nn.Transformer()
|
||||
optimizer = torch.optim.Adam(model.parameters())
|
||||
dataloader = torch.utils.data.DataLoader(dataset)
|
||||
|
||||
+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
|
||||
|
||||
for batch in dataloader:
|
||||
optimizer.zero_grad()
|
||||
loss = model(batch)
|
||||
- loss.backward()
|
||||
+ accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Run** (single command):
|
||||
```bash
|
||||
accelerate launch train.py
|
||||
```
|
||||
|
||||
## Common workflows
|
||||
|
||||
### Workflow 1: From single GPU to multi-GPU
|
||||
|
||||
**Original script**:
|
||||
```python
|
||||
# train.py
|
||||
import torch
|
||||
|
||||
model = torch.nn.Linear(10, 2).to('cuda')
|
||||
optimizer = torch.optim.Adam(model.parameters())
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
|
||||
|
||||
for epoch in range(10):
|
||||
for batch in dataloader:
|
||||
batch = batch.to('cuda')
|
||||
optimizer.zero_grad()
|
||||
loss = model(batch).mean()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**With Accelerate** (4 lines added):
|
||||
```python
|
||||
# train.py
|
||||
import torch
|
||||
from accelerate import Accelerator # +1
|
||||
|
||||
accelerator = Accelerator() # +2
|
||||
|
||||
model = torch.nn.Linear(10, 2)
|
||||
optimizer = torch.optim.Adam(model.parameters())
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
|
||||
|
||||
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) # +3
|
||||
|
||||
for epoch in range(10):
|
||||
for batch in dataloader:
|
||||
# No .to('cuda') needed - automatic!
|
||||
optimizer.zero_grad()
|
||||
loss = model(batch).mean()
|
||||
accelerator.backward(loss) # +4
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Configure** (interactive):
|
||||
```bash
|
||||
accelerate config
|
||||
```
|
||||
|
||||
**Questions**:
|
||||
- Which machine? (single/multi GPU/TPU/CPU)
|
||||
- How many machines? (1)
|
||||
- Mixed precision? (no/fp16/bf16/fp8)
|
||||
- DeepSpeed? (no/yes)
|
||||
|
||||
**Launch** (works on any setup):
|
||||
```bash
|
||||
# Single GPU
|
||||
accelerate launch train.py
|
||||
|
||||
# Multi-GPU (8 GPUs)
|
||||
accelerate launch --multi_gpu --num_processes 8 train.py
|
||||
|
||||
# Multi-node
|
||||
accelerate launch --multi_gpu --num_processes 16 \
|
||||
--num_machines 2 --machine_rank 0 \
|
||||
--main_process_ip $MASTER_ADDR \
|
||||
train.py
|
||||
```
|
||||
|
||||
### Workflow 2: Mixed precision training
|
||||
|
||||
**Enable FP16/BF16**:
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
|
||||
# FP16 (with gradient scaling)
|
||||
accelerator = Accelerator(mixed_precision='fp16')
|
||||
|
||||
# BF16 (no scaling, more stable)
|
||||
accelerator = Accelerator(mixed_precision='bf16')
|
||||
|
||||
# FP8 (H100+)
|
||||
accelerator = Accelerator(mixed_precision='fp8')
|
||||
|
||||
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
|
||||
|
||||
# Everything else is automatic!
|
||||
for batch in dataloader:
|
||||
with accelerator.autocast(): # Optional, done automatically
|
||||
loss = model(batch)
|
||||
accelerator.backward(loss)
|
||||
```
|
||||
|
||||
### Workflow 3: DeepSpeed ZeRO integration
|
||||
|
||||
**Enable DeepSpeed ZeRO-2**:
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='bf16',
|
||||
deepspeed_plugin={
|
||||
"zero_stage": 2, # ZeRO-2
|
||||
"offload_optimizer": False,
|
||||
"gradient_accumulation_steps": 4
|
||||
}
|
||||
)
|
||||
|
||||
# Same code as before!
|
||||
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
|
||||
```
|
||||
|
||||
**Or via config**:
|
||||
```bash
|
||||
accelerate config
|
||||
# Select: DeepSpeed → ZeRO-2
|
||||
```
|
||||
|
||||
**deepspeed_config.json**:
|
||||
```json
|
||||
{
|
||||
"fp16": {"enabled": false},
|
||||
"bf16": {"enabled": true},
|
||||
"zero_optimization": {
|
||||
"stage": 2,
|
||||
"offload_optimizer": {"device": "cpu"},
|
||||
"allgather_bucket_size": 5e8,
|
||||
"reduce_bucket_size": 5e8
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Launch**:
|
||||
```bash
|
||||
accelerate launch --config_file deepspeed_config.json train.py
|
||||
```
|
||||
|
||||
### Workflow 4: FSDP (Fully Sharded Data Parallel)
|
||||
|
||||
**Enable FSDP**:
|
||||
```python
|
||||
from accelerate import Accelerator, FullyShardedDataParallelPlugin
|
||||
|
||||
fsdp_plugin = FullyShardedDataParallelPlugin(
|
||||
sharding_strategy="FULL_SHARD", # ZeRO-3 equivalent
|
||||
auto_wrap_policy="TRANSFORMER_AUTO_WRAP",
|
||||
cpu_offload=False
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='bf16',
|
||||
fsdp_plugin=fsdp_plugin
|
||||
)
|
||||
|
||||
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
|
||||
```
|
||||
|
||||
**Or via config**:
|
||||
```bash
|
||||
accelerate config
|
||||
# Select: FSDP → Full Shard → No CPU Offload
|
||||
```
|
||||
|
||||
### Workflow 5: Gradient accumulation
|
||||
|
||||
**Accumulate gradients**:
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
|
||||
accelerator = Accelerator(gradient_accumulation_steps=4)
|
||||
|
||||
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
|
||||
|
||||
for batch in dataloader:
|
||||
with accelerator.accumulate(model): # Handles accumulation
|
||||
optimizer.zero_grad()
|
||||
loss = model(batch)
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Effective batch size**: `batch_size * num_gpus * gradient_accumulation_steps`
|
||||
|
||||
## When to use vs alternatives
|
||||
|
||||
**Use Accelerate when**:
|
||||
- Want simplest distributed training
|
||||
- Need single script for any hardware
|
||||
- Use HuggingFace ecosystem
|
||||
- Want flexibility (DDP/DeepSpeed/FSDP/Megatron)
|
||||
- Need quick prototyping
|
||||
|
||||
**Key advantages**:
|
||||
- **4 lines**: Minimal code changes
|
||||
- **Unified API**: Same code for DDP, DeepSpeed, FSDP, Megatron
|
||||
- **Automatic**: Device placement, mixed precision, sharding
|
||||
- **Interactive config**: No manual launcher setup
|
||||
- **Single launch**: Works everywhere
|
||||
|
||||
**Use alternatives instead**:
|
||||
- **PyTorch Lightning**: Need callbacks, high-level abstractions
|
||||
- **Ray Train**: Multi-node orchestration, hyperparameter tuning
|
||||
- **DeepSpeed**: Direct API control, advanced features
|
||||
- **Raw DDP**: Maximum control, minimal abstraction
|
||||
|
||||
## Common issues
|
||||
|
||||
**Issue: Wrong device placement**
|
||||
|
||||
Don't manually move to device:
|
||||
```python
|
||||
# WRONG
|
||||
batch = batch.to('cuda')
|
||||
|
||||
# CORRECT
|
||||
# Accelerate handles it automatically after prepare()
|
||||
```
|
||||
|
||||
**Issue: Gradient accumulation not working**
|
||||
|
||||
Use context manager:
|
||||
```python
|
||||
# CORRECT
|
||||
with accelerator.accumulate(model):
|
||||
optimizer.zero_grad()
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Issue: Checkpointing in distributed**
|
||||
|
||||
Use accelerator methods:
|
||||
```python
|
||||
# Save only on main process
|
||||
if accelerator.is_main_process:
|
||||
accelerator.save_state('checkpoint/')
|
||||
|
||||
# Load on all processes
|
||||
accelerator.load_state('checkpoint/')
|
||||
```
|
||||
|
||||
**Issue: Different results with FSDP**
|
||||
|
||||
Ensure same random seed:
|
||||
```python
|
||||
from accelerate.utils import set_seed
|
||||
set_seed(42)
|
||||
```
|
||||
|
||||
## Advanced topics
|
||||
|
||||
**Megatron integration**: See [references/megatron-integration.md](references/megatron-integration.md) for tensor parallelism, pipeline parallelism, and sequence parallelism setup.
|
||||
|
||||
**Custom plugins**: See [references/custom-plugins.md](references/custom-plugins.md) for creating custom distributed plugins and advanced configuration.
|
||||
|
||||
**Performance tuning**: See [references/performance.md](references/performance.md) for profiling, memory optimization, and best practices.
|
||||
|
||||
## Hardware requirements
|
||||
|
||||
- **CPU**: Works (slow)
|
||||
- **Single GPU**: Works
|
||||
- **Multi-GPU**: DDP (default), DeepSpeed, or FSDP
|
||||
- **Multi-node**: DDP, DeepSpeed, FSDP, Megatron
|
||||
- **TPU**: Supported
|
||||
- **Apple MPS**: Supported
|
||||
|
||||
**Launcher requirements**:
|
||||
- **DDP**: `torch.distributed.run` (built-in)
|
||||
- **DeepSpeed**: `deepspeed` (pip install deepspeed)
|
||||
- **FSDP**: PyTorch 1.12+ (built-in)
|
||||
- **Megatron**: Custom setup
|
||||
|
||||
## Resources
|
||||
|
||||
- Docs: https://huggingface.co/docs/accelerate
|
||||
- GitHub: https://github.com/huggingface/accelerate
|
||||
- Version: 1.11.0+
|
||||
- Tutorial: "Accelerate your scripts"
|
||||
- Examples: https://github.com/huggingface/accelerate/tree/main/examples
|
||||
- Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries
|
||||
|
||||
|
||||
|
||||
@@ -1,453 +0,0 @@
|
||||
# Custom Plugins for Accelerate
|
||||
|
||||
## Overview
|
||||
|
||||
Accelerate allows creating **custom plugins** to extend distributed training strategies beyond built-in options (DDP, FSDP, DeepSpeed).
|
||||
|
||||
## Plugin Architecture
|
||||
|
||||
### Base Plugin Structure
|
||||
|
||||
```python
|
||||
from accelerate.utils import DistributedDataParallelKwargs
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class CustomPlugin:
|
||||
"""Custom training plugin."""
|
||||
|
||||
# Plugin configuration
|
||||
param1: int = 1
|
||||
param2: str = "default"
|
||||
|
||||
def __post_init__(self):
|
||||
# Validation logic
|
||||
if self.param1 < 1:
|
||||
raise ValueError("param1 must be >= 1")
|
||||
```
|
||||
|
||||
### Using Custom Plugin
|
||||
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
|
||||
# Create plugin
|
||||
custom_plugin = CustomPlugin(param1=4, param2="value")
|
||||
|
||||
# Pass to Accelerator
|
||||
accelerator = Accelerator(
|
||||
custom_plugin=custom_plugin # Not a real parameter, example only
|
||||
)
|
||||
```
|
||||
|
||||
## Built-In Plugin Examples
|
||||
|
||||
### 1. GradScalerKwargs (FP16 Configuration)
|
||||
|
||||
```python
|
||||
from accelerate.utils import GradScalerKwargs
|
||||
|
||||
# Configure gradient scaler for FP16
|
||||
scaler_kwargs = GradScalerKwargs(
|
||||
init_scale=2.**16, # Initial loss scale
|
||||
growth_factor=2.0, # Scale growth rate
|
||||
backoff_factor=0.5, # Scale backoff rate
|
||||
growth_interval=2000, # Steps between scale increases
|
||||
enabled=True # Enable scaler
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='fp16',
|
||||
kwargs_handlers=[scaler_kwargs] # Pass as kwargs handler
|
||||
)
|
||||
```
|
||||
|
||||
**Use case**: Fine-tune FP16 gradient scaling behavior
|
||||
|
||||
### 2. DistributedDataParallelKwargs
|
||||
|
||||
```python
|
||||
from accelerate.utils import DistributedDataParallelKwargs
|
||||
|
||||
# Configure DDP behavior
|
||||
ddp_kwargs = DistributedDataParallelKwargs(
|
||||
bucket_cap_mb=25, # Gradient bucketing size
|
||||
find_unused_parameters=False, # Find unused params (slower)
|
||||
check_reduction=False, # Check gradient reduction
|
||||
gradient_as_bucket_view=True, # Memory optimization
|
||||
static_graph=False # Static computation graph
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
kwargs_handlers=[ddp_kwargs]
|
||||
)
|
||||
```
|
||||
|
||||
**Use case**: Optimize DDP performance for specific models
|
||||
|
||||
### 3. FP8RecipeKwargs (H100 FP8)
|
||||
|
||||
```python
|
||||
from accelerate.utils import FP8RecipeKwargs
|
||||
|
||||
# Configure FP8 training (H100)
|
||||
fp8_recipe = FP8RecipeKwargs(
|
||||
backend="te", # TransformerEngine backend
|
||||
margin=0, # Scaling margin
|
||||
interval=1, # Scaling interval
|
||||
fp8_format="HYBRID", # E4M3 + E5M2 hybrid
|
||||
amax_history_len=1024, # AMAX history length
|
||||
amax_compute_algo="max" # AMAX computation algorithm
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='fp8',
|
||||
kwargs_handlers=[fp8_recipe]
|
||||
)
|
||||
```
|
||||
|
||||
**Use case**: Ultra-fast training on H100 GPUs
|
||||
|
||||
## Custom DeepSpeed Configuration
|
||||
|
||||
### ZeRO-3 with CPU Offload
|
||||
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
from accelerate.utils import DeepSpeedPlugin
|
||||
|
||||
# Custom DeepSpeed config
|
||||
ds_plugin = DeepSpeedPlugin(
|
||||
zero_stage=3, # ZeRO-3
|
||||
offload_optimizer_device="cpu", # CPU offload optimizer
|
||||
offload_param_device="cpu", # CPU offload parameters
|
||||
zero3_init_flag=True, # ZeRO-3 initialization
|
||||
zero3_save_16bit_model=True, # Save FP16 weights
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
deepspeed_plugin=ds_plugin,
|
||||
mixed_precision='bf16'
|
||||
)
|
||||
```
|
||||
|
||||
### ZeRO-2 with NVMe Offload
|
||||
|
||||
```python
|
||||
ds_plugin = DeepSpeedPlugin(
|
||||
zero_stage=2,
|
||||
offload_optimizer_device="nvme", # NVMe offload
|
||||
offload_param_device="nvme",
|
||||
nvme_path="/local_nvme", # NVMe mount path
|
||||
)
|
||||
```
|
||||
|
||||
### Custom JSON Config
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
# Load custom DeepSpeed config
|
||||
with open('deepspeed_config.json', 'r') as f:
|
||||
ds_config = json.load(f)
|
||||
|
||||
ds_plugin = DeepSpeedPlugin(hf_ds_config=ds_config)
|
||||
|
||||
accelerator = Accelerator(deepspeed_plugin=ds_plugin)
|
||||
```
|
||||
|
||||
**Example config** (`deepspeed_config.json`):
|
||||
```json
|
||||
{
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": 1.0,
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"offload_optimizer": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"offload_param": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 1e9,
|
||||
"reduce_bucket_size": 5e8,
|
||||
"stage3_prefetch_bucket_size": 5e8,
|
||||
"stage3_param_persistence_threshold": 1e6,
|
||||
"stage3_max_live_parameters": 1e9,
|
||||
"stage3_max_reuse_distance": 1e9,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"steps_per_print": 100,
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
```
|
||||
|
||||
## Custom FSDP Configuration
|
||||
|
||||
### FSDP with Custom Auto-Wrap Policy
|
||||
|
||||
```python
|
||||
from accelerate.utils import FullyShardedDataParallelPlugin
|
||||
from torch.distributed.fsdp import BackwardPrefetch, ShardingStrategy
|
||||
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
|
||||
import functools
|
||||
|
||||
# Custom wrap policy (size-based)
|
||||
wrap_policy = functools.partial(
|
||||
size_based_auto_wrap_policy,
|
||||
min_num_params=1e6 # Wrap layers with 1M+ params
|
||||
)
|
||||
|
||||
fsdp_plugin = FullyShardedDataParallelPlugin(
|
||||
sharding_strategy=ShardingStrategy.FULL_SHARD, # ZeRO-3 equivalent
|
||||
backward_prefetch=BackwardPrefetch.BACKWARD_PRE, # Prefetch strategy
|
||||
mixed_precision_policy=None, # Use Accelerator's mixed precision
|
||||
auto_wrap_policy=wrap_policy, # Custom wrapping
|
||||
cpu_offload=False,
|
||||
ignored_modules=None, # Modules to not wrap
|
||||
state_dict_type="FULL_STATE_DICT", # Save format
|
||||
optim_state_dict_config=None,
|
||||
limit_all_gathers=False,
|
||||
use_orig_params=True, # Use original param shapes
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
fsdp_plugin=fsdp_plugin,
|
||||
mixed_precision='bf16'
|
||||
)
|
||||
```
|
||||
|
||||
### FSDP with Transformer Auto-Wrap
|
||||
|
||||
```python
|
||||
from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
|
||||
from transformers.models.gpt2.modeling_gpt2 import GPT2Block
|
||||
|
||||
# Wrap at transformer block level
|
||||
wrap_policy = functools.partial(
|
||||
transformer_auto_wrap_policy,
|
||||
transformer_layer_cls={GPT2Block} # Wrap GPT2Block layers
|
||||
)
|
||||
|
||||
fsdp_plugin = FullyShardedDataParallelPlugin(
|
||||
auto_wrap_policy=wrap_policy
|
||||
)
|
||||
```
|
||||
|
||||
## Creating Custom Training Strategy
|
||||
|
||||
### Example: Custom Gradient Accumulation
|
||||
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
|
||||
class CustomGradientAccumulation:
|
||||
def __init__(self, steps=4, adaptive=False):
|
||||
self.steps = steps
|
||||
self.adaptive = adaptive
|
||||
self.current_step = 0
|
||||
|
||||
def should_sync(self, loss):
|
||||
"""Decide whether to sync gradients."""
|
||||
self.current_step += 1
|
||||
|
||||
# Adaptive: sync on high loss
|
||||
if self.adaptive and loss > threshold:
|
||||
self.current_step = 0
|
||||
return True
|
||||
|
||||
# Regular: sync every N steps
|
||||
if self.current_step >= self.steps:
|
||||
self.current_step = 0
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
# Usage
|
||||
custom_accum = CustomGradientAccumulation(steps=8, adaptive=True)
|
||||
accelerator = Accelerator()
|
||||
|
||||
for batch in dataloader:
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
|
||||
# Scale loss
|
||||
loss = loss / custom_accum.steps
|
||||
accelerator.backward(loss)
|
||||
|
||||
# Conditional sync
|
||||
if custom_accum.should_sync(loss.item()):
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
```
|
||||
|
||||
### Example: Custom Mixed Precision
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
class CustomMixedPrecision:
|
||||
"""Custom mixed precision with dynamic loss scaling."""
|
||||
|
||||
def __init__(self, init_scale=2**16, scale_window=2000):
|
||||
self.scaler = torch.cuda.amp.GradScaler(
|
||||
init_scale=init_scale,
|
||||
growth_interval=scale_window
|
||||
)
|
||||
self.scale_history = []
|
||||
|
||||
def scale_loss(self, loss):
|
||||
"""Scale loss for backward."""
|
||||
return self.scaler.scale(loss)
|
||||
|
||||
def unscale_and_clip(self, optimizer, max_norm=1.0):
|
||||
"""Unscale gradients and clip."""
|
||||
self.scaler.unscale_(optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(
|
||||
optimizer.param_groups[0]['params'],
|
||||
max_norm
|
||||
)
|
||||
|
||||
def step(self, optimizer):
|
||||
"""Optimizer step with scaler update."""
|
||||
scale_before = self.scaler.get_scale()
|
||||
self.scaler.step(optimizer)
|
||||
self.scaler.update()
|
||||
scale_after = self.scaler.get_scale()
|
||||
|
||||
# Track scale changes
|
||||
if scale_before != scale_after:
|
||||
self.scale_history.append(scale_after)
|
||||
|
||||
# Usage
|
||||
custom_mp = CustomMixedPrecision()
|
||||
|
||||
for batch in dataloader:
|
||||
with torch.cuda.amp.autocast(dtype=torch.float16):
|
||||
loss = model(**batch).loss
|
||||
|
||||
scaled_loss = custom_mp.scale_loss(loss)
|
||||
scaled_loss.backward()
|
||||
|
||||
custom_mp.unscale_and_clip(optimizer, max_norm=1.0)
|
||||
custom_mp.step(optimizer)
|
||||
optimizer.zero_grad()
|
||||
```
|
||||
|
||||
## Advanced: Custom Distributed Backend
|
||||
|
||||
### Custom AllReduce Strategy
|
||||
|
||||
```python
|
||||
import torch.distributed as dist
|
||||
|
||||
class CustomAllReduce:
|
||||
"""Custom all-reduce with compression."""
|
||||
|
||||
def __init__(self, compression_ratio=0.1):
|
||||
self.compression_ratio = compression_ratio
|
||||
|
||||
def compress_gradients(self, tensor):
|
||||
"""Top-k gradient compression."""
|
||||
k = int(tensor.numel() * self.compression_ratio)
|
||||
values, indices = torch.topk(tensor.abs().view(-1), k)
|
||||
return values, indices
|
||||
|
||||
def all_reduce_compressed(self, tensor):
|
||||
"""All-reduce with gradient compression."""
|
||||
# Compress
|
||||
values, indices = self.compress_gradients(tensor)
|
||||
|
||||
# All-reduce compressed gradients
|
||||
dist.all_reduce(values, op=dist.ReduceOp.SUM)
|
||||
|
||||
# Decompress
|
||||
tensor_compressed = torch.zeros_like(tensor).view(-1)
|
||||
tensor_compressed[indices] = values / dist.get_world_size()
|
||||
|
||||
return tensor_compressed.view_as(tensor)
|
||||
|
||||
# Usage in training loop
|
||||
custom_ar = CustomAllReduce(compression_ratio=0.1)
|
||||
|
||||
for batch in dataloader:
|
||||
loss = model(**batch).loss
|
||||
loss.backward()
|
||||
|
||||
# Custom all-reduce
|
||||
for param in model.parameters():
|
||||
if param.grad is not None:
|
||||
param.grad.data = custom_ar.all_reduce_compressed(param.grad.data)
|
||||
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
```
|
||||
|
||||
## Plugin Best Practices
|
||||
|
||||
### 1. Validation in `__post_init__`
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class CustomPlugin:
|
||||
learning_rate: float = 1e-3
|
||||
warmup_steps: int = 1000
|
||||
|
||||
def __post_init__(self):
|
||||
# Validate parameters
|
||||
if self.learning_rate <= 0:
|
||||
raise ValueError("learning_rate must be positive")
|
||||
if self.warmup_steps < 0:
|
||||
raise ValueError("warmup_steps must be non-negative")
|
||||
|
||||
# Compute derived values
|
||||
self.min_lr = self.learning_rate * 0.1
|
||||
```
|
||||
|
||||
### 2. Compatibility Checks
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class CustomPlugin:
|
||||
feature_enabled: bool = True
|
||||
|
||||
def is_compatible(self, accelerator):
|
||||
"""Check if plugin is compatible with accelerator config."""
|
||||
if self.feature_enabled and accelerator.mixed_precision == 'fp8':
|
||||
raise ValueError("Custom plugin not compatible with FP8")
|
||||
return True
|
||||
```
|
||||
|
||||
### 3. State Management
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class CustomPlugin:
|
||||
counter: int = 0
|
||||
history: list = None
|
||||
|
||||
def __post_init__(self):
|
||||
if self.history is None:
|
||||
self.history = []
|
||||
|
||||
def update_state(self, value):
|
||||
"""Update plugin state during training."""
|
||||
self.counter += 1
|
||||
self.history.append(value)
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
- Accelerate Plugins: https://huggingface.co/docs/accelerate/package_reference/kwargs
|
||||
- DeepSpeed Config: https://www.deepspeed.ai/docs/config-json/
|
||||
- FSDP Guide: https://pytorch.org/docs/stable/fsdp.html
|
||||
- Custom Training Loops: https://huggingface.co/docs/accelerate/usage_guides/training_tpu
|
||||
@@ -1,489 +0,0 @@
|
||||
# Megatron Integration with Accelerate
|
||||
|
||||
## Overview
|
||||
|
||||
Accelerate supports Megatron-LM for massive model training with tensor parallelism and pipeline parallelism.
|
||||
|
||||
**Megatron capabilities**:
|
||||
- **Tensor Parallelism (TP)**: Split layers across GPUs
|
||||
- **Pipeline Parallelism (PP)**: Split model depth across GPUs
|
||||
- **Data Parallelism (DP)**: Replicate model across GPU groups
|
||||
- **Sequence Parallelism**: Split sequences for long contexts
|
||||
|
||||
## Setup
|
||||
|
||||
### Install Megatron-LM
|
||||
|
||||
```bash
|
||||
# Clone Megatron-LM repository
|
||||
git clone https://github.com/NVIDIA/Megatron-LM.git
|
||||
cd Megatron-LM
|
||||
pip install -e .
|
||||
|
||||
# Install Apex (NVIDIA optimizations)
|
||||
git clone https://github.com/NVIDIA/apex
|
||||
cd apex
|
||||
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation \
|
||||
--config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
|
||||
```
|
||||
|
||||
### Accelerate Configuration
|
||||
|
||||
```bash
|
||||
accelerate config
|
||||
```
|
||||
|
||||
**Questions**:
|
||||
```
|
||||
In which compute environment are you running?
|
||||
> This machine
|
||||
|
||||
Which type of machine are you using?
|
||||
> Multi-GPU
|
||||
|
||||
How many different machines will you use?
|
||||
> 1
|
||||
|
||||
Do you want to use DeepSpeed/FSDP?
|
||||
> No
|
||||
|
||||
Do you want to use Megatron-LM?
|
||||
> Yes
|
||||
|
||||
What is the Tensor Parallelism degree? [1-8]
|
||||
> 2
|
||||
|
||||
Do you want to enable Sequence Parallelism?
|
||||
> No
|
||||
|
||||
What is the Pipeline Parallelism degree? [1-8]
|
||||
> 2
|
||||
|
||||
What is the Data Parallelism degree? [1-8]
|
||||
> 2
|
||||
|
||||
Where to perform activation checkpointing? ['SELECTIVE', 'FULL', 'NONE']
|
||||
> SELECTIVE
|
||||
|
||||
Where to perform activation partitioning? ['SEQUENTIAL', 'UNIFORM']
|
||||
> SEQUENTIAL
|
||||
```
|
||||
|
||||
**Generated config** (`~/.cache/huggingface/accelerate/default_config.yaml`):
|
||||
```yaml
|
||||
compute_environment: LOCAL_MACHINE
|
||||
distributed_type: MEGATRON_LM
|
||||
downcast_bf16: 'no'
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
megatron_lm_config:
|
||||
megatron_lm_gradient_clipping: 1.0
|
||||
megatron_lm_learning_rate_decay_iters: 320000
|
||||
megatron_lm_num_micro_batches: 1
|
||||
megatron_lm_pp_degree: 2
|
||||
megatron_lm_recompute_activations: true
|
||||
megatron_lm_sequence_parallelism: false
|
||||
megatron_lm_tp_degree: 2
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
```
|
||||
|
||||
## Parallelism Strategies
|
||||
|
||||
### Tensor Parallelism (TP)
|
||||
|
||||
**Splits each transformer layer across GPUs**:
|
||||
|
||||
```python
|
||||
# Layer split across 2 GPUs
|
||||
# GPU 0: First half of attention heads
|
||||
# GPU 1: Second half of attention heads
|
||||
|
||||
# Each GPU computes partial outputs
|
||||
# All-reduce combines results
|
||||
```
|
||||
|
||||
**TP degree recommendations**:
|
||||
- **TP=1**: No tensor parallelism (single GPU per layer)
|
||||
- **TP=2**: 2 GPUs per layer (good for 7-13B models)
|
||||
- **TP=4**: 4 GPUs per layer (good for 20-40B models)
|
||||
- **TP=8**: 8 GPUs per layer (good for 70B+ models)
|
||||
|
||||
**Benefits**:
|
||||
- Reduces memory per GPU
|
||||
- All-reduce communication (fast)
|
||||
|
||||
**Drawbacks**:
|
||||
- Requires fast inter-GPU bandwidth (NVLink)
|
||||
- Communication overhead per layer
|
||||
|
||||
### Pipeline Parallelism (PP)
|
||||
|
||||
**Splits model depth across GPUs**:
|
||||
|
||||
```python
|
||||
# 12-layer model, PP=4
|
||||
# GPU 0: Layers 0-2
|
||||
# GPU 1: Layers 3-5
|
||||
# GPU 2: Layers 6-8
|
||||
# GPU 3: Layers 9-11
|
||||
```
|
||||
|
||||
**PP degree recommendations**:
|
||||
- **PP=1**: No pipeline parallelism
|
||||
- **PP=2**: 2 pipeline stages (good for 20-40B models)
|
||||
- **PP=4**: 4 pipeline stages (good for 70B+ models)
|
||||
- **PP=8**: 8 pipeline stages (good for 175B+ models)
|
||||
|
||||
**Benefits**:
|
||||
- Linear memory reduction (4× PP = 4× less memory)
|
||||
- Works across nodes (slower interconnect OK)
|
||||
|
||||
**Drawbacks**:
|
||||
- Pipeline bubbles (idle time)
|
||||
- Requires micro-batching
|
||||
|
||||
### Data Parallelism (DP)
|
||||
|
||||
**Replicates model across GPU groups**:
|
||||
|
||||
```python
|
||||
# 8 GPUs, TP=2, PP=2, DP=2
|
||||
# Group 0 (GPUs 0-3): Full model replica
|
||||
# Group 1 (GPUs 4-7): Full model replica
|
||||
```
|
||||
|
||||
**DP degree**:
|
||||
- `DP = total_gpus / (TP × PP)`
|
||||
- Example: 8 GPUs, TP=2, PP=2 → DP=2
|
||||
|
||||
**Benefits**:
|
||||
- Increases throughput
|
||||
- Scales batch size
|
||||
|
||||
### Sequence Parallelism
|
||||
|
||||
**Splits long sequences across GPUs** (extends TP):
|
||||
|
||||
```python
|
||||
# 8K sequence, TP=2, Sequence Parallel=True
|
||||
# GPU 0: Tokens 0-4095
|
||||
# GPU 1: Tokens 4096-8191
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
- Enables very long sequences (100K+ tokens)
|
||||
- Reduces activation memory
|
||||
|
||||
**Requirements**:
|
||||
- Must use with TP > 1
|
||||
- RoPE/ALiBi position encodings work best
|
||||
|
||||
## Accelerate Code Example
|
||||
|
||||
### Basic Setup
|
||||
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
from accelerate.utils import MegatronLMPlugin
|
||||
|
||||
# Configure Megatron
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
tp_degree=2, # Tensor parallelism degree
|
||||
pp_degree=2, # Pipeline parallelism degree
|
||||
num_micro_batches=4, # Micro-batches for pipeline
|
||||
gradient_clipping=1.0, # Gradient clipping value
|
||||
sequence_parallelism=False, # Enable sequence parallelism
|
||||
recompute_activations=True, # Activation checkpointing
|
||||
use_distributed_optimizer=True, # Distributed optimizer
|
||||
custom_prepare_model_function=None, # Custom model prep
|
||||
)
|
||||
|
||||
# Initialize accelerator
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='bf16',
|
||||
megatron_lm_plugin=megatron_plugin
|
||||
)
|
||||
|
||||
# Prepare model and optimizer
|
||||
model, optimizer, train_dataloader = accelerator.prepare(
|
||||
model, optimizer, train_dataloader
|
||||
)
|
||||
|
||||
# Training loop (same as DDP!)
|
||||
for batch in train_dataloader:
|
||||
optimizer.zero_grad()
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
### Full Training Script
|
||||
|
||||
```python
|
||||
import torch
|
||||
from accelerate import Accelerator
|
||||
from accelerate.utils import MegatronLMPlugin
|
||||
from transformers import GPT2Config, GPT2LMHeadModel
|
||||
|
||||
def main():
|
||||
# Megatron configuration
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
tp_degree=2,
|
||||
pp_degree=2,
|
||||
num_micro_batches=4,
|
||||
gradient_clipping=1.0,
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='bf16',
|
||||
gradient_accumulation_steps=8,
|
||||
megatron_lm_plugin=megatron_plugin
|
||||
)
|
||||
|
||||
# Model
|
||||
config = GPT2Config(
|
||||
n_layer=24,
|
||||
n_head=16,
|
||||
n_embd=1024,
|
||||
)
|
||||
model = GPT2LMHeadModel(config)
|
||||
|
||||
# Optimizer
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=6e-4)
|
||||
|
||||
# Prepare
|
||||
model, optimizer, train_loader = accelerator.prepare(
|
||||
model, optimizer, train_loader
|
||||
)
|
||||
|
||||
# Training loop
|
||||
for epoch in range(num_epochs):
|
||||
for batch in train_loader:
|
||||
with accelerator.accumulate(model):
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Save checkpoint
|
||||
accelerator.wait_for_everyone()
|
||||
accelerator.save_state(f'checkpoint-epoch-{epoch}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
```
|
||||
|
||||
### Launch Command
|
||||
|
||||
```bash
|
||||
# 8 GPUs, TP=2, PP=2, DP=2
|
||||
accelerate launch --multi_gpu --num_processes 8 train.py
|
||||
|
||||
# Multi-node (2 nodes, 8 GPUs each)
|
||||
# Node 0
|
||||
accelerate launch --multi_gpu --num_processes 16 \
|
||||
--num_machines 2 --machine_rank 0 \
|
||||
--main_process_ip $MASTER_ADDR \
|
||||
--main_process_port 29500 \
|
||||
train.py
|
||||
|
||||
# Node 1
|
||||
accelerate launch --multi_gpu --num_processes 16 \
|
||||
--num_machines 2 --machine_rank 1 \
|
||||
--main_process_ip $MASTER_ADDR \
|
||||
--main_process_port 29500 \
|
||||
train.py
|
||||
```
|
||||
|
||||
## Activation Checkpointing
|
||||
|
||||
**Reduces memory by recomputing activations**:
|
||||
|
||||
```python
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
recompute_activations=True, # Enable checkpointing
|
||||
checkpoint_num_layers=1, # Checkpoint every N layers
|
||||
distribute_checkpointed_activations=True, # Distribute across TP
|
||||
partition_activations=True, # Partition in PP
|
||||
check_for_nan_in_loss_and_grad=True, # Stability check
|
||||
)
|
||||
```
|
||||
|
||||
**Strategies**:
|
||||
- `SELECTIVE`: Checkpoint transformer blocks only
|
||||
- `FULL`: Checkpoint all layers
|
||||
- `NONE`: No checkpointing
|
||||
|
||||
**Memory savings**: 30-50% with 10-15% slowdown
|
||||
|
||||
## Distributed Optimizer
|
||||
|
||||
**Shards optimizer state across DP ranks**:
|
||||
|
||||
```python
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
use_distributed_optimizer=True, # Enable sharded optimizer
|
||||
)
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
- Reduces optimizer memory by DP degree
|
||||
- Example: DP=4 → 4× less optimizer memory per GPU
|
||||
|
||||
**Compatible with**:
|
||||
- AdamW, Adam, SGD
|
||||
- Mixed precision training
|
||||
|
||||
## Performance Tuning
|
||||
|
||||
### Micro-Batch Size
|
||||
|
||||
```python
|
||||
# Pipeline parallelism requires micro-batching
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
pp_degree=4,
|
||||
num_micro_batches=16, # 16 micro-batches per pipeline
|
||||
)
|
||||
|
||||
# Effective batch = num_micro_batches × micro_batch_size × DP
|
||||
# Example: 16 × 2 × 4 = 128
|
||||
```
|
||||
|
||||
**Recommendations**:
|
||||
- More micro-batches → less pipeline bubble
|
||||
- Typical: 4-16 micro-batches
|
||||
|
||||
### Sequence Length
|
||||
|
||||
```python
|
||||
# For long sequences, enable sequence parallelism
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
tp_degree=4,
|
||||
sequence_parallelism=True, # Required: TP > 1
|
||||
)
|
||||
|
||||
# Enables sequences up to TP × normal limit
|
||||
# Example: TP=4, 8K normal → 32K with sequence parallel
|
||||
```
|
||||
|
||||
### GPU Topology
|
||||
|
||||
**NVLink required for TP**:
|
||||
```bash
|
||||
# Check NVLink topology
|
||||
nvidia-smi topo -m
|
||||
|
||||
# Good topology (NVLink between all GPUs)
|
||||
# GPU0 - GPU1: NV12 (fast)
|
||||
# GPU0 - GPU2: NV12 (fast)
|
||||
|
||||
# Bad topology (PCIe only)
|
||||
# GPU0 - GPU4: PHB (slow, avoid TP across these)
|
||||
```
|
||||
|
||||
**Recommendations**:
|
||||
- **TP**: Within same node (NVLink)
|
||||
- **PP**: Across nodes (slower interconnect OK)
|
||||
- **DP**: Any topology
|
||||
|
||||
## Model Size Guidelines
|
||||
|
||||
| Model Size | GPUs | TP | PP | DP | Micro-Batches |
|
||||
|------------|------|----|----|----|--------------|
|
||||
| 7B | 8 | 1 | 1 | 8 | 1 |
|
||||
| 13B | 8 | 2 | 1 | 4 | 1 |
|
||||
| 20B | 16 | 4 | 1 | 4 | 1 |
|
||||
| 40B | 32 | 4 | 2 | 4 | 4 |
|
||||
| 70B | 64 | 8 | 2 | 4 | 8 |
|
||||
| 175B | 128 | 8 | 4 | 4 | 16 |
|
||||
|
||||
**Assumptions**: BF16, 2K sequence length, A100 80GB
|
||||
|
||||
## Checkpointing
|
||||
|
||||
### Save Checkpoint
|
||||
|
||||
```python
|
||||
# Save full model state
|
||||
accelerator.save_state('checkpoint-1000')
|
||||
|
||||
# Megatron saves separate files per rank
|
||||
# checkpoint-1000/
|
||||
# pytorch_model_tp_0_pp_0.bin
|
||||
# pytorch_model_tp_0_pp_1.bin
|
||||
# pytorch_model_tp_1_pp_0.bin
|
||||
# pytorch_model_tp_1_pp_1.bin
|
||||
# optimizer_tp_0_pp_0.bin
|
||||
# ...
|
||||
```
|
||||
|
||||
### Load Checkpoint
|
||||
|
||||
```python
|
||||
# Resume training
|
||||
accelerator.load_state('checkpoint-1000')
|
||||
|
||||
# Automatically loads correct shard per rank
|
||||
```
|
||||
|
||||
### Convert to Standard PyTorch
|
||||
|
||||
```bash
|
||||
# Merge Megatron checkpoint to single file
|
||||
python merge_megatron_checkpoint.py \
|
||||
--checkpoint-dir checkpoint-1000 \
|
||||
--output pytorch_model.bin
|
||||
```
|
||||
|
||||
## Common Issues
|
||||
|
||||
### Issue: OOM with Pipeline Parallelism
|
||||
|
||||
**Solution**: Increase micro-batches
|
||||
```python
|
||||
megatron_plugin = MegatronLMPlugin(
|
||||
pp_degree=4,
|
||||
num_micro_batches=16, # Increase from 4
|
||||
)
|
||||
```
|
||||
|
||||
### Issue: Slow Training
|
||||
|
||||
**Check 1**: Pipeline bubbles (PP too high)
|
||||
```python
|
||||
# Reduce PP, increase TP
|
||||
tp_degree=4 # Increase
|
||||
pp_degree=2 # Decrease
|
||||
```
|
||||
|
||||
**Check 2**: Micro-batch size too small
|
||||
```python
|
||||
num_micro_batches=8 # Increase
|
||||
```
|
||||
|
||||
### Issue: NVLink Not Detected
|
||||
|
||||
```bash
|
||||
# Verify NVLink
|
||||
nvidia-smi nvlink -s
|
||||
|
||||
# If no NVLink, avoid TP > 1
|
||||
# Use PP or DP instead
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
- Megatron-LM: https://github.com/NVIDIA/Megatron-LM
|
||||
- Accelerate Megatron docs: https://huggingface.co/docs/accelerate/usage_guides/megatron_lm
|
||||
- Paper: "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism"
|
||||
- NVIDIA Apex: https://github.com/NVIDIA/apex
|
||||
@@ -1,525 +0,0 @@
|
||||
# Accelerate Performance Tuning
|
||||
|
||||
## Profiling
|
||||
|
||||
### Basic Profiling
|
||||
|
||||
```python
|
||||
from accelerate import Accelerator
|
||||
import time
|
||||
|
||||
accelerator = Accelerator()
|
||||
|
||||
# Warmup
|
||||
for _ in range(10):
|
||||
batch = next(iter(dataloader))
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Profile training loop
|
||||
start = time.time()
|
||||
total_batches = 100
|
||||
|
||||
for i, batch in enumerate(dataloader):
|
||||
if i >= total_batches:
|
||||
break
|
||||
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
accelerator.wait_for_everyone() # Sync all processes
|
||||
elapsed = time.time() - start
|
||||
|
||||
# Metrics
|
||||
batches_per_sec = total_batches / elapsed
|
||||
samples_per_sec = (total_batches * batch_size * accelerator.num_processes) / elapsed
|
||||
|
||||
print(f"Throughput: {samples_per_sec:.2f} samples/sec")
|
||||
print(f"Batches/sec: {batches_per_sec:.2f}")
|
||||
```
|
||||
|
||||
### PyTorch Profiler Integration
|
||||
|
||||
```python
|
||||
from torch.profiler import profile, ProfilerActivity
|
||||
|
||||
with profile(
|
||||
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
|
||||
record_shapes=True,
|
||||
profile_memory=True,
|
||||
with_stack=True
|
||||
) as prof:
|
||||
for i, batch in enumerate(dataloader):
|
||||
if i >= 10: # Profile first 10 batches
|
||||
break
|
||||
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Print profiling results
|
||||
print(prof.key_averages().table(
|
||||
sort_by="cuda_time_total", row_limit=20
|
||||
))
|
||||
|
||||
# Export to Chrome tracing
|
||||
prof.export_chrome_trace("trace.json")
|
||||
# View at chrome://tracing
|
||||
```
|
||||
|
||||
## Memory Optimization
|
||||
|
||||
### 1. Gradient Accumulation
|
||||
|
||||
**Problem**: Large batch size causes OOM
|
||||
|
||||
**Solution**: Accumulate gradients across micro-batches
|
||||
|
||||
```python
|
||||
accelerator = Accelerator(gradient_accumulation_steps=8)
|
||||
|
||||
# Effective batch = batch_size × accumulation_steps × num_gpus
|
||||
# Example: 4 × 8 × 8 = 256
|
||||
|
||||
for batch in dataloader:
|
||||
with accelerator.accumulate(model): # Handles accumulation logic
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
```
|
||||
|
||||
**Memory savings**: 8× less activation memory (with 8 accumulation steps)
|
||||
|
||||
### 2. Gradient Checkpointing
|
||||
|
||||
**Enable in model**:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"gpt2",
|
||||
use_cache=False # Required for gradient checkpointing
|
||||
)
|
||||
|
||||
# Enable checkpointing
|
||||
model.gradient_checkpointing_enable()
|
||||
|
||||
# Prepare with Accelerate
|
||||
model = accelerator.prepare(model)
|
||||
```
|
||||
|
||||
**Memory savings**: 30-50% with 10-15% slowdown
|
||||
|
||||
### 3. Mixed Precision
|
||||
|
||||
**BF16 (A100/H100)**:
|
||||
```python
|
||||
accelerator = Accelerator(mixed_precision='bf16')
|
||||
|
||||
# Automatic mixed precision
|
||||
for batch in dataloader:
|
||||
outputs = model(**batch) # Forward in BF16
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss) # Backward in FP32
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**FP16 (V100, older GPUs)**:
|
||||
```python
|
||||
from accelerate.utils import GradScalerKwargs
|
||||
|
||||
scaler_kwargs = GradScalerKwargs(
|
||||
init_scale=2.**16,
|
||||
growth_interval=2000
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
mixed_precision='fp16',
|
||||
kwargs_handlers=[scaler_kwargs]
|
||||
)
|
||||
```
|
||||
|
||||
**Memory savings**: 50% compared to FP32
|
||||
|
||||
### 4. CPU Offloading (DeepSpeed)
|
||||
|
||||
```python
|
||||
from accelerate.utils import DeepSpeedPlugin
|
||||
|
||||
ds_plugin = DeepSpeedPlugin(
|
||||
zero_stage=3,
|
||||
offload_optimizer_device="cpu", # Offload optimizer to CPU
|
||||
offload_param_device="cpu", # Offload parameters to CPU
|
||||
)
|
||||
|
||||
accelerator = Accelerator(
|
||||
deepspeed_plugin=ds_plugin,
|
||||
mixed_precision='bf16'
|
||||
)
|
||||
```
|
||||
|
||||
**Memory savings**: 10-20× for optimizer state, 5-10× for parameters
|
||||
|
||||
**Trade-off**: 20-30% slower due to CPU-GPU transfers
|
||||
|
||||
### 5. Flash Attention
|
||||
|
||||
```python
|
||||
# Install flash-attn
|
||||
# pip install flash-attn
|
||||
|
||||
from transformers import AutoModelForCausalLM
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"gpt2",
|
||||
attn_implementation="flash_attention_2" # Enable Flash Attention 2
|
||||
)
|
||||
|
||||
model = accelerator.prepare(model)
|
||||
```
|
||||
|
||||
**Memory savings**: 50% for attention, 2× faster
|
||||
|
||||
**Requirements**: A100/H100, sequence length must be multiple of 128
|
||||
|
||||
## Communication Optimization
|
||||
|
||||
### 1. Gradient Bucketing (DDP)
|
||||
|
||||
```python
|
||||
from accelerate.utils import DistributedDataParallelKwargs
|
||||
|
||||
ddp_kwargs = DistributedDataParallelKwargs(
|
||||
bucket_cap_mb=25, # Bucket size for gradient reduction
|
||||
gradient_as_bucket_view=True, # Reduce memory copies
|
||||
static_graph=False # Set True if model doesn't change
|
||||
)
|
||||
|
||||
accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
|
||||
```
|
||||
|
||||
**Recommended bucket sizes**:
|
||||
- Small models (<1B): 25 MB
|
||||
- Medium models (1-10B): 50-100 MB
|
||||
- Large models (>10B): 100-200 MB
|
||||
|
||||
### 2. Find Unused Parameters
|
||||
|
||||
```python
|
||||
# Only enable if model has unused parameters (slower!)
|
||||
ddp_kwargs = DistributedDataParallelKwargs(
|
||||
find_unused_parameters=True
|
||||
)
|
||||
```
|
||||
|
||||
**Use case**: Models with conditional branches (e.g., mixture of experts)
|
||||
|
||||
**Cost**: 10-20% slower
|
||||
|
||||
### 3. NCCL Tuning
|
||||
|
||||
```bash
|
||||
# Set environment variables before launch
|
||||
export NCCL_DEBUG=INFO # Debug info
|
||||
export NCCL_IB_DISABLE=0 # Enable InfiniBand
|
||||
export NCCL_SOCKET_IFNAME=eth0 # Network interface
|
||||
export NCCL_P2P_LEVEL=NVL # Use NVLink
|
||||
|
||||
accelerate launch train.py
|
||||
```
|
||||
|
||||
**NCCL_P2P_LEVEL options**:
|
||||
- `NVL`: NVLink (fastest, within node)
|
||||
- `PIX`: PCIe (fast, within node)
|
||||
- `PHB`: PCIe host bridge (slow, cross-node)
|
||||
|
||||
## Data Loading Optimization
|
||||
|
||||
### 1. DataLoader Workers
|
||||
|
||||
```python
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
train_loader = DataLoader(
|
||||
dataset,
|
||||
batch_size=32,
|
||||
num_workers=4, # Parallel data loading
|
||||
pin_memory=True, # Pin memory for faster GPU transfer
|
||||
prefetch_factor=2, # Prefetch batches per worker
|
||||
persistent_workers=True # Keep workers alive between epochs
|
||||
)
|
||||
|
||||
train_loader = accelerator.prepare(train_loader)
|
||||
```
|
||||
|
||||
**Recommendations**:
|
||||
- `num_workers`: 2-4 per GPU (8 GPUs → 16-32 workers)
|
||||
- `pin_memory`: Always True for GPU training
|
||||
- `prefetch_factor`: 2-4 (higher for slow data loading)
|
||||
|
||||
### 2. Data Preprocessing
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
|
||||
# Bad: Preprocess during training (slow)
|
||||
dataset = load_dataset("openwebtext")
|
||||
|
||||
for batch in dataset:
|
||||
tokens = tokenizer(batch['text']) # Slow!
|
||||
...
|
||||
|
||||
# Good: Preprocess once, save
|
||||
dataset = load_dataset("openwebtext")
|
||||
tokenized = dataset.map(
|
||||
lambda x: tokenizer(x['text']),
|
||||
batched=True,
|
||||
num_proc=8, # Parallel preprocessing
|
||||
remove_columns=['text']
|
||||
)
|
||||
tokenized.save_to_disk("preprocessed_data")
|
||||
|
||||
# Load preprocessed
|
||||
dataset = load_from_disk("preprocessed_data")
|
||||
```
|
||||
|
||||
### 3. Faster Tokenization
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
# Enable Rust-based tokenizers (10× faster)
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "true"
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"gpt2",
|
||||
use_fast=True # Use fast Rust tokenizer
|
||||
)
|
||||
```
|
||||
|
||||
## Compilation (PyTorch 2.0+)
|
||||
|
||||
### Compile Model
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Compile model for faster execution
|
||||
model = torch.compile(
|
||||
model,
|
||||
mode="reduce-overhead", # Options: default, reduce-overhead, max-autotune
|
||||
fullgraph=False, # Compile entire graph (stricter)
|
||||
dynamic=True # Support dynamic shapes
|
||||
)
|
||||
|
||||
model = accelerator.prepare(model)
|
||||
```
|
||||
|
||||
**Speedup**: 10-50% depending on model
|
||||
|
||||
**Compilation modes**:
|
||||
- `default`: Balanced (best for most cases)
|
||||
- `reduce-overhead`: Min overhead (best for small batches)
|
||||
- `max-autotune`: Max performance (slow compile, best for production)
|
||||
|
||||
### Compilation Best Practices
|
||||
|
||||
```python
|
||||
# Bad: Compile after prepare (won't work)
|
||||
model = accelerator.prepare(model)
|
||||
model = torch.compile(model) # Error!
|
||||
|
||||
# Good: Compile before prepare
|
||||
model = torch.compile(model)
|
||||
model = accelerator.prepare(model)
|
||||
|
||||
# Training loop
|
||||
for batch in dataloader:
|
||||
# First iteration: slow (compilation)
|
||||
# Subsequent iterations: fast (compiled)
|
||||
outputs = model(**batch)
|
||||
...
|
||||
```
|
||||
|
||||
## Benchmarking Different Strategies
|
||||
|
||||
### Script Template
|
||||
|
||||
```python
|
||||
import time
|
||||
import torch
|
||||
from accelerate import Accelerator
|
||||
|
||||
def benchmark_strategy(strategy_name, accelerator_kwargs):
|
||||
"""Benchmark a specific training strategy."""
|
||||
accelerator = Accelerator(**accelerator_kwargs)
|
||||
|
||||
# Setup
|
||||
model = create_model()
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
|
||||
dataloader = create_dataloader()
|
||||
|
||||
model, optimizer, dataloader = accelerator.prepare(
|
||||
model, optimizer, dataloader
|
||||
)
|
||||
|
||||
# Warmup
|
||||
for i, batch in enumerate(dataloader):
|
||||
if i >= 10:
|
||||
break
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Benchmark
|
||||
accelerator.wait_for_everyone()
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
|
||||
num_batches = 100
|
||||
for i, batch in enumerate(dataloader):
|
||||
if i >= num_batches:
|
||||
break
|
||||
|
||||
outputs = model(**batch)
|
||||
loss = outputs.loss
|
||||
accelerator.backward(loss)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
torch.cuda.synchronize()
|
||||
elapsed = time.time() - start
|
||||
|
||||
# Metrics
|
||||
throughput = (num_batches * batch_size * accelerator.num_processes) / elapsed
|
||||
memory_used = torch.cuda.max_memory_allocated() / 1e9 # GB
|
||||
|
||||
if accelerator.is_main_process:
|
||||
print(f"\n{strategy_name}:")
|
||||
print(f" Throughput: {throughput:.2f} samples/sec")
|
||||
print(f" Memory: {memory_used:.2f} GB")
|
||||
print(f" Time: {elapsed:.2f} sec")
|
||||
|
||||
torch.cuda.reset_peak_memory_stats()
|
||||
|
||||
# Benchmark different strategies
|
||||
strategies = [
|
||||
("DDP + FP32", {}),
|
||||
("DDP + BF16", {"mixed_precision": "bf16"}),
|
||||
("DDP + BF16 + GradAccum", {"mixed_precision": "bf16", "gradient_accumulation_steps": 4}),
|
||||
("FSDP", {"fsdp_plugin": fsdp_plugin}),
|
||||
("DeepSpeed ZeRO-2", {"deepspeed_plugin": ds_plugin_stage2}),
|
||||
("DeepSpeed ZeRO-3", {"deepspeed_plugin": ds_plugin_stage3}),
|
||||
]
|
||||
|
||||
for name, kwargs in strategies:
|
||||
benchmark_strategy(name, kwargs)
|
||||
```
|
||||
|
||||
## Performance Checklist
|
||||
|
||||
**Before training**:
|
||||
- [ ] Use BF16/FP16 mixed precision
|
||||
- [ ] Enable gradient checkpointing (if OOM)
|
||||
- [ ] Set appropriate `num_workers` (2-4 per GPU)
|
||||
- [ ] Enable `pin_memory=True`
|
||||
- [ ] Preprocess data once, not during training
|
||||
- [ ] Compile model with `torch.compile` (PyTorch 2.0+)
|
||||
|
||||
**For large models**:
|
||||
- [ ] Use FSDP or DeepSpeed ZeRO-3
|
||||
- [ ] Enable CPU offloading (if still OOM)
|
||||
- [ ] Use Flash Attention
|
||||
- [ ] Increase gradient accumulation
|
||||
|
||||
**For multi-node**:
|
||||
- [ ] Check network topology (InfiniBand > Ethernet)
|
||||
- [ ] Tune NCCL settings
|
||||
- [ ] Use larger bucket sizes for DDP
|
||||
- [ ] Verify NVLink for tensor parallelism
|
||||
|
||||
**Profiling**:
|
||||
- [ ] Profile first 10-100 batches
|
||||
- [ ] Check GPU utilization (`nvidia-smi dmon`)
|
||||
- [ ] Check data loading time (should be <5% of iteration)
|
||||
- [ ] Identify communication bottlenecks
|
||||
|
||||
## Common Performance Issues
|
||||
|
||||
### Issue: Low GPU Utilization (<80%)
|
||||
|
||||
**Cause 1**: Data loading bottleneck
|
||||
```python
|
||||
# Solution: Increase workers and prefetch
|
||||
num_workers=8
|
||||
prefetch_factor=4
|
||||
```
|
||||
|
||||
**Cause 2**: Small batch size
|
||||
```python
|
||||
# Solution: Increase batch size or use gradient accumulation
|
||||
batch_size=32 # Increase
|
||||
gradient_accumulation_steps=4 # Or accumulate
|
||||
```
|
||||
|
||||
### Issue: High Memory Usage
|
||||
|
||||
**Solution 1**: Gradient checkpointing
|
||||
```python
|
||||
model.gradient_checkpointing_enable()
|
||||
```
|
||||
|
||||
**Solution 2**: Reduce batch size, increase accumulation
|
||||
```python
|
||||
batch_size=8 # Reduce from 32
|
||||
gradient_accumulation_steps=16 # Maintain effective batch
|
||||
```
|
||||
|
||||
**Solution 3**: Use FSDP or DeepSpeed ZeRO-3
|
||||
```python
|
||||
accelerator = Accelerator(fsdp_plugin=fsdp_plugin)
|
||||
```
|
||||
|
||||
### Issue: Slow Multi-GPU Training
|
||||
|
||||
**Cause**: Communication bottleneck
|
||||
|
||||
**Check 1**: Gradient bucket size
|
||||
```python
|
||||
ddp_kwargs = DistributedDataParallelKwargs(bucket_cap_mb=100)
|
||||
```
|
||||
|
||||
**Check 2**: NCCL settings
|
||||
```bash
|
||||
export NCCL_DEBUG=INFO
|
||||
# Check for "Using NVLS" (good) vs "Using PHB" (bad)
|
||||
```
|
||||
|
||||
**Check 3**: Network bandwidth
|
||||
```bash
|
||||
# Test inter-GPU bandwidth
|
||||
nvidia-smi nvlink -s
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
- Accelerate Performance: https://huggingface.co/docs/accelerate/usage_guides/performance
|
||||
- PyTorch Profiler: https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html
|
||||
- NCCL Tuning: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
|
||||
- Flash Attention: https://github.com/Dao-AILab/flash-attention
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user