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1 Commits

Author SHA1 Message Date
Brooklyn Nicholson c275423d0d feat(tui): add /mouse [on|off|toggle] runtime slash command
Toggle SGR mouse tracking (DEC 1000/1002/1003/1006) at runtime without
restart or env-var spelunking. Fix path when a terminal doesn't honor raw
mode / no-echo and echoes mouse events as visible escape sequences (e.g.
`<35;111;133M` scrolling up the transcript on every mouse move).

- New `/mouse [on|off|toggle]` slash command (persists via config.set key=mouse
  → display.tui_mouse in ~/.hermes/config.yaml).
- New hermes-ink export `setAltScreenMouseTracking(enabled)` that writes
  ENABLE/DISABLE bytes and updates the instance flag without re-entering
  the alt-screen — so live toggles are flicker-free.
- `<AlternateScreen>` mouseTracking prop is frozen at initial value (from
  `HERMES_TUI_DISABLE_MOUSE` env); runtime state lives in `$uiState` and is
  applied via useEffect. Env-var opt-out wins over config so explicit
  HERMES_TUI_DISABLE_MOUSE=1 stays off regardless of persisted state.
- Server: folds `mouse` into the existing compact/statusbar branch in
  config.set/get, defaulting to on.
2026-04-21 17:31:01 -05:00
313 changed files with 6579 additions and 27491 deletions
-3
View File
@@ -14,6 +14,3 @@ node_modules
.env
*.md
# Runtime data (bind-mounted at /opt/data; must not leak into build context)
data/
-1
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@@ -1,4 +1,3 @@
.DS_Store
/venv/
/_pycache/
*.pyc*
+1 -1
View File
@@ -69,7 +69,7 @@ hermes-agent/
│ ├── server.py # RPC handlers and session logic
│ ├── render.py # Optional rich/ANSI bridge
│ └── slash_worker.py # Persistent HermesCLI subprocess for slash commands
├── hermes_agent/acp/ # ACP server (VS Code / Zed / JetBrains integration)
├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)
├── cron/ # Scheduler (jobs.py, scheduler.py)
├── environments/ # RL training environments (Atropos)
├── tests/ # Pytest suite (~3000 tests)
+3 -3
View File
@@ -55,10 +55,10 @@ If your skill is specialized, community-contributed, or niche, it's better suite
| Requirement | Notes |
|-------------|-------|
| **Git** | With `--recurse-submodules` support, and the `git-lfs` extension installed |
| **Git** | With `--recurse-submodules` support |
| **Python 3.11+** | uv will install it if missing |
| **uv** | Fast Python package manager ([install](https://docs.astral.sh/uv/)) |
| **Node.js 20+** | Optional — needed for browser tools and WhatsApp bridge (matches root `package.json` engines) |
| **Node.js 18+** | Optional — needed for browser tools and WhatsApp bridge |
### Clone and install
@@ -88,7 +88,7 @@ cp cli-config.yaml.example ~/.hermes/config.yaml
touch ~/.hermes/.env
# Add at minimum an LLM provider key:
echo "OPENROUTER_API_KEY=***" >> ~/.hermes/.env
echo 'OPENROUTER_API_KEY=sk-or-v1-your-key' >> ~/.hermes/.env
```
### Run
+1 -2
View File
@@ -12,7 +12,7 @@ ENV PLAYWRIGHT_BROWSERS_PATH=/opt/hermes/.playwright
# Install system dependencies in one layer, clear APT cache
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential nodejs npm python3 ripgrep ffmpeg gcc python3-dev libffi-dev procps git openssh-client docker-cli && \
build-essential nodejs npm python3 ripgrep ffmpeg gcc python3-dev libffi-dev procps git && \
rm -rf /var/lib/apt/lists/*
# Non-root user for runtime; UID can be overridden via HERMES_UID at runtime
@@ -50,6 +50,5 @@ RUN uv venv && \
# ---------- Runtime ----------
ENV HERMES_WEB_DIST=/opt/hermes/hermes_cli/web_dist
ENV HERMES_HOME=/opt/data
ENV PATH="/opt/data/.local/bin:${PATH}"
VOLUME [ "/opt/data" ]
ENTRYPOINT [ "/opt/hermes/docker/entrypoint.sh" ]
+1
View File
@@ -173,6 +173,7 @@ python -m pytest tests/ -q
- 💬 [Discord](https://discord.gg/NousResearch)
- 📚 [Skills Hub](https://agentskills.io)
- 🐛 [Issues](https://github.com/NousResearch/hermes-agent/issues)
- 💡 [Discussions](https://github.com/NousResearch/hermes-agent/discussions)
- 🔌 [HermesClaw](https://github.com/AaronWong1999/hermesclaw) — Community WeChat bridge: Run Hermes Agent and OpenClaw on the same WeChat account.
---
+5
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@@ -0,0 +1,5 @@
"""Allow running the ACP adapter as ``python -m acp_adapter``."""
from .entry import main
main()
@@ -6,7 +6,7 @@ and starts the ACP agent server.
Usage::
python -m hermes_agent.acp.entry
python -m acp_adapter.entry
# or
hermes acp
# or
@@ -16,6 +16,7 @@ Usage::
import asyncio
import logging
import sys
from pathlib import Path
from hermes_constants import get_hermes_home
@@ -103,8 +104,13 @@ def main() -> None:
logger = logging.getLogger(__name__)
logger.info("Starting hermes-agent ACP adapter")
# Ensure the project root is on sys.path so ``from run_agent import AIAgent`` works
project_root = str(Path(__file__).resolve().parent.parent)
if project_root not in sys.path:
sys.path.insert(0, project_root)
import acp
from hermes_agent.acp.server import HermesACPAgent
from .server import HermesACPAgent
agent = HermesACPAgent()
try:
@@ -15,7 +15,7 @@ from typing import Any, Callable, Deque, Dict
import acp
from hermes_agent.acp.tools import (
from .tools import (
build_tool_complete,
build_tool_start,
make_tool_call_id,
@@ -52,15 +52,15 @@ try:
except ImportError:
from acp.schema import AuthMethod as AuthMethodAgent # type: ignore[attr-defined]
from hermes_agent.acp.auth import detect_provider
from hermes_agent.acp.events import (
from acp_adapter.auth import detect_provider
from acp_adapter.events import (
make_message_cb,
make_step_cb,
make_thinking_cb,
make_tool_progress_cb,
)
from hermes_agent.acp.permissions import make_approval_callback
from hermes_agent.acp.session import SessionManager, SessionState
from acp_adapter.permissions import make_approval_callback
from acp_adapter.session import SessionManager, SessionState
logger = logging.getLogger(__name__)
+111 -142
View File
@@ -17,6 +17,7 @@ import os
from pathlib import Path
from hermes_constants import get_hermes_home
from types import SimpleNamespace
from typing import Any, Dict, List, Optional, Tuple
from utils import normalize_proxy_env_vars
@@ -116,63 +117,6 @@ def _get_anthropic_max_output(model: str) -> int:
return best_val
def _resolve_positive_anthropic_max_tokens(value) -> Optional[int]:
"""Return ``value`` floored to a positive int, or ``None`` if it is not a
finite positive number. Ported from openclaw/openclaw#66664.
Anthropic's Messages API rejects ``max_tokens`` values that are 0,
negative, non-integer, or non-finite with HTTP 400. Python's ``or``
idiom (``max_tokens or fallback``) correctly catches ``0`` but lets
negative ints and fractional floats (``-1``, ``0.5``) through to the
API, producing a user-visible failure instead of a local error.
"""
# Booleans are a subclass of int — exclude explicitly so ``True`` doesn't
# silently become 1 and ``False`` doesn't become 0.
if isinstance(value, bool):
return None
if not isinstance(value, (int, float)):
return None
try:
import math
if not math.isfinite(value):
return None
except Exception:
return None
floored = int(value) # truncates toward zero for floats
return floored if floored > 0 else None
def _resolve_anthropic_messages_max_tokens(
requested,
model: str,
context_length: Optional[int] = None,
) -> int:
"""Resolve the ``max_tokens`` budget for an Anthropic Messages call.
Prefers ``requested`` when it is a positive finite number; otherwise
falls back to the model's output ceiling. Raises ``ValueError`` if no
positive budget can be resolved (should not happen with current model
table defaults, but guards against a future regression where
``_get_anthropic_max_output`` could return ``0``).
Separately, callers apply a context-window clamp — this resolver does
not, to keep the positive-value contract independent of endpoint
specifics.
Ported from openclaw/openclaw#66664 (resolveAnthropicMessagesMaxTokens).
"""
resolved = _resolve_positive_anthropic_max_tokens(requested)
if resolved is not None:
return resolved
fallback = _get_anthropic_max_output(model)
if fallback > 0:
return fallback
raise ValueError(
f"Anthropic Messages adapter requires a positive max_tokens value for "
f"model {model!r}; got {requested!r} and no model default resolved."
)
def _supports_adaptive_thinking(model: str) -> bool:
"""Return True for Claude 4.6+ models that support adaptive thinking."""
return any(v in model for v in _ADAPTIVE_THINKING_SUBSTRINGS)
@@ -322,14 +266,6 @@ def _is_third_party_anthropic_endpoint(base_url: str | None) -> bool:
return True # Any other endpoint is a third-party proxy
def _is_kimi_coding_endpoint(base_url: str | None) -> bool:
"""Return True for Kimi's /coding endpoint that requires claude-code UA."""
normalized = _normalize_base_url_text(base_url)
if not normalized:
return False
return normalized.rstrip("/").lower().startswith("https://api.kimi.com/coding")
def _requires_bearer_auth(base_url: str | None) -> bool:
"""Return True for Anthropic-compatible providers that require Bearer auth.
@@ -387,18 +323,9 @@ def build_anthropic_client(api_key: str, base_url: str = None, timeout: float =
kwargs["base_url"] = normalized_base_url
common_betas = _common_betas_for_base_url(normalized_base_url)
if _is_kimi_coding_endpoint(base_url):
# Kimi's /coding endpoint requires User-Agent: claude-code/0.1.0
# to be recognized as a valid Coding Agent. Without it, returns 403.
# Check this BEFORE _requires_bearer_auth since both match api.kimi.com/coding.
kwargs["api_key"] = api_key
kwargs["default_headers"] = {
"User-Agent": "claude-code/0.1.0",
**( {"anthropic-beta": ",".join(common_betas)} if common_betas else {} )
}
elif _requires_bearer_auth(normalized_base_url):
if _requires_bearer_auth(normalized_base_url):
# Some Anthropic-compatible providers (e.g. MiniMax) expect the API key in
# Authorization: Bearer *** for regular API keys. Route those endpoints
# Authorization: Bearer even for regular API keys. Route those endpoints
# through auth_token so the SDK sends Bearer auth instead of x-api-key.
# Check this before OAuth token shape detection because MiniMax secrets do
# not use Anthropic's sk-ant-api prefix and would otherwise be misread as
@@ -1139,31 +1066,6 @@ def convert_messages_to_anthropic(
"name": fn.get("name", ""),
"input": parsed_args,
})
# Kimi's /coding endpoint (Anthropic protocol) requires assistant
# tool-call messages to carry reasoning_content when thinking is
# enabled server-side. Preserve it as a thinking block so Kimi
# can validate the message history. See hermes-agent#13848.
#
# Accept empty string "" — _copy_reasoning_content_for_api()
# injects "" as a tier-3 fallback for Kimi tool-call messages
# that had no reasoning. Kimi requires the field to exist, even
# if empty.
#
# Prepend (not append): Anthropic protocol requires thinking
# blocks before text and tool_use blocks.
#
# Guard: only add when reasoning_details didn't already contribute
# thinking blocks. On native Anthropic, reasoning_details produces
# signed thinking blocks — adding another unsigned one from
# reasoning_content would create a duplicate (same text) that gets
# downgraded to a spurious text block on the last assistant message.
reasoning_content = m.get("reasoning_content")
_already_has_thinking = any(
isinstance(b, dict) and b.get("type") in ("thinking", "redacted_thinking")
for b in blocks
)
if isinstance(reasoning_content, str) and not _already_has_thinking:
blocks.insert(0, {"type": "thinking", "thinking": reasoning_content})
# Anthropic rejects empty assistant content
effective = blocks or content
if not effective or effective == "":
@@ -1319,7 +1221,6 @@ def convert_messages_to_anthropic(
# cache markers can interfere with signature validation.
_THINKING_TYPES = frozenset(("thinking", "redacted_thinking"))
_is_third_party = _is_third_party_anthropic_endpoint(base_url)
_is_kimi = _is_kimi_coding_endpoint(base_url)
last_assistant_idx = None
for i in range(len(result) - 1, -1, -1):
@@ -1331,25 +1232,7 @@ def convert_messages_to_anthropic(
if m.get("role") != "assistant" or not isinstance(m.get("content"), list):
continue
if _is_kimi:
# Kimi's /coding endpoint enables thinking server-side and
# requires unsigned thinking blocks on replayed assistant
# tool-call messages. Strip signed Anthropic blocks (Kimi
# can't validate signatures) but preserve the unsigned ones
# we synthesised from reasoning_content above.
new_content = []
for b in m["content"]:
if not isinstance(b, dict) or b.get("type") not in _THINKING_TYPES:
new_content.append(b)
continue
if b.get("signature") or b.get("data"):
# Anthropic-signed block — Kimi can't validate, strip
continue
# Unsigned thinking (synthesised from reasoning_content) —
# keep it: Kimi needs it for message-history validation.
new_content.append(b)
m["content"] = new_content or [{"type": "text", "text": "(empty)"}]
elif _is_third_party or idx != last_assistant_idx:
if _is_third_party or idx != last_assistant_idx:
# Third-party endpoint: strip ALL thinking blocks from every
# assistant message — signatures are Anthropic-proprietary.
# Direct Anthropic: strip from non-latest assistant messages only.
@@ -1447,12 +1330,7 @@ def build_anthropic_kwargs(
model = normalize_model_name(model, preserve_dots=preserve_dots)
# effective_max_tokens = output cap for this call (≠ total context window)
# Use the resolver helper so non-positive values (negative ints,
# fractional floats, NaN, non-numeric) fail locally with a clear error
# rather than 400-ing at the Anthropic API. See openclaw/openclaw#66664.
effective_max_tokens = _resolve_anthropic_messages_max_tokens(
max_tokens, model, context_length=context_length
)
effective_max_tokens = max_tokens or _get_anthropic_max_output(model)
# Clamp output cap to fit inside the total context window.
# Only matters for small custom endpoints where context_length < native
@@ -1531,25 +1409,11 @@ def build_anthropic_kwargs(
# MiniMax Anthropic-compat endpoints support thinking (manual mode only,
# not adaptive). Haiku does NOT support extended thinking — skip entirely.
#
# Kimi's /coding endpoint speaks the Anthropic Messages protocol but has
# its own thinking semantics: when ``thinking.enabled`` is sent, Kimi
# validates the message history and requires every prior assistant
# tool-call message to carry OpenAI-style ``reasoning_content``. The
# Anthropic path never populates that field, and
# ``convert_messages_to_anthropic`` strips all Anthropic thinking blocks
# on third-party endpoints — so the request fails with HTTP 400
# "thinking is enabled but reasoning_content is missing in assistant
# tool call message at index N". Kimi's reasoning is driven server-side
# on the /coding route, so skip Anthropic's thinking parameter entirely
# for that host. (Kimi on chat_completions enables thinking via
# extra_body in the ChatCompletionsTransport — see #13503.)
#
# On 4.7+ the `thinking.display` field defaults to "omitted", which
# silently hides reasoning text that Hermes surfaces in its CLI. We
# request "summarized" so the reasoning blocks stay populated — matching
# 4.6 behavior and preserving the activity-feed UX during long tool runs.
_is_kimi_coding = _is_kimi_coding_endpoint(base_url)
if reasoning_config and isinstance(reasoning_config, dict) and not _is_kimi_coding:
if reasoning_config and isinstance(reasoning_config, dict):
if reasoning_config.get("enabled") is not False and "haiku" not in model.lower():
effort = str(reasoning_config.get("effort", "medium")).lower()
budget = THINKING_BUDGET.get(effort, 8000)
@@ -1598,4 +1462,109 @@ def build_anthropic_kwargs(
return kwargs
def normalize_anthropic_response(
response,
strip_tool_prefix: bool = False,
) -> Tuple[SimpleNamespace, str]:
"""Normalize Anthropic response to match the shape expected by AIAgent.
Returns (assistant_message, finish_reason) where assistant_message has
.content, .tool_calls, and .reasoning attributes.
When *strip_tool_prefix* is True, removes the ``mcp_`` prefix that was
added to tool names for OAuth Claude Code compatibility.
"""
text_parts = []
reasoning_parts = []
reasoning_details = []
tool_calls = []
for block in response.content:
if block.type == "text":
text_parts.append(block.text)
elif block.type == "thinking":
reasoning_parts.append(block.thinking)
block_dict = _to_plain_data(block)
if isinstance(block_dict, dict):
reasoning_details.append(block_dict)
elif block.type == "tool_use":
name = block.name
if strip_tool_prefix and name.startswith(_MCP_TOOL_PREFIX):
name = name[len(_MCP_TOOL_PREFIX):]
tool_calls.append(
SimpleNamespace(
id=block.id,
type="function",
function=SimpleNamespace(
name=name,
arguments=json.dumps(block.input),
),
)
)
# Map Anthropic stop_reason to OpenAI finish_reason.
# Newer stop reasons added in Claude 4.5+ / 4.7:
# - refusal: the model declined to answer (cyber safeguards, CSAM, etc.)
# - model_context_window_exceeded: hit context limit (not max_tokens)
# Both need distinct handling upstream — a refusal should surface to the
# user with a clear message, and a context-window overflow should trigger
# compression/truncation rather than be treated as normal end-of-turn.
stop_reason_map = {
"end_turn": "stop",
"tool_use": "tool_calls",
"max_tokens": "length",
"stop_sequence": "stop",
"refusal": "content_filter",
"model_context_window_exceeded": "length",
}
finish_reason = stop_reason_map.get(response.stop_reason, "stop")
return (
SimpleNamespace(
content="\n".join(text_parts) if text_parts else None,
tool_calls=tool_calls or None,
reasoning="\n\n".join(reasoning_parts) if reasoning_parts else None,
reasoning_content=None,
reasoning_details=reasoning_details or None,
),
finish_reason,
)
def normalize_anthropic_response_v2(
response,
strip_tool_prefix: bool = False,
) -> "NormalizedResponse":
"""Normalize Anthropic response to NormalizedResponse.
Wraps the existing normalize_anthropic_response() and maps its output
to the shared transport types. This allows incremental migration —
one call site at a time — without changing the original function.
"""
from agent.transports.types import NormalizedResponse, build_tool_call
assistant_msg, finish_reason = normalize_anthropic_response(response, strip_tool_prefix)
tool_calls = None
if assistant_msg.tool_calls:
tool_calls = [
build_tool_call(
id=tc.id,
name=tc.function.name,
arguments=tc.function.arguments,
)
for tc in assistant_msg.tool_calls
]
provider_data = {}
if getattr(assistant_msg, "reasoning_details", None):
provider_data["reasoning_details"] = assistant_msg.reasoning_details
return NormalizedResponse(
content=assistant_msg.content,
tool_calls=tool_calls,
finish_reason=finish_reason,
reasoning=getattr(assistant_msg, "reasoning", None),
usage=None, # Anthropic usage is on the raw response, not the normaliser
provider_data=provider_data or None,
)
+24 -49
View File
@@ -134,7 +134,6 @@ _API_KEY_PROVIDER_AUX_MODELS: Dict[str, str] = {
"gemini": "gemini-3-flash-preview",
"zai": "glm-4.5-flash",
"kimi-coding": "kimi-k2-turbo-preview",
"stepfun": "step-3.5-flash",
"kimi-coding-cn": "kimi-k2-turbo-preview",
"minimax": "MiniMax-M2.7",
"minimax-cn": "MiniMax-M2.7",
@@ -183,6 +182,8 @@ auxiliary_is_nous: bool = False
# Default auxiliary models per provider
_OPENROUTER_MODEL = "google/gemini-3-flash-preview"
_NOUS_MODEL = "google/gemini-3-flash-preview"
_NOUS_FREE_TIER_VISION_MODEL = "xiaomi/mimo-v2-omni"
_NOUS_FREE_TIER_AUX_MODEL = "xiaomi/mimo-v2-pro"
_NOUS_DEFAULT_BASE_URL = "https://inference-api.nousresearch.com/v1"
_ANTHROPIC_DEFAULT_BASE_URL = "https://api.anthropic.com"
_AUTH_JSON_PATH = get_hermes_home() / "auth.json"
@@ -573,8 +574,7 @@ class _AnthropicCompletionsAdapter:
self._is_oauth = is_oauth
def create(self, **kwargs) -> Any:
from agent.anthropic_adapter import build_anthropic_kwargs
from agent.transports import get_transport
from agent.anthropic_adapter import build_anthropic_kwargs, normalize_anthropic_response
messages = kwargs.get("messages", [])
model = kwargs.get("model", self._model)
@@ -611,19 +611,7 @@ class _AnthropicCompletionsAdapter:
anthropic_kwargs["temperature"] = temperature
response = self._client.messages.create(**anthropic_kwargs)
_transport = get_transport("anthropic_messages")
_nr = _transport.normalize_response(
response, strip_tool_prefix=self._is_oauth
)
# ToolCall already duck-types as OpenAI shape (.type, .function.name,
# .function.arguments) via properties, so no wrapping needed.
assistant_message = SimpleNamespace(
content=_nr.content,
tool_calls=_nr.tool_calls,
reasoning=_nr.reasoning,
)
finish_reason = _nr.finish_reason
assistant_message, finish_reason = normalize_anthropic_response(response)
usage = None
if hasattr(response, "usage") and response.usage:
@@ -857,7 +845,7 @@ def _resolve_api_key_provider() -> Tuple[Optional[OpenAI], Optional[str]]:
return GeminiNativeClient(api_key=api_key, base_url=base_url), model
extra = {}
if base_url_host_matches(base_url, "api.kimi.com"):
extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
extra["default_headers"] = {"User-Agent": "KimiCLI/1.30.0"}
elif base_url_host_matches(base_url, "api.githubcopilot.com"):
from hermes_cli.models import copilot_default_headers
@@ -883,7 +871,7 @@ def _resolve_api_key_provider() -> Tuple[Optional[OpenAI], Optional[str]]:
return GeminiNativeClient(api_key=api_key, base_url=base_url), model
extra = {}
if base_url_host_matches(base_url, "api.kimi.com"):
extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
extra["default_headers"] = {"User-Agent": "KimiCLI/1.30.0"}
elif base_url_host_matches(base_url, "api.githubcopilot.com"):
from hermes_cli.models import copilot_default_headers
@@ -939,35 +927,22 @@ def _try_nous(vision: bool = False) -> Tuple[Optional[OpenAI], Optional[str]]:
global auxiliary_is_nous
auxiliary_is_nous = True
logger.debug("Auxiliary client: Nous Portal")
# Ask the Portal which model it currently recommends for this task type.
# The /api/nous/recommended-models endpoint is the authoritative source:
# it distinguishes paid vs free tier recommendations, and get_nous_recommended_aux_model
# auto-detects the caller's tier via check_nous_free_tier(). Fall back to
# _NOUS_MODEL (google/gemini-3-flash-preview) when the Portal is unreachable
# or returns a null recommendation for this task type.
model = _NOUS_MODEL
if nous.get("source") == "pool":
model = "gemini-3-flash"
else:
model = _NOUS_MODEL
# Free-tier users can't use paid auxiliary models — use the free
# models instead: mimo-v2-omni for vision, mimo-v2-pro for text tasks.
# Paid accounts keep their tier-appropriate models: gemini-3-flash-preview
# for both text and vision tasks.
try:
from hermes_cli.models import get_nous_recommended_aux_model
recommended = get_nous_recommended_aux_model(vision=vision)
if recommended:
model = recommended
logger.debug(
"Auxiliary/%s: using Portal-recommended model %s",
"vision" if vision else "text", model,
)
else:
logger.debug(
"Auxiliary/%s: no Portal recommendation, falling back to %s",
"vision" if vision else "text", model,
)
except Exception as exc:
logger.debug(
"Auxiliary/%s: recommended-models lookup failed (%s); "
"falling back to %s",
"vision" if vision else "text", exc, model,
)
from hermes_cli.models import check_nous_free_tier
if check_nous_free_tier():
model = _NOUS_FREE_TIER_VISION_MODEL if vision else _NOUS_FREE_TIER_AUX_MODEL
logger.debug("Free-tier Nous account — using %s for auxiliary/%s",
model, "vision" if vision else "text")
except Exception:
pass
if runtime is not None:
api_key, base_url = runtime
else:
@@ -1512,7 +1487,7 @@ def _to_async_client(sync_client, model: str):
async_kwargs["default_headers"] = copilot_default_headers()
elif base_url_host_matches(sync_base_url, "api.kimi.com"):
async_kwargs["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
async_kwargs["default_headers"] = {"User-Agent": "KimiCLI/1.30.0"}
return AsyncOpenAI(**async_kwargs), model
@@ -1699,7 +1674,7 @@ def resolve_provider_client(
)
extra = {}
if base_url_host_matches(custom_base, "api.kimi.com"):
extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
extra["default_headers"] = {"User-Agent": "KimiCLI/1.30.0"}
elif base_url_host_matches(custom_base, "api.githubcopilot.com"):
from hermes_cli.models import copilot_default_headers
extra["default_headers"] = copilot_default_headers()
@@ -1806,7 +1781,7 @@ def resolve_provider_client(
# Provider-specific headers
headers = {}
if base_url_host_matches(base_url, "api.kimi.com"):
headers["User-Agent"] = "claude-code/0.1.0"
headers["User-Agent"] = "KimiCLI/1.30.0"
elif base_url_host_matches(base_url, "api.githubcopilot.com"):
from hermes_cli.models import copilot_default_headers
+7 -54
View File
@@ -64,47 +64,6 @@ _CHARS_PER_TOKEN = 4
_SUMMARY_FAILURE_COOLDOWN_SECONDS = 600
def _content_text_for_contains(content: Any) -> str:
"""Return a best-effort text view of message content.
Used only for substring checks when we need to know whether we've already
appended a note to a message. Keeps multimodal lists intact elsewhere.
"""
if content is None:
return ""
if isinstance(content, str):
return content
if isinstance(content, list):
parts: list[str] = []
for item in content:
if isinstance(item, str):
parts.append(item)
elif isinstance(item, dict):
text = item.get("text")
if isinstance(text, str):
parts.append(text)
return "\n".join(part for part in parts if part)
return str(content)
def _append_text_to_content(content: Any, text: str, *, prepend: bool = False) -> Any:
"""Append or prepend plain text to message content safely.
Compression sometimes needs to add a note or merge a summary into an
existing message. Message content may be plain text or a multimodal list of
blocks, so direct string concatenation is not always safe.
"""
if content is None:
return text
if isinstance(content, str):
return text + content if prepend else content + text
if isinstance(content, list):
text_block = {"type": "text", "text": text}
return [text_block, *content] if prepend else [*content, text_block]
rendered = str(content)
return text + rendered if prepend else rendered + text
def _truncate_tool_call_args_json(args: str, head_chars: int = 200) -> str:
"""Shrink long string values inside a tool-call arguments JSON blob while
preserving JSON validity.
@@ -848,7 +807,7 @@ The user has requested that this compaction PRIORITISE preserving all informatio
)
self.summary_model = "" # empty = use main model
self._summary_failure_cooldown_until = 0.0 # no cooldown
return self._generate_summary(turns_to_summarize, focus_topic=focus_topic) # retry immediately
return self._generate_summary(turns_to_summarize) # retry immediately
# Transient errors (timeout, rate limit, network) — shorter cooldown
_transient_cooldown = 60
@@ -1185,13 +1144,10 @@ The user has requested that this compaction PRIORITISE preserving all informatio
for i in range(compress_start):
msg = messages[i].copy()
if i == 0 and msg.get("role") == "system":
existing = msg.get("content")
existing = msg.get("content") or ""
_compression_note = "[Note: Some earlier conversation turns have been compacted into a handoff summary to preserve context space. The current session state may still reflect earlier work, so build on that summary and state rather than re-doing work.]"
if _compression_note not in _content_text_for_contains(existing):
msg["content"] = _append_text_to_content(
existing,
"\n\n" + _compression_note if isinstance(existing, str) and existing else _compression_note,
)
if _compression_note not in existing:
msg["content"] = existing + "\n\n" + _compression_note
compressed.append(msg)
# If LLM summary failed, insert a static fallback so the model
@@ -1235,15 +1191,12 @@ The user has requested that this compaction PRIORITISE preserving all informatio
for i in range(compress_end, n_messages):
msg = messages[i].copy()
if _merge_summary_into_tail and i == compress_end:
merged_prefix = (
original = msg.get("content") or ""
msg["content"] = (
summary
+ "\n\n--- END OF CONTEXT SUMMARY — "
"respond to the message below, not the summary above ---\n\n"
)
msg["content"] = _append_text_to_content(
msg.get("content"),
merged_prefix,
prepend=True,
+ original
)
_merge_summary_into_tail = False
compressed.append(msg)
+10 -74
View File
@@ -220,25 +220,12 @@ _TRANSPORT_ERROR_TYPES = frozenset({
"ConnectionAbortedError", "BrokenPipeError",
"TimeoutError", "ReadError",
"ServerDisconnectedError",
# SSL/TLS transport errors — transient mid-stream handshake/record
# failures that should retry rather than surface as a stalled session.
# ssl.SSLError subclasses OSError (caught by isinstance) but we list
# the type names here so provider-wrapped SSL errors (e.g. when the
# SDK re-raises without preserving the exception chain) still classify
# as transport rather than falling through to the unknown bucket.
"SSLError", "SSLZeroReturnError", "SSLWantReadError",
"SSLWantWriteError", "SSLEOFError", "SSLSyscallError",
# OpenAI SDK errors (not subclasses of Python builtins)
"APIConnectionError",
"APITimeoutError",
})
# Server disconnect patterns (no status code, but transport-level).
# These are the "ambiguous" patterns — a plain connection close could be
# transient transport hiccup OR server-side context overflow rejection
# (common when the API gateway disconnects instead of returning an HTTP
# error for oversized requests). A large session + one of these patterns
# triggers the context-overflow-with-compression recovery path.
# Server disconnect patterns (no status code, but transport-level)
_SERVER_DISCONNECT_PATTERNS = [
"server disconnected",
"peer closed connection",
@@ -249,40 +236,6 @@ _SERVER_DISCONNECT_PATTERNS = [
"incomplete chunked read",
]
# SSL/TLS transient failure patterns — intentionally distinct from
# _SERVER_DISCONNECT_PATTERNS above.
#
# An SSL alert mid-stream is almost always a transport-layer hiccup
# (flaky network, mid-session TLS renegotiation failure, load balancer
# dropping the connection) — NOT a server-side context overflow signal.
# So we want the retry path but NOT the compression path; lumping these
# into _SERVER_DISCONNECT_PATTERNS would trigger unnecessary (and
# expensive) context compression on any large-session SSL hiccup.
#
# The OpenSSL library constructs error codes by prepending a format string
# to the uppercased alert reason; OpenSSL 3.x changed the separator
# (e.g. `SSLV3_ALERT_BAD_RECORD_MAC` → `SSL/TLS_ALERT_BAD_RECORD_MAC`),
# which silently stopped matching anything explicit. Matching on the
# stable substrings (`bad record mac`, `ssl alert`, `tls alert`, etc.)
# survives future OpenSSL format churn without code changes.
_SSL_TRANSIENT_PATTERNS = [
# Space-separated (human-readable form, Python ssl module, most SDKs)
"bad record mac",
"ssl alert",
"tls alert",
"ssl handshake failure",
"tlsv1 alert",
"sslv3 alert",
# Underscore-separated (OpenSSL error code tokens, e.g.
# `ERR_SSL_SSL/TLS_ALERT_BAD_RECORD_MAC`, `SSLV3_ALERT_BAD_RECORD_MAC`)
"bad_record_mac",
"ssl_alert",
"tls_alert",
"tls_alert_internal_error",
# Python ssl module prefix, e.g. "[SSL: BAD_RECORD_MAC]"
"[ssl:",
]
# ── Classification pipeline ─────────────────────────────────────────────
@@ -302,10 +255,9 @@ def classify_api_error(
2. HTTP status code + message-aware refinement
3. Error code classification (from body)
4. Message pattern matching (billing vs rate_limit vs context vs auth)
5. SSL/TLS transient alert patterns → retry as timeout
5. Transport error heuristics
6. Server disconnect + large session → context overflow
7. Transport error heuristics
8. Fallback: unknown (retryable with backoff)
7. Fallback: unknown (retryable with backoff)
Args:
error: The exception from the API call.
@@ -436,18 +388,7 @@ def classify_api_error(
if classified is not None:
return classified
# ── 5. SSL/TLS transient errors → retry as timeout (not compression) ──
# SSL alerts mid-stream are transport hiccups, not server-side context
# overflow signals. Classify before the disconnect check so a large
# session doesn't incorrectly trigger context compression when the real
# cause is a flaky TLS handshake. Also matches when the error is
# wrapped in a generic exception whose message string carries the SSL
# alert text but the type isn't ssl.SSLError (happens with some SDKs
# that re-raise without chaining).
if any(p in error_msg for p in _SSL_TRANSIENT_PATTERNS):
return _result(FailoverReason.timeout, retryable=True)
# ── 6. Server disconnect + large session → context overflow ─────
# ── 5. Server disconnect + large session → context overflow ─────
# Must come BEFORE generic transport error catch — a disconnect on
# a large session is more likely context overflow than a transient
# transport hiccup. Without this ordering, RemoteProtocolError
@@ -464,12 +405,12 @@ def classify_api_error(
)
return _result(FailoverReason.timeout, retryable=True)
# ── 7. Transport / timeout heuristics ───────────────────────────
# ── 6. Transport / timeout heuristics ───────────────────────────
if error_type in _TRANSPORT_ERROR_TYPES or isinstance(error, (TimeoutError, ConnectionError, OSError)):
return _result(FailoverReason.timeout, retryable=True)
# ── 8. Fallback: unknown ────────────────────────────────────────
# ── 7. Fallback: unknown ────────────────────────────────────────
return _result(FailoverReason.unknown, retryable=True)
@@ -529,16 +470,11 @@ def _classify_by_status(
retryable=False,
should_fallback=True,
)
# Generic 404 with no "model not found" signal — could be a wrong
# endpoint path (common with local llama.cpp / Ollama / vLLM when
# the URL is slightly misconfigured), a proxy routing glitch, or
# a transient backend issue. Classifying these as model_not_found
# silently falls back to a different provider and tells the model
# the model is missing, which is wrong and wastes a turn. Treat
# as unknown so the retry loop surfaces the real error instead.
# Generic 404 — could be model or endpoint
return result_fn(
FailoverReason.unknown,
retryable=True,
FailoverReason.model_not_found,
retryable=False,
should_fallback=True,
)
if status_code == 413:
-242
View File
@@ -1,242 +0,0 @@
"""
Image Generation Provider ABC
=============================
Defines the pluggable-backend interface for image generation. Providers register
instances via ``PluginContext.register_image_gen_provider()``; the active one
(selected via ``image_gen.provider`` in ``config.yaml``) services every
``image_generate`` tool call.
Providers live in ``<repo>/plugins/image_gen/<name>/`` (built-in, auto-loaded
as ``kind: backend``) or ``~/.hermes/plugins/image_gen/<name>/`` (user, opt-in
via ``plugins.enabled``).
Response shape
--------------
All providers return a dict that :func:`success_response` / :func:`error_response`
produce. The tool wrapper JSON-serializes it. Keys:
success bool
image str | None URL or absolute file path
model str provider-specific model identifier
prompt str echoed prompt
aspect_ratio str "landscape" | "square" | "portrait"
provider str provider name (for diagnostics)
error str only when success=False
error_type str only when success=False
"""
from __future__ import annotations
import abc
import base64
import datetime
import logging
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
logger = logging.getLogger(__name__)
VALID_ASPECT_RATIOS: Tuple[str, ...] = ("landscape", "square", "portrait")
DEFAULT_ASPECT_RATIO = "landscape"
# ---------------------------------------------------------------------------
# ABC
# ---------------------------------------------------------------------------
class ImageGenProvider(abc.ABC):
"""Abstract base class for an image generation backend.
Subclasses must implement :meth:`generate`. Everything else has sane
defaults — override only what your provider needs.
"""
@property
@abc.abstractmethod
def name(self) -> str:
"""Stable short identifier used in ``image_gen.provider`` config.
Lowercase, no spaces. Examples: ``fal``, ``openai``, ``replicate``.
"""
@property
def display_name(self) -> str:
"""Human-readable label shown in ``hermes tools``. Defaults to ``name.title()``."""
return self.name.title()
def is_available(self) -> bool:
"""Return True when this provider can service calls.
Typically checks for a required API key. Default: True
(providers with no external dependencies are always available).
"""
return True
def list_models(self) -> List[Dict[str, Any]]:
"""Return catalog entries for ``hermes tools`` model picker.
Each entry::
{
"id": "gpt-image-1.5", # required
"display": "GPT Image 1.5", # optional; defaults to id
"speed": "~10s", # optional
"strengths": "...", # optional
"price": "$...", # optional
}
Default: empty list (provider has no user-selectable models).
"""
return []
def get_setup_schema(self) -> Dict[str, Any]:
"""Return provider metadata for the ``hermes tools`` picker.
Used by ``tools_config.py`` to inject this provider as a row in
the Image Generation provider list. Shape::
{
"name": "OpenAI", # picker label
"badge": "paid", # optional short tag
"tag": "One-line description...", # optional subtitle
"env_vars": [ # keys to prompt for
{"key": "OPENAI_API_KEY",
"prompt": "OpenAI API key",
"url": "https://platform.openai.com/api-keys"},
],
}
Default: minimal entry derived from ``display_name``. Override to
expose API key prompts and custom badges.
"""
return {
"name": self.display_name,
"badge": "",
"tag": "",
"env_vars": [],
}
def default_model(self) -> Optional[str]:
"""Return the default model id, or None if not applicable."""
models = self.list_models()
if models:
return models[0].get("id")
return None
@abc.abstractmethod
def generate(
self,
prompt: str,
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
**kwargs: Any,
) -> Dict[str, Any]:
"""Generate an image.
Implementations should return the dict from :func:`success_response`
or :func:`error_response`. ``kwargs`` may contain forward-compat
parameters future versions of the schema will expose — implementations
should ignore unknown keys.
"""
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def resolve_aspect_ratio(value: Optional[str]) -> str:
"""Clamp an aspect_ratio value to the valid set, defaulting to landscape.
Invalid values are coerced rather than rejected so the tool surface is
forgiving of agent mistakes.
"""
if not isinstance(value, str):
return DEFAULT_ASPECT_RATIO
v = value.strip().lower()
if v in VALID_ASPECT_RATIOS:
return v
return DEFAULT_ASPECT_RATIO
def _images_cache_dir() -> Path:
"""Return ``$HERMES_HOME/cache/images/``, creating parents as needed."""
from hermes_constants import get_hermes_home
path = get_hermes_home() / "cache" / "images"
path.mkdir(parents=True, exist_ok=True)
return path
def save_b64_image(
b64_data: str,
*,
prefix: str = "image",
extension: str = "png",
) -> Path:
"""Decode base64 image data and write it under ``$HERMES_HOME/cache/images/``.
Returns the absolute :class:`Path` to the saved file.
Filename format: ``<prefix>_<YYYYMMDD_HHMMSS>_<short-uuid>.<ext>``.
"""
raw = base64.b64decode(b64_data)
ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
short = uuid.uuid4().hex[:8]
path = _images_cache_dir() / f"{prefix}_{ts}_{short}.{extension}"
path.write_bytes(raw)
return path
def success_response(
*,
image: str,
model: str,
prompt: str,
aspect_ratio: str,
provider: str,
extra: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Build a uniform success response dict.
``image`` may be an HTTP URL or an absolute filesystem path (for b64
providers like OpenAI). Callers that need to pass through additional
backend-specific fields can supply ``extra``.
"""
payload: Dict[str, Any] = {
"success": True,
"image": image,
"model": model,
"prompt": prompt,
"aspect_ratio": aspect_ratio,
"provider": provider,
}
if extra:
for k, v in extra.items():
payload.setdefault(k, v)
return payload
def error_response(
*,
error: str,
error_type: str = "provider_error",
provider: str = "",
model: str = "",
prompt: str = "",
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
) -> Dict[str, Any]:
"""Build a uniform error response dict."""
return {
"success": False,
"image": None,
"error": error,
"error_type": error_type,
"model": model,
"prompt": prompt,
"aspect_ratio": aspect_ratio,
"provider": provider,
}
-120
View File
@@ -1,120 +0,0 @@
"""
Image Generation Provider Registry
==================================
Central map of registered providers. Populated by plugins at import-time via
``PluginContext.register_image_gen_provider()``; consumed by the
``image_generate`` tool to dispatch each call to the active backend.
Active selection
----------------
The active provider is chosen by ``image_gen.provider`` in ``config.yaml``.
If unset, :func:`get_active_provider` applies fallback logic:
1. If exactly one provider is registered, use it.
2. Otherwise if a provider named ``fal`` is registered, use it (legacy
default — matches pre-plugin behavior).
3. Otherwise return ``None`` (the tool surfaces a helpful error pointing
the user at ``hermes tools``).
"""
from __future__ import annotations
import logging
import threading
from typing import Dict, List, Optional
from agent.image_gen_provider import ImageGenProvider
logger = logging.getLogger(__name__)
_providers: Dict[str, ImageGenProvider] = {}
_lock = threading.Lock()
def register_provider(provider: ImageGenProvider) -> None:
"""Register an image generation provider.
Re-registration (same ``name``) overwrites the previous entry and logs
a debug message — this makes hot-reload scenarios (tests, dev loops)
behave predictably.
"""
if not isinstance(provider, ImageGenProvider):
raise TypeError(
f"register_provider() expects an ImageGenProvider instance, "
f"got {type(provider).__name__}"
)
name = provider.name
if not isinstance(name, str) or not name.strip():
raise ValueError("Image gen provider .name must be a non-empty string")
with _lock:
existing = _providers.get(name)
_providers[name] = provider
if existing is not None:
logger.debug("Image gen provider '%s' re-registered (was %r)", name, type(existing).__name__)
else:
logger.debug("Registered image gen provider '%s' (%s)", name, type(provider).__name__)
def list_providers() -> List[ImageGenProvider]:
"""Return all registered providers, sorted by name."""
with _lock:
items = list(_providers.values())
return sorted(items, key=lambda p: p.name)
def get_provider(name: str) -> Optional[ImageGenProvider]:
"""Return the provider registered under *name*, or None."""
if not isinstance(name, str):
return None
with _lock:
return _providers.get(name.strip())
def get_active_provider() -> Optional[ImageGenProvider]:
"""Resolve the currently-active provider.
Reads ``image_gen.provider`` from config.yaml; falls back per the
module docstring.
"""
configured: Optional[str] = None
try:
from hermes_cli.config import load_config
cfg = load_config()
section = cfg.get("image_gen") if isinstance(cfg, dict) else None
if isinstance(section, dict):
raw = section.get("provider")
if isinstance(raw, str) and raw.strip():
configured = raw.strip()
except Exception as exc:
logger.debug("Could not read image_gen.provider from config: %s", exc)
with _lock:
snapshot = dict(_providers)
if configured:
provider = snapshot.get(configured)
if provider is not None:
return provider
logger.debug(
"image_gen.provider='%s' configured but not registered; falling back",
configured,
)
# Fallback: single-provider case
if len(snapshot) == 1:
return next(iter(snapshot.values()))
# Fallback: prefer legacy FAL for backward compat
if "fal" in snapshot:
return snapshot["fal"]
return None
def _reset_for_tests() -> None:
"""Clear the registry. **Test-only.**"""
with _lock:
_providers.clear()
+6 -35
View File
@@ -4,7 +4,6 @@ Pure utility functions with no AIAgent dependency. Used by ContextCompressor
and run_agent.py for pre-flight context checks.
"""
import ipaddress
import logging
import re
import time
@@ -26,7 +25,7 @@ logger = logging.getLogger(__name__)
# are preserved so the full model name reaches cache lookups and server queries.
_PROVIDER_PREFIXES: frozenset[str] = frozenset({
"openrouter", "nous", "openai-codex", "copilot", "copilot-acp",
"gemini", "ollama-cloud", "zai", "kimi-coding", "kimi-coding-cn", "stepfun", "minimax", "minimax-cn", "anthropic", "deepseek",
"gemini", "ollama-cloud", "zai", "kimi-coding", "kimi-coding-cn", "minimax", "minimax-cn", "anthropic", "deepseek",
"opencode-zen", "opencode-go", "ai-gateway", "kilocode", "alibaba",
"qwen-oauth",
"xiaomi",
@@ -37,7 +36,7 @@ _PROVIDER_PREFIXES: frozenset[str] = frozenset({
"glm", "z-ai", "z.ai", "zhipu", "github", "github-copilot",
"github-models", "kimi", "moonshot", "kimi-cn", "moonshot-cn", "claude", "deep-seek",
"ollama",
"stepfun", "opencode", "zen", "go", "vercel", "kilo", "dashscope", "aliyun", "qwen",
"opencode", "zen", "go", "vercel", "kilo", "dashscope", "aliyun", "qwen",
"mimo", "xiaomi-mimo",
"arcee-ai", "arceeai",
"xai", "x-ai", "x.ai", "grok",
@@ -52,13 +51,6 @@ _OLLAMA_TAG_PATTERN = re.compile(
)
# Tailscale's CGNAT range (RFC 6598). `ipaddress.is_private` excludes this
# block, so without an explicit check Ollama reached over Tailscale (e.g.
# `http://100.77.243.5:11434`) wouldn't be treated as local and its stream
# read / stale timeouts wouldn't get auto-bumped. Built once at import time.
_TAILSCALE_CGNAT = ipaddress.IPv4Network("100.64.0.0/10")
def _strip_provider_prefix(model: str) -> str:
"""Strip a recognised provider prefix from a model string.
@@ -133,8 +125,6 @@ DEFAULT_CONTEXT_LENGTHS = {
# Google
"gemini": 1048576,
# Gemma (open models served via AI Studio)
"gemma-4": 256000, # Gemma 4 family
"gemma4": 256000, # Ollama-style naming (e.g. gemma4:31b-cloud)
"gemma-4-31b": 256000,
"gemma-3": 131072,
"gemma": 8192, # fallback for older gemma models
@@ -187,8 +177,6 @@ DEFAULT_CONTEXT_LENGTHS = {
"mimo-v2-pro": 1000000,
"mimo-v2-omni": 256000,
"mimo-v2-flash": 256000,
"mimo-v2.5-pro": 1000000,
"mimo-v2.5": 1000000,
"zai-org/GLM-5": 202752,
}
@@ -203,7 +191,6 @@ _CONTEXT_LENGTH_KEYS = (
"max_seq_len",
"n_ctx_train",
"n_ctx",
"ctx_size",
)
_MAX_COMPLETION_KEYS = (
@@ -247,12 +234,9 @@ _URL_TO_PROVIDER: Dict[str, str] = {
"chatgpt.com": "openai",
"api.anthropic.com": "anthropic",
"api.z.ai": "zai",
"open.bigmodel.cn": "zai",
"api.moonshot.ai": "kimi-coding",
"api.moonshot.cn": "kimi-coding-cn",
"api.kimi.com": "kimi-coding",
"api.stepfun.ai": "stepfun",
"api.stepfun.com": "stepfun",
"api.arcee.ai": "arcee",
"api.minimax": "minimax",
"dashscope.aliyuncs.com": "alibaba",
@@ -297,15 +281,7 @@ def _is_known_provider_base_url(base_url: str) -> bool:
def is_local_endpoint(base_url: str) -> bool:
"""Return True if base_url points to a local machine.
Recognises loopback (``localhost``, ``127.0.0.0/8``, ``::1``),
container-internal DNS names (``host.docker.internal`` et al.),
RFC-1918 private ranges (``10/8``, ``172.16/12``, ``192.168/16``),
link-local, and Tailscale CGNAT (``100.64.0.0/10``). Tailscale CGNAT
is included so remote-but-trusted Ollama boxes reached over a
Tailscale mesh get the same timeout auto-bumps as localhost Ollama.
"""
"""Return True if base_url points to a local machine (localhost / RFC-1918 / WSL)."""
normalized = _normalize_base_url(base_url)
if not normalized:
return False
@@ -320,17 +296,14 @@ def is_local_endpoint(base_url: str) -> bool:
# Docker / Podman / Lima internal DNS names (e.g. host.docker.internal)
if any(host.endswith(suffix) for suffix in _CONTAINER_LOCAL_SUFFIXES):
return True
# RFC-1918 private ranges, link-local, and Tailscale CGNAT
# RFC-1918 private ranges and link-local
import ipaddress
try:
addr = ipaddress.ip_address(host)
if addr.is_private or addr.is_loopback or addr.is_link_local:
return True
if isinstance(addr, ipaddress.IPv4Address) and addr in _TAILSCALE_CGNAT:
return True
return addr.is_private or addr.is_loopback or addr.is_link_local
except ValueError:
pass
# Bare IP that looks like a private range (e.g. 172.26.x.x for WSL)
# or Tailscale CGNAT (100.64.x.x100.127.x.x).
parts = host.split(".")
if len(parts) == 4:
try:
@@ -341,8 +314,6 @@ def is_local_endpoint(base_url: str) -> bool:
return True
if first == 192 and second == 168:
return True
if first == 100 and 64 <= second <= 127:
return True
except ValueError:
pass
return False
-4
View File
@@ -146,7 +146,6 @@ PROVIDER_TO_MODELS_DEV: Dict[str, str] = {
"openai-codex": "openai",
"zai": "zai",
"kimi-coding": "kimi-for-coding",
"stepfun": "stepfun",
"kimi-coding-cn": "kimi-for-coding",
"minimax": "minimax",
"minimax-cn": "minimax-cn",
@@ -418,9 +417,6 @@ def list_provider_models(provider: str) -> List[str]:
Returns an empty list if the provider is unknown or has no data.
"""
from hermes_cli.models import normalize_provider
provider = normalize_provider(provider) or provider
models = _get_provider_models(provider)
if models is None:
return []
+1 -33
View File
@@ -350,13 +350,7 @@ PLATFORM_HINTS = {
),
"cli": (
"You are a CLI AI Agent. Try not to use markdown but simple text "
"renderable inside a terminal. "
"File delivery: there is no attachment channel — the user reads your "
"response directly in their terminal. Do NOT emit MEDIA:/path tags "
"(those are only intercepted on messaging platforms like Telegram, "
"Discord, Slack, etc.; on the CLI they render as literal text). "
"When referring to a file you created or changed, just state its "
"absolute path in plain text; the user can open it from there."
"renderable inside a terminal."
),
"sms": (
"You are communicating via SMS. Keep responses concise and use plain text "
@@ -370,32 +364,6 @@ PLATFORM_HINTS = {
"MEDIA:/absolute/path/to/file in your response. Images (.jpg, .png, "
".heic) appear as photos and other files arrive as attachments."
),
"mattermost": (
"You are in a Mattermost workspace communicating with your user. "
"Mattermost renders standard Markdown — headings, bold, italic, code "
"blocks, and tables all work. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.jpg, .png, .webp) are uploaded as photo "
"attachments, audio and video as file attachments. "
"Image URLs in markdown format ![alt](url) are rendered as inline previews automatically."
),
"matrix": (
"You are in a Matrix room communicating with your user. "
"Matrix renders Markdown — bold, italic, code blocks, and links work; "
"the adapter converts your Markdown to HTML for rich display. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.jpg, .png, .webp) are sent as inline photos, "
"audio (.ogg, .mp3) as voice/audio messages, video (.mp4) inline, "
"and other files as downloadable attachments."
),
"feishu": (
"You are in a Feishu (Lark) workspace communicating with your user. "
"Feishu renders Markdown in messages — bold, italic, code blocks, and "
"links are supported. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.jpg, .png, .webp) are uploaded and displayed "
"inline, audio files as voice messages, and other files as attachments."
),
"weixin": (
"You are on Weixin/WeChat. Markdown formatting is supported, so you may use it when "
"it improves readability, but keep the message compact and chat-friendly. You can send media files natively: "
+1 -1
View File
@@ -435,7 +435,7 @@ def iter_skill_index_files(skills_dir: Path, filename: str):
Excludes ``.git``, ``.github``, ``.hub`` directories.
"""
matches = []
for root, dirs, files in os.walk(skills_dir, followlinks=True):
for root, dirs, files in os.walk(skills_dir):
dirs[:] = [d for d in dirs if d not in EXCLUDED_SKILL_DIRS]
if filename in files:
matches.append(Path(root) / filename)
+1 -1
View File
@@ -38,7 +38,7 @@ def generate_title(user_message: str, assistant_response: str, timeout: float =
response = call_llm(
task="title_generation",
messages=messages,
max_tokens=500,
max_tokens=30,
temperature=0.3,
timeout=timeout,
)
-12
View File
@@ -37,15 +37,3 @@ def _discover_transports() -> None:
import agent.transports.anthropic # noqa: F401
except ImportError:
pass
try:
import agent.transports.codex # noqa: F401
except ImportError:
pass
try:
import agent.transports.chat_completions # noqa: F401
except ImportError:
pass
try:
import agent.transports.bedrock # noqa: F401
except ImportError:
pass
+6 -54
View File
@@ -78,71 +78,23 @@ class AnthropicTransport(ProviderTransport):
def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
"""Normalize Anthropic response to NormalizedResponse.
Parses content blocks (text, thinking, tool_use), maps stop_reason
to OpenAI finish_reason, and collects reasoning_details in provider_data.
kwargs:
strip_tool_prefix: bool — strip 'mcp_mcp_' prefixes from tool names.
"""
import json
from agent.anthropic_adapter import _to_plain_data
from agent.transports.types import ToolCall
from agent.anthropic_adapter import normalize_anthropic_response_v2
strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
_MCP_PREFIX = "mcp_"
text_parts = []
reasoning_parts = []
reasoning_details = []
tool_calls = []
for block in response.content:
if block.type == "text":
text_parts.append(block.text)
elif block.type == "thinking":
reasoning_parts.append(block.thinking)
block_dict = _to_plain_data(block)
if isinstance(block_dict, dict):
reasoning_details.append(block_dict)
elif block.type == "tool_use":
name = block.name
if strip_tool_prefix and name.startswith(_MCP_PREFIX):
name = name[len(_MCP_PREFIX):]
tool_calls.append(
ToolCall(
id=block.id,
name=name,
arguments=json.dumps(block.input),
)
)
finish_reason = self._STOP_REASON_MAP.get(response.stop_reason, "stop")
provider_data = {}
if reasoning_details:
provider_data["reasoning_details"] = reasoning_details
return NormalizedResponse(
content="\n".join(text_parts) if text_parts else None,
tool_calls=tool_calls or None,
finish_reason=finish_reason,
reasoning="\n\n".join(reasoning_parts) if reasoning_parts else None,
usage=None,
provider_data=provider_data or None,
)
return normalize_anthropic_response_v2(response, strip_tool_prefix=strip_tool_prefix)
def validate_response(self, response: Any) -> bool:
"""Check Anthropic response structure is valid.
An empty content list is legitimate when ``stop_reason == "end_turn"``
— the model's canonical way of signalling "nothing more to add" after
a tool turn that already delivered the user-facing text. Treating it
as invalid falsely retries a completed response.
"""
"""Check Anthropic response structure is valid."""
if response is None:
return False
content_blocks = getattr(response, "content", None)
if not isinstance(content_blocks, list):
return False
if not content_blocks:
return getattr(response, "stop_reason", None) == "end_turn"
return False
return True
def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
-154
View File
@@ -1,154 +0,0 @@
"""AWS Bedrock Converse API transport.
Delegates to the existing adapter functions in agent/bedrock_adapter.py.
Bedrock uses its own boto3 client (not the OpenAI SDK), so the transport
owns format conversion and normalization, while client construction and
boto3 calls stay on AIAgent.
"""
from typing import Any, Dict, List, Optional
from agent.transports.base import ProviderTransport
from agent.transports.types import NormalizedResponse, ToolCall, Usage
class BedrockTransport(ProviderTransport):
"""Transport for api_mode='bedrock_converse'."""
@property
def api_mode(self) -> str:
return "bedrock_converse"
def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
"""Convert OpenAI messages to Bedrock Converse format."""
from agent.bedrock_adapter import convert_messages_to_converse
return convert_messages_to_converse(messages)
def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
"""Convert OpenAI tool schemas to Bedrock Converse toolConfig."""
from agent.bedrock_adapter import convert_tools_to_converse
return convert_tools_to_converse(tools)
def build_kwargs(
self,
model: str,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None,
**params,
) -> Dict[str, Any]:
"""Build Bedrock converse() kwargs.
Calls convert_messages and convert_tools internally.
params:
max_tokens: int — output token limit (default 4096)
temperature: float | None
guardrail_config: dict | None — Bedrock guardrails
region: str — AWS region (default 'us-east-1')
"""
from agent.bedrock_adapter import build_converse_kwargs
region = params.get("region", "us-east-1")
guardrail = params.get("guardrail_config")
kwargs = build_converse_kwargs(
model=model,
messages=messages,
tools=tools,
max_tokens=params.get("max_tokens", 4096),
temperature=params.get("temperature"),
guardrail_config=guardrail,
)
# Sentinel keys for dispatch — agent pops these before the boto3 call
kwargs["__bedrock_converse__"] = True
kwargs["__bedrock_region__"] = region
return kwargs
def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
"""Normalize Bedrock response to NormalizedResponse.
Handles two shapes:
1. Raw boto3 dict (from direct converse() calls)
2. Already-normalized SimpleNamespace with .choices (from dispatch site)
"""
from agent.bedrock_adapter import normalize_converse_response
# Normalize to OpenAI-compatible SimpleNamespace
if hasattr(response, "choices") and response.choices:
# Already normalized at dispatch site
ns = response
else:
# Raw boto3 dict
ns = normalize_converse_response(response)
choice = ns.choices[0]
msg = choice.message
finish_reason = choice.finish_reason or "stop"
tool_calls = None
if msg.tool_calls:
tool_calls = [
ToolCall(
id=tc.id,
name=tc.function.name,
arguments=tc.function.arguments,
)
for tc in msg.tool_calls
]
usage = None
if hasattr(ns, "usage") and ns.usage:
u = ns.usage
usage = Usage(
prompt_tokens=getattr(u, "prompt_tokens", 0) or 0,
completion_tokens=getattr(u, "completion_tokens", 0) or 0,
total_tokens=getattr(u, "total_tokens", 0) or 0,
)
reasoning = getattr(msg, "reasoning", None) or getattr(msg, "reasoning_content", None)
return NormalizedResponse(
content=msg.content,
tool_calls=tool_calls,
finish_reason=finish_reason,
reasoning=reasoning,
usage=usage,
)
def validate_response(self, response: Any) -> bool:
"""Check Bedrock response structure.
After normalize_converse_response, the response has OpenAI-compatible
.choices — same check as chat_completions.
"""
if response is None:
return False
# Raw Bedrock dict response — check for 'output' key
if isinstance(response, dict):
return "output" in response
# Already-normalized SimpleNamespace
if hasattr(response, "choices"):
return bool(response.choices)
return False
def map_finish_reason(self, raw_reason: str) -> str:
"""Map Bedrock stop reason to OpenAI finish_reason.
The adapter already does this mapping inside normalize_converse_response,
so this is only used for direct access to raw responses.
"""
_MAP = {
"end_turn": "stop",
"tool_use": "tool_calls",
"max_tokens": "length",
"stop_sequence": "stop",
"guardrail_intervened": "content_filter",
"content_filtered": "content_filter",
}
return _MAP.get(raw_reason, "stop")
# Auto-register on import
from agent.transports import register_transport # noqa: E402
register_transport("bedrock_converse", BedrockTransport)
-387
View File
@@ -1,387 +0,0 @@
"""OpenAI Chat Completions transport.
Handles the default api_mode ('chat_completions') used by ~16 OpenAI-compatible
providers (OpenRouter, Nous, NVIDIA, Qwen, Ollama, DeepSeek, xAI, Kimi, etc.).
Messages and tools are already in OpenAI format — convert_messages and
convert_tools are near-identity. The complexity lives in build_kwargs
which has provider-specific conditionals for max_tokens defaults,
reasoning configuration, temperature handling, and extra_body assembly.
"""
import copy
from typing import Any, Dict, List, Optional
from agent.prompt_builder import DEVELOPER_ROLE_MODELS
from agent.transports.base import ProviderTransport
from agent.transports.types import NormalizedResponse, ToolCall, Usage
class ChatCompletionsTransport(ProviderTransport):
"""Transport for api_mode='chat_completions'.
The default path for OpenAI-compatible providers.
"""
@property
def api_mode(self) -> str:
return "chat_completions"
def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> List[Dict[str, Any]]:
"""Messages are already in OpenAI format — sanitize Codex leaks only.
Strips Codex Responses API fields (``codex_reasoning_items`` on the
message, ``call_id``/``response_item_id`` on tool_calls) that strict
chat-completions providers reject with 400/422.
"""
needs_sanitize = False
for msg in messages:
if not isinstance(msg, dict):
continue
if "codex_reasoning_items" in msg:
needs_sanitize = True
break
tool_calls = msg.get("tool_calls")
if isinstance(tool_calls, list):
for tc in tool_calls:
if isinstance(tc, dict) and ("call_id" in tc or "response_item_id" in tc):
needs_sanitize = True
break
if needs_sanitize:
break
if not needs_sanitize:
return messages
sanitized = copy.deepcopy(messages)
for msg in sanitized:
if not isinstance(msg, dict):
continue
msg.pop("codex_reasoning_items", None)
tool_calls = msg.get("tool_calls")
if isinstance(tool_calls, list):
for tc in tool_calls:
if isinstance(tc, dict):
tc.pop("call_id", None)
tc.pop("response_item_id", None)
return sanitized
def convert_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Tools are already in OpenAI format — identity."""
return tools
def build_kwargs(
self,
model: str,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None,
**params,
) -> Dict[str, Any]:
"""Build chat.completions.create() kwargs.
This is the most complex transport method — it handles ~16 providers
via params rather than subclasses.
params:
timeout: float — API call timeout
max_tokens: int | None — user-configured max tokens
ephemeral_max_output_tokens: int | None — one-shot override (error recovery)
max_tokens_param_fn: callable — returns {max_tokens: N} or {max_completion_tokens: N}
reasoning_config: dict | None
request_overrides: dict | None
session_id: str | None
qwen_session_metadata: dict | None — {sessionId, promptId} precomputed
model_lower: str — lowercase model name for pattern matching
# Provider detection flags (all optional, default False)
is_openrouter: bool
is_nous: bool
is_qwen_portal: bool
is_github_models: bool
is_nvidia_nim: bool
is_kimi: bool
is_custom_provider: bool
ollama_num_ctx: int | None
# Provider routing
provider_preferences: dict | None
# Qwen-specific
qwen_prepare_fn: callable | None — runs AFTER codex sanitization
qwen_prepare_inplace_fn: callable | None — in-place variant for deepcopied lists
# Temperature
fixed_temperature: Any — from _fixed_temperature_for_model()
omit_temperature: bool
# Reasoning
supports_reasoning: bool
github_reasoning_extra: dict | None
# Claude on OpenRouter/Nous max output
anthropic_max_output: int | None
# Extra
extra_body_additions: dict | None — pre-built extra_body entries
"""
# Codex sanitization: drop reasoning_items / call_id / response_item_id
sanitized = self.convert_messages(messages)
# Qwen portal prep AFTER codex sanitization. If sanitize already
# deepcopied, reuse that copy via the in-place variant to avoid a
# second deepcopy.
is_qwen = params.get("is_qwen_portal", False)
if is_qwen:
qwen_prep = params.get("qwen_prepare_fn")
qwen_prep_inplace = params.get("qwen_prepare_inplace_fn")
if sanitized is messages:
if qwen_prep is not None:
sanitized = qwen_prep(sanitized)
else:
# Already deepcopied — transform in place
if qwen_prep_inplace is not None:
qwen_prep_inplace(sanitized)
elif qwen_prep is not None:
sanitized = qwen_prep(sanitized)
# Developer role swap for GPT-5/Codex models
model_lower = params.get("model_lower", (model or "").lower())
if (
sanitized
and isinstance(sanitized[0], dict)
and sanitized[0].get("role") == "system"
and any(p in model_lower for p in DEVELOPER_ROLE_MODELS)
):
sanitized = list(sanitized)
sanitized[0] = {**sanitized[0], "role": "developer"}
api_kwargs: Dict[str, Any] = {
"model": model,
"messages": sanitized,
}
timeout = params.get("timeout")
if timeout is not None:
api_kwargs["timeout"] = timeout
# Temperature
fixed_temp = params.get("fixed_temperature")
omit_temp = params.get("omit_temperature", False)
if omit_temp:
api_kwargs.pop("temperature", None)
elif fixed_temp is not None:
api_kwargs["temperature"] = fixed_temp
# Qwen metadata (caller precomputes {sessionId, promptId})
qwen_meta = params.get("qwen_session_metadata")
if qwen_meta and is_qwen:
api_kwargs["metadata"] = qwen_meta
# Tools
if tools:
api_kwargs["tools"] = tools
# max_tokens resolution — priority: ephemeral > user > provider default
max_tokens_fn = params.get("max_tokens_param_fn")
ephemeral = params.get("ephemeral_max_output_tokens")
max_tokens = params.get("max_tokens")
anthropic_max_out = params.get("anthropic_max_output")
is_nvidia_nim = params.get("is_nvidia_nim", False)
is_kimi = params.get("is_kimi", False)
reasoning_config = params.get("reasoning_config")
if ephemeral is not None and max_tokens_fn:
api_kwargs.update(max_tokens_fn(ephemeral))
elif max_tokens is not None and max_tokens_fn:
api_kwargs.update(max_tokens_fn(max_tokens))
elif is_nvidia_nim and max_tokens_fn:
api_kwargs.update(max_tokens_fn(16384))
elif is_qwen and max_tokens_fn:
api_kwargs.update(max_tokens_fn(65536))
elif is_kimi and max_tokens_fn:
# Kimi/Moonshot: 32000 matches Kimi CLI's default
api_kwargs.update(max_tokens_fn(32000))
elif anthropic_max_out is not None:
api_kwargs["max_tokens"] = anthropic_max_out
# Kimi: top-level reasoning_effort (unless thinking disabled)
if is_kimi:
_kimi_thinking_off = bool(
reasoning_config
and isinstance(reasoning_config, dict)
and reasoning_config.get("enabled") is False
)
if not _kimi_thinking_off:
_kimi_effort = "medium"
if reasoning_config and isinstance(reasoning_config, dict):
_e = (reasoning_config.get("effort") or "").strip().lower()
if _e in ("low", "medium", "high"):
_kimi_effort = _e
api_kwargs["reasoning_effort"] = _kimi_effort
# extra_body assembly
extra_body: Dict[str, Any] = {}
is_openrouter = params.get("is_openrouter", False)
is_nous = params.get("is_nous", False)
is_github_models = params.get("is_github_models", False)
provider_prefs = params.get("provider_preferences")
if provider_prefs and is_openrouter:
extra_body["provider"] = provider_prefs
# Kimi extra_body.thinking
if is_kimi:
_kimi_thinking_enabled = True
if reasoning_config and isinstance(reasoning_config, dict):
if reasoning_config.get("enabled") is False:
_kimi_thinking_enabled = False
extra_body["thinking"] = {
"type": "enabled" if _kimi_thinking_enabled else "disabled",
}
# Reasoning
if params.get("supports_reasoning", False):
if is_github_models:
gh_reasoning = params.get("github_reasoning_extra")
if gh_reasoning is not None:
extra_body["reasoning"] = gh_reasoning
else:
if reasoning_config is not None:
rc = dict(reasoning_config)
if is_nous and rc.get("enabled") is False:
pass # omit for Nous when disabled
else:
extra_body["reasoning"] = rc
else:
extra_body["reasoning"] = {"enabled": True, "effort": "medium"}
if is_nous:
extra_body["tags"] = ["product=hermes-agent"]
# Ollama num_ctx
ollama_ctx = params.get("ollama_num_ctx")
if ollama_ctx:
options = extra_body.get("options", {})
options["num_ctx"] = ollama_ctx
extra_body["options"] = options
# Ollama/custom think=false
if params.get("is_custom_provider", False):
if reasoning_config and isinstance(reasoning_config, dict):
_effort = (reasoning_config.get("effort") or "").strip().lower()
_enabled = reasoning_config.get("enabled", True)
if _effort == "none" or _enabled is False:
extra_body["think"] = False
if is_qwen:
extra_body["vl_high_resolution_images"] = True
# Merge any pre-built extra_body additions
additions = params.get("extra_body_additions")
if additions:
extra_body.update(additions)
if extra_body:
api_kwargs["extra_body"] = extra_body
# Request overrides last (service_tier etc.)
overrides = params.get("request_overrides")
if overrides:
api_kwargs.update(overrides)
return api_kwargs
def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
"""Normalize OpenAI ChatCompletion to NormalizedResponse.
For chat_completions, this is near-identity — the response is already
in OpenAI format. extra_content on tool_calls (Gemini thought_signature)
is preserved via ToolCall.provider_data. reasoning_details (OpenRouter
unified format) and reasoning_content (DeepSeek/Moonshot) are also
preserved for downstream replay.
"""
choice = response.choices[0]
msg = choice.message
finish_reason = choice.finish_reason or "stop"
tool_calls = None
if msg.tool_calls:
tool_calls = []
for tc in msg.tool_calls:
# Preserve provider-specific extras on the tool call.
# Gemini 3 thinking models attach extra_content with
# thought_signature — without replay on the next turn the API
# rejects the request with 400.
tc_provider_data: Dict[str, Any] = {}
extra = getattr(tc, "extra_content", None)
if extra is None and hasattr(tc, "model_extra"):
extra = (tc.model_extra or {}).get("extra_content")
if extra is not None:
if hasattr(extra, "model_dump"):
try:
extra = extra.model_dump()
except Exception:
pass
tc_provider_data["extra_content"] = extra
tool_calls.append(ToolCall(
id=tc.id,
name=tc.function.name,
arguments=tc.function.arguments,
provider_data=tc_provider_data or None,
))
usage = None
if hasattr(response, "usage") and response.usage:
u = response.usage
usage = Usage(
prompt_tokens=getattr(u, "prompt_tokens", 0) or 0,
completion_tokens=getattr(u, "completion_tokens", 0) or 0,
total_tokens=getattr(u, "total_tokens", 0) or 0,
)
# Preserve reasoning fields separately. DeepSeek/Moonshot use
# ``reasoning_content``; others use ``reasoning``. Downstream code
# (_extract_reasoning, thinking-prefill retry) reads both distinctly,
# so keep them apart in provider_data rather than merging.
reasoning = getattr(msg, "reasoning", None)
reasoning_content = getattr(msg, "reasoning_content", None)
provider_data: Dict[str, Any] = {}
if reasoning_content:
provider_data["reasoning_content"] = reasoning_content
rd = getattr(msg, "reasoning_details", None)
if rd:
provider_data["reasoning_details"] = rd
return NormalizedResponse(
content=msg.content,
tool_calls=tool_calls,
finish_reason=finish_reason,
reasoning=reasoning,
usage=usage,
provider_data=provider_data or None,
)
def validate_response(self, response: Any) -> bool:
"""Check that response has valid choices."""
if response is None:
return False
if not hasattr(response, "choices") or response.choices is None:
return False
if not response.choices:
return False
return True
def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
"""Extract OpenRouter/OpenAI cache stats from prompt_tokens_details."""
usage = getattr(response, "usage", None)
if usage is None:
return None
details = getattr(usage, "prompt_tokens_details", None)
if details is None:
return None
cached = getattr(details, "cached_tokens", 0) or 0
written = getattr(details, "cache_write_tokens", 0) or 0
if cached or written:
return {"cached_tokens": cached, "creation_tokens": written}
return None
# Auto-register on import
from agent.transports import register_transport # noqa: E402
register_transport("chat_completions", ChatCompletionsTransport)
-217
View File
@@ -1,217 +0,0 @@
"""OpenAI Responses API (Codex) transport.
Delegates to the existing adapter functions in agent/codex_responses_adapter.py.
This transport owns format conversion and normalization — NOT client lifecycle,
streaming, or the _run_codex_stream() call path.
"""
from typing import Any, Dict, List, Optional
from agent.transports.base import ProviderTransport
from agent.transports.types import NormalizedResponse, ToolCall, Usage
class ResponsesApiTransport(ProviderTransport):
"""Transport for api_mode='codex_responses'.
Wraps the functions extracted into codex_responses_adapter.py (PR 1).
"""
@property
def api_mode(self) -> str:
return "codex_responses"
def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
"""Convert OpenAI chat messages to Responses API input items."""
from agent.codex_responses_adapter import _chat_messages_to_responses_input
return _chat_messages_to_responses_input(messages)
def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
"""Convert OpenAI tool schemas to Responses API function definitions."""
from agent.codex_responses_adapter import _responses_tools
return _responses_tools(tools)
def build_kwargs(
self,
model: str,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None,
**params,
) -> Dict[str, Any]:
"""Build Responses API kwargs.
Calls convert_messages and convert_tools internally.
params:
instructions: str — system prompt (extracted from messages[0] if not given)
reasoning_config: dict | None — {effort, enabled}
session_id: str | None — used for prompt_cache_key + xAI conv header
max_tokens: int | None — max_output_tokens
request_overrides: dict | None — extra kwargs merged in
provider: str | None — provider name for backend-specific logic
base_url: str | None — endpoint URL
base_url_hostname: str | None — hostname for backend detection
is_github_responses: bool — Copilot/GitHub models backend
is_codex_backend: bool — chatgpt.com/backend-api/codex
is_xai_responses: bool — xAI/Grok backend
github_reasoning_extra: dict | None — Copilot reasoning params
"""
from agent.codex_responses_adapter import (
_chat_messages_to_responses_input,
_responses_tools,
)
from run_agent import DEFAULT_AGENT_IDENTITY
instructions = params.get("instructions", "")
payload_messages = messages
if not instructions:
if messages and messages[0].get("role") == "system":
instructions = str(messages[0].get("content") or "").strip()
payload_messages = messages[1:]
if not instructions:
instructions = DEFAULT_AGENT_IDENTITY
is_github_responses = params.get("is_github_responses", False)
is_codex_backend = params.get("is_codex_backend", False)
is_xai_responses = params.get("is_xai_responses", False)
# Resolve reasoning effort
reasoning_effort = "medium"
reasoning_enabled = True
reasoning_config = params.get("reasoning_config")
if reasoning_config and isinstance(reasoning_config, dict):
if reasoning_config.get("enabled") is False:
reasoning_enabled = False
elif reasoning_config.get("effort"):
reasoning_effort = reasoning_config["effort"]
_effort_clamp = {"minimal": "low"}
reasoning_effort = _effort_clamp.get(reasoning_effort, reasoning_effort)
kwargs = {
"model": model,
"instructions": instructions,
"input": _chat_messages_to_responses_input(payload_messages),
"tools": _responses_tools(tools),
"tool_choice": "auto",
"parallel_tool_calls": True,
"store": False,
}
session_id = params.get("session_id")
if not is_github_responses and session_id:
kwargs["prompt_cache_key"] = session_id
if reasoning_enabled and is_xai_responses:
kwargs["include"] = ["reasoning.encrypted_content"]
elif reasoning_enabled:
if is_github_responses:
github_reasoning = params.get("github_reasoning_extra")
if github_reasoning is not None:
kwargs["reasoning"] = github_reasoning
else:
kwargs["reasoning"] = {"effort": reasoning_effort, "summary": "auto"}
kwargs["include"] = ["reasoning.encrypted_content"]
elif not is_github_responses and not is_xai_responses:
kwargs["include"] = []
request_overrides = params.get("request_overrides")
if request_overrides:
kwargs.update(request_overrides)
max_tokens = params.get("max_tokens")
if max_tokens is not None and not is_codex_backend:
kwargs["max_output_tokens"] = max_tokens
if is_xai_responses and session_id:
kwargs["extra_headers"] = {"x-grok-conv-id": session_id}
return kwargs
def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
"""Normalize Codex Responses API response to NormalizedResponse."""
from agent.codex_responses_adapter import (
_normalize_codex_response,
_extract_responses_message_text,
_extract_responses_reasoning_text,
)
# _normalize_codex_response returns (SimpleNamespace, finish_reason_str)
msg, finish_reason = _normalize_codex_response(response)
tool_calls = None
if msg and msg.tool_calls:
tool_calls = []
for tc in msg.tool_calls:
provider_data = {}
if hasattr(tc, "call_id") and tc.call_id:
provider_data["call_id"] = tc.call_id
if hasattr(tc, "response_item_id") and tc.response_item_id:
provider_data["response_item_id"] = tc.response_item_id
tool_calls.append(ToolCall(
id=tc.id if hasattr(tc, "id") else (tc.function.name if hasattr(tc, "function") else None),
name=tc.function.name if hasattr(tc, "function") else getattr(tc, "name", ""),
arguments=tc.function.arguments if hasattr(tc, "function") else getattr(tc, "arguments", "{}"),
provider_data=provider_data or None,
))
# Extract reasoning items for provider_data
provider_data = {}
if msg and hasattr(msg, "codex_reasoning_items") and msg.codex_reasoning_items:
provider_data["codex_reasoning_items"] = msg.codex_reasoning_items
if msg and hasattr(msg, "reasoning_details") and msg.reasoning_details:
provider_data["reasoning_details"] = msg.reasoning_details
return NormalizedResponse(
content=msg.content if msg else None,
tool_calls=tool_calls,
finish_reason=finish_reason or "stop",
reasoning=msg.reasoning if msg and hasattr(msg, "reasoning") else None,
usage=None, # Codex usage is extracted separately in normalize_usage()
provider_data=provider_data or None,
)
def validate_response(self, response: Any) -> bool:
"""Check Codex Responses API response has valid output structure.
Returns True only if response.output is a non-empty list.
Does NOT check output_text fallback — the caller handles that
with diagnostic logging for stream backfill recovery.
"""
if response is None:
return False
output = getattr(response, "output", None)
if not isinstance(output, list) or not output:
return False
return True
def preflight_kwargs(self, api_kwargs: Any, *, allow_stream: bool = False) -> dict:
"""Validate and sanitize Codex API kwargs before the call.
Normalizes input items, strips unsupported fields, validates structure.
"""
from agent.codex_responses_adapter import _preflight_codex_api_kwargs
return _preflight_codex_api_kwargs(api_kwargs, allow_stream=allow_stream)
def map_finish_reason(self, raw_reason: str) -> str:
"""Map Codex response.status to OpenAI finish_reason.
Codex uses response.status ('completed', 'incomplete') +
response.incomplete_details.reason for granular mapping.
This method handles the simple status string; the caller
should check incomplete_details separately for 'max_output_tokens'.
"""
_MAP = {
"completed": "stop",
"incomplete": "length",
"failed": "stop",
"cancelled": "stop",
}
return _MAP.get(raw_reason, "stop")
# Auto-register on import
from agent.transports import register_transport # noqa: E402
register_transport("codex_responses", ResponsesApiTransport)
-42
View File
@@ -37,30 +37,6 @@ class ToolCall:
arguments: str # JSON string
provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
# ── Backward compatibility ──────────────────────────────────
# The agent loop reads tc.function.name / tc.function.arguments
# throughout run_agent.py (45+ sites). These properties let
# NormalizedResponse pass through without the _nr_to_assistant_message
# shim, while keeping ToolCall's canonical fields flat.
@property
def type(self) -> str:
return "function"
@property
def function(self) -> "ToolCall":
"""Return self so tc.function.name / tc.function.arguments work."""
return self
@property
def call_id(self) -> Optional[str]:
"""Codex call_id from provider_data, accessed via getattr by _build_assistant_message."""
return (self.provider_data or {}).get("call_id")
@property
def response_item_id(self) -> Optional[str]:
"""Codex response_item_id from provider_data."""
return (self.provider_data or {}).get("response_item_id")
@dataclass
class Usage:
@@ -94,24 +70,6 @@ class NormalizedResponse:
usage: Optional[Usage] = None
provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
# ── Backward compatibility ──────────────────────────────────
# The shim _nr_to_assistant_message() mapped these from provider_data.
# These properties let NormalizedResponse pass through directly.
@property
def reasoning_content(self) -> Optional[str]:
pd = self.provider_data or {}
return pd.get("reasoning_content")
@property
def reasoning_details(self):
pd = self.provider_data or {}
return pd.get("reasoning_details")
@property
def codex_reasoning_items(self):
pd = self.provider_data or {}
return pd.get("codex_reasoning_items")
# ---------------------------------------------------------------------------
# Factory helpers
-12
View File
@@ -533,22 +533,10 @@ def normalize_usage(
prompt_total = _to_int(getattr(response_usage, "prompt_tokens", 0))
output_tokens = _to_int(getattr(response_usage, "completion_tokens", 0))
details = getattr(response_usage, "prompt_tokens_details", None)
# Primary: OpenAI-style prompt_tokens_details. Fallback: Anthropic-style
# top-level fields that some OpenAI-compatible proxies (OpenRouter, Vercel
# AI Gateway, Cline) expose when routing Claude models — without this
# fallback, cache writes are undercounted as 0 and cache reads can be
# missed when the proxy only surfaces them at the top level.
# Port of cline/cline#10266.
cache_read_tokens = _to_int(getattr(details, "cached_tokens", 0) if details else 0)
if not cache_read_tokens:
cache_read_tokens = _to_int(getattr(response_usage, "cache_read_input_tokens", 0))
cache_write_tokens = _to_int(
getattr(details, "cache_write_tokens", 0) if details else 0
)
if not cache_write_tokens:
cache_write_tokens = _to_int(
getattr(response_usage, "cache_creation_input_tokens", 0)
)
input_tokens = max(0, prompt_total - cache_read_tokens - cache_write_tokens)
reasoning_tokens = 0
-1
View File
@@ -776,7 +776,6 @@ delegation:
# max_concurrent_children: 3 # Max parallel child agents (default: 3)
# max_spawn_depth: 1 # Tree depth cap (1-3, default: 1 = flat). Raise to 2 or 3 to allow orchestrator children to spawn their own workers.
# orchestrator_enabled: true # Kill switch for role="orchestrator" children (default: true).
# inherit_mcp_toolsets: true # When explicit child toolsets are narrowed, also keep the parent's MCP toolsets (default: true). Set false for strict intersection.
# model: "google/gemini-3-flash-preview" # Override model for subagents (empty = inherit parent)
# provider: "openrouter" # Override provider for subagents (empty = inherit parent)
# # Resolves full credentials (base_url, api_key) automatically.
+3 -86
View File
@@ -108,11 +108,6 @@ def _strip_reasoning_tags(text: str) -> str:
``<thought>`` (Gemma 4). Must stay in sync with
``run_agent.py::_strip_think_blocks`` and the stream consumer's
``_OPEN_THINK_TAGS`` / ``_CLOSE_THINK_TAGS`` tuples.
Also strips tool-call XML blocks some open models leak into visible
content (``<tool_call>``, ``<function_calls>``, Gemma-style
``<function name=""></function>``). Ported from
openclaw/openclaw#67318.
"""
cleaned = text
for tag in _REASONING_TAGS:
@@ -137,31 +132,6 @@ def _strip_reasoning_tags(text: str) -> str:
cleaned,
flags=re.IGNORECASE,
)
# Tool-call XML blocks (openclaw/openclaw#67318).
for tc_tag in ("tool_call", "tool_calls", "tool_result",
"function_call", "function_calls"):
cleaned = re.sub(
rf"<{tc_tag}\b[^>]*>.*?</{tc_tag}>\s*",
"",
cleaned,
flags=re.DOTALL | re.IGNORECASE,
)
# <function name="..."> — boundary + attribute gated to avoid prose FPs.
cleaned = re.sub(
r'(?:(?<=^)|(?<=[\n\r.!?:]))[ \t]*'
r'<function\b[^>]*\bname\s*=[^>]*>'
r'(?:(?:(?!</function>).)*)</function>\s*',
'',
cleaned,
flags=re.DOTALL | re.IGNORECASE,
)
# Stray tool-call close tags.
cleaned = re.sub(
r'</(?:tool_call|tool_calls|tool_result|function_call|function_calls|function)>\s*',
'',
cleaned,
flags=re.IGNORECASE,
)
return cleaned.strip()
@@ -305,23 +275,13 @@ def load_cli_config() -> Dict[str, Any]:
Environment variables take precedence over config file values.
Returns default values if no config file exists.
If HERMES_IGNORE_USER_CONFIG=1 is set (via ``hermes chat --ignore-user-config``),
the user config at ``~/.hermes/config.yaml`` is skipped entirely and only the
built-in defaults plus the project-level ``cli-config.yaml`` (if any) are used.
Credentials in ``.env`` are still loaded this flag only suppresses
behavioral/config settings.
"""
# Check user config first ({HERMES_HOME}/config.yaml)
user_config_path = _hermes_home / 'config.yaml'
project_config_path = Path(__file__).parent / 'cli-config.yaml'
# --ignore-user-config: force-skip the user config.yaml (still honor project
# config as a fallback so defaults stay sensible).
ignore_user_config = os.environ.get("HERMES_IGNORE_USER_CONFIG") == "1"
# Use user config if it exists, otherwise project config
if user_config_path.exists() and not ignore_user_config:
if user_config_path.exists():
config_path = user_config_path
else:
config_path = project_config_path
@@ -954,32 +914,6 @@ def _cleanup_worktree(info: Dict[str, str] = None) -> None:
print(f"\033[32m✓ Worktree cleaned up: {wt_path}\033[0m")
def _run_state_db_auto_maintenance(session_db) -> None:
"""Call ``SessionDB.maybe_auto_prune_and_vacuum`` using current config.
Reads the ``sessions:`` section from config.yaml via
:func:`hermes_cli.config.load_config` (the authoritative loader that
deep-merges DEFAULT_CONFIG, so unmigrated configs still get default
values). Honours ``auto_prune`` / ``retention_days`` /
``vacuum_after_prune`` / ``min_interval_hours``, and delegates to the
DB. Never raises maintenance must never block interactive startup.
"""
if session_db is None:
return
try:
from hermes_cli.config import load_config as _load_full_config
cfg = (_load_full_config().get("sessions") or {})
if not cfg.get("auto_prune", False):
return
session_db.maybe_auto_prune_and_vacuum(
retention_days=int(cfg.get("retention_days", 90)),
min_interval_hours=int(cfg.get("min_interval_hours", 24)),
vacuum=bool(cfg.get("vacuum_after_prune", True)),
)
except Exception as exc:
logger.debug("state.db auto-maintenance skipped: %s", exc)
def _prune_stale_worktrees(repo_root: str, max_age_hours: int = 24) -> None:
"""Remove stale worktrees and orphaned branches on startup.
@@ -1812,7 +1746,6 @@ class HermesCLI:
resume: str = None,
checkpoints: bool = False,
pass_session_id: bool = False,
ignore_rules: bool = False,
):
"""
Initialize the Hermes CLI.
@@ -1966,11 +1899,6 @@ class HermesCLI:
self.checkpoints_enabled = checkpoints or cp_cfg.get("enabled", False)
self.checkpoint_max_snapshots = cp_cfg.get("max_snapshots", 50)
self.pass_session_id = pass_session_id
# --ignore-rules: honor either the constructor flag or the env var set
# by `hermes chat --ignore-rules` in hermes_cli/main.py. When true we
# pass skip_context_files=True and skip_memory=True to AIAgent so
# AGENTS.md/SOUL.md/.cursorrules and persistent memory are not loaded.
self.ignore_rules = ignore_rules or os.environ.get("HERMES_IGNORE_RULES") == "1"
# Ephemeral system prompt: env var takes precedence, then config
self.system_prompt = (
@@ -2033,13 +1961,7 @@ class HermesCLI:
self._session_db = SessionDB()
except Exception as e:
logger.warning("Failed to initialize SessionDB — session will NOT be indexed for search: %s", e)
# Opportunistic state.db maintenance — runs at most once per
# min_interval_hours, tracked via state_meta in state.db itself so
# it's shared across all Hermes processes for this HERMES_HOME.
# Never blocks startup on failure.
_run_state_db_auto_maintenance(self._session_db)
# Deferred title: stored in memory until the session is created in the DB
self._pending_title: Optional[str] = None
@@ -3328,8 +3250,6 @@ class HermesCLI:
checkpoints_enabled=self.checkpoints_enabled,
checkpoint_max_snapshots=self.checkpoint_max_snapshots,
pass_session_id=self.pass_session_id,
skip_context_files=self.ignore_rules,
skip_memory=self.ignore_rules,
tool_progress_callback=self._on_tool_progress,
tool_start_callback=self._on_tool_start if self._inline_diffs_enabled else None,
tool_complete_callback=self._on_tool_complete if self._inline_diffs_enabled else None,
@@ -8454,7 +8374,7 @@ class HermesCLI:
# in terminal_tool is populated for this thread. The main thread
# registration (run() line ~9046) is invisible here because
# _callback_tls is threading.local(). Matches the pattern used
# by hermes_agent/acp/server.py for ACP sessions.
# by acp_adapter/server.py for ACP sessions.
set_sudo_password_callback(self._sudo_password_callback)
set_approval_callback(self._approval_callback)
try:
@@ -10834,8 +10754,6 @@ def main(
w: bool = False,
checkpoints: bool = False,
pass_session_id: bool = False,
ignore_user_config: bool = False,
ignore_rules: bool = False,
):
"""
Hermes Agent CLI - Interactive AI Assistant
@@ -10945,7 +10863,6 @@ def main(
resume=resume,
checkpoints=checkpoints,
pass_session_id=pass_session_id,
ignore_rules=ignore_rules,
)
if parsed_skills:
-6
View File
@@ -972,12 +972,6 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
f"— last activity: {_last_desc}"
)
# Guard against non-dict returns from run_conversation under error conditions
if not isinstance(result, dict):
raise RuntimeError(
f"agent.run_conversation returned {type(result).__name__} instead of dict: {result!r}"
)
final_response = result.get("final_response", "") or ""
# Strip leaked placeholder text that upstream may inject on empty completions.
if final_response.strip() == "(No response generated)":
-22
View File
@@ -58,13 +58,6 @@ if [ ! -f "$HERMES_HOME/config.yaml" ]; then
cp "$INSTALL_DIR/cli-config.yaml.example" "$HERMES_HOME/config.yaml"
fi
# Ensure the main config file remains accessible to the hermes runtime user
# even if it was edited on the host after initial ownership setup.
if [ -f "$HERMES_HOME/config.yaml" ]; then
chown hermes:hermes "$HERMES_HOME/config.yaml"
chmod 640 "$HERMES_HOME/config.yaml"
fi
# SOUL.md
if [ ! -f "$HERMES_HOME/SOUL.md" ]; then
cp "$INSTALL_DIR/docker/SOUL.md" "$HERMES_HOME/SOUL.md"
@@ -75,19 +68,4 @@ if [ -d "$INSTALL_DIR/skills" ]; then
python3 "$INSTALL_DIR/tools/skills_sync.py"
fi
# Final exec: two supported invocation patterns.
#
# docker run <image> -> exec `hermes` with no args (legacy default)
# docker run <image> chat -q "..." -> exec `hermes chat -q "..."` (legacy wrap)
# docker run <image> sleep infinity -> exec `sleep infinity` directly
# docker run <image> bash -> exec `bash` directly
#
# If the first positional arg resolves to an executable on PATH, we assume the
# caller wants to run it directly (needed by the launcher which runs long-lived
# `sleep infinity` sandbox containers — see tools/environments/docker.py).
# Otherwise we treat the args as a hermes subcommand and wrap with `hermes`,
# preserving the documented `docker run <image> <subcommand>` behavior.
if [ $# -gt 0 ] && command -v "$1" >/dev/null 2>&1; then
exec "$@"
fi
exec hermes "$@"
-2
View File
@@ -616,8 +616,6 @@ def load_gateway_config() -> GatewayConfig:
if isinstance(frc, list):
frc = ",".join(str(v) for v in frc)
os.environ["SLACK_FREE_RESPONSE_CHANNELS"] = str(frc)
if "reactions" in slack_cfg and not os.getenv("SLACK_REACTIONS"):
os.environ["SLACK_REACTIONS"] = str(slack_cfg["reactions"]).lower()
# Discord settings → env vars (env vars take precedence)
discord_cfg = yaml_cfg.get("discord", {})
+11 -44
View File
@@ -135,22 +135,9 @@ class HookRegistry:
except Exception as e:
print(f"[hooks] Error loading hook {hook_dir.name}: {e}", flush=True)
def _resolve_handlers(self, event_type: str) -> List[Callable]:
"""Return all handlers that should fire for ``event_type``.
Exact matches fire first, followed by wildcard matches (e.g.
``command:*`` matches ``command:reset``).
"""
handlers = list(self._handlers.get(event_type, []))
if ":" in event_type:
base = event_type.split(":")[0]
wildcard_key = f"{base}:*"
handlers.extend(self._handlers.get(wildcard_key, []))
return handlers
async def emit(self, event_type: str, context: Optional[Dict[str, Any]] = None) -> None:
"""
Fire all handlers registered for an event, discarding return values.
Fire all handlers registered for an event.
Supports wildcard matching: handlers registered for "command:*" will
fire for any "command:..." event. Handlers registered for a base type
@@ -164,7 +151,16 @@ class HookRegistry:
if context is None:
context = {}
for fn in self._resolve_handlers(event_type):
# Collect handlers: exact match + wildcard match
handlers = list(self._handlers.get(event_type, []))
# Check for wildcard patterns (e.g., "command:*" matches "command:reset")
if ":" in event_type:
base = event_type.split(":")[0]
wildcard_key = f"{base}:*"
handlers.extend(self._handlers.get(wildcard_key, []))
for fn in handlers:
try:
result = fn(event_type, context)
# Support both sync and async handlers
@@ -172,32 +168,3 @@ class HookRegistry:
await result
except Exception as e:
print(f"[hooks] Error in handler for '{event_type}': {e}", flush=True)
async def emit_collect(
self,
event_type: str,
context: Optional[Dict[str, Any]] = None,
) -> List[Any]:
"""Fire handlers and return their non-None return values in order.
Like :meth:`emit` but captures each handler's return value. Used for
decision-style hooks (e.g. ``command:<name>`` policies that want to
allow/deny/rewrite the command before normal dispatch).
Exceptions from individual handlers are logged but do not abort the
remaining handlers.
"""
if context is None:
context = {}
results: List[Any] = []
for fn in self._resolve_handlers(event_type):
try:
result = fn(event_type, context)
if asyncio.iscoroutine(result):
result = await result
if result is not None:
results.append(result)
except Exception as e:
print(f"[hooks] Error in handler for '{event_type}': {e}", flush=True)
return results
+21 -270
View File
@@ -752,10 +752,7 @@ class MessageEvent:
if not self.is_command():
return self.text
parts = self.text.split(maxsplit=1)
args = parts[1] if len(parts) > 1 else ""
# iOS auto-corrects -- to — (em dash) and - to (en dash)
args = args.replace("\u2014\u2014", "--").replace("\u2014", "--").replace("\u2013", "-")
return args
return parts[1] if len(parts) > 1 else ""
@dataclass
@@ -900,16 +897,10 @@ class BasePlatformAdapter(ABC):
self._fatal_error_retryable = True
self._fatal_error_handler: Optional[Callable[["BasePlatformAdapter"], Awaitable[None] | None]] = None
# Track active message handlers per session for interrupt support.
# _active_sessions stores the per-session interrupt Event; _session_tasks
# maps session → the specific Task currently processing it so that
# session-terminating commands (/stop, /new, /reset) can cancel the
# right task and release the adapter-level guard deterministically.
# Without the owner-task map, an old task's finally block could delete
# a newer task's guard, leaving stale busy state.
# 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] = {}
self._session_tasks: Dict[str, asyncio.Task] = {}
# Background message-processing tasks spawned by handle_message().
# Gateway shutdown cancels these so an old gateway instance doesn't keep
# working on a task after --replace or manual restarts.
@@ -1352,7 +1343,7 @@ class BasePlatformAdapter(ABC):
# Extract MEDIA:<path> tags, allowing optional whitespace after the colon
# and quoted/backticked paths for LLM-formatted outputs.
media_pattern = re.compile(
r'''[`"']?MEDIA:\s*(?P<path>`[^`\n]+`|"[^"\n]+"|'[^'\n]+'|(?:~/|/)\S+(?:[^\S\n]+\S+)*?\.(?:png|jpe?g|gif|webp|mp4|mov|avi|mkv|webm|ogg|opus|mp3|wav|m4a|epub|pdf|zip|rar|7z|docx?|xlsx?|pptx?|txt|csv|apk|ipa)(?=[\s`"',;:)\]}]|$)|\S+)[`"']?'''
r'''[`"']?MEDIA:\s*(?P<path>`[^`\n]+`|"[^"\n]+"|'[^'\n]+'|(?:~/|/)\S+(?:[^\S\n]+\S+)*?\.(?:png|jpe?g|gif|webp|mp4|mov|avi|mkv|webm|ogg|opus|mp3|wav|m4a|pdf)(?=[\s`"',;:)\]}]|$)|\S+)[`"']?'''
)
for match in media_pattern.finditer(content):
path = match.group("path").strip()
@@ -1686,222 +1677,6 @@ class BasePlatformAdapter(ABC):
return f"{existing_text}\n\n{new_text}".strip()
return existing_text
# ------------------------------------------------------------------
# Session task + guard ownership helpers
# ------------------------------------------------------------------
# These were introduced together with the _session_tasks owner map to
# make session lifecycle reconciliation deterministic across (a) the
# normal completion path, (b) /stop/ /new/ /reset bypass commands,
# and (c) stale-lock self-heal on the next inbound message.
def _release_session_guard(
self,
session_key: str,
*,
guard: Optional[asyncio.Event] = None,
) -> None:
"""Release the adapter-level guard for a session.
When ``guard`` is provided, only release the entry if it still points
at that exact Event. This lets reset-like commands swap in a temporary
guard while the old processing task unwinds, without having the old
task's cleanup accidentally clear the replacement guard.
"""
current_guard = self._active_sessions.get(session_key)
if current_guard is None:
return
if guard is not None and current_guard is not guard:
return
del self._active_sessions[session_key]
def _session_task_is_stale(self, session_key: str) -> bool:
"""Return True if the owner task for ``session_key`` is done/cancelled.
A lock is "stale" when the adapter still has ``_active_sessions[key]``
AND a known owner task in ``_session_tasks`` that has already exited.
When there is no owner task at all, that usually means the guard was
installed by some path other than handle_message() (tests sometimes
install guards directly) don't treat that as stale. The on-entry
self-heal only needs to handle the production split-brain case where
an owner task was recorded, then exited without clearing its guard.
"""
task = self._session_tasks.get(session_key)
if task is None:
return False
done = getattr(task, "done", None)
return bool(done and done())
def _heal_stale_session_lock(self, session_key: str) -> bool:
"""Clear a stale session lock if the owner task is already gone.
Returns True if a stale lock was healed. Returns False if there is
no lock, or the owner task is still alive (the normal busy case).
This is the on-entry safety net sidbin's issue #11016 analysis calls
for: without it, a split-brain adapter still thinks the session is
active, but nothing is actually processing traps the chat in
infinite "Interrupting current task..." until the gateway is
restarted.
"""
if session_key not in self._active_sessions:
return False
if not self._session_task_is_stale(session_key):
return False
logger.warning(
"[%s] Healing stale session lock for %s (owner task is done/absent)",
self.name,
session_key,
)
self._active_sessions.pop(session_key, None)
self._pending_messages.pop(session_key, None)
self._session_tasks.pop(session_key, None)
return True
def _start_session_processing(
self,
event: MessageEvent,
session_key: str,
*,
interrupt_event: Optional[asyncio.Event] = None,
) -> bool:
"""Spawn a background processing task under the given session guard.
Returns True on success. If the runtime stubs ``create_task`` with a
non-Task sentinel (some tests do this), the guard is rolled back and
False is returned so the caller isn't left holding a half-installed
session lock.
"""
guard = interrupt_event or asyncio.Event()
self._active_sessions[session_key] = guard
task = asyncio.create_task(self._process_message_background(event, session_key))
self._session_tasks[session_key] = task
try:
self._background_tasks.add(task)
except TypeError:
# Tests stub create_task() with lightweight sentinels that are not
# hashable and do not support lifecycle callbacks.
self._session_tasks.pop(session_key, None)
self._release_session_guard(session_key, guard=guard)
return False
if hasattr(task, "add_done_callback"):
task.add_done_callback(self._background_tasks.discard)
task.add_done_callback(self._expected_cancelled_tasks.discard)
return True
async def cancel_session_processing(
self,
session_key: str,
*,
release_guard: bool = True,
discard_pending: bool = True,
) -> None:
"""Cancel in-flight processing for a single session.
``release_guard=False`` keeps the adapter-level session guard in place
so reset-like commands can finish atomically before follow-up messages
are allowed to start a fresh background task.
"""
task = self._session_tasks.pop(session_key, None)
if task is not None and not task.done():
logger.debug(
"[%s] Cancelling active processing for session %s",
self.name,
session_key,
)
self._expected_cancelled_tasks.add(task)
task.cancel()
try:
await task
except asyncio.CancelledError:
pass
except Exception:
logger.debug(
"[%s] Session cancellation raised while unwinding %s",
self.name,
session_key,
exc_info=True,
)
if discard_pending:
self._pending_messages.pop(session_key, None)
if release_guard:
self._release_session_guard(session_key)
async def _drain_pending_after_session_command(
self,
session_key: str,
command_guard: asyncio.Event,
) -> None:
"""Resume the latest queued follow-up once a session command completes.
Called at the tail of /stop, /new, and /reset dispatch. Releases the
command-scoped guard, then if a follow-up message landed while the
command was running spawns a fresh processing task for it.
"""
pending_event = self._pending_messages.pop(session_key, None)
self._release_session_guard(session_key, guard=command_guard)
if pending_event is None:
return
self._start_session_processing(pending_event, session_key)
async def _dispatch_active_session_command(
self,
event: MessageEvent,
session_key: str,
cmd: str,
) -> None:
"""Dispatch a reset-like bypass command while preserving guard ordering.
/stop, /new, and /reset must:
1. Keep the session guard installed while the runner processes the
command (so a racing follow-up message stays queued, not
dispatched as a second parallel run).
2. Cancel the old in-flight adapter task only AFTER the runner has
finished handling the command (so the runner sees consistent
state and its response is sent in order).
3. Release the command-scoped guard and drain the latest queued
follow-up exactly once, after 1 and 2 complete.
"""
logger.debug(
"[%s] Command '/%s' bypassing active-session guard for %s",
self.name,
cmd,
session_key,
)
current_guard = self._active_sessions.get(session_key)
command_guard = asyncio.Event()
self._active_sessions[session_key] = command_guard
thread_meta = {"thread_id": event.source.thread_id} if event.source.thread_id else None
try:
response = await self._message_handler(event)
# Old adapter task (if any) is cancelled AFTER the runner has
# fully handled the command — keeps ordering deterministic.
await self.cancel_session_processing(
session_key,
release_guard=False,
discard_pending=False,
)
if response:
await self._send_with_retry(
chat_id=event.source.chat_id,
content=response,
reply_to=event.message_id,
metadata=thread_meta,
)
except Exception:
# On failure, restore the original guard if one still exists so
# we don't leave the session in a half-reset state.
if self._active_sessions.get(session_key) is command_guard:
if session_key in self._session_tasks and current_guard is not None:
self._active_sessions[session_key] = current_guard
else:
self._release_session_guard(session_key, guard=command_guard)
raise
await self._drain_pending_after_session_command(session_key, command_guard)
async def handle_message(self, event: MessageEvent) -> None:
"""
Process an incoming message.
@@ -1918,15 +1693,7 @@ class BasePlatformAdapter(ABC):
group_sessions_per_user=self.config.extra.get("group_sessions_per_user", True),
thread_sessions_per_user=self.config.extra.get("thread_sessions_per_user", False),
)
# On-entry self-heal: if the adapter still has an _active_sessions
# entry for this key but the owner task has already exited (done or
# cancelled), the lock is stale. Clear it and fall through to
# normal dispatch so the user isn't trapped behind a dead guard —
# this is the split-brain tail described in issue #11016.
if session_key in self._active_sessions:
self._heal_stale_session_lock(session_key)
# Check if there's already an active handler for this session
if session_key in self._active_sessions:
# Certain commands must bypass the active-session guard and be
@@ -1943,23 +1710,6 @@ class BasePlatformAdapter(ABC):
from hermes_cli.commands import should_bypass_active_session
if should_bypass_active_session(cmd):
# /stop, /new, /reset must cancel the in-flight adapter task
# and preserve ordering of queued follow-ups. Route those
# through the dedicated handoff path that serializes
# cancellation + runner response + pending drain.
if cmd in ("stop", "new", "reset"):
try:
await self._dispatch_active_session_command(event, session_key, cmd)
except Exception as e:
logger.error(
"[%s] Command '/%s' dispatch failed: %s",
self.name, cmd, e, exc_info=True,
)
return
# Other bypass commands (/approve, /deny, /status,
# /background, /restart) just need direct dispatch — they
# don't cancel the running task.
logger.debug(
"[%s] Command '/%s' bypassing active-session guard for %s",
self.name, cmd, session_key,
@@ -2005,9 +1755,19 @@ class BasePlatformAdapter(ABC):
# starts would also pass the _active_sessions check and spawn a
# duplicate task. (grammY sequentialize / aiogram EventIsolation
# pattern — set the guard synchronously, not inside the task.)
# _start_session_processing installs the guard AND the owner-task
# mapping atomically so stale-lock detection works.
self._start_session_processing(event, session_key)
self._active_sessions[session_key] = asyncio.Event()
# Spawn background task to process this message
task = asyncio.create_task(self._process_message_background(event, session_key))
try:
self._background_tasks.add(task)
except TypeError:
# Some tests stub create_task() with lightweight sentinels that are not
# hashable and do not support lifecycle callbacks.
return
if hasattr(task, "add_done_callback"):
task.add_done_callback(self._background_tasks.discard)
task.add_done_callback(self._expected_cancelled_tasks.discard)
@staticmethod
def _get_human_delay() -> float:
@@ -2367,9 +2127,6 @@ class BasePlatformAdapter(ABC):
drain_task = asyncio.create_task(
self._process_message_background(late_pending, session_key)
)
# Hand ownership of the session to the drain task so stale-lock
# detection keeps working while it runs.
self._session_tasks[session_key] = drain_task
try:
self._background_tasks.add(drain_task)
drain_task.add_done_callback(self._background_tasks.discard)
@@ -2379,14 +2136,9 @@ class BasePlatformAdapter(ABC):
# Leave _active_sessions[session_key] populated — the drain
# task's own lifecycle will clean it up.
else:
# Clean up session tracking. Guard-match both deletes so a
# reset-like command that already swapped in its own
# command_guard (and cancelled us) can't be accidentally
# cleared by our unwind. The command owns the session now.
current_task = asyncio.current_task()
if current_task is not None and self._session_tasks.get(session_key) is current_task:
del self._session_tasks[session_key]
self._release_session_guard(session_key, guard=interrupt_event)
# Clean up session tracking
if session_key in self._active_sessions:
del self._active_sessions[session_key]
async def cancel_background_tasks(self) -> None:
"""Cancel any in-flight background message-processing tasks.
@@ -2416,7 +2168,6 @@ class BasePlatformAdapter(ABC):
# will be in self._background_tasks now. Re-check.
self._background_tasks.clear()
self._expected_cancelled_tasks.clear()
self._session_tasks.clear()
self._pending_messages.clear()
self._active_sessions.clear()
+34 -69
View File
@@ -527,7 +527,6 @@ class DiscordAdapter(BasePlatformAdapter):
# Reply threading mode: "off" (no replies), "first" (reply on first
# chunk only, default), "all" (reply-reference on every chunk).
self._reply_to_mode: str = getattr(config, 'reply_to_mode', 'first') or 'first'
self._slash_commands: bool = self.config.extra.get("slash_commands", True)
async def connect(self) -> bool:
"""Connect to Discord and start receiving events."""
@@ -745,8 +744,7 @@ class DiscordAdapter(BasePlatformAdapter):
)
# Register slash commands
if self._slash_commands:
self._register_slash_commands()
self._register_slash_commands()
# Start the bot in background
self._bot_task = asyncio.create_task(self._client.start(self.config.token))
@@ -2131,42 +2129,10 @@ class DiscordAdapter(BasePlatformAdapter):
# This ensures new commands added to COMMAND_REGISTRY in
# hermes_cli/commands.py automatically appear as Discord slash
# commands without needing a manual entry here.
def _build_auto_slash_command(_name: str, _description: str, _args_hint: str = ""):
"""Build a discord.app_commands.Command that proxies to _run_simple_slash."""
discord_name = _name.lower()[:32]
desc = (_description or f"Run /{_name}")[:100]
has_args = bool(_args_hint)
if has_args:
def _make_args_handler(__name: str, __hint: str):
@discord.app_commands.describe(args=f"Arguments: {__hint}"[:100])
async def _handler(interaction: discord.Interaction, args: str = ""):
await self._run_simple_slash(
interaction, f"/{__name} {args}".strip()
)
_handler.__name__ = f"auto_slash_{__name.replace('-', '_')}"
return _handler
handler = _make_args_handler(_name, _args_hint)
else:
def _make_simple_handler(__name: str):
async def _handler(interaction: discord.Interaction):
await self._run_simple_slash(interaction, f"/{__name}")
_handler.__name__ = f"auto_slash_{__name.replace('-', '_')}"
return _handler
handler = _make_simple_handler(_name)
return discord.app_commands.Command(
name=discord_name,
description=desc,
callback=handler,
)
already_registered: set[str] = set()
try:
from hermes_cli.commands import COMMAND_REGISTRY, _is_gateway_available, _resolve_config_gates
already_registered = set()
try:
already_registered = {cmd.name for cmd in tree.get_commands()}
except Exception:
@@ -2181,10 +2147,38 @@ class DiscordAdapter(BasePlatformAdapter):
discord_name = cmd_def.name.lower()[:32]
if discord_name in already_registered:
continue
auto_cmd = _build_auto_slash_command(
cmd_def.name,
cmd_def.description,
cmd_def.args_hint,
# Skip aliases that overlap with already-registered names
# (aliases for explicitly registered commands are handled above).
desc = (cmd_def.description or f"Run /{cmd_def.name}")[:100]
has_args = bool(cmd_def.args_hint)
if has_args:
# Command takes optional arguments — create handler with
# an optional ``args`` string parameter.
def _make_args_handler(_name: str, _hint: str):
@discord.app_commands.describe(args=f"Arguments: {_hint}"[:100])
async def _handler(interaction: discord.Interaction, args: str = ""):
await self._run_simple_slash(
interaction, f"/{_name} {args}".strip()
)
_handler.__name__ = f"auto_slash_{_name.replace('-', '_')}"
return _handler
handler = _make_args_handler(cmd_def.name, cmd_def.args_hint)
else:
# Parameterless command.
def _make_simple_handler(_name: str):
async def _handler(interaction: discord.Interaction):
await self._run_simple_slash(interaction, f"/{_name}")
_handler.__name__ = f"auto_slash_{_name.replace('-', '_')}"
return _handler
handler = _make_simple_handler(cmd_def.name)
auto_cmd = discord.app_commands.Command(
name=discord_name,
description=desc,
callback=handler,
)
try:
tree.add_command(auto_cmd)
@@ -2201,35 +2195,6 @@ class DiscordAdapter(BasePlatformAdapter):
except Exception as e:
logger.warning("Discord auto-register from COMMAND_REGISTRY failed: %s", e)
# ── Plugin-registered slash commands ──
# Plugins register via PluginContext.register_command(); we mirror
# those into Discord's native slash picker so users get the same
# autocomplete UX as for built-in commands. No per-platform plugin
# API needed — plugin commands are platform-agnostic.
try:
from hermes_cli.commands import _iter_plugin_command_entries
for plugin_name, plugin_desc, plugin_args_hint in _iter_plugin_command_entries():
discord_name = plugin_name.lower()[:32]
if discord_name in already_registered:
continue
auto_cmd = _build_auto_slash_command(
plugin_name,
plugin_desc,
plugin_args_hint,
)
try:
tree.add_command(auto_cmd)
already_registered.add(discord_name)
except Exception:
# Silently skip commands that fail registration (e.g.
# name conflict with a subcommand group).
pass
except Exception as e:
logger.warning(
"Discord auto-register from plugin commands failed: %s", e
)
# Register skills under a single /skill command group with category
# subcommand groups. This uses 1 top-level slot instead of N,
# supporting up to 25 categories × 25 skills = 625 skills.
-1
View File
@@ -545,7 +545,6 @@ class EmailAdapter(BasePlatformAdapter):
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
**kwargs,
) -> SendResult:
"""Send a file as an email attachment."""
try:
+80 -349
View File
@@ -14,35 +14,6 @@ Supports:
- Interactive card button-click events routed as synthetic COMMAND events
- Webhook anomaly tracking (matches openclaw createWebhookAnomalyTracker)
- Verification token validation as second auth layer (matches openclaw)
Feishu identity model
---------------------
Feishu uses three user-ID tiers (official docs:
https://open.feishu.cn/document/home/user-identity-introduction/introduction):
open_id (ou_xxx) **App-scoped**. The same person gets a different
open_id under each Feishu app. Always available in
event payloads without extra permissions.
user_id (u_xxx) **Tenant-scoped**. Stable within a company but
requires the ``contact:user.employee_id:readonly``
scope. May not be present.
union_id (on_xxx) **Developer-scoped**. Same across all apps owned by
one developer/ISV. Best cross-app stable ID.
For bots specifically:
app_id The application's canonical credential identifier.
bot open_id Returned by ``/bot/v3/info``. This is the bot's own
open_id *within its app context* and is what Feishu
puts in ``mentions[].id.open_id`` when someone
@-mentions the bot. Used for mention gating only.
In single-bot mode (what Hermes currently supports), open_id works as a
de-facto unique user identifier since there is only one app context.
Session-key participant isolation prefers ``union_id`` (via user_id_alt)
over ``open_id`` (via user_id) so that sessions stay stable if the same
user is seen through different apps in the future.
"""
from __future__ import annotations
@@ -64,7 +35,7 @@ from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from types import SimpleNamespace
from typing import Any, Dict, List, Optional, Sequence
from typing import Any, Dict, List, Optional
from urllib.error import HTTPError, URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
@@ -102,9 +73,7 @@ try:
UpdateMessageRequest,
UpdateMessageRequestBody,
)
from lark_oapi.core import AccessTokenType, HttpMethod
from lark_oapi.core.const import FEISHU_DOMAIN, LARK_DOMAIN
from lark_oapi.core.model import BaseRequest
from lark_oapi.event.callback.model.p2_card_action_trigger import (
CallBackCard,
P2CardActionTriggerResponse,
@@ -265,8 +234,6 @@ FALLBACK_ATTACHMENT_TEXT = "[Attachment]"
_PREFERRED_LOCALES = ("zh_cn", "en_us")
_MARKDOWN_SPECIAL_CHARS_RE = re.compile(r"([\\`*_{}\[\]()#+\-!|>~])")
_MENTION_PLACEHOLDER_RE = re.compile(r"@_user_\d+")
_MENTION_BOUNDARY_CHARS = frozenset(" \t\n\r.,;:!?、,。;:!?()[]{}<>\"'`")
_TRAILING_TERMINAL_PUNCT = frozenset(" \t\n\r.!?。!?")
_WHITESPACE_RE = re.compile(r"\s+")
_SUPPORTED_CARD_TEXT_KEYS = (
"title",
@@ -310,36 +277,12 @@ class FeishuPostMediaRef:
resource_type: str = "file"
@dataclass(frozen=True)
class FeishuMentionRef:
name: str = ""
open_id: str = ""
is_all: bool = False
is_self: bool = False
@dataclass(frozen=True)
class _FeishuBotIdentity:
open_id: str = ""
user_id: str = ""
name: str = ""
def matches(self, *, open_id: str, user_id: str, name: str) -> bool:
# Precedence: open_id > user_id > name. IDs are authoritative when both
# sides have them; the next tier is only considered when either side
# lacks the current one.
if open_id and self.open_id:
return open_id == self.open_id
if user_id and self.user_id:
return user_id == self.user_id
return bool(self.name) and name == self.name
@dataclass(frozen=True)
class FeishuPostParseResult:
text_content: str
image_keys: List[str] = field(default_factory=list)
media_refs: List[FeishuPostMediaRef] = field(default_factory=list)
mentioned_ids: List[str] = field(default_factory=list)
@dataclass(frozen=True)
@@ -349,14 +292,14 @@ class FeishuNormalizedMessage:
preferred_message_type: str = "text"
image_keys: List[str] = field(default_factory=list)
media_refs: List[FeishuPostMediaRef] = field(default_factory=list)
mentions: List[FeishuMentionRef] = field(default_factory=list)
mentioned_ids: List[str] = field(default_factory=list)
relation_kind: str = "plain"
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass(frozen=True)
class FeishuAdapterSettings:
app_id: str # Canonical bot/app identifier (credential, not from event payloads)
app_id: str
app_secret: str
domain_name: str
connection_mode: str
@@ -364,11 +307,7 @@ class FeishuAdapterSettings:
verification_token: str
group_policy: str
allowed_group_users: frozenset[str]
# Bot's own open_id (app-scoped) — returned by /bot/v3/info. Used only for
# @mention matching: Feishu puts this value in mentions[].id.open_id when
# a user @-mentions the bot in a group chat.
bot_open_id: str
# Bot's user_id (tenant-scoped) — optional, used as fallback mention match.
bot_user_id: str
bot_name: str
dedup_cache_size: int
@@ -566,17 +505,14 @@ def _build_markdown_post_rows(content: str) -> List[List[Dict[str, str]]]:
return rows or [[{"tag": "md", "text": content}]]
def parse_feishu_post_payload(
payload: Any,
*,
mentions_map: Optional[Dict[str, FeishuMentionRef]] = None,
) -> FeishuPostParseResult:
def parse_feishu_post_payload(payload: Any) -> FeishuPostParseResult:
resolved = _resolve_post_payload(payload)
if not resolved:
return FeishuPostParseResult(text_content=FALLBACK_POST_TEXT)
image_keys: List[str] = []
media_refs: List[FeishuPostMediaRef] = []
mentioned_ids: List[str] = []
parts: List[str] = []
title = _normalize_feishu_text(str(resolved.get("title", "")).strip())
@@ -587,10 +523,7 @@ def parse_feishu_post_payload(
if not isinstance(row, list):
continue
row_text = _normalize_feishu_text(
"".join(
_render_post_element(item, image_keys, media_refs, mentions_map)
for item in row
)
"".join(_render_post_element(item, image_keys, media_refs, mentioned_ids) for item in row)
)
if row_text:
parts.append(row_text)
@@ -599,6 +532,7 @@ def parse_feishu_post_payload(
text_content="\n".join(parts).strip() or FALLBACK_POST_TEXT,
image_keys=image_keys,
media_refs=media_refs,
mentioned_ids=mentioned_ids,
)
@@ -650,7 +584,7 @@ def _render_post_element(
element: Any,
image_keys: List[str],
media_refs: List[FeishuPostMediaRef],
mentions_map: Optional[Dict[str, FeishuMentionRef]] = None,
mentioned_ids: List[str],
) -> str:
if isinstance(element, str):
return element
@@ -668,21 +602,19 @@ def _render_post_element(
escaped_label = _escape_markdown_text(label)
return f"[{escaped_label}]({href})" if href else escaped_label
if tag == "at":
# Post <at>.user_id is a placeholder ("@_user_N" or "@_all"); look up
# the real ref in mentions_map for the display name.
placeholder = str(element.get("user_id", "")).strip()
if placeholder == "@_all":
# Feishu SDK sometimes omits @_all from the top-level mentions
# payload; record it here so the caller's mention list stays complete.
if mentions_map is not None and "@_all" not in mentions_map:
mentions_map["@_all"] = FeishuMentionRef(is_all=True)
return "@all"
ref = (mentions_map or {}).get(placeholder)
if ref is not None:
display_name = ref.name or ref.open_id or "user"
else:
display_name = str(element.get("user_name", "")).strip() or "user"
return f"@{_escape_markdown_text(display_name)}"
mentioned_id = (
str(element.get("open_id", "")).strip()
or str(element.get("user_id", "")).strip()
)
if mentioned_id and mentioned_id not in mentioned_ids:
mentioned_ids.append(mentioned_id)
display_name = (
str(element.get("user_name", "")).strip()
or str(element.get("name", "")).strip()
or str(element.get("text", "")).strip()
or mentioned_id
)
return f"@{_escape_markdown_text(display_name)}" if display_name else "@"
if tag in {"img", "image"}:
image_key = str(element.get("image_key", "")).strip()
if image_key and image_key not in image_keys:
@@ -720,7 +652,8 @@ def _render_post_element(
nested_parts: List[str] = []
for key in ("text", "title", "content", "children", "elements"):
extracted = _render_nested_post(element.get(key), image_keys, media_refs, mentions_map)
value = element.get(key)
extracted = _render_nested_post(value, image_keys, media_refs, mentioned_ids)
if extracted:
nested_parts.append(extracted)
return " ".join(part for part in nested_parts if part)
@@ -730,7 +663,7 @@ def _render_nested_post(
value: Any,
image_keys: List[str],
media_refs: List[FeishuPostMediaRef],
mentions_map: Optional[Dict[str, FeishuMentionRef]] = None,
mentioned_ids: List[str],
) -> str:
if isinstance(value, str):
return _escape_markdown_text(value)
@@ -738,17 +671,17 @@ def _render_nested_post(
return " ".join(
part
for item in value
for part in [_render_nested_post(item, image_keys, media_refs, mentions_map)]
for part in [_render_nested_post(item, image_keys, media_refs, mentioned_ids)]
if part
)
if isinstance(value, dict):
direct = _render_post_element(value, image_keys, media_refs, mentions_map)
direct = _render_post_element(value, image_keys, media_refs, mentioned_ids)
if direct:
return direct
return " ".join(
part
for item in value.values()
for part in [_render_nested_post(item, image_keys, media_refs, mentions_map)]
for part in [_render_nested_post(item, image_keys, media_refs, mentioned_ids)]
if part
)
return ""
@@ -759,48 +692,31 @@ def _render_nested_post(
# ---------------------------------------------------------------------------
def normalize_feishu_message(
*,
message_type: str,
raw_content: str,
mentions: Optional[Sequence[Any]] = None,
bot: _FeishuBotIdentity = _FeishuBotIdentity(),
) -> FeishuNormalizedMessage:
def normalize_feishu_message(*, message_type: str, raw_content: str) -> FeishuNormalizedMessage:
normalized_type = str(message_type or "").strip().lower()
payload = _load_feishu_payload(raw_content)
mentions_map = _build_mentions_map(mentions, bot)
if normalized_type == "text":
text = str(payload.get("text", "") or "")
# Feishu SDK sometimes omits @_all from the mentions payload even when
# the text literal contains it (confirmed via im.v1.message.get).
if "@_all" in text and "@_all" not in mentions_map:
mentions_map["@_all"] = FeishuMentionRef(is_all=True)
return FeishuNormalizedMessage(
raw_type=normalized_type,
text_content=_normalize_feishu_text(text, mentions_map),
mentions=list(mentions_map.values()),
text_content=_normalize_feishu_text(str(payload.get("text", "") or "")),
)
if normalized_type == "post":
# The walker writes back to mentions_map if it encounters
# <at user_id="@_all">, so reading .values() after parsing is enough.
parsed_post = parse_feishu_post_payload(payload, mentions_map=mentions_map)
parsed_post = parse_feishu_post_payload(payload)
return FeishuNormalizedMessage(
raw_type=normalized_type,
text_content=parsed_post.text_content,
image_keys=list(parsed_post.image_keys),
media_refs=list(parsed_post.media_refs),
mentions=list(mentions_map.values()),
mentioned_ids=list(parsed_post.mentioned_ids),
relation_kind="post",
)
mention_refs = list(mentions_map.values())
if normalized_type == "image":
image_key = str(payload.get("image_key", "") or "").strip()
alt_text = _normalize_feishu_text(
str(payload.get("text", "") or "")
or str(payload.get("alt", "") or "")
or FALLBACK_IMAGE_TEXT,
mentions_map,
or FALLBACK_IMAGE_TEXT
)
return FeishuNormalizedMessage(
raw_type=normalized_type,
@@ -808,7 +724,6 @@ def normalize_feishu_message(
preferred_message_type="photo",
image_keys=[image_key] if image_key else [],
relation_kind="image",
mentions=mention_refs,
)
if normalized_type in {"file", "audio", "media"}:
media_ref = _build_media_ref_from_payload(payload, resource_type=normalized_type)
@@ -820,7 +735,6 @@ def normalize_feishu_message(
media_refs=[media_ref] if media_ref.file_key else [],
relation_kind=normalized_type,
metadata={"placeholder_text": placeholder},
mentions=mention_refs,
)
if normalized_type == "merge_forward":
return _normalize_merge_forward_message(payload)
@@ -1095,20 +1009,8 @@ def _first_non_empty_text(*values: Any) -> str:
# ---------------------------------------------------------------------------
def _normalize_feishu_text(
text: str,
mentions_map: Optional[Dict[str, FeishuMentionRef]] = None,
) -> str:
def _sub(match: "re.Match[str]") -> str:
key = match.group(0)
ref = (mentions_map or {}).get(key)
if ref is None:
return " "
name = ref.name or ref.open_id or "user"
return f"@{name}"
cleaned = _MENTION_PLACEHOLDER_RE.sub(_sub, text or "")
cleaned = cleaned.replace("@_all", "@all")
def _normalize_feishu_text(text: str) -> str:
cleaned = _MENTION_PLACEHOLDER_RE.sub(" ", text or "")
cleaned = cleaned.replace("\r\n", "\n").replace("\r", "\n")
cleaned = "\n".join(_WHITESPACE_RE.sub(" ", line).strip() for line in cleaned.split("\n"))
cleaned = "\n".join(line for line in cleaned.split("\n") if line)
@@ -1127,117 +1029,6 @@ def _unique_lines(lines: List[str]) -> List[str]:
return unique
# ---------------------------------------------------------------------------
# Mention helpers
# ---------------------------------------------------------------------------
def _extract_mention_ids(mention: Any) -> tuple[str, str]:
# Returns (open_id, user_id). im.v1.message.get hands back id as a string
# plus id_type discriminator; event payloads hand back a nested UserId
# object carrying both fields.
mention_id = getattr(mention, "id", None)
if isinstance(mention_id, str):
id_type = str(getattr(mention, "id_type", "") or "").lower()
if id_type == "open_id":
return mention_id, ""
if id_type == "user_id":
return "", mention_id
return "", ""
if mention_id is None:
return "", ""
return (
str(getattr(mention_id, "open_id", "") or ""),
str(getattr(mention_id, "user_id", "") or ""),
)
def _build_mentions_map(
mentions: Optional[Sequence[Any]],
bot: _FeishuBotIdentity,
) -> Dict[str, FeishuMentionRef]:
result: Dict[str, FeishuMentionRef] = {}
for mention in mentions or []:
key = str(getattr(mention, "key", "") or "")
if not key:
continue
if key == "@_all":
result[key] = FeishuMentionRef(is_all=True)
continue
open_id, user_id = _extract_mention_ids(mention)
name = str(getattr(mention, "name", "") or "").strip()
result[key] = FeishuMentionRef(
name=name,
open_id=open_id,
is_self=bot.matches(open_id=open_id, user_id=user_id, name=name),
)
return result
def _build_mention_hint(mentions: Sequence[FeishuMentionRef]) -> str:
parts: List[str] = []
seen: set = set()
for ref in mentions:
if ref.is_self:
continue
signature = (ref.is_all, ref.open_id, ref.name)
if signature in seen:
continue
seen.add(signature)
if ref.is_all:
parts.append("@all")
elif ref.open_id:
parts.append(f"{ref.name or 'unknown'} (open_id={ref.open_id})")
else:
parts.append(ref.name or "unknown")
return f"[Mentioned: {', '.join(parts)}]" if parts else ""
def _strip_edge_self_mentions(
text: str,
mentions: Sequence[FeishuMentionRef],
) -> str:
# Leading: strip consecutive self-mentions unconditionally.
# Trailing: strip only when followed by whitespace/terminal punct, so
# mid-sentence references ("don't @Bot again") stay intact.
# Leading word-boundary prevents @Al from eating @Alice.
if not text:
return text
self_names = [
f"@{ref.name or ref.open_id or 'user'}"
for ref in mentions
if ref.is_self
]
if not self_names:
return text
remaining = text.lstrip()
while True:
for nm in self_names:
if not remaining.startswith(nm):
continue
after = remaining[len(nm):]
if after and after[0] not in _MENTION_BOUNDARY_CHARS:
continue
remaining = after.lstrip()
break
else:
break
while True:
i = len(remaining)
while i > 0 and remaining[i - 1] in _TRAILING_TERMINAL_PUNCT:
i -= 1
body = remaining[:i]
tail = remaining[i:]
for nm in self_names:
if body.endswith(nm):
remaining = body[: -len(nm)].rstrip() + tail
break
else:
return remaining
def _run_official_feishu_ws_client(ws_client: Any, adapter: Any) -> None:
"""Run the official Lark WS client in its own thread-local event loop."""
import lark_oapi.ws.client as ws_client_module
@@ -1700,7 +1491,6 @@ class FeishuAdapter(BasePlatformAdapter):
if not self._client:
return SendResult(success=False, error="Not connected")
content = self.format_message(content)
try:
msg_type, payload = self._build_outbound_payload(content)
body = self._build_update_message_body(msg_type=msg_type, content=payload)
@@ -2680,22 +2470,13 @@ class FeishuAdapter(BasePlatformAdapter):
chat_type: str,
message_id: str,
) -> None:
text, inbound_type, media_urls, media_types, mentions = await self._extract_message_content(message)
if inbound_type == MessageType.TEXT:
text = _strip_edge_self_mentions(text, mentions)
if text.startswith("/"):
inbound_type = MessageType.COMMAND
# Guard runs post-strip so a pure "@Bot" message (stripped to "") is dropped.
text, inbound_type, media_urls, media_types = await self._extract_message_content(message)
if inbound_type == MessageType.TEXT and not text and not media_urls:
logger.debug("[Feishu] Ignoring empty text message id=%s", message_id)
logger.debug("[Feishu] Ignoring unsupported or empty message type: %s", getattr(message, "message_type", ""))
return
if inbound_type != MessageType.COMMAND:
hint = _build_mention_hint(mentions)
if hint:
text = f"{hint}\n\n{text}" if text else hint
if inbound_type == MessageType.TEXT and text.startswith("/"):
inbound_type = MessageType.COMMAND
reply_to_message_id = (
getattr(message, "parent_id", None)
@@ -3154,20 +2935,14 @@ class FeishuAdapter(BasePlatformAdapter):
# Message content extraction and resource download
# =========================================================================
async def _extract_message_content(
self, message: Any
) -> tuple[str, MessageType, List[str], List[str], List[FeishuMentionRef]]:
async def _extract_message_content(self, message: Any) -> tuple[str, MessageType, List[str], List[str]]:
"""Extract text and cached media from a normalized Feishu message."""
raw_content = getattr(message, "content", "") or ""
raw_type = getattr(message, "message_type", "") or ""
message_id = str(getattr(message, "message_id", "") or "")
logger.info("[Feishu] Received raw message type=%s message_id=%s", raw_type, message_id)
normalized = normalize_feishu_message(
message_type=raw_type,
raw_content=raw_content,
mentions=getattr(message, "mentions", None),
bot=self._bot_identity(),
)
normalized = normalize_feishu_message(message_type=raw_type, raw_content=raw_content)
media_urls, media_types = await self._download_feishu_message_resources(
message_id=message_id,
normalized=normalized,
@@ -3184,7 +2959,7 @@ class FeishuAdapter(BasePlatformAdapter):
if injected:
text = injected
return text, inbound_type, media_urls, media_types, list(normalized.mentions)
return text, inbound_type, media_urls, media_types
async def _download_feishu_message_resources(
self,
@@ -3448,22 +3223,10 @@ class FeishuAdapter(BasePlatformAdapter):
return "group"
async def _resolve_sender_profile(self, sender_id: Any) -> Dict[str, Optional[str]]:
"""Map Feishu's three-tier user IDs onto Hermes' SessionSource fields.
Preference order for the primary ``user_id`` field:
1. user_id (tenant-scoped, most stable requires permission scope)
2. open_id (app-scoped, always available different per bot app)
``user_id_alt`` carries the union_id (developer-scoped, stable across
all apps by the same developer). Session-key generation prefers
user_id_alt when present, so participant isolation stays stable even
if the primary ID is the app-scoped open_id.
"""
open_id = getattr(sender_id, "open_id", None) or None
user_id = getattr(sender_id, "user_id", None) or None
union_id = getattr(sender_id, "union_id", None) or None
# Prefer tenant-scoped user_id; fall back to app-scoped open_id.
primary_id = user_id or open_id
primary_id = open_id or user_id
display_name = await self._resolve_sender_name_from_api(primary_id or union_id)
return {
"user_id": primary_id,
@@ -3545,31 +3308,15 @@ class FeishuAdapter(BasePlatformAdapter):
body = getattr(parent, "body", None)
msg_type = getattr(parent, "msg_type", "") or ""
raw_content = getattr(body, "content", "") or ""
parent_mentions = getattr(parent, "mentions", None) if parent else None
text = self._extract_text_from_raw_content(
msg_type=msg_type,
raw_content=raw_content,
mentions=parent_mentions,
)
text = self._extract_text_from_raw_content(msg_type=msg_type, raw_content=raw_content)
self._message_text_cache[message_id] = text
return text
except Exception:
logger.warning("[Feishu] Failed to fetch parent message %s", message_id, exc_info=True)
return None
def _extract_text_from_raw_content(
self,
*,
msg_type: str,
raw_content: str,
mentions: Optional[Sequence[Any]] = None,
) -> Optional[str]:
normalized = normalize_feishu_message(
message_type=msg_type,
raw_content=raw_content,
mentions=mentions,
bot=self._bot_identity(),
)
def _extract_text_from_raw_content(self, *, msg_type: str, raw_content: str) -> Optional[str]:
normalized = normalize_feishu_message(message_type=msg_type, raw_content=raw_content)
if normalized.text_content:
return normalized.text_content
placeholder = normalized.metadata.get("placeholder_text") if isinstance(normalized.metadata, dict) else None
@@ -3639,10 +3386,10 @@ class FeishuAdapter(BasePlatformAdapter):
normalized = normalize_feishu_message(
message_type=getattr(message, "message_type", "") or "",
raw_content=raw_content,
mentions=getattr(message, "mentions", None),
bot=self._bot_identity(),
)
return self._post_mentions_bot(normalized.mentions)
if normalized.mentioned_ids:
return self._post_mentions_bot(normalized.mentioned_ids)
return False
def _is_self_sent_bot_message(self, event: Any) -> bool:
"""Return True only for Feishu events emitted by this Hermes bot."""
@@ -3662,37 +3409,30 @@ class FeishuAdapter(BasePlatformAdapter):
return False
def _message_mentions_bot(self, mentions: List[Any]) -> bool:
# IDs trump names: when both sides have open_id (or both user_id),
# match requires equal IDs. Name fallback only when either side
# lacks an ID.
"""Check whether any mention targets the configured or inferred bot identity."""
for mention in mentions:
mention_id = getattr(mention, "id", None)
mention_open_id = (getattr(mention_id, "open_id", None) or "").strip()
mention_user_id = (getattr(mention_id, "user_id", None) or "").strip()
mention_open_id = getattr(mention_id, "open_id", None)
mention_user_id = getattr(mention_id, "user_id", None)
mention_name = (getattr(mention, "name", None) or "").strip()
if mention_open_id and self._bot_open_id:
if mention_open_id == self._bot_open_id:
return True
continue # IDs differ — not the bot; skip name fallback.
if mention_user_id and self._bot_user_id:
if mention_user_id == self._bot_user_id:
return True
continue
if self._bot_open_id and mention_open_id == self._bot_open_id:
return True
if self._bot_user_id and mention_user_id == self._bot_user_id:
return True
if self._bot_name and mention_name == self._bot_name:
return True
return False
def _post_mentions_bot(self, mentions: List[FeishuMentionRef]) -> bool:
return any(m.is_self for m in mentions)
def _bot_identity(self) -> _FeishuBotIdentity:
return _FeishuBotIdentity(
open_id=self._bot_open_id,
user_id=self._bot_user_id,
name=self._bot_name,
)
def _post_mentions_bot(self, mentioned_ids: List[str]) -> bool:
if not mentioned_ids:
return False
if self._bot_open_id and self._bot_open_id in mentioned_ids:
return True
if self._bot_user_id and self._bot_user_id in mentioned_ids:
return True
return False
async def _hydrate_bot_identity(self) -> None:
"""Best-effort discovery of bot identity for precise group mention gating
@@ -3717,15 +3457,14 @@ class FeishuAdapter(BasePlatformAdapter):
# uses via probe_bot().
if not self._bot_open_id or not self._bot_name:
try:
req = (
BaseRequest.builder()
.http_method(HttpMethod.GET)
.uri("/open-apis/bot/v3/info")
.token_types({AccessTokenType.TENANT})
.build()
resp = await asyncio.to_thread(
self._client.request,
method="GET",
url="/open-apis/bot/v3/info",
body=None,
raw_response=True,
)
resp = await asyncio.to_thread(self._client.request, req)
content = getattr(getattr(resp, "raw", None), "content", None)
content = getattr(resp, "content", None)
if content:
payload = json.loads(content)
parsed = _parse_bot_response(payload) or {}
@@ -4473,9 +4212,6 @@ def probe_bot(app_id: str, app_secret: str, domain: str) -> Optional[dict]:
Uses lark_oapi SDK when available, falls back to raw HTTP otherwise.
Returns {"bot_name": ..., "bot_open_id": ...} on success, None on failure.
Note: ``bot_open_id`` here is the bot's app-scoped open_id — the same ID
that Feishu puts in @mention payloads. It is NOT the app_id.
"""
if FEISHU_AVAILABLE:
return _probe_bot_sdk(app_id, app_secret, domain)
@@ -4496,12 +4232,12 @@ def _build_onboard_client(app_id: str, app_secret: str, domain: str) -> Any:
def _parse_bot_response(data: dict) -> Optional[dict]:
# /bot/v3/info returns bot.app_name; legacy paths used bot_name — accept both.
"""Extract bot_name and bot_open_id from a /bot/v3/info response."""
if data.get("code") != 0:
return None
bot = data.get("bot") or data.get("data", {}).get("bot") or {}
return {
"bot_name": bot.get("app_name") or bot.get("bot_name"),
"bot_name": bot.get("bot_name"),
"bot_open_id": bot.get("open_id"),
}
@@ -4510,18 +4246,13 @@ def _probe_bot_sdk(app_id: str, app_secret: str, domain: str) -> Optional[dict]:
"""Probe bot info using lark_oapi SDK."""
try:
client = _build_onboard_client(app_id, app_secret, domain)
req = (
BaseRequest.builder()
.http_method(HttpMethod.GET)
.uri("/open-apis/bot/v3/info")
.token_types({AccessTokenType.TENANT})
.build()
resp = client.request(
method="GET",
url="/open-apis/bot/v3/info",
body=None,
raw_response=True,
)
resp = client.request(req)
content = getattr(getattr(resp, "raw", None), "content", None)
if content is None:
return None
return _parse_bot_response(json.loads(content))
return _parse_bot_response(json.loads(resp.content))
except Exception as exc:
logger.debug("[Feishu onboard] SDK probe failed: %s", exc)
return None
+4 -2
View File
@@ -26,8 +26,9 @@ from .adapter import ( # noqa: F401
# -- Onboard (QR-code scan-to-configure) -----------------------------------
from .onboard import ( # noqa: F401
BindStatus,
create_bind_task,
poll_bind_result,
build_connect_url,
qr_register,
)
from .crypto import decrypt_secret, generate_bind_key # noqa: F401
@@ -43,8 +44,9 @@ __all__ = [
"_ssrf_redirect_guard",
# onboard
"BindStatus",
"create_bind_task",
"poll_bind_result",
"build_connect_url",
"qr_register",
# crypto
"decrypt_secret",
"generate_bind_key",
-3
View File
@@ -535,9 +535,6 @@ class QQAdapter(BasePlatformAdapter):
quick_disconnect_count = 0
else:
backoff_idx += 1
if backoff_idx >= MAX_RECONNECT_ATTEMPTS:
logger.error("[%s] Max reconnect attempts reached (QQCloseError)", self._log_tag)
return
except Exception as exc:
if not self._running:
+21 -117
View File
@@ -1,10 +1,6 @@
"""
QQBot scan-to-configure (QR code onboard) module.
Mirrors the Feishu onboarding pattern: synchronous HTTP + a single public
entry-point ``qr_register()`` that handles the full flow (create task
display QR code poll decrypt credentials).
Calls the ``q.qq.com`` ``create_bind_task`` / ``poll_bind_result`` APIs to
generate a QR-code URL and poll for scan completion. On success the caller
receives the bot's *app_id*, *client_secret* (decrypted locally), and the
@@ -16,20 +12,18 @@ Reference: https://bot.q.qq.com/wiki/develop/api-v2/
from __future__ import annotations
import logging
import time
from enum import IntEnum
from typing import Optional, Tuple
from typing import Tuple
from urllib.parse import quote
from .constants import (
ONBOARD_API_TIMEOUT,
ONBOARD_CREATE_PATH,
ONBOARD_POLL_INTERVAL,
ONBOARD_POLL_PATH,
PORTAL_HOST,
QR_URL_TEMPLATE,
)
from .crypto import decrypt_secret, generate_bind_key
from .crypto import generate_bind_key
from .utils import get_api_headers
logger = logging.getLogger(__name__)
@@ -41,7 +35,7 @@ logger = logging.getLogger(__name__)
class BindStatus(IntEnum):
"""Status codes returned by ``_poll_bind_result``."""
"""Status codes returned by ``poll_bind_result``."""
NONE = 0
PENDING = 1
@@ -50,40 +44,18 @@ class BindStatus(IntEnum):
# ---------------------------------------------------------------------------
# QR rendering
# ---------------------------------------------------------------------------
try:
import qrcode as _qrcode_mod
except (ImportError, TypeError):
_qrcode_mod = None # type: ignore[assignment]
def _render_qr(url: str) -> bool:
"""Try to render a QR code in the terminal. Returns True if successful."""
if _qrcode_mod is None:
return False
try:
qr = _qrcode_mod.QRCode(
error_correction=_qrcode_mod.constants.ERROR_CORRECT_M,
border=2,
)
qr.add_data(url)
qr.make(fit=True)
qr.print_ascii(invert=True)
return True
except Exception:
return False
# ---------------------------------------------------------------------------
# Synchronous HTTP helpers (mirrors Feishu _post_registration pattern)
# Public API
# ---------------------------------------------------------------------------
def _create_bind_task(timeout: float = ONBOARD_API_TIMEOUT) -> Tuple[str, str]:
async def create_bind_task(
timeout: float = ONBOARD_API_TIMEOUT,
) -> Tuple[str, str]:
"""Create a bind task and return *(task_id, aes_key_base64)*.
The AES key is generated locally and sent to the server so it can
encrypt the bot credentials before returning them.
Raises:
RuntimeError: If the API returns a non-zero ``retcode``.
"""
@@ -92,8 +64,8 @@ def _create_bind_task(timeout: float = ONBOARD_API_TIMEOUT) -> Tuple[str, str]:
url = f"https://{PORTAL_HOST}{ONBOARD_CREATE_PATH}"
key = generate_bind_key()
with httpx.Client(timeout=timeout, follow_redirects=True) as client:
resp = client.post(url, json={"key": key}, headers=get_api_headers())
async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client:
resp = await client.post(url, json={"key": key}, headers=get_api_headers())
resp.raise_for_status()
data = resp.json()
@@ -108,7 +80,7 @@ def _create_bind_task(timeout: float = ONBOARD_API_TIMEOUT) -> Tuple[str, str]:
return task_id, key
def _poll_bind_result(
async def poll_bind_result(
task_id: str,
timeout: float = ONBOARD_API_TIMEOUT,
) -> Tuple[BindStatus, str, str, str]:
@@ -117,6 +89,12 @@ def _poll_bind_result(
Returns:
A 4-tuple of ``(status, bot_appid, bot_encrypt_secret, user_openid)``.
* ``bot_encrypt_secret`` is AES-256-GCM encrypted decrypt it with
:func:`~gateway.platforms.qqbot.crypto.decrypt_secret` using the
key from :func:`create_bind_task`.
* ``user_openid`` is the OpenID of the person who scanned the code
(available when ``status == COMPLETED``).
Raises:
RuntimeError: If the API returns a non-zero ``retcode``.
"""
@@ -124,8 +102,8 @@ def _poll_bind_result(
url = f"https://{PORTAL_HOST}{ONBOARD_POLL_PATH}"
with httpx.Client(timeout=timeout, follow_redirects=True) as client:
resp = client.post(url, json={"task_id": task_id}, headers=get_api_headers())
async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client:
resp = await client.post(url, json={"task_id": task_id}, headers=get_api_headers())
resp.raise_for_status()
data = resp.json()
@@ -144,77 +122,3 @@ def _poll_bind_result(
def build_connect_url(task_id: str) -> str:
"""Build the QR-code target URL for a given *task_id*."""
return QR_URL_TEMPLATE.format(task_id=quote(task_id))
# ---------------------------------------------------------------------------
# Public entry-point
# ---------------------------------------------------------------------------
_MAX_REFRESHES = 3
def qr_register(timeout_seconds: int = 600) -> Optional[dict]:
"""Run the QQBot scan-to-configure QR registration flow.
Mirrors ``feishu.qr_register()``: handles create display poll
decrypt in one call. Unexpected errors propagate to the caller.
:returns:
``{"app_id": ..., "client_secret": ..., "user_openid": ...}`` on
success, or ``None`` on failure / expiry / cancellation.
"""
deadline = time.monotonic() + timeout_seconds
for refresh_count in range(_MAX_REFRESHES + 1):
# ── Create bind task ──
try:
task_id, aes_key = _create_bind_task()
except Exception as exc:
logger.warning("[QQBot onboard] Failed to create bind task: %s", exc)
return None
url = build_connect_url(task_id)
# ── Display QR code + URL ──
print()
if _render_qr(url):
print(f" Scan the QR code above, or open this URL directly:\n {url}")
else:
print(f" Open this URL in QQ on your phone:\n {url}")
print(" Tip: pip install qrcode to display a scannable QR code here")
print()
# ── Poll loop ──
while time.monotonic() < deadline:
try:
status, app_id, encrypted_secret, user_openid = _poll_bind_result(task_id)
except Exception:
time.sleep(ONBOARD_POLL_INTERVAL)
continue
if status == BindStatus.COMPLETED:
client_secret = decrypt_secret(encrypted_secret, aes_key)
print()
print(f" QR scan complete! (App ID: {app_id})")
if user_openid:
print(f" Scanner's OpenID: {user_openid}")
return {
"app_id": app_id,
"client_secret": client_secret,
"user_openid": user_openid,
}
if status == BindStatus.EXPIRED:
if refresh_count >= _MAX_REFRESHES:
logger.warning("[QQBot onboard] QR code expired %d times — giving up", _MAX_REFRESHES)
return None
print(f"\n QR code expired, refreshing... ({refresh_count + 1}/{_MAX_REFRESHES})")
break # next for-loop iteration creates a new task
time.sleep(ONBOARD_POLL_INTERVAL)
else:
# deadline reached without completing
logger.warning("[QQBot onboard] Poll timed out after %ds", timeout_seconds)
return None
return None
+7 -57
View File
@@ -38,7 +38,6 @@ from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
ProcessingOutcome,
SendResult,
SUPPORTED_DOCUMENT_TYPES,
safe_url_for_log,
@@ -114,11 +113,6 @@ class SlackAdapter(BasePlatformAdapter):
# Cache for _fetch_thread_context results: cache_key → _ThreadContextCache
self._thread_context_cache: Dict[str, _ThreadContextCache] = {}
self._THREAD_CACHE_TTL = 60.0
# Track message IDs that should get reaction lifecycle (DMs / @mentions).
self._reacting_message_ids: set = set()
# Track active assistant thread status indicators so stop_typing can
# clear them (chat_id → thread_ts).
self._active_status_threads: Dict[str, str] = {}
async def connect(self) -> bool:
"""Connect to Slack via Socket Mode."""
@@ -368,7 +362,6 @@ class SlackAdapter(BasePlatformAdapter):
if not thread_ts:
return # Can only set status in a thread context
self._active_status_threads[chat_id] = thread_ts
try:
await self._get_client(chat_id).assistant_threads_setStatus(
channel_id=chat_id,
@@ -380,22 +373,6 @@ class SlackAdapter(BasePlatformAdapter):
# in an assistant-enabled context. Falls back to reactions.
logger.debug("[Slack] assistant.threads.setStatus failed: %s", e)
async def stop_typing(self, chat_id: str) -> None:
"""Clear the assistant thread status indicator."""
if not self._app:
return
thread_ts = self._active_status_threads.pop(chat_id, None)
if not thread_ts:
return
try:
await self._get_client(chat_id).assistant_threads_setStatus(
channel_id=chat_id,
thread_ts=thread_ts,
status="",
)
except Exception as e:
logger.debug("[Slack] assistant.threads.setStatus clear failed: %s", e)
def _dm_top_level_threads_as_sessions(self) -> bool:
"""Whether top-level Slack DMs get per-message session threads.
@@ -607,38 +584,6 @@ class SlackAdapter(BasePlatformAdapter):
logger.debug("[Slack] reactions.remove failed (%s): %s", emoji, e)
return False
def _reactions_enabled(self) -> bool:
"""Check if message reactions are enabled via config/env."""
return os.getenv("SLACK_REACTIONS", "true").lower() not in ("false", "0", "no")
async def on_processing_start(self, event: MessageEvent) -> None:
"""Add an in-progress reaction when message processing begins."""
if not self._reactions_enabled():
return
ts = getattr(event, "message_id", None)
if not ts or ts not in self._reacting_message_ids:
return
channel_id = getattr(event.source, "chat_id", None)
if channel_id:
await self._add_reaction(channel_id, ts, "eyes")
async def on_processing_complete(self, event: MessageEvent, outcome: ProcessingOutcome) -> None:
"""Swap the in-progress reaction for a final success/failure reaction."""
if not self._reactions_enabled():
return
ts = getattr(event, "message_id", None)
if not ts or ts not in self._reacting_message_ids:
return
self._reacting_message_ids.discard(ts)
channel_id = getattr(event.source, "chat_id", None)
if not channel_id:
return
await self._remove_reaction(channel_id, ts, "eyes")
if outcome == ProcessingOutcome.SUCCESS:
await self._add_reaction(channel_id, ts, "white_check_mark")
elif outcome == ProcessingOutcome.FAILURE:
await self._add_reaction(channel_id, ts, "x")
# ----- User identity resolution -----
async def _resolve_user_name(self, user_id: str, chat_id: str = "") -> str:
@@ -1268,12 +1213,17 @@ class SlackAdapter(BasePlatformAdapter):
# Only react when bot is directly addressed (DM or @mention).
# In listen-all channels (require_mention=false), reacting to every
# casual message would be noisy.
_should_react = (is_dm or is_mentioned) and self._reactions_enabled()
_should_react = is_dm or is_mentioned
if _should_react:
self._reacting_message_ids.add(ts)
await self._add_reaction(channel_id, ts, "eyes")
await self.handle_message(msg_event)
if _should_react:
await self._remove_reaction(channel_id, ts, "eyes")
await self._add_reaction(channel_id, ts, "white_check_mark")
# ----- Approval button support (Block Kit) -----
async def send_exec_approval(
-136
View File
@@ -508,11 +508,6 @@ class WeComAdapter(BasePlatformAdapter):
self._remember_chat_req_id(chat_id, self._payload_req_id(payload))
text, reply_text = self._extract_text(body)
# Strip leading @mention in group chats so slash commands like
# "@BotName /approve" are correctly recognized as "/approve".
# Mirrors what the Telegram adapter does (re.sub @botname).
if is_group and text:
text = re.sub(r"^@\S+\s*", "", text).strip()
media_urls, media_types = await self._extract_media(body)
message_type = self._derive_message_type(body, text, media_types)
has_reply_context = bool(reply_text and (text or media_urls))
@@ -1469,134 +1464,3 @@ class WeComAdapter(BasePlatformAdapter):
"name": chat_id,
"type": "group" if chat_id and chat_id.lower().startswith("group") else "dm",
}
# ------------------------------------------------------------------
# QR code scan flow for obtaining bot credentials
# ------------------------------------------------------------------
_QR_GENERATE_URL = "https://work.weixin.qq.com/ai/qc/generate"
_QR_QUERY_URL = "https://work.weixin.qq.com/ai/qc/query_result"
_QR_CODE_PAGE = "https://work.weixin.qq.com/ai/qc/gen?source=hermes&scode="
_QR_POLL_INTERVAL = 3 # seconds
_QR_POLL_TIMEOUT = 300 # 5 minutes
def qr_scan_for_bot_info(
*,
timeout_seconds: int = _QR_POLL_TIMEOUT,
) -> Optional[Dict[str, str]]:
"""Run the WeCom QR scan flow to obtain bot_id and secret.
Fetches a QR code from WeCom, renders it in the terminal, and polls
until the user scans it or the timeout expires.
Returns ``{"bot_id": ..., "secret": ...}`` on success, ``None`` on
failure or timeout.
Note: the ``work.weixin.qq.com/ai/qc/{generate,query_result}`` endpoints
used here are not part of WeCom's public developer API — they back the
admin-console web UI's bot-creation flow and may change without notice.
The same pattern is used by the feishu/dingtalk QR setup wizards.
"""
try:
import urllib.request
import urllib.parse
except ImportError: # pragma: no cover
logger.error("urllib is required for WeCom QR scan")
return None
generate_url = f"{_QR_GENERATE_URL}?source=hermes"
# ── Step 1: Fetch QR code ──
print(" Connecting to WeCom...", end="", flush=True)
try:
req = urllib.request.Request(generate_url, headers={"User-Agent": "HermesAgent/1.0"})
with urllib.request.urlopen(req, timeout=15) as resp:
raw = json.loads(resp.read().decode("utf-8"))
except Exception as exc:
logger.error("WeCom QR: failed to fetch QR code: %s", exc)
print(f" failed: {exc}")
return None
data = raw.get("data") or {}
scode = str(data.get("scode") or "").strip()
auth_url = str(data.get("auth_url") or "").strip()
if not scode or not auth_url:
logger.error("WeCom QR: unexpected response format: %s", raw)
print(" failed: unexpected response format")
return None
print(" done.")
# ── Step 2: Render QR code in terminal ──
print()
qr_rendered = False
try:
import qrcode as _qrcode
qr = _qrcode.QRCode()
qr.add_data(auth_url)
qr.make(fit=True)
qr.print_ascii(invert=True)
qr_rendered = True
except ImportError:
pass
except Exception:
pass
page_url = f"{_QR_CODE_PAGE}{urllib.parse.quote(scode)}"
if qr_rendered:
print(f"\n Scan the QR code above, or open this URL directly:\n {page_url}")
else:
print(f" Open this URL in WeCom on your phone:\n\n {page_url}\n")
print(" Tip: pip install qrcode to display a scannable QR code here next time")
print()
print(" Fetching configuration results...", end="", flush=True)
# ── Step 3: Poll for result ──
import time
deadline = time.time() + timeout_seconds
query_url = f"{_QR_QUERY_URL}?scode={urllib.parse.quote(scode)}"
poll_count = 0
while time.time() < deadline:
try:
req = urllib.request.Request(query_url, headers={"User-Agent": "HermesAgent/1.0"})
with urllib.request.urlopen(req, timeout=10) as resp:
result = json.loads(resp.read().decode("utf-8"))
except Exception as exc:
logger.debug("WeCom QR poll error: %s", exc)
time.sleep(_QR_POLL_INTERVAL)
continue
poll_count += 1
# Print a dot on every poll so progress is visible within 3s.
print(".", end="", flush=True)
result_data = result.get("data") or {}
status = str(result_data.get("status") or "").lower()
if status == "success":
print() # newline after "Fetching configuration results..." dots
bot_info = result_data.get("bot_info") or {}
bot_id = str(bot_info.get("botid") or bot_info.get("bot_id") or "").strip()
secret = str(bot_info.get("secret") or "").strip()
if bot_id and secret:
return {"bot_id": bot_id, "secret": secret}
logger.warning(
"WeCom QR: scan reported success but bot_info missing or incomplete: %s",
result_data,
)
print(
" QR scan reported success but no bot credentials were returned.\n"
" This usually means the bot was not actually created on the WeCom side.\n"
" Falling back to manual credential entry."
)
return None
time.sleep(_QR_POLL_INTERVAL)
print() # newline after dots
print(f" QR scan timed out ({timeout_seconds // 60} minutes). Please try again.")
return None
+24 -189
View File
@@ -710,26 +710,7 @@ class GatewayRunner:
self._session_db = SessionDB()
except Exception as e:
logger.debug("SQLite session store not available: %s", e)
# Opportunistic state.db maintenance: prune ended sessions older
# than sessions.retention_days + optional VACUUM. Tracks last-run
# in state_meta so it only actually executes once per
# sessions.min_interval_hours. Gateway is long-lived so blocking
# a few seconds once per day is acceptable; failures are logged
# but never raised.
if self._session_db is not None:
try:
from hermes_cli.config import load_config as _load_full_config
_sess_cfg = (_load_full_config().get("sessions") or {})
if _sess_cfg.get("auto_prune", False):
self._session_db.maybe_auto_prune_and_vacuum(
retention_days=int(_sess_cfg.get("retention_days", 90)),
min_interval_hours=int(_sess_cfg.get("min_interval_hours", 24)),
vacuum=bool(_sess_cfg.get("vacuum_after_prune", True)),
)
except Exception as exc:
logger.debug("state.db auto-maintenance skipped: %s", exc)
# DM pairing store for code-based user authorization
from gateway.pairing import PairingStore
self.pairing_store = PairingStore()
@@ -2687,9 +2668,8 @@ class GatewayRunner:
except Exception as _e:
logger.debug("SessionDB close error: %s", _e)
from gateway.status import remove_pid_file, release_gateway_runtime_lock
from gateway.status import remove_pid_file
remove_pid_file()
release_gateway_runtime_lock()
# Write a clean-shutdown marker so the next startup knows this
# wasn't a crash. suspend_recently_active() only needs to run
@@ -3486,72 +3466,22 @@ class GatewayRunner:
# Check for commands
command = event.get_command()
from hermes_cli.commands import (
GATEWAY_KNOWN_COMMANDS,
is_gateway_known_command,
resolve_command as _resolve_cmd,
)
# Resolve aliases to canonical name so dispatch and hook names
# don't depend on the exact alias the user typed.
_cmd_def = _resolve_cmd(command) if command else None
canonical = _cmd_def.name if _cmd_def else command
# Fire the ``command:<canonical>`` hook for any recognized slash
# command — built-in OR plugin-registered. Handlers can return a
# dict with ``{"decision": "deny" | "handled" | "rewrite", ...}``
# to intercept dispatch before core handling runs. This replaces
# the previous fire-and-forget emit(): return values are now
# honored, but handlers that return nothing behave exactly as
# before (telemetry-style hooks keep working).
if command and is_gateway_known_command(canonical):
raw_args = event.get_command_args().strip()
hook_ctx = {
# Emit command:* hook for any recognized slash command.
# GATEWAY_KNOWN_COMMANDS is derived from the central COMMAND_REGISTRY
# in hermes_cli/commands.py — no hardcoded set to maintain here.
from hermes_cli.commands import GATEWAY_KNOWN_COMMANDS, resolve_command as _resolve_cmd
if command and command in GATEWAY_KNOWN_COMMANDS:
await self.hooks.emit(f"command:{command}", {
"platform": source.platform.value if source.platform else "",
"user_id": source.user_id,
"command": canonical,
"raw_command": command,
"args": raw_args,
"raw_args": raw_args,
}
try:
hook_results = await self.hooks.emit_collect(
f"command:{canonical}", hook_ctx
)
except Exception as _hook_err:
logger.debug(
"command:%s hook dispatch failed (non-fatal): %s",
canonical, _hook_err,
)
hook_results = []
"command": command,
"args": event.get_command_args().strip(),
})
for hook_result in hook_results:
if not isinstance(hook_result, dict):
continue
decision = str(hook_result.get("decision", "")).strip().lower()
if not decision or decision == "allow":
continue
if decision == "deny":
message = hook_result.get("message")
if isinstance(message, str) and message:
return message
return f"Command `/{command}` was blocked by a hook."
if decision == "handled":
message = hook_result.get("message")
return message if isinstance(message, str) and message else None
if decision == "rewrite":
new_command = str(
hook_result.get("command_name", "")
).strip().lstrip("/")
if not new_command:
continue
new_args = str(hook_result.get("raw_args", "")).strip()
event.text = f"/{new_command} {new_args}".strip()
command = event.get_command()
_cmd_def = _resolve_cmd(command) if command else None
canonical = _cmd_def.name if _cmd_def else command
break
# Resolve aliases to canonical name so dispatch only checks canonicals.
_cmd_def = _resolve_cmd(command) if command else None
canonical = _cmd_def.name if _cmd_def else command
if canonical == "new":
return await self._handle_reset_command(event)
@@ -4971,11 +4901,6 @@ class GatewayRunner:
# the configured default instead of the previously switched model.
self._session_model_overrides.pop(session_key, None)
# Clear session-scoped dangerous-command approvals and /yolo state.
# /new is a conversation-boundary operation — approval state from the
# previous conversation must not survive the reset.
self._clear_session_boundary_security_state(session_key)
# Fire plugin on_session_finalize hook (session boundary)
try:
from hermes_cli.plugins import invoke_hook as _invoke_hook
@@ -5484,7 +5409,6 @@ class GatewayRunner:
try:
providers = list_authenticated_providers(
current_provider=current_provider,
current_base_url=current_base_url,
user_providers=user_provs,
custom_providers=custom_provs,
max_models=50,
@@ -5596,7 +5520,6 @@ class GatewayRunner:
try:
providers = list_authenticated_providers(
current_provider=current_provider,
current_base_url=current_base_url,
user_providers=user_provs,
custom_providers=custom_provs,
max_models=5,
@@ -6533,11 +6456,6 @@ class GatewayRunner:
session_id=task_id,
platform=platform_key,
user_id=source.user_id,
user_name=source.user_name,
chat_id=source.chat_id,
chat_name=source.chat_name,
chat_type=source.chat_type,
thread_id=source.thread_id,
session_db=self._session_db,
fallback_model=self._fallback_model,
)
@@ -7224,7 +7142,6 @@ class GatewayRunner:
new_entry = self.session_store.switch_session(session_key, target_id)
if not new_entry:
return "Failed to switch session."
self._clear_session_boundary_security_state(session_key)
# Get the title for confirmation
title = self._session_db.get_session_title(target_id) or name
@@ -7299,7 +7216,6 @@ class GatewayRunner:
tool_calls=msg.get("tool_calls"),
tool_call_id=msg.get("tool_call_id"),
reasoning=msg.get("reasoning"),
reasoning_content=msg.get("reasoning_content"),
)
except Exception:
pass # Best-effort copy
@@ -7314,7 +7230,6 @@ class GatewayRunner:
new_entry = self.session_store.switch_session(session_key, new_session_id)
if not new_entry:
return "Branch created but failed to switch to it."
self._clear_session_boundary_security_state(session_key)
# Evict any cached agent for this session
self._evict_cached_agent(session_key)
@@ -7705,14 +7620,13 @@ class GatewayRunner:
from hermes_cli.debug import (
_capture_dump, collect_debug_report,
upload_to_pastebin, _schedule_auto_delete,
_GATEWAY_PRIVACY_NOTICE, _best_effort_sweep_expired_pastes,
_GATEWAY_PRIVACY_NOTICE,
)
loop = asyncio.get_running_loop()
# Run blocking I/O (dump capture, log reads, uploads) in a thread.
def _collect_and_upload():
_best_effort_sweep_expired_pastes()
dump_text = _capture_dump()
report = collect_debug_report(log_lines=200, dump_text=dump_text)
@@ -8665,12 +8579,7 @@ class GatewayRunner:
override = self._session_model_overrides.get(session_key)
return override is not None and override.get("model") == agent_model
def _release_running_agent_state(
self,
session_key: str,
*,
run_generation: Optional[int] = None,
) -> bool:
def _release_running_agent_state(self, session_key: str) -> None:
"""Pop ALL per-running-agent state entries for ``session_key``.
Replaces ad-hoc ``del self._running_agents[key]`` calls scattered
@@ -8686,48 +8595,13 @@ class GatewayRunner:
across turns (``_session_model_overrides``, ``_voice_mode``,
``_pending_approvals``, ``_update_prompt_pending``) is NOT
touched here those have their own lifecycles.
When ``run_generation`` is provided, only clear the slot if that
generation is still current for the session. This prevents an
older async run whose generation was bumped by /stop or /new from
clobbering a newer run's state during its own unwind. Returns
True when the slot was cleared, False when an ownership guard
blocked it.
"""
if not session_key:
return False
if run_generation is not None and not self._is_session_run_current(
session_key, run_generation
):
return False
return
self._running_agents.pop(session_key, None)
self._running_agents_ts.pop(session_key, None)
if hasattr(self, "_busy_ack_ts"):
self._busy_ack_ts.pop(session_key, None)
return True
def _clear_session_boundary_security_state(self, session_key: str) -> None:
"""Clear approval state that must not survive a real conversation switch."""
if not session_key:
return
pending_approvals = getattr(self, "_pending_approvals", None)
if isinstance(pending_approvals, dict):
pending_approvals.pop(session_key, None)
try:
from tools.approval import clear_session as _clear_approval_session
except Exception:
return
try:
_clear_approval_session(session_key)
except Exception as e:
logger.debug(
"Failed to clear approval state for session boundary %s: %s",
session_key,
e,
)
def _begin_session_run_generation(self, session_key: str) -> int:
"""Claim a fresh run generation token for ``session_key``.
@@ -9824,11 +9698,6 @@ class GatewayRunner:
session_id=session_id,
platform=platform_key,
user_id=source.user_id,
user_name=source.user_name,
chat_id=source.chat_id,
chat_name=source.chat_name,
chat_type=source.chat_type,
thread_id=source.thread_id,
gateway_session_key=session_key,
session_db=self._session_db,
fallback_model=self._fallback_model,
@@ -10266,24 +10135,10 @@ class GatewayRunner:
# Wait for agent to be created
while agent_holder[0] is None:
await asyncio.sleep(0.05)
if not session_key:
return
# Only promote the sentinel to the real agent if this run is still
# current. If /stop or /new bumped the generation while we were
# spinning up, leave the newer run's slot alone — we'll be
# discarded by the stale-result check in _handle_message_with_agent.
if run_generation is not None and not self._is_session_run_current(
session_key, run_generation
):
logger.info(
"Skipping stale agent promotion for %s — generation %s is no longer current",
(session_key or "")[:20],
run_generation,
)
return
self._running_agents[session_key] = agent_holder[0]
if self._draining:
self._update_runtime_status("draining")
if session_key:
self._running_agents[session_key] = agent_holder[0]
if self._draining:
self._update_runtime_status("draining")
tracking_task = asyncio.create_task(track_agent())
@@ -10789,14 +10644,7 @@ class GatewayRunner:
# Clean up tracking
tracking_task.cancel()
if session_key:
# Only release the slot if this run's generation still owns
# it. A /stop or /new that bumped the generation while we
# were unwinding has already installed its own state; this
# guard prevents an old run from clobbering it on the way
# out.
self._release_running_agent_state(
session_key, run_generation=run_generation
)
self._release_running_agent_state(session_key)
if self._draining:
self._update_runtime_status("draining")
@@ -10916,13 +10764,7 @@ async def start_gateway(config: Optional[GatewayConfig] = None, replace: bool =
# The PID file is scoped to HERMES_HOME, so future multi-profile
# setups (each profile using a distinct HERMES_HOME) will naturally
# allow concurrent instances without tripping this guard.
from gateway.status import (
acquire_gateway_runtime_lock,
get_running_pid,
release_gateway_runtime_lock,
remove_pid_file,
terminate_pid,
)
from gateway.status import get_running_pid, remove_pid_file, terminate_pid
existing_pid = get_running_pid()
if existing_pid is not None and existing_pid != os.getpid():
if replace:
@@ -11135,21 +10977,14 @@ async def start_gateway(config: Optional[GatewayConfig] = None, replace: bool =
"Exiting to avoid double-running.", _current_pid
)
return False
if not acquire_gateway_runtime_lock():
logger.error(
"Gateway runtime lock is already held by another instance. Exiting."
)
return False
try:
write_pid_file()
except FileExistsError:
release_gateway_runtime_lock()
logger.error(
"PID file race lost to another gateway instance. Exiting."
)
return False
atexit.register(remove_pid_file)
atexit.register(release_gateway_runtime_lock)
# Start the gateway
success = await runner.start()
+1 -6
View File
@@ -80,7 +80,7 @@ class SessionSource:
user_name: Optional[str] = None
thread_id: Optional[str] = None # For forum topics, Discord threads, etc.
chat_topic: Optional[str] = None # Channel topic/description (Discord, Slack)
user_id_alt: Optional[str] = None # Platform-specific stable alt ID (Signal UUID, Feishu union_id)
user_id_alt: Optional[str] = None # Signal UUID (alternative to phone number)
chat_id_alt: Optional[str] = None # Signal group internal ID
is_bot: bool = False # True when the message author is a bot/webhook (Discord)
@@ -1147,10 +1147,6 @@ class SessionStore:
tool_name=message.get("tool_name"),
tool_calls=message.get("tool_calls"),
tool_call_id=message.get("tool_call_id"),
reasoning=message.get("reasoning") if message.get("role") == "assistant" else None,
reasoning_content=message.get("reasoning_content") if message.get("role") == "assistant" else None,
reasoning_details=message.get("reasoning_details") if message.get("role") == "assistant" else None,
codex_reasoning_items=message.get("codex_reasoning_items") if message.get("role") == "assistant" else None,
)
except Exception as e:
logger.debug("Session DB operation failed: %s", e)
@@ -1180,7 +1176,6 @@ class SessionStore:
tool_calls=msg.get("tool_calls"),
tool_call_id=msg.get("tool_call_id"),
reasoning=msg.get("reasoning") if role == "assistant" else None,
reasoning_content=msg.get("reasoning_content") if role == "assistant" else None,
reasoning_details=msg.get("reasoning_details") if role == "assistant" else None,
codex_reasoning_items=msg.get("codex_reasoning_items") if role == "assistant" else None,
)
+27 -174
View File
@@ -22,18 +22,11 @@ from pathlib import Path
from hermes_constants import get_hermes_home
from typing import Any, Optional
if sys.platform == "win32":
import msvcrt
else:
import fcntl
_GATEWAY_KIND = "hermes-gateway"
_RUNTIME_STATUS_FILE = "gateway_state.json"
_LOCKS_DIRNAME = "gateway-locks"
_IS_WINDOWS = sys.platform == "win32"
_UNSET = object()
_GATEWAY_LOCK_FILENAME = "gateway.lock"
_gateway_lock_handle = None
def _get_pid_path() -> Path:
@@ -42,14 +35,6 @@ def _get_pid_path() -> Path:
return home / "gateway.pid"
def _get_gateway_lock_path(pid_path: Optional[Path] = None) -> Path:
"""Return the path to the runtime gateway lock file."""
if pid_path is not None:
return pid_path.with_name(_GATEWAY_LOCK_FILENAME)
home = get_hermes_home()
return home / _GATEWAY_LOCK_FILENAME
def _get_runtime_status_path() -> Path:
"""Return the persisted runtime health/status file path."""
return _get_pid_path().with_name(_RUNTIME_STATUS_FILE)
@@ -136,7 +121,6 @@ def _looks_like_gateway_process(pid: int) -> bool:
"hermes_cli.main gateway",
"hermes_cli/main.py gateway",
"hermes gateway",
"hermes-gateway",
"gateway/run.py",
)
return any(pattern in cmdline for pattern in patterns)
@@ -228,135 +212,16 @@ def _read_pid_record(pid_path: Optional[Path] = None) -> Optional[dict]:
return None
def _read_gateway_lock_record(lock_path: Optional[Path] = None) -> Optional[dict[str, Any]]:
return _read_pid_record(lock_path or _get_gateway_lock_path())
def _pid_from_record(record: Optional[dict[str, Any]]) -> Optional[int]:
if not record:
return None
try:
return int(record["pid"])
except (KeyError, TypeError, ValueError):
return None
def _cleanup_invalid_pid_path(pid_path: Path, *, cleanup_stale: bool) -> None:
"""Delete a stale gateway PID file (and its sibling lock metadata).
Called from ``get_running_pid()`` after the runtime lock has already been
confirmed inactive, so the on-disk metadata is known to belong to a dead
process. Unlike ``remove_pid_file()`` (which defensively refuses to delete
a PID file whose ``pid`` field differs from ``os.getpid()`` to protect
``--replace`` handoffs), this path force-unlinks both files so the next
startup sees a clean slate.
"""
if not cleanup_stale:
return
try:
pid_path.unlink(missing_ok=True)
if pid_path == _get_pid_path():
remove_pid_file()
else:
pid_path.unlink(missing_ok=True)
except Exception:
pass
try:
_get_gateway_lock_path(pid_path).unlink(missing_ok=True)
except Exception:
pass
def _write_gateway_lock_record(handle) -> None:
handle.seek(0)
handle.truncate()
json.dump(_build_pid_record(), handle)
handle.flush()
try:
os.fsync(handle.fileno())
except OSError:
pass
def _try_acquire_file_lock(handle) -> bool:
try:
if _IS_WINDOWS:
handle.seek(0, os.SEEK_END)
if handle.tell() == 0:
handle.write("\n")
handle.flush()
handle.seek(0)
msvcrt.locking(handle.fileno(), msvcrt.LK_NBLCK, 1)
else:
fcntl.flock(handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
return True
except (BlockingIOError, OSError):
return False
def _release_file_lock(handle) -> None:
try:
if _IS_WINDOWS:
handle.seek(0)
msvcrt.locking(handle.fileno(), msvcrt.LK_UNLCK, 1)
else:
fcntl.flock(handle.fileno(), fcntl.LOCK_UN)
except OSError:
pass
def acquire_gateway_runtime_lock() -> bool:
"""Claim the cross-process runtime lock for the gateway.
Unlike the PID file, the lock is owned by the live process itself. If the
process dies abruptly, the OS releases the lock automatically.
"""
global _gateway_lock_handle
if _gateway_lock_handle is not None:
return True
path = _get_gateway_lock_path()
path.parent.mkdir(parents=True, exist_ok=True)
handle = open(path, "a+", encoding="utf-8")
if not _try_acquire_file_lock(handle):
handle.close()
return False
_write_gateway_lock_record(handle)
_gateway_lock_handle = handle
return True
def release_gateway_runtime_lock() -> None:
"""Release the gateway runtime lock when owned by this process."""
global _gateway_lock_handle
handle = _gateway_lock_handle
if handle is None:
return
_gateway_lock_handle = None
_release_file_lock(handle)
try:
handle.close()
except OSError:
pass
def is_gateway_runtime_lock_active(lock_path: Optional[Path] = None) -> bool:
"""Return True when some process currently owns the gateway runtime lock."""
global _gateway_lock_handle
resolved_lock_path = lock_path or _get_gateway_lock_path()
if _gateway_lock_handle is not None and resolved_lock_path == _get_gateway_lock_path():
return True
if not resolved_lock_path.exists():
return False
handle = open(resolved_lock_path, "a+", encoding="utf-8")
try:
if _try_acquire_file_lock(handle):
_release_file_lock(handle)
return False
return True
finally:
try:
handle.close()
except OSError:
pass
def write_pid_file() -> None:
@@ -496,8 +361,7 @@ def acquire_scoped_lock(scope: str, identity: str, metadata: Optional[dict[str,
if not stale:
try:
os.kill(existing_pid, 0)
except (ProcessLookupError, PermissionError, OSError):
# Windows raises OSError with WinError 87 for invalid pid check
except (ProcessLookupError, PermissionError):
stale = True
else:
current_start = _get_process_start_time(existing_pid)
@@ -719,46 +583,35 @@ def get_running_pid(
Cleans up stale PID files automatically.
"""
resolved_pid_path = pid_path or _get_pid_path()
resolved_lock_path = _get_gateway_lock_path(resolved_pid_path)
lock_active = is_gateway_runtime_lock_active(resolved_lock_path)
if not lock_active:
record = _read_pid_record(resolved_pid_path)
if not record:
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
primary_record = _read_pid_record(resolved_pid_path)
fallback_record = _read_gateway_lock_record(resolved_lock_path)
try:
pid = int(record["pid"])
except (KeyError, TypeError, ValueError):
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
for record in (primary_record, fallback_record):
pid = _pid_from_record(record)
if pid is None:
continue
try:
os.kill(pid, 0) # signal 0 = existence check, no actual signal sent
except (ProcessLookupError, PermissionError):
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
try:
os.kill(pid, 0) # signal 0 = existence check, no actual signal sent
except ProcessLookupError:
continue
except PermissionError:
# The process exists but belongs to another user/service scope.
# With the runtime lock still held, prefer keeping it visible
# rather than deleting the PID file as "stale".
if _record_looks_like_gateway(record):
return pid
continue
except OSError:
# Windows raises OSError with WinError 87 for an invalid pid
# (process is definitely gone). Treat as "process doesn't exist".
continue
recorded_start = record.get("start_time")
current_start = _get_process_start_time(pid)
if recorded_start is not None and current_start is not None and current_start != recorded_start:
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
recorded_start = record.get("start_time")
current_start = _get_process_start_time(pid)
if recorded_start is not None and current_start is not None and current_start != recorded_start:
continue
if not _looks_like_gateway_process(pid):
if not _record_looks_like_gateway(record):
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
if _looks_like_gateway_process(pid) or _record_looks_like_gateway(record):
return pid
_cleanup_invalid_pid_path(resolved_pid_path, cleanup_stale=cleanup_stale)
return None
return pid
def is_gateway_running(
-1
View File
@@ -1 +0,0 @@
"""Hermes Agent — The self-improving AI agent."""
-5
View File
@@ -1,5 +0,0 @@
"""Allow running the ACP adapter as ``python -m hermes_agent.acp``."""
from hermes_agent.acp.entry import main
main()
+6 -26
View File
@@ -72,8 +72,6 @@ DEFAULT_QWEN_BASE_URL = "https://portal.qwen.ai/v1"
DEFAULT_GITHUB_MODELS_BASE_URL = "https://api.githubcopilot.com"
DEFAULT_COPILOT_ACP_BASE_URL = "acp://copilot"
DEFAULT_OLLAMA_CLOUD_BASE_URL = "https://ollama.com/v1"
STEPFUN_STEP_PLAN_INTL_BASE_URL = "https://api.stepfun.ai/step_plan/v1"
STEPFUN_STEP_PLAN_CN_BASE_URL = "https://api.stepfun.com/step_plan/v1"
CODEX_OAUTH_CLIENT_ID = "app_EMoamEEZ73f0CkXaXp7hrann"
CODEX_OAUTH_TOKEN_URL = "https://auth.openai.com/oauth/token"
CODEX_ACCESS_TOKEN_REFRESH_SKEW_SECONDS = 120
@@ -170,11 +168,8 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
id="kimi-coding",
name="Kimi / Moonshot",
auth_type="api_key",
# Legacy platform.moonshot.ai keys use this endpoint (OpenAI-compat).
# sk-kimi- (Kimi Code) keys are auto-redirected to api.kimi.com/coding
# by _resolve_kimi_base_url() below.
inference_base_url="https://api.moonshot.ai/v1",
api_key_env_vars=("KIMI_API_KEY", "KIMI_CODING_API_KEY"),
api_key_env_vars=("KIMI_API_KEY",),
base_url_env_var="KIMI_BASE_URL",
),
"kimi-coding-cn": ProviderConfig(
@@ -184,14 +179,6 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
inference_base_url="https://api.moonshot.cn/v1",
api_key_env_vars=("KIMI_CN_API_KEY",),
),
"stepfun": ProviderConfig(
id="stepfun",
name="StepFun Step Plan",
auth_type="api_key",
inference_base_url=STEPFUN_STEP_PLAN_INTL_BASE_URL,
api_key_env_vars=("STEPFUN_API_KEY",),
base_url_env_var="STEPFUN_BASE_URL",
),
"arcee": ProviderConfig(
id="arcee",
name="Arcee AI",
@@ -214,7 +201,6 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
auth_type="api_key",
inference_base_url="https://api.anthropic.com",
api_key_env_vars=("ANTHROPIC_API_KEY", "ANTHROPIC_TOKEN", "CLAUDE_CODE_OAUTH_TOKEN"),
base_url_env_var="ANTHROPIC_BASE_URL",
),
"alibaba": ProviderConfig(
id="alibaba",
@@ -354,16 +340,10 @@ def get_anthropic_key() -> str:
# =============================================================================
# Kimi Code (kimi.com/code) issues keys prefixed "sk-kimi-" that only work
# on api.kimi.com/coding. Legacy keys from platform.moonshot.ai work on
# api.moonshot.ai/v1 (the old default). Auto-detect when user hasn't set
# on api.kimi.com/coding/v1. Legacy keys from platform.moonshot.ai work on
# api.moonshot.ai/v1 (the default). Auto-detect when user hasn't set
# KIMI_BASE_URL explicitly.
#
# Note: the base URL intentionally has NO /v1 suffix. The /coding endpoint
# speaks the Anthropic Messages protocol, and the anthropic SDK appends
# "/v1/messages" internally — so "/coding" + SDK suffix → "/coding/v1/messages"
# (the correct target). Using "/coding/v1" here would produce
# "/coding/v1/v1/messages" (a 404).
KIMI_CODE_BASE_URL = "https://api.kimi.com/coding"
KIMI_CODE_BASE_URL = "https://api.kimi.com/coding/v1"
def _resolve_kimi_base_url(api_key: str, default_url: str, env_override: str) -> str:
@@ -1003,7 +983,6 @@ def resolve_provider(
"x-ai": "xai", "x.ai": "xai", "grok": "xai",
"kimi": "kimi-coding", "kimi-for-coding": "kimi-coding", "moonshot": "kimi-coding",
"kimi-cn": "kimi-coding-cn", "moonshot-cn": "kimi-coding-cn",
"step": "stepfun", "stepfun-coding-plan": "stepfun",
"arcee-ai": "arcee", "arceeai": "arcee",
"minimax-china": "minimax-cn", "minimax_cn": "minimax-cn",
"claude": "anthropic", "claude-code": "anthropic",
@@ -3396,7 +3375,7 @@ def _login_nous(args, pconfig: ProviderConfig) -> None:
)
from hermes_cli.models import (
_PROVIDER_MODELS, get_pricing_for_provider,
_PROVIDER_MODELS, get_pricing_for_provider, filter_nous_free_models,
check_nous_free_tier, partition_nous_models_by_tier,
)
model_ids = _PROVIDER_MODELS.get("nous", [])
@@ -3405,6 +3384,7 @@ def _login_nous(args, pconfig: ProviderConfig) -> None:
unavailable_models: list = []
if model_ids:
pricing = get_pricing_for_provider("nous")
model_ids = filter_nous_free_models(model_ids, pricing)
free_tier = check_nous_free_tier()
if free_tier:
model_ids, unavailable_models = partition_nous_models_by_tier(
+1 -1
View File
@@ -249,7 +249,7 @@ def _scan_workspace_state(source_dir: Path) -> list[tuple[Path, str]]:
state_path = child / state_name
if state_path.exists():
kind = "directory" if state_path.is_dir() else "file"
rel = state_path.relative_to(source_dir).as_posix()
rel = state_path.relative_to(source_dir)
findings.append((state_path, f"Workspace {kind}: {rel}"))
return findings
-65
View File
@@ -260,26 +260,6 @@ GATEWAY_KNOWN_COMMANDS: frozenset[str] = frozenset(
)
def is_gateway_known_command(name: str | None) -> bool:
"""Return True if ``name`` resolves to a gateway-dispatchable slash command.
This covers both built-in commands (``GATEWAY_KNOWN_COMMANDS`` derived
from ``COMMAND_REGISTRY``) and plugin-registered commands, which are
looked up lazily so importing this module never forces plugin
discovery. Gateway code uses this to decide whether to emit
``command:<name>`` hooks plugin commands get the same lifecycle
events as built-ins.
"""
if not name:
return False
if name in GATEWAY_KNOWN_COMMANDS:
return True
for plugin_name, _description, _args_hint in _iter_plugin_command_entries():
if plugin_name == name:
return True
return False
# Commands with explicit Level-2 running-agent handlers in gateway/run.py.
# Listed here for introspection / tests; semantically a subset of
# "all resolvable commands" — which is the real bypass set (see
@@ -391,47 +371,12 @@ def gateway_help_lines() -> list[str]:
return lines
def _iter_plugin_command_entries() -> list[tuple[str, str, str]]:
"""Yield (name, description, args_hint) tuples for all plugin slash commands.
Plugin commands are registered via
:func:`hermes_cli.plugins.PluginContext.register_command`. They behave
like ``CommandDef`` entries for gateway surfacing: they appear in the
Telegram command menu, in Slack's ``/hermes`` subcommand mapping, and
(via :func:`gateway.platforms.discord._register_slash_commands`) in
Discord's native slash command picker.
Lookup is lazy so importing this module never forces plugin discovery
(which can trigger filesystem scans and environment-dependent
behavior).
"""
try:
from hermes_cli.plugins import get_plugin_commands
except Exception:
return []
try:
commands = get_plugin_commands() or {}
except Exception:
return []
entries: list[tuple[str, str, str]] = []
for name, meta in commands.items():
if not isinstance(name, str) or not isinstance(meta, dict):
continue
description = str(meta.get("description") or f"Run /{name}")
args_hint = str(meta.get("args_hint") or "").strip()
entries.append((name, description, args_hint))
return entries
def telegram_bot_commands() -> list[tuple[str, str]]:
"""Return (command_name, description) pairs for Telegram setMyCommands.
Telegram command names cannot contain hyphens, so they are replaced with
underscores. Aliases are skipped -- Telegram shows one menu entry per
canonical command.
Plugin-registered slash commands are included so plugins get native
autocomplete in Telegram without touching core code.
"""
overrides = _resolve_config_gates()
result: list[tuple[str, str]] = []
@@ -441,10 +386,6 @@ def telegram_bot_commands() -> list[tuple[str, str]]:
tg_name = _sanitize_telegram_name(cmd.name)
if tg_name:
result.append((tg_name, cmd.description))
for name, description, _args_hint in _iter_plugin_command_entries():
tg_name = _sanitize_telegram_name(name)
if tg_name:
result.append((tg_name, description))
return result
@@ -809,9 +750,6 @@ def slack_subcommand_map() -> dict[str, str]:
Maps both canonical names and aliases so /hermes bg do stuff works
the same as /hermes background do stuff.
Plugin-registered slash commands are included so ``/hermes <plugin-cmd>``
routes through the plugin handler.
"""
overrides = _resolve_config_gates()
mapping: dict[str, str] = {}
@@ -821,9 +759,6 @@ def slack_subcommand_map() -> dict[str, str]:
mapping[cmd.name] = f"/{cmd.name}"
for alias in cmd.aliases:
mapping[alias] = f"/{alias}"
for name, _description, _args_hint in _iter_plugin_command_entries():
if name not in mapping:
mapping[name] = f"/{name}"
return mapping
+8 -89
View File
@@ -394,23 +394,17 @@ DEFAULT_CONFIG = {
# (bash doesn't source bashrc in non-interactive login mode) or
# zsh-specific files like ``~/.zshrc`` / ``~/.zprofile``.
# Paths support ``~`` / ``${VAR}``. Missing files are silently
# skipped. When empty, Hermes auto-sources ``~/.profile``,
# ``~/.bash_profile``, and ``~/.bashrc`` (in that order) if the
# skipped. When empty, Hermes auto-appends ``~/.bashrc`` if the
# snapshot shell is bash (this is the ``auto_source_bashrc``
# behaviour — disable with that key if you want strict login-only
# semantics).
"shell_init_files": [],
# When true (default), Hermes sources the user's shell rc files
# (``~/.profile``, ``~/.bash_profile``, ``~/.bashrc``) in the
# login shell used to build the environment snapshot. This
# captures PATH additions, shell functions, and aliases — which a
# plain ``bash -l -c`` would otherwise miss because bash skips
# bashrc in non-interactive login mode, and because a default
# Debian/Ubuntu ``~/.bashrc`` short-circuits on non-interactive
# sources. ``~/.profile`` and ``~/.bash_profile`` are tried first
# because ``n`` / ``nvm`` / ``asdf`` installers typically write
# their PATH exports there without an interactivity guard. Turn
# this off if your rc files misbehave when sourced
# When true (default), Hermes sources ``~/.bashrc`` in the login
# shell used to build the environment snapshot. This captures
# PATH additions, shell functions, and aliases defined in the
# user's bashrc — which a plain ``bash -l -c`` would otherwise
# miss because bash skips bashrc in non-interactive login mode.
# Turn this off if you have a bashrc that misbehaves when sourced
# non-interactively (e.g. one that hard-exits on TTY checks).
"auto_source_bashrc": True,
"docker_image": "nikolaik/python-nodejs:python3.11-nodejs20",
@@ -619,10 +613,6 @@ DEFAULT_CONFIG = {
},
# Text-to-speech configuration
# Each provider supports an optional `max_text_length:` override for the
# per-request input-character cap. Omit it to use the provider's documented
# limit (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k model-aware,
# Gemini 5000, Edge 5000, Mistral 4000, NeuTTS/KittenTTS 2000).
"tts": {
"provider": "edge", # "edge" (free) | "elevenlabs" (premium) | "openai" | "xai" | "minimax" | "mistral" | "neutts" (local)
"edge": {
@@ -718,12 +708,6 @@ DEFAULT_CONFIG = {
"provider": "", # e.g. "openrouter" (empty = inherit parent provider + credentials)
"base_url": "", # direct OpenAI-compatible endpoint for subagents
"api_key": "", # API key for delegation.base_url (falls back to OPENAI_API_KEY)
# When delegate_task narrows child toolsets explicitly, preserve any
# MCP toolsets the parent already has enabled. On by default so
# narrowing (e.g. toolsets=["web","browser"]) expresses "I want these
# extras" without silently stripping MCP tools the parent already has.
# Set to false for strict intersection.
"inherit_mcp_toolsets": True,
"max_iterations": 50, # per-subagent iteration cap (each subagent gets its own budget,
# independent of the parent's max_iterations)
"reasoning_effort": "", # reasoning effort for subagents: "xhigh", "high", "medium",
@@ -760,17 +744,6 @@ DEFAULT_CONFIG = {
"inline_shell": False,
# Timeout (seconds) for each !`cmd` snippet when inline_shell is on.
"inline_shell_timeout": 10,
# Run the keyword/pattern security scanner on skills the agent
# writes via skill_manage (create/edit/patch). Off by default
# because the agent can already execute the same code paths via
# terminal() with no gate, so the scan adds friction (blocks
# skills that mention risky keywords in prose) without meaningful
# security. Turn on if you want the belt-and-suspenders — a
# dangerous verdict will then surface as a tool error to the
# agent, which can retry with the flagged content removed.
# External hub installs (trusted/community sources) are always
# scanned regardless of this setting.
"guard_agent_created": False,
},
# Honcho AI-native memory -- reads ~/.honcho/config.json as single source of truth.
@@ -863,7 +836,6 @@ DEFAULT_CONFIG = {
# Pre-exec security scanning via tirith
"security": {
"allow_private_urls": False, # Allow requests to private/internal IPs (for OpenWrt, proxies, VPNs)
"redact_secrets": True,
"tirith_enabled": True,
"tirith_path": "tirith",
@@ -917,34 +889,6 @@ DEFAULT_CONFIG = {
"force_ipv4": False,
},
# Session storage — controls automatic cleanup of ~/.hermes/state.db.
# state.db accumulates every session, message, tool call, and FTS5 index
# entry forever. Without auto-pruning, a heavy user (gateway + cron)
# reports 384MB+ databases with 68K+ messages, which slows down FTS5
# inserts, /resume listing, and insights queries.
"sessions": {
# When true, prune ended sessions older than retention_days once
# per (roughly) min_interval_hours at CLI/gateway/cron startup.
# Only touches ended sessions — active sessions are always preserved.
# Default false: session history is valuable for search recall, and
# silently deleting it could surprise users. Opt in explicitly.
"auto_prune": False,
# How many days of ended-session history to keep. Matches the
# default of ``hermes sessions prune``.
"retention_days": 90,
# VACUUM after a prune that actually deleted rows. SQLite does not
# reclaim disk space on DELETE — freed pages are just reused on
# subsequent INSERTs — so without VACUUM the file stays bloated
# even after pruning. VACUUM blocks writes for a few seconds per
# 100MB, so it only runs at startup, and only when prune deleted
# ≥1 session.
"vacuum_after_prune": True,
# Minimum hours between auto-maintenance runs (avoids repeating
# the sweep on every CLI invocation). Tracked via state_meta in
# state.db itself, so it's shared across all processes.
"min_interval_hours": 24,
},
# Config schema version - bump this when adding new required fields
"_config_version": 22,
}
@@ -1102,22 +1046,6 @@ OPTIONAL_ENV_VARS = {
"category": "provider",
"advanced": True,
},
"STEPFUN_API_KEY": {
"description": "StepFun Step Plan API key",
"prompt": "StepFun Step Plan API key",
"url": "https://platform.stepfun.com/",
"password": True,
"category": "provider",
"advanced": True,
},
"STEPFUN_BASE_URL": {
"description": "StepFun Step Plan base URL override",
"prompt": "StepFun Step Plan base URL (leave empty for default)",
"url": None,
"password": False,
"category": "provider",
"advanced": True,
},
"ARCEEAI_API_KEY": {
"description": "Arcee AI API key",
"prompt": "Arcee AI API key",
@@ -2072,14 +2000,6 @@ def _normalize_custom_provider_entry(
models = entry.get("models")
if isinstance(models, dict) and models:
normalized["models"] = models
elif isinstance(models, list) and models:
# Hand-edited configs (and older Hermes versions) write ``models`` as
# a plain list of model ids. Preserve them by converting to the dict
# shape downstream code expects; otherwise normalize silently drops
# the list and /model shows the provider with (0) models.
normalized["models"] = {
str(m): {} for m in models if isinstance(m, str) and m.strip()
}
context_length = entry.get("context_length")
if isinstance(context_length, int) and context_length > 0:
@@ -2178,7 +2098,6 @@ _KNOWN_ROOT_KEYS = {
"fallback_providers", "credential_pool_strategies", "toolsets",
"agent", "terminal", "display", "compression", "delegation",
"auxiliary", "custom_providers", "context", "memory", "gateway",
"sessions",
}
# Valid fields inside a custom_providers list entry
@@ -3194,7 +3113,7 @@ def save_config(config: Dict[str, Any]):
if not sec or sec.get("redact_secrets") is None:
parts.append(_SECURITY_COMMENT)
fb = normalized.get("fallback_model", {})
if not fb or not isinstance(fb, dict) or not (fb.get("provider") and fb.get("model")):
if not fb or not (fb.get("provider") and fb.get("model")):
parts.append(_FALLBACK_COMMENT)
atomic_yaml_write(
+48 -128
View File
@@ -13,7 +13,6 @@ import time
import urllib.error
import urllib.parse
import urllib.request
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
@@ -148,14 +147,6 @@ def _sweep_expired_pastes(now: Optional[float] = None) -> tuple[int, int]:
return (deleted, len(remaining))
def _best_effort_sweep_expired_pastes() -> None:
"""Attempt pending-paste cleanup without letting /debug fail offline."""
try:
_sweep_expired_pastes()
except Exception:
pass
# ---------------------------------------------------------------------------
# Privacy / delete helpers
# ---------------------------------------------------------------------------
@@ -323,128 +314,72 @@ def upload_to_pastebin(content: str, expiry_days: int = 7) -> str:
# Log file reading
# ---------------------------------------------------------------------------
@dataclass
class LogSnapshot:
"""Single-read snapshot of a log file used by debug-share."""
path: Optional[Path]
tail_text: str
full_text: Optional[str]
def _primary_log_path(log_name: str) -> Optional[Path]:
"""Where *log_name* would live if present. Doesn't check existence."""
from hermes_cli.logs import LOG_FILES
filename = LOG_FILES.get(log_name)
return (get_hermes_home() / "logs" / filename) if filename else None
def _resolve_log_path(log_name: str) -> Optional[Path]:
"""Find the log file for *log_name*, falling back to the .1 rotation.
Returns the first non-empty candidate (primary, then .1), or None.
Callers distinguish 'empty primary' from 'truly missing' via
:func:`_primary_log_path`.
Returns the path if found, or None.
"""
primary = _primary_log_path(log_name)
if primary is None:
from hermes_cli.logs import LOG_FILES
filename = LOG_FILES.get(log_name)
if not filename:
return None
log_dir = get_hermes_home() / "logs"
primary = log_dir / filename
if primary.exists() and primary.stat().st_size > 0:
return primary
rotated = primary.parent / f"{primary.name}.1"
# Fall back to the most recent rotated file (.1).
rotated = log_dir / f"{filename}.1"
if rotated.exists() and rotated.stat().st_size > 0:
return rotated
return None
def _capture_log_snapshot(
log_name: str,
*,
tail_lines: int,
max_bytes: int = _MAX_LOG_BYTES,
) -> LogSnapshot:
"""Capture a log once and derive summary/full-log views from it.
def _read_log_tail(log_name: str, num_lines: int) -> str:
"""Read the last *num_lines* from a log file, or return a placeholder."""
from hermes_cli.logs import _read_last_n_lines
The report tail and standalone log upload must come from the same file
snapshot. Otherwise a rotation/truncate between reads can make the report
look newer than the uploaded ``agent.log`` paste.
log_path = _resolve_log_path(log_name)
if log_path is None:
return "(file not found)"
try:
lines = _read_last_n_lines(log_path, num_lines)
return "".join(lines).rstrip("\n")
except Exception as exc:
return f"(error reading: {exc})"
def _read_full_log(log_name: str, max_bytes: int = _MAX_LOG_BYTES) -> Optional[str]:
"""Read a log file for standalone upload.
Returns the file content (last *max_bytes* if truncated), or None if the
file doesn't exist or is empty.
"""
log_path = _resolve_log_path(log_name)
if log_path is None:
primary = _primary_log_path(log_name)
tail = "(file empty)" if primary and primary.exists() else "(file not found)"
return LogSnapshot(path=None, tail_text=tail, full_text=None)
return None
try:
size = log_path.stat().st_size
if size == 0:
# race: file was truncated between _resolve_log_path and stat
return LogSnapshot(path=log_path, tail_text="(file empty)", full_text=None)
return None
if size <= max_bytes:
return log_path.read_text(encoding="utf-8", errors="replace")
# File is larger than max_bytes — read the tail.
with open(log_path, "rb") as f:
if size <= max_bytes:
raw = f.read()
truncated = False
else:
# Read from the end until we have enough bytes for the
# standalone upload and enough newline context to render the
# summary tail from the same snapshot.
chunk_size = 8192
pos = size
chunks: list[bytes] = []
total = 0
newline_count = 0
while pos > 0 and (total < max_bytes or newline_count <= tail_lines + 1) and total < max_bytes * 2:
read_size = min(chunk_size, pos)
pos -= read_size
f.seek(pos)
chunk = f.read(read_size)
chunks.insert(0, chunk)
total += len(chunk)
newline_count += chunk.count(b"\n")
chunk_size = min(chunk_size * 2, 65536)
raw = b"".join(chunks)
truncated = pos > 0
full_raw = raw
if truncated and len(full_raw) > max_bytes:
cut = len(full_raw) - max_bytes
# Check whether the cut lands exactly on a line boundary. If the
# byte just before the cut position is a newline the first retained
# byte starts a complete line and we should keep it. Only drop a
# partial first line when we're genuinely mid-line.
on_boundary = cut > 0 and full_raw[cut - 1 : cut] == b"\n"
full_raw = full_raw[cut:]
if not on_boundary and b"\n" in full_raw:
full_raw = full_raw.split(b"\n", 1)[1]
all_text = raw.decode("utf-8", errors="replace")
tail_text = "".join(all_text.splitlines(keepends=True)[-tail_lines:]).rstrip("\n")
full_text = full_raw.decode("utf-8", errors="replace")
if truncated:
full_text = f"[... truncated — showing last ~{max_bytes // 1024}KB ...]\n{full_text}"
return LogSnapshot(path=log_path, tail_text=tail_text, full_text=full_text)
except Exception as exc:
return LogSnapshot(path=log_path, tail_text=f"(error reading: {exc})", full_text=None)
def _capture_default_log_snapshots(log_lines: int) -> dict[str, LogSnapshot]:
"""Capture all logs used by debug-share exactly once."""
errors_lines = min(log_lines, 100)
return {
"agent": _capture_log_snapshot("agent", tail_lines=log_lines),
"errors": _capture_log_snapshot("errors", tail_lines=errors_lines),
"gateway": _capture_log_snapshot("gateway", tail_lines=errors_lines),
}
f.seek(size - max_bytes)
# Skip partial line at the seek point.
f.readline()
content = f.read().decode("utf-8", errors="replace")
return f"[... truncated — showing last ~{max_bytes // 1024}KB ...]\n{content}"
except Exception:
return None
# ---------------------------------------------------------------------------
@@ -470,12 +405,7 @@ def _capture_dump() -> str:
return capture.getvalue()
def collect_debug_report(
*,
log_lines: int = 200,
dump_text: str = "",
log_snapshots: Optional[dict[str, LogSnapshot]] = None,
) -> str:
def collect_debug_report(*, log_lines: int = 200, dump_text: str = "") -> str:
"""Build the summary debug report: system dump + log tails.
Parameters
@@ -494,22 +424,19 @@ def collect_debug_report(
dump_text = _capture_dump()
buf.write(dump_text)
if log_snapshots is None:
log_snapshots = _capture_default_log_snapshots(log_lines)
# ── Recent log tails (summary only) ──────────────────────────────────
buf.write("\n\n")
buf.write(f"--- agent.log (last {log_lines} lines) ---\n")
buf.write(log_snapshots["agent"].tail_text)
buf.write(_read_log_tail("agent", log_lines))
buf.write("\n\n")
errors_lines = min(log_lines, 100)
buf.write(f"--- errors.log (last {errors_lines} lines) ---\n")
buf.write(log_snapshots["errors"].tail_text)
buf.write(_read_log_tail("errors", errors_lines))
buf.write("\n\n")
buf.write(f"--- gateway.log (last {errors_lines} lines) ---\n")
buf.write(log_snapshots["gateway"].tail_text)
buf.write(_read_log_tail("gateway", errors_lines))
buf.write("\n")
return buf.getvalue()
@@ -521,8 +448,6 @@ def collect_debug_report(
def run_debug_share(args):
"""Collect debug report + full logs, upload each, print URLs."""
_best_effort_sweep_expired_pastes()
log_lines = getattr(args, "lines", 200)
expiry = getattr(args, "expire", 7)
local_only = getattr(args, "local", False)
@@ -534,15 +459,10 @@ def run_debug_share(args):
# Capture dump once — prepended to every paste for context.
dump_text = _capture_dump()
log_snapshots = _capture_default_log_snapshots(log_lines)
report = collect_debug_report(
log_lines=log_lines,
dump_text=dump_text,
log_snapshots=log_snapshots,
)
agent_log = log_snapshots["agent"].full_text
gateway_log = log_snapshots["gateway"].full_text
report = collect_debug_report(log_lines=log_lines, dump_text=dump_text)
agent_log = _read_full_log("agent")
gateway_log = _read_full_log("gateway")
# Prepend dump header to each full log so every paste is self-contained.
if agent_log:
+4 -9
View File
@@ -912,7 +912,6 @@ def run_doctor(args):
_apikey_providers = [
("Z.AI / GLM", ("GLM_API_KEY", "ZAI_API_KEY", "Z_AI_API_KEY"), "https://api.z.ai/api/paas/v4/models", "GLM_BASE_URL", True),
("Kimi / Moonshot", ("KIMI_API_KEY",), "https://api.moonshot.ai/v1/models", "KIMI_BASE_URL", True),
("StepFun Step Plan", ("STEPFUN_API_KEY",), "https://api.stepfun.ai/step_plan/v1/models", "STEPFUN_BASE_URL", True),
("Kimi / Moonshot (China)", ("KIMI_CN_API_KEY",), "https://api.moonshot.cn/v1/models", None, True),
("Arcee AI", ("ARCEEAI_API_KEY",), "https://api.arcee.ai/api/v1/models", "ARCEE_BASE_URL", True),
("DeepSeek", ("DEEPSEEK_API_KEY",), "https://api.deepseek.com/v1/models", "DEEPSEEK_BASE_URL", True),
@@ -944,22 +943,18 @@ def run_doctor(args):
try:
import httpx
_base = os.getenv(_base_env, "") if _base_env else ""
# Auto-detect Kimi Code keys (sk-kimi-) → api.kimi.com/coding/v1
# (OpenAI-compat surface, which exposes /models for health check).
# Auto-detect Kimi Code keys (sk-kimi-) → api.kimi.com
if not _base and _key.startswith("sk-kimi-"):
_base = "https://api.kimi.com/coding/v1"
# Anthropic-compat endpoints (/anthropic, api.kimi.com/coding
# with no /v1) don't support /models. Rewrite to the OpenAI-compat
# /v1 surface for health checks.
# Anthropic-compat endpoints (/anthropic) don't support /models.
# Rewrite to the OpenAI-compat /v1 surface for health checks.
if _base and _base.rstrip("/").endswith("/anthropic"):
from agent.auxiliary_client import _to_openai_base_url
_base = _to_openai_base_url(_base)
if base_url_host_matches(_base, "api.kimi.com") and _base.rstrip("/").endswith("/coding"):
_base = _base.rstrip("/") + "/v1"
_url = (_base.rstrip("/") + "/models") if _base else _default_url
_headers = {"Authorization": f"Bearer {_key}"}
if base_url_host_matches(_base, "api.kimi.com"):
_headers["User-Agent"] = "claude-code/0.1.0"
_headers["User-Agent"] = "KimiCLI/1.30.0"
_resp = httpx.get(
_url,
headers=_headers,
-2
View File
@@ -160,8 +160,6 @@ def load_hermes_dotenv(
# Fix corrupted .env files before python-dotenv parses them (#8908).
if user_env.exists():
_sanitize_env_file_if_needed(user_env)
if project_env_path and project_env_path.exists():
_sanitize_env_file_if_needed(project_env_path)
if user_env.exists():
_load_dotenv_with_fallback(user_env, override=True)
+148 -483
View File
@@ -333,147 +333,6 @@ def _probe_systemd_service_running(system: bool = False) -> tuple[bool, bool]:
return selected_system, result.stdout.strip() == "active"
def _read_systemd_unit_properties(
system: bool = False,
properties: tuple[str, ...] = (
"ActiveState",
"SubState",
"Result",
"ExecMainStatus",
),
) -> dict[str, str]:
"""Return selected ``systemctl show`` properties for the gateway unit."""
selected_system = _select_systemd_scope(system)
try:
result = _run_systemctl(
[
"show",
get_service_name(),
"--no-pager",
"--property",
",".join(properties),
],
system=selected_system,
capture_output=True,
text=True,
timeout=10,
)
except (RuntimeError, subprocess.TimeoutExpired, OSError):
return {}
if result.returncode != 0:
return {}
parsed: dict[str, str] = {}
for line in result.stdout.splitlines():
if "=" not in line:
continue
key, value = line.split("=", 1)
parsed[key] = value.strip()
return parsed
def _wait_for_systemd_service_restart(
*,
system: bool = False,
previous_pid: int | None = None,
timeout: float = 60.0,
) -> bool:
"""Wait for the gateway service to become active after a restart handoff."""
import time
svc = get_service_name()
scope_label = _service_scope_label(system).capitalize()
deadline = time.time() + timeout
while time.time() < deadline:
props = _read_systemd_unit_properties(system=system)
active_state = props.get("ActiveState", "")
sub_state = props.get("SubState", "")
new_pid = None
try:
from gateway.status import get_running_pid
new_pid = get_running_pid()
except Exception:
new_pid = None
if active_state == "active":
if new_pid and (previous_pid is None or new_pid != previous_pid):
print(f"{scope_label} service restarted (PID {new_pid})")
return True
if previous_pid is None:
print(f"{scope_label} service restarted")
return True
if active_state == "activating" and sub_state == "auto-restart":
time.sleep(1)
continue
time.sleep(2)
print(
f"{scope_label} service did not become active within {int(timeout)}s.\n"
f" Check status: {'sudo ' if system else ''}hermes gateway status\n"
f" Check logs: journalctl {'--user ' if not system else ''}-u {svc} -l --since '2 min ago'"
)
return False
def _recover_pending_systemd_restart(system: bool = False, previous_pid: int | None = None) -> bool:
"""Recover a planned service restart that is stuck in systemd state."""
props = _read_systemd_unit_properties(system=system)
if not props:
return False
try:
from gateway.status import read_runtime_status
except Exception:
return False
runtime_state = read_runtime_status() or {}
if not runtime_state.get("restart_requested"):
return False
active_state = props.get("ActiveState", "")
sub_state = props.get("SubState", "")
exec_main_status = props.get("ExecMainStatus", "")
result = props.get("Result", "")
if active_state == "activating" and sub_state == "auto-restart":
print("⏳ Service restart already pending — waiting for systemd relaunch...")
return _wait_for_systemd_service_restart(
system=system,
previous_pid=previous_pid,
)
if active_state == "failed" and (
exec_main_status == str(GATEWAY_SERVICE_RESTART_EXIT_CODE)
or result == "exit-code"
):
svc = get_service_name()
scope_label = _service_scope_label(system).capitalize()
print(f"↻ Clearing failed state for pending {scope_label.lower()} service restart...")
_run_systemctl(
["reset-failed", svc],
system=system,
check=False,
timeout=30,
)
_run_systemctl(
["start", svc],
system=system,
check=False,
timeout=90,
)
return _wait_for_systemd_service_restart(
system=system,
previous_pid=previous_pid,
)
return False
def _probe_launchd_service_running() -> bool:
if not get_launchd_plist_path().exists():
return False
@@ -611,8 +470,7 @@ def stop_profile_gateway() -> bool:
except (ProcessLookupError, PermissionError):
break
if get_running_pid() is None:
remove_pid_file()
remove_pid_file()
return True
@@ -761,21 +619,6 @@ def get_systemd_unit_path(system: bool = False) -> Path:
return Path.home() / ".config" / "systemd" / "user" / f"{name}.service"
class UserSystemdUnavailableError(RuntimeError):
"""Raised when ``systemctl --user`` cannot reach the user D-Bus session.
Typically hit on fresh RHEL/Debian SSH sessions where linger is disabled
and no user@.service is running, so ``/run/user/$UID/bus`` never exists.
Carries a user-facing remediation message in ``args[0]``.
"""
def _user_dbus_socket_path() -> Path:
"""Return the expected per-user D-Bus socket path (regardless of existence)."""
xdg = os.environ.get("XDG_RUNTIME_DIR") or f"/run/user/{os.getuid()}"
return Path(xdg) / "bus"
def _ensure_user_systemd_env() -> None:
"""Ensure DBUS_SESSION_BUS_ADDRESS and XDG_RUNTIME_DIR are set for systemctl --user.
@@ -798,126 +641,6 @@ def _ensure_user_systemd_env() -> None:
os.environ["DBUS_SESSION_BUS_ADDRESS"] = f"unix:path={bus_path}"
def _wait_for_user_dbus_socket(timeout: float = 3.0) -> bool:
"""Poll for the user D-Bus socket to appear, up to ``timeout`` seconds.
Linger-enabled user@.service can take a second or two to spawn the socket
after ``loginctl enable-linger`` runs. Returns True once the socket exists.
"""
import time
deadline = time.monotonic() + timeout
while time.monotonic() < deadline:
if _user_dbus_socket_path().exists():
_ensure_user_systemd_env()
return True
time.sleep(0.2)
return _user_dbus_socket_path().exists()
def _preflight_user_systemd(*, auto_enable_linger: bool = True) -> None:
"""Ensure ``systemctl --user`` will reach the user D-Bus session bus.
No-op when the bus socket is already there (the common case on desktops
and linger-enabled servers). On fresh SSH sessions where the socket is
missing:
* If linger is already enabled, wait briefly for user@.service to spawn
the socket.
* If linger is disabled and ``auto_enable_linger`` is True, try
``loginctl enable-linger $USER`` (works as non-root when polkit permits
it, otherwise needs sudo).
* If the socket is still missing afterwards, raise
:class:`UserSystemdUnavailableError` with a precise remediation message.
Callers should treat the exception as a terminal condition for user-scope
systemd operations and surface the message to the user.
"""
_ensure_user_systemd_env()
bus_path = _user_dbus_socket_path()
if bus_path.exists():
return
import getpass
username = getpass.getuser()
linger_enabled, linger_detail = get_systemd_linger_status()
if linger_enabled is True:
if _wait_for_user_dbus_socket(timeout=3.0):
return
# Linger is on but socket still missing — unusual; fall through to error.
_raise_user_systemd_unavailable(
username,
reason="User D-Bus socket is missing even though linger is enabled.",
fix_hint=(
f" systemctl start user@{os.getuid()}.service\n"
" (may require sudo; try again after the command succeeds)"
),
)
if auto_enable_linger and shutil.which("loginctl"):
try:
result = subprocess.run(
["loginctl", "enable-linger", username],
capture_output=True,
text=True,
check=False,
timeout=30,
)
except Exception as exc:
_raise_user_systemd_unavailable(
username,
reason=f"loginctl enable-linger failed ({exc}).",
fix_hint=f" sudo loginctl enable-linger {username}",
)
else:
if result.returncode == 0:
if _wait_for_user_dbus_socket(timeout=5.0):
print(f"✓ Enabled linger for {username} — user D-Bus now available")
return
# enable-linger succeeded but the socket never appeared.
_raise_user_systemd_unavailable(
username,
reason="Linger was enabled, but the user D-Bus socket did not appear.",
fix_hint=(
" Log out and log back in, then re-run the command.\n"
f" Or reboot and run: systemctl --user start {get_service_name()}"
),
)
detail = (result.stderr or result.stdout or f"exit {result.returncode}").strip()
_raise_user_systemd_unavailable(
username,
reason=f"loginctl enable-linger was denied: {detail}",
fix_hint=f" sudo loginctl enable-linger {username}",
)
_raise_user_systemd_unavailable(
username,
reason=(
"User D-Bus session is not available "
f"({linger_detail or 'linger disabled'})."
),
fix_hint=f" sudo loginctl enable-linger {username}",
)
def _raise_user_systemd_unavailable(username: str, *, reason: str, fix_hint: str) -> None:
"""Build a user-facing error message and raise UserSystemdUnavailableError."""
msg = (
f"{reason}\n"
" systemctl --user cannot reach the user D-Bus session in this shell.\n"
"\n"
" To fix:\n"
f"{fix_hint}\n"
"\n"
" Alternative: run the gateway in the foreground (stays up until\n"
" you exit / close the terminal):\n"
" hermes gateway run"
)
raise UserSystemdUnavailableError(msg)
def _systemctl_cmd(system: bool = False) -> list[str]:
if not system:
_ensure_user_systemd_env()
@@ -1758,11 +1481,6 @@ def systemd_start(system: bool = False):
system = _select_systemd_scope(system)
if system:
_require_root_for_system_service("start")
else:
# Fail fast with actionable guidance if the user D-Bus session is not
# reachable (common on fresh RHEL/Debian SSH sessions without linger).
# Raises UserSystemdUnavailableError with a remediation message.
_preflight_user_systemd()
refresh_systemd_unit_if_needed(system=system)
_run_systemctl(["start", get_service_name()], system=system, check=True, timeout=30)
print(f"{_service_scope_label(system).capitalize()} service started")
@@ -1782,16 +1500,19 @@ def systemd_restart(system: bool = False):
system = _select_systemd_scope(system)
if system:
_require_root_for_system_service("restart")
else:
_preflight_user_systemd()
refresh_systemd_unit_if_needed(system=system)
from gateway.status import get_running_pid
pid = get_running_pid()
if pid is not None and _request_gateway_self_restart(pid):
# SIGUSR1 sent — the gateway will drain active agents, exit with
# code 75, and systemd will restart it after RestartSec (30s).
# Wait for the old process to die and the new one to become active
# so the CLI doesn't return while the service is still restarting.
import time
scope_label = _service_scope_label(system).capitalize()
svc = get_service_name()
scope_cmd = _systemctl_cmd(system)
# Phase 1: wait for old process to exit (drain + shutdown)
print(f"{scope_label} service draining active work...")
@@ -1805,41 +1526,48 @@ def systemd_restart(system: bool = False):
else:
print(f"⚠ Old process (PID {pid}) still alive after 90s")
# The gateway exits with code 75 for a planned service restart.
# systemd can sit in the RestartSec window or even wedge itself into a
# failed/rate-limited state if the operator asks for another restart in
# the middle of that handoff. Clear any stale failed state and kick the
# unit immediately so `hermes gateway restart` behaves idempotently.
_run_systemctl(
["reset-failed", svc],
system=system,
check=False,
timeout=30,
)
_run_systemctl(
["start", svc],
system=system,
check=False,
timeout=90,
)
_wait_for_systemd_service_restart(system=system, previous_pid=pid)
return
# Phase 2: wait for systemd to start the new process
print(f"⏳ Waiting for {svc} to restart...")
deadline = time.time() + 60
while time.time() < deadline:
try:
result = subprocess.run(
scope_cmd + ["is-active", svc],
capture_output=True, text=True, timeout=5,
)
if result.stdout.strip() == "active":
# Verify it's a NEW process, not the old one somehow
new_pid = get_running_pid()
if new_pid and new_pid != pid:
print(f"{scope_label} service restarted (PID {new_pid})")
return
except (subprocess.TimeoutExpired, FileNotFoundError):
pass
time.sleep(2)
if _recover_pending_systemd_restart(system=system, previous_pid=pid):
# Timed out — check final state
try:
result = subprocess.run(
scope_cmd + ["is-active", svc],
capture_output=True, text=True, timeout=5,
)
if result.stdout.strip() == "active":
print(f"{scope_label} service restarted")
return
except Exception:
pass
print(
f"{scope_label} service did not become active within 60s.\n"
f" Check status: {'sudo ' if system else ''}hermes gateway status\n"
f" Check logs: journalctl {'--user ' if not system else ''}-u {svc} --since '2 min ago'"
)
return
_run_systemctl(
["reset-failed", get_service_name()],
system=system,
check=False,
timeout=30,
)
_run_systemctl(["reload-or-restart", get_service_name()], system=system, check=True, timeout=90)
print(f"{_service_scope_label(system).capitalize()} service restarted")
def systemd_status(deep: bool = False, system: bool = False, full: bool = False):
def systemd_status(deep: bool = False, system: bool = False):
system = _select_systemd_scope(system)
unit_path = get_systemd_unit_path(system=system)
scope_flag = " --system" if system else ""
@@ -1862,12 +1590,8 @@ def systemd_status(deep: bool = False, system: bool = False, full: bool = False)
print(f" Run: {'sudo ' if system else ''}hermes gateway restart{scope_flag} # auto-refreshes the unit")
print()
status_cmd = ["status", get_service_name(), "--no-pager"]
if full:
status_cmd.append("-l")
_run_systemctl(
status_cmd,
["status", get_service_name(), "--no-pager"],
system=system,
capture_output=False,
timeout=10,
@@ -1900,19 +1624,6 @@ def systemd_status(deep: bool = False, system: bool = False, full: bool = False)
for line in runtime_lines:
print(f" {line}")
unit_props = _read_systemd_unit_properties(system=system)
active_state = unit_props.get("ActiveState", "")
sub_state = unit_props.get("SubState", "")
exec_main_status = unit_props.get("ExecMainStatus", "")
result_code = unit_props.get("Result", "")
if active_state == "activating" and sub_state == "auto-restart":
print(" ⏳ Restart pending: systemd is waiting to relaunch the gateway")
elif active_state == "failed" and exec_main_status == str(GATEWAY_SERVICE_RESTART_EXIT_CODE):
print(" ⚠ Planned restart is stuck in systemd failed state (exit 75)")
print(f" Run: systemctl {'--user ' if not system else ''}reset-failed {get_service_name()} && {'sudo ' if system else ''}hermes gateway start{scope_flag}")
elif active_state == "failed" and result_code:
print(f" ⚠ Systemd unit result: {result_code}")
if system:
print("✓ System service starts at boot without requiring systemd linger")
elif deep:
@@ -1928,10 +1639,7 @@ def systemd_status(deep: bool = False, system: bool = False, full: bool = False)
if deep:
print()
print("Recent logs:")
log_cmd = _journalctl_cmd(system) + ["-u", get_service_name(), "-n", "20", "--no-pager"]
if full:
log_cmd.append("-l")
subprocess.run(log_cmd, timeout=10)
subprocess.run(_journalctl_cmd(system) + ["-u", get_service_name(), "-n", "20", "--no-pager"], timeout=10)
# =============================================================================
@@ -2931,120 +2639,9 @@ def _setup_dingtalk():
def _setup_wecom():
"""Interactive setup for WeCom — scan QR code or manual credential input."""
print()
print(color(" ─── 💬 WeCom (Enterprise WeChat) Setup ───", Colors.CYAN))
existing_bot_id = get_env_value("WECOM_BOT_ID")
existing_secret = get_env_value("WECOM_SECRET")
if existing_bot_id and existing_secret:
print()
print_success("WeCom is already configured.")
if not prompt_yes_no(" Reconfigure WeCom?", False):
return
# ── Choose setup method ──
print()
method_choices = [
"Scan QR code to obtain Bot ID and Secret automatically (recommended)",
"Enter existing Bot ID and Secret manually",
]
method_idx = prompt_choice(" How would you like to set up WeCom?", method_choices, 0)
bot_id = None
secret = None
if method_idx == 0:
# ── QR scan flow ──
try:
from gateway.platforms.wecom import qr_scan_for_bot_info
except Exception as exc:
print_error(f" WeCom QR scan import failed: {exc}")
qr_scan_for_bot_info = None
if qr_scan_for_bot_info is not None:
try:
credentials = qr_scan_for_bot_info()
except KeyboardInterrupt:
print()
print_warning(" WeCom setup cancelled.")
return
except Exception as exc:
print_warning(f" QR scan failed: {exc}")
credentials = None
if credentials:
bot_id = credentials.get("bot_id", "")
secret = credentials.get("secret", "")
print_success(" ✔ QR scan successful! Bot ID and Secret obtained.")
if not bot_id or not secret:
print_info(" QR scan did not complete. Continuing with manual input.")
bot_id = None
secret = None
# ── Manual credential input ──
if not bot_id or not secret:
print()
print_info(" 1. Go to WeCom Application → Workspace → Smart Robot -> Create smart robots")
print_info(" 2. Select API Mode")
print_info(" 3. Copy the Bot ID and Secret from the bot's credentials info")
print_info(" 4. The bot connects via WebSocket — no public endpoint needed")
print()
bot_id = prompt(" Bot ID", password=False)
if not bot_id:
print_warning(" Skipped — WeCom won't work without a Bot ID.")
return
secret = prompt(" Secret", password=True)
if not secret:
print_warning(" Skipped — WeCom won't work without a Secret.")
return
# ── Save core credentials ──
save_env_value("WECOM_BOT_ID", bot_id)
save_env_value("WECOM_SECRET", secret)
# ── Allowed users (deny-by-default security) ──
print()
print_info(" The gateway DENIES all users by default for security.")
print_info(" Enter user IDs to create an allowlist, or leave empty.")
allowed = prompt(" Allowed user IDs (comma-separated, or empty)", password=False)
if allowed:
cleaned = allowed.replace(" ", "")
save_env_value("WECOM_ALLOWED_USERS", cleaned)
print_success(" Saved — only these users can interact with the bot.")
else:
print()
access_choices = [
"Enable open access (anyone can message the bot)",
"Use DM pairing (unknown users request access, you approve with 'hermes pairing approve')",
"Disable direct messages",
"Skip for now (bot will deny all users until configured)",
]
access_idx = prompt_choice(" How should unauthorized users be handled?", access_choices, 1)
if access_idx == 0:
save_env_value("WECOM_DM_POLICY", "open")
save_env_value("GATEWAY_ALLOW_ALL_USERS", "true")
print_warning(" Open access enabled — anyone can use your bot!")
elif access_idx == 1:
save_env_value("WECOM_DM_POLICY", "pairing")
print_success(" DM pairing mode — users will receive a code to request access.")
print_info(" Approve with: hermes pairing approve <platform> <code>")
elif access_idx == 2:
save_env_value("WECOM_DM_POLICY", "disabled")
print_warning(" Direct messages disabled.")
else:
print_info(" Skipped — configure later with 'hermes gateway setup'")
# ── Home channel (optional) ──
print()
print_info(" Chat ID for scheduled results and notifications.")
home = prompt(" Home chat ID (optional, for cron/notifications)", password=False)
if home:
save_env_value("WECOM_HOME_CHANNEL", home)
print_success(f" Home channel set to {home}")
print()
print_success("💬 WeCom configured!")
"""Configure WeCom (Enterprise WeChat) via the standard platform setup."""
wecom_platform = next(p for p in _PLATFORMS if p["key"] == "wecom")
_setup_standard_platform(wecom_platform)
def _is_service_installed() -> bool:
@@ -3424,8 +3021,7 @@ def _setup_qqbot():
if method_idx == 0:
# ── QR scan-to-configure ──
try:
from gateway.platforms.qqbot import qr_register
credentials = qr_register()
credentials = _qqbot_qr_flow()
except KeyboardInterrupt:
print()
print_warning(" QQ Bot setup cancelled.")
@@ -3507,6 +3103,106 @@ def _setup_qqbot():
print_info(f" App ID: {credentials['app_id']}")
def _qqbot_render_qr(url: str) -> bool:
"""Try to render a QR code in the terminal. Returns True if successful."""
try:
import qrcode as _qr
qr = _qr.QRCode(border=1,error_correction=_qr.constants.ERROR_CORRECT_L)
qr.add_data(url)
qr.make(fit=True)
qr.print_ascii(invert=True)
return True
except Exception:
return False
def _qqbot_qr_flow():
"""Run the QR-code scan-to-configure flow.
Returns a dict with app_id, client_secret, user_openid on success,
or None on failure/cancel.
"""
try:
from gateway.platforms.qqbot import (
create_bind_task, poll_bind_result, build_connect_url,
decrypt_secret, BindStatus,
)
from gateway.platforms.qqbot.constants import ONBOARD_POLL_INTERVAL
except Exception as exc:
print_error(f" QQBot onboard import failed: {exc}")
return None
import asyncio
import time
MAX_REFRESHES = 3
refresh_count = 0
while refresh_count <= MAX_REFRESHES:
loop = asyncio.new_event_loop()
# ── Create bind task ──
try:
task_id, aes_key = loop.run_until_complete(create_bind_task())
except Exception as e:
print_warning(f" Failed to create bind task: {e}")
loop.close()
return None
url = build_connect_url(task_id)
# ── Display QR code + URL ──
print()
if _qqbot_render_qr(url):
print(f" Scan the QR code above, or open this URL directly:\n {url}")
else:
print(f" Open this URL in QQ on your phone:\n {url}")
print_info(" Tip: pip install qrcode to show a scannable QR code here")
# ── Poll loop (silent — keep QR visible at bottom) ──
try:
while True:
try:
status, app_id, encrypted_secret, user_openid = loop.run_until_complete(
poll_bind_result(task_id)
)
except Exception:
time.sleep(ONBOARD_POLL_INTERVAL)
continue
if status == BindStatus.COMPLETED:
client_secret = decrypt_secret(encrypted_secret, aes_key)
print()
print_success(f" QR scan complete! (App ID: {app_id})")
if user_openid:
print_info(f" Scanner's OpenID: {user_openid}")
return {
"app_id": app_id,
"client_secret": client_secret,
"user_openid": user_openid,
}
if status == BindStatus.EXPIRED:
refresh_count += 1
if refresh_count > MAX_REFRESHES:
print()
print_warning(f" QR code expired {MAX_REFRESHES} times — giving up.")
return None
print()
print_warning(f" QR code expired, refreshing... ({refresh_count}/{MAX_REFRESHES})")
loop.close()
break # outer while creates a new task
time.sleep(ONBOARD_POLL_INTERVAL)
except KeyboardInterrupt:
loop.close()
raise
finally:
loop.close()
return None
def _setup_signal():
"""Interactive setup for Signal messenger."""
import shutil
@@ -3658,10 +3354,6 @@ def gateway_setup():
systemd_start()
elif is_macos():
launchd_start()
except UserSystemdUnavailableError as e:
print_error(" Failed to start — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except subprocess.CalledProcessError as e:
print_error(f" Failed to start: {e}")
else:
@@ -3698,8 +3390,6 @@ def gateway_setup():
_setup_feishu()
elif platform["key"] == "qqbot":
_setup_qqbot()
elif platform["key"] == "wecom":
_setup_wecom()
else:
_setup_standard_platform(platform)
@@ -3726,10 +3416,6 @@ def gateway_setup():
else:
stop_profile_gateway()
print_info("Start manually: hermes gateway")
except UserSystemdUnavailableError as e:
print_error(" Restart failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except subprocess.CalledProcessError as e:
print_error(f" Restart failed: {e}")
elif service_installed:
@@ -3739,10 +3425,6 @@ def gateway_setup():
systemd_start()
elif is_macos():
launchd_start()
except UserSystemdUnavailableError as e:
print_error(" Start failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except subprocess.CalledProcessError as e:
print_error(f" Start failed: {e}")
else:
@@ -3766,10 +3448,6 @@ def gateway_setup():
systemd_start(system=installed_scope == "system")
else:
launchd_start()
except UserSystemdUnavailableError as e:
print_error(" Start failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except subprocess.CalledProcessError as e:
print_error(f" Start failed: {e}")
except subprocess.CalledProcessError as e:
@@ -3807,18 +3485,6 @@ def gateway_setup():
def gateway_command(args):
"""Handle gateway subcommands."""
try:
return _gateway_command_inner(args)
except UserSystemdUnavailableError as e:
# Clean, actionable message instead of a traceback when the user D-Bus
# session is unreachable (fresh SSH shell, no linger, container, etc.).
print_error("User systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
sys.exit(1)
def _gateway_command_inner(args):
subcmd = getattr(args, 'gateway_command', None)
# Default to run if no subcommand
@@ -4082,13 +3748,12 @@ def _gateway_command_inner(args):
elif subcmd == "status":
deep = getattr(args, 'deep', False)
full = getattr(args, 'full', False)
system = getattr(args, 'system', False)
snapshot = get_gateway_runtime_snapshot(system=system)
# Check for service first
if supports_systemd_services() and (get_systemd_unit_path(system=False).exists() or get_systemd_unit_path(system=True).exists()):
systemd_status(deep, system=system, full=full)
systemd_status(deep, system=system)
_print_gateway_process_mismatch(snapshot)
elif is_macos() and get_launchd_plist_path().exists():
launchd_status(deep)
+6 -186
View File
@@ -1131,20 +1131,6 @@ def cmd_chat(args):
if getattr(args, "yolo", False):
os.environ["HERMES_YOLO_MODE"] = "1"
# --ignore-user-config: make load_cli_config() / load_config() skip the
# user's ~/.hermes/config.yaml and return built-in defaults. Set BEFORE
# importing cli (which runs `CLI_CONFIG = load_cli_config()` at module
# import time). Credentials in .env are still loaded — this flag only
# ignores behavioral/config settings.
if getattr(args, "ignore_user_config", False):
os.environ["HERMES_IGNORE_USER_CONFIG"] = "1"
# --ignore-rules: skip auto-injection of AGENTS.md/SOUL.md/.cursorrules
# (rules), memory entries, and any preloaded skills coming from user config.
# Maps to AIAgent(skip_context_files=True, skip_memory=True).
if getattr(args, "ignore_rules", False):
os.environ["HERMES_IGNORE_RULES"] = "1"
# --source: tag session source for filtering (e.g. 'tool' for third-party integrations)
if getattr(args, "source", None):
os.environ["HERMES_SESSION_SOURCE"] = args.source
@@ -1173,8 +1159,6 @@ def cmd_chat(args):
"checkpoints": getattr(args, "checkpoints", False),
"pass_session_id": getattr(args, "pass_session_id", False),
"max_turns": getattr(args, "max_turns", None),
"ignore_rules": getattr(args, "ignore_rules", False),
"ignore_user_config": getattr(args, "ignore_user_config", False),
}
# Filter out None values
kwargs = {k: v for k, v in kwargs.items() if v is not None}
@@ -1582,8 +1566,6 @@ def select_provider_and_model(args=None):
_model_flow_anthropic(config, current_model)
elif selected_provider == "kimi-coding":
_model_flow_kimi(config, current_model)
elif selected_provider == "stepfun":
_model_flow_stepfun(config, current_model)
elif selected_provider == "bedrock":
_model_flow_bedrock(config, current_model)
elif selected_provider in (
@@ -2183,6 +2165,7 @@ def _model_flow_nous(config, current_model="", args=None):
from hermes_cli.models import (
_PROVIDER_MODELS,
get_pricing_for_provider,
filter_nous_free_models,
check_nous_free_tier,
partition_nous_models_by_tier,
)
@@ -2225,8 +2208,10 @@ def _model_flow_nous(config, current_model="", args=None):
# Check if user is on free tier
free_tier = check_nous_free_tier()
# For free users: partition models into selectable/unavailable based on
# whether they are free per the Portal-reported pricing.
# For both tiers: apply the allowlist filter first (removes non-allowlisted
# free models and allowlist models that aren't actually free).
# Then for free users: partition remaining models into selectable/unavailable.
model_ids = filter_nous_free_models(model_ids, pricing)
unavailable_models: list[str] = []
if free_tier:
model_ids, unavailable_models = partition_nous_models_by_tier(
@@ -3480,140 +3465,6 @@ def _model_flow_kimi(config, current_model=""):
print("No change.")
def _infer_stepfun_region(base_url: str) -> str:
"""Infer the current StepFun region from the configured endpoint."""
normalized = (base_url or "").strip().lower()
if "api.stepfun.com" in normalized:
return "china"
return "international"
def _stepfun_base_url_for_region(region: str) -> str:
from hermes_cli.auth import (
STEPFUN_STEP_PLAN_CN_BASE_URL,
STEPFUN_STEP_PLAN_INTL_BASE_URL,
)
return (
STEPFUN_STEP_PLAN_CN_BASE_URL
if region == "china"
else STEPFUN_STEP_PLAN_INTL_BASE_URL
)
def _model_flow_stepfun(config, current_model=""):
"""StepFun Step Plan flow with region-specific endpoints."""
from hermes_cli.auth import (
PROVIDER_REGISTRY,
_prompt_model_selection,
_save_model_choice,
deactivate_provider,
)
from hermes_cli.config import get_env_value, save_env_value, load_config, save_config
from hermes_cli.models import fetch_api_models
provider_id = "stepfun"
pconfig = PROVIDER_REGISTRY[provider_id]
key_env = pconfig.api_key_env_vars[0] if pconfig.api_key_env_vars else ""
base_url_env = pconfig.base_url_env_var or ""
existing_key = ""
for ev in pconfig.api_key_env_vars:
existing_key = get_env_value(ev) or os.getenv(ev, "")
if existing_key:
break
if not existing_key:
print(f"No {pconfig.name} API key configured.")
if key_env:
try:
import getpass
new_key = getpass.getpass(f"{key_env} (or Enter to cancel): ").strip()
except (KeyboardInterrupt, EOFError):
print()
return
if not new_key:
print("Cancelled.")
return
save_env_value(key_env, new_key)
existing_key = new_key
print("API key saved.")
print()
else:
print(f" {pconfig.name} API key: {existing_key[:8]}... ✓")
print()
current_base = ""
if base_url_env:
current_base = get_env_value(base_url_env) or os.getenv(base_url_env, "")
if not current_base:
model_cfg = config.get("model")
if isinstance(model_cfg, dict):
current_base = str(model_cfg.get("base_url") or "").strip()
current_region = _infer_stepfun_region(current_base or pconfig.inference_base_url)
region_choices = [
("international", f"International ({_stepfun_base_url_for_region('international')})"),
("china", f"China ({_stepfun_base_url_for_region('china')})"),
]
ordered_regions = []
for region_key, label in region_choices:
if region_key == current_region:
ordered_regions.insert(0, (region_key, f"{label} ← currently active"))
else:
ordered_regions.append((region_key, label))
ordered_regions.append(("cancel", "Cancel"))
region_idx = _prompt_provider_choice([label for _, label in ordered_regions])
if region_idx is None or ordered_regions[region_idx][0] == "cancel":
print("No change.")
return
selected_region = ordered_regions[region_idx][0]
effective_base = _stepfun_base_url_for_region(selected_region)
if base_url_env:
save_env_value(base_url_env, effective_base)
live_models = fetch_api_models(existing_key, effective_base)
if live_models:
model_list = live_models
print(f" Found {len(model_list)} model(s) from {pconfig.name} API")
else:
model_list = _PROVIDER_MODELS.get(provider_id, [])
if model_list:
print(
f" Could not auto-detect models from {pconfig.name} API — "
"showing Step Plan fallback catalog."
)
if model_list:
selected = _prompt_model_selection(model_list, current_model=current_model)
else:
try:
selected = input("Model name: ").strip()
except (KeyboardInterrupt, EOFError):
selected = None
if selected:
_save_model_choice(selected)
cfg = load_config()
model = cfg.get("model")
if not isinstance(model, dict):
model = {"default": model} if model else {}
cfg["model"] = model
model["provider"] = provider_id
model["base_url"] = effective_base
model.pop("api_mode", None)
save_config(cfg)
deactivate_provider()
config["model"] = dict(model)
print(f"Default model set to: {selected} (via {pconfig.name})")
else:
print("No change.")
def _model_flow_bedrock_api_key(config, region, current_model=""):
"""Bedrock API Key mode — uses the OpenAI-compatible bedrock-mantle endpoint.
@@ -6622,18 +6473,6 @@ For more help on a command:
default=False,
help="Include the session ID in the agent's system prompt",
)
parser.add_argument(
"--ignore-user-config",
action="store_true",
default=False,
help="Ignore ~/.hermes/config.yaml and fall back to built-in defaults (credentials in .env are still loaded)",
)
parser.add_argument(
"--ignore-rules",
action="store_true",
default=False,
help="Skip auto-injection of AGENTS.md, SOUL.md, .cursorrules, memory, and preloaded skills",
)
parser.add_argument(
"--tui",
action="store_true",
@@ -6694,7 +6533,6 @@ For more help on a command:
"zai",
"kimi-coding",
"kimi-coding-cn",
"stepfun",
"minimax",
"minimax-cn",
"kilocode",
@@ -6773,18 +6611,6 @@ For more help on a command:
default=argparse.SUPPRESS,
help="Include the session ID in the agent's system prompt",
)
chat_parser.add_argument(
"--ignore-user-config",
action="store_true",
default=argparse.SUPPRESS,
help="Ignore ~/.hermes/config.yaml and fall back to built-in defaults (credentials in .env are still loaded). Useful for isolated CI runs, reproduction, and third-party integrations.",
)
chat_parser.add_argument(
"--ignore-rules",
action="store_true",
default=argparse.SUPPRESS,
help="Skip auto-injection of AGENTS.md, SOUL.md, .cursorrules, memory, and preloaded skills. Combine with --ignore-user-config for a fully isolated run.",
)
chat_parser.add_argument(
"--source",
default=None,
@@ -6928,12 +6754,6 @@ For more help on a command:
# 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_status.add_argument(
"-l",
"--full",
action="store_true",
help="Show full, untruncated service/log output where supported",
)
gateway_status.add_argument(
"--system",
action="store_true",
@@ -8516,7 +8336,7 @@ Examples:
def cmd_acp(args):
"""Launch Hermes Agent as an ACP server."""
try:
from hermes_agent.acp.entry import main as acp_main
from acp_adapter.entry import main as acp_main
acp_main()
except ImportError:
+30 -95
View File
@@ -143,7 +143,7 @@ MODEL_ALIASES: dict[str, ModelIdentity] = {
# Z.AI / GLM
"glm": ModelIdentity("z-ai", "glm"),
# Step Plan (StepFun)
# StepFun
"step": ModelIdentity("stepfun", "step"),
# Xiaomi
@@ -678,7 +678,6 @@ def switch_model(
_da = DIRECT_ALIASES.get(resolved_alias)
if _da is not None and _da.base_url:
base_url = _da.base_url
api_mode = "" # clear so determine_api_mode re-detects from URL
if not api_key:
api_key = "no-key-required"
@@ -782,7 +781,6 @@ def switch_model(
def list_authenticated_providers(
current_provider: str = "",
current_base_url: str = "",
user_providers: dict = None,
custom_providers: list | None = None,
max_models: int = 8,
@@ -811,10 +809,7 @@ def list_authenticated_providers(
get_provider_info as _mdev_pinfo,
)
from hermes_cli.auth import PROVIDER_REGISTRY
from hermes_cli.models import (
OPENROUTER_MODELS, _PROVIDER_MODELS,
_MODELS_DEV_PREFERRED, _merge_with_models_dev,
)
from hermes_cli.models import OPENROUTER_MODELS, _PROVIDER_MODELS
results: List[dict] = []
seen_slugs: set = set() # lowercase-normalized to catch case variants (#9545)
@@ -848,10 +843,6 @@ def list_authenticated_providers(
# source of truth. models.dev can have wrong mappings (e.g.
# minimax-cn → MINIMAX_API_KEY instead of MINIMAX_CN_API_KEY).
pconfig = PROVIDER_REGISTRY.get(hermes_id)
# Skip non-API-key auth providers here — they are handled in
# section 2 (HERMES_OVERLAYS) with proper auth store checking.
if pconfig and pconfig.auth_type != "api_key":
continue
if pconfig and pconfig.api_key_env_vars:
env_vars = list(pconfig.api_key_env_vars)
else:
@@ -864,13 +855,8 @@ def list_authenticated_providers(
if not has_creds:
continue
# Use curated list, falling back to models.dev if no curated list.
# For preferred providers, merge models.dev entries into the curated
# catalog so newly released models (e.g. mimo-v2.5-pro on opencode-go)
# show up in the picker without requiring a Hermes release.
# Use curated list, falling back to models.dev if no curated list
model_ids = curated.get(hermes_id, [])
if hermes_id in _MODELS_DEV_PREFERRED:
model_ids = _merge_with_models_dev(hermes_id, model_ids)
total = len(model_ids)
top = model_ids[:max_models]
@@ -974,9 +960,6 @@ def list_authenticated_providers(
# Use curated list — look up by Hermes slug, fall back to overlay key
model_ids = curated.get(hermes_slug, []) or curated.get(pid, [])
# Merge with models.dev for preferred providers (same rationale as above).
if hermes_slug in _MODELS_DEV_PREFERRED:
model_ids = _merge_with_models_dev(hermes_slug, model_ids)
total = len(model_ids)
top = model_ids[:max_models]
@@ -1122,113 +1105,66 @@ def list_authenticated_providers(
# --- 4. Saved custom providers from config ---
# Each ``custom_providers`` entry represents one model under a named
# provider. Entries sharing the same endpoint (``base_url`` + ``api_key``)
# are grouped into a single picker row, so e.g. four Ollama entries
# pointing at ``http://localhost:11434/v1`` with per-model display names
# ("Ollama — GLM 5.1", "Ollama — Qwen3-coder", ...) appear as one
# "Ollama" row with four models inside instead of four near-duplicates
# that differ only by suffix. Entries with distinct endpoints still
# produce separate rows.
#
# When the grouped endpoint matches ``current_base_url`` the group's
# slug becomes ``current_provider`` so that selecting a model from the
# picker flows back through the runtime provider that already holds
# valid credentials — no re-resolution needed.
# provider. Entries sharing the same provider name are grouped into a
# single picker row so that e.g. four Ollama Cloud entries
# (qwen3-coder, glm-5.1, kimi-k2, minimax-m2.7) appear as one
# "Ollama Cloud" row with four models inside instead of four
# duplicate "Ollama Cloud" rows. Entries with distinct provider names
# still produce separate rows (e.g. Ollama Cloud vs Moonshot).
if custom_providers and isinstance(custom_providers, list):
from collections import OrderedDict
# Key by (base_url, api_key) instead of slug: names frequently
# differ per model ("Ollama — X") while the endpoint stays the
# same. Slug-based grouping left them as separate rows.
groups: "OrderedDict[tuple, dict]" = OrderedDict()
groups: "OrderedDict[str, dict]" = OrderedDict()
for entry in custom_providers:
if not isinstance(entry, dict):
continue
raw_name = (entry.get("name") or "").strip()
display_name = (entry.get("name") or "").strip()
api_url = (
entry.get("base_url", "")
or entry.get("url", "")
or entry.get("api", "")
or ""
).strip().rstrip("/")
if not raw_name or not api_url:
).strip()
if not display_name or not api_url:
continue
api_key = (entry.get("api_key") or "").strip()
group_key = (api_url, api_key)
if group_key not in groups:
# Strip per-model suffix so "Ollama — GLM 5.1" becomes
# "Ollama" for the grouped row. Em dash is the convention
# Hermes's own writer uses; a hyphen variant is accepted
# for hand-edited configs.
display_name = raw_name
for sep in ("", " - "):
if sep in display_name:
display_name = display_name.split(sep)[0].strip()
break
if not display_name:
display_name = raw_name
# If this endpoint matches the currently active one, use
# ``current_provider`` as the slug so picker-driven switches
# route through the live credential pipeline.
if (
current_base_url
and api_url == current_base_url.strip().rstrip("/")
):
slug = current_provider or custom_provider_slug(display_name)
else:
slug = custom_provider_slug(display_name)
groups[group_key] = {
"slug": slug,
slug = custom_provider_slug(display_name)
if slug not in groups:
groups[slug] = {
"name": display_name,
"api_url": api_url,
"models": [],
}
# The singular ``model:`` field only holds the currently
# active model. Hermes's own writer (main.py::_save_custom_provider)
# stores every configured model as a dict under ``models:``;
# downstream readers (agent/models_dev.py, gateway/run.py,
# run_agent.py, hermes_cli/config.py) already consume that dict.
# The /model picker previously ignored it, so multi-model
# custom providers appeared to have only the active model.
default_model = (entry.get("model") or "").strip()
if default_model and default_model not in groups[group_key]["models"]:
groups[group_key]["models"].append(default_model)
if default_model and default_model not in groups[slug]["models"]:
groups[slug]["models"].append(default_model)
cfg_models = entry.get("models", {})
if isinstance(cfg_models, dict):
for m in cfg_models:
if m and m not in groups[group_key]["models"]:
groups[group_key]["models"].append(m)
if m and m not in groups[slug]["models"]:
groups[slug]["models"].append(m)
elif isinstance(cfg_models, list):
for m in cfg_models:
if m and m not in groups[group_key]["models"]:
groups[group_key]["models"].append(m)
if m and m not in groups[slug]["models"]:
groups[slug]["models"].append(m)
_section4_emitted_slugs: set = set()
for grp in groups.values():
slug = grp["slug"]
# If the slug is already claimed by a built-in / overlay /
# user-provider row (sections 1-3), skip this custom group
# to avoid shadowing a real provider.
if slug.lower() in seen_slugs and slug.lower() not in _section4_emitted_slugs:
for slug, grp in groups.items():
if slug.lower() in seen_slugs:
continue
# If a prior section-4 group already used this slug (two custom
# endpoints with the same cleaned name — e.g. two OpenAI-
# compatible gateways named identically with different keys),
# append a counter so both rows stay visible in the picker.
if slug.lower() in _section4_emitted_slugs:
base_slug = slug
n = 2
while f"{base_slug}-{n}".lower() in seen_slugs:
n += 1
slug = f"{base_slug}-{n}"
grp["slug"] = slug
# Skip if section 3 already emitted this endpoint under its
# ``providers:`` dict key — matches on (display_name, base_url).
# Prevents two picker rows labelled identically when callers
# pass both ``user_providers`` and a compatibility-merged
# ``custom_providers`` list.
# ``providers:`` dict key — matches on (display_name, base_url),
# the tuple section 4 groups by. Prevents two picker rows
# labelled identically when callers pass both ``user_providers``
# and a compatibility-merged ``custom_providers`` list.
_pair_key = (
str(grp["name"]).strip().lower(),
str(grp["api_url"]).strip().rstrip("/").lower(),
@@ -1246,7 +1182,6 @@ def list_authenticated_providers(
"api_url": grp["api_url"],
})
seen_slugs.add(slug.lower())
_section4_emitted_slugs.add(slug.lower())
# Sort: current provider first, then by model count descending
results.sort(key=lambda r: (not r["is_current"], -r["total_models"]))
+48 -260
View File
@@ -42,8 +42,7 @@ OPENROUTER_MODELS: list[tuple[str, str]] = [
("openrouter/elephant-alpha", "free"),
("openai/gpt-5.4", ""),
("openai/gpt-5.4-mini", ""),
("xiaomi/mimo-v2.5-pro", ""),
("xiaomi/mimo-v2.5", ""),
("xiaomi/mimo-v2-pro", ""),
("openai/gpt-5.3-codex", ""),
("google/gemini-3-pro-image-preview", ""),
("google/gemini-3-flash-preview", ""),
@@ -54,7 +53,6 @@ OPENROUTER_MODELS: list[tuple[str, str]] = [
("stepfun/step-3.5-flash", ""),
("minimax/minimax-m2.7", ""),
("minimax/minimax-m2.5", ""),
("minimax/minimax-m2.5:free", "free"),
("z-ai/glm-5.1", ""),
("z-ai/glm-5v-turbo", ""),
("z-ai/glm-5-turbo", ""),
@@ -109,8 +107,7 @@ def _codex_curated_models() -> list[str]:
_PROVIDER_MODELS: dict[str, list[str]] = {
"nous": [
"moonshotai/kimi-k2.6",
"xiaomi/mimo-v2.5-pro",
"xiaomi/mimo-v2.5",
"xiaomi/mimo-v2-pro",
"anthropic/claude-opus-4.7",
"anthropic/claude-opus-4.6",
"anthropic/claude-sonnet-4.6",
@@ -128,15 +125,17 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
"stepfun/step-3.5-flash",
"minimax/minimax-m2.7",
"minimax/minimax-m2.5",
"minimax/minimax-m2.5:free",
"z-ai/glm-5.1",
"z-ai/glm-5v-turbo",
"z-ai/glm-5-turbo",
"x-ai/grok-4.20-beta",
"nvidia/nemotron-3-super-120b-a12b",
"nvidia/nemotron-3-super-120b-a12b:free",
"arcee-ai/trinity-large-preview:free",
"arcee-ai/trinity-large-thinking",
"openai/gpt-5.4-pro",
"openai/gpt-5.4-nano",
"openrouter/elephant-alpha",
],
"openai-codex": _codex_curated_models(),
"copilot-acp": [
@@ -212,10 +211,6 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
"kimi-k2-turbo-preview",
"kimi-k2-0905-preview",
],
"stepfun": [
"step-3.5-flash",
"step-3.5-flash-2603",
],
"moonshot": [
"kimi-k2.6",
"kimi-k2.5",
@@ -367,11 +362,17 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
_PROVIDER_MODELS["ai-gateway"] = [mid for mid, _ in VERCEL_AI_GATEWAY_MODELS]
# ---------------------------------------------------------------------------
# Nous Portal free-model helper
# Nous Portal free-model filtering
# ---------------------------------------------------------------------------
# The Nous Portal models endpoint is the source of truth for which models
# are currently offered (free or paid). We trust whatever it returns and
# surface it to users as-is — no local allowlist filtering.
# Models that are ALLOWED to appear when priced as free on Nous Portal.
# Any other free model is hidden — prevents promotional/temporary free models
# from cluttering the selection when users are paying subscribers.
# Models in this list are ALSO filtered out if they are NOT free (i.e. they
# should only appear in the menu when they are genuinely free).
_NOUS_ALLOWED_FREE_MODELS: frozenset[str] = frozenset({
"xiaomi/mimo-v2-pro",
"xiaomi/mimo-v2-omni",
})
def _is_model_free(model_id: str, pricing: dict[str, dict[str, str]]) -> bool:
@@ -385,6 +386,35 @@ def _is_model_free(model_id: str, pricing: dict[str, dict[str, str]]) -> bool:
return False
def filter_nous_free_models(
model_ids: list[str],
pricing: dict[str, dict[str, str]],
) -> list[str]:
"""Filter the Nous Portal model list according to free-model policy.
Rules:
Paid models that are NOT in the allowlist keep (normal case).
Free models that are NOT in the allowlist drop.
Allowlist models that ARE free keep.
Allowlist models that are NOT free drop.
"""
if not pricing:
return model_ids # no pricing data — can't filter, show everything
result: list[str] = []
for mid in model_ids:
free = _is_model_free(mid, pricing)
if mid in _NOUS_ALLOWED_FREE_MODELS:
# Allowlist model: only show when it's actually free
if free:
result.append(mid)
else:
# Regular model: keep only when it's NOT free
if not free:
result.append(mid)
return result
# ---------------------------------------------------------------------------
# Nous Portal account tier detection
# ---------------------------------------------------------------------------
@@ -448,7 +478,8 @@ def partition_nous_models_by_tier(
) -> tuple[list[str], list[str]]:
"""Split Nous models into (selectable, unavailable) based on user tier.
For paid-tier users: all models are selectable, none unavailable.
For paid-tier users: all models are selectable, none unavailable
(free-model filtering is handled separately by ``filter_nous_free_models``).
For free-tier users: only free models are selectable; paid models
are returned as unavailable (shown grayed out in the menu).
@@ -518,157 +549,6 @@ def check_nous_free_tier() -> bool:
return False # default to paid on error — don't block users
# ---------------------------------------------------------------------------
# Nous Portal recommended models
#
# The Portal publishes a curated list of suggested models (separated into
# paid and free tiers) plus dedicated recommendations for compaction (text
# summarisation / auxiliary) and vision tasks. We fetch it once per process
# with a TTL cache so callers can ask "what's the best aux model right now?"
# without hitting the network on every lookup.
#
# Shape of the response (fields we care about):
# {
# "paidRecommendedModels": [ {modelName, ...}, ... ],
# "freeRecommendedModels": [ {modelName, ...}, ... ],
# "paidRecommendedCompactionModel": {modelName, ...} | null,
# "paidRecommendedVisionModel": {modelName, ...} | null,
# "freeRecommendedCompactionModel": {modelName, ...} | null,
# "freeRecommendedVisionModel": {modelName, ...} | null,
# }
# ---------------------------------------------------------------------------
NOUS_RECOMMENDED_MODELS_PATH = "/api/nous/recommended-models"
_NOUS_RECOMMENDED_CACHE_TTL: int = 600 # seconds (10 minutes)
# (result_dict, timestamp) keyed by portal_base_url so staging vs prod don't collide.
_nous_recommended_cache: dict[str, tuple[dict[str, Any], float]] = {}
def fetch_nous_recommended_models(
portal_base_url: str = "",
timeout: float = 5.0,
*,
force_refresh: bool = False,
) -> dict[str, Any]:
"""Fetch the Nous Portal's curated recommended-models payload.
Hits ``<portal>/api/nous/recommended-models``. The endpoint is public
no auth is required. Results are cached per portal URL for
``_NOUS_RECOMMENDED_CACHE_TTL`` seconds; pass ``force_refresh=True`` to
bypass the cache.
Returns the parsed JSON dict on success, or ``{}`` on any failure
(network, parse, non-2xx). Callers must treat missing/null fields as
"no recommendation" and fall back to their own default.
"""
base = (portal_base_url or "https://portal.nousresearch.com").rstrip("/")
now = time.monotonic()
cached = _nous_recommended_cache.get(base)
if not force_refresh and cached is not None:
payload, cached_at = cached
if now - cached_at < _NOUS_RECOMMENDED_CACHE_TTL:
return payload
url = f"{base}{NOUS_RECOMMENDED_MODELS_PATH}"
try:
req = urllib.request.Request(
url,
headers={"Accept": "application/json"},
)
with urllib.request.urlopen(req, timeout=timeout) as resp:
data = json.loads(resp.read().decode())
if not isinstance(data, dict):
data = {}
except Exception:
data = {}
_nous_recommended_cache[base] = (data, now)
return data
def _resolve_nous_portal_url() -> str:
"""Best-effort lookup of the Portal base URL the user is authed against."""
try:
from hermes_cli.auth import (
DEFAULT_NOUS_PORTAL_URL,
get_provider_auth_state,
)
state = get_provider_auth_state("nous") or {}
portal = str(state.get("portal_base_url") or "").strip()
if portal:
return portal.rstrip("/")
return str(DEFAULT_NOUS_PORTAL_URL).rstrip("/")
except Exception:
return "https://portal.nousresearch.com"
def _extract_model_name(entry: Any) -> Optional[str]:
"""Pull the ``modelName`` field from a recommended-model entry, else None."""
if not isinstance(entry, dict):
return None
model_name = entry.get("modelName")
if isinstance(model_name, str) and model_name.strip():
return model_name.strip()
return None
def get_nous_recommended_aux_model(
*,
vision: bool = False,
free_tier: Optional[bool] = None,
portal_base_url: str = "",
force_refresh: bool = False,
) -> Optional[str]:
"""Return the Portal's recommended model name for an auxiliary task.
Picks the best field from the Portal's recommended-models payload:
* ``vision=True`` ``paidRecommendedVisionModel`` (paid tier) or
``freeRecommendedVisionModel`` (free tier)
* ``vision=False`` ``paidRecommendedCompactionModel`` or
``freeRecommendedCompactionModel``
When ``free_tier`` is ``None`` (default) the user's tier is auto-detected
via :func:`check_nous_free_tier`. Pass an explicit bool to bypass the
detection useful for tests or when the caller already knows the tier.
For paid-tier users we prefer the paid recommendation but gracefully fall
back to the free recommendation if the Portal returned ``null`` for the
paid field (common during the staged rollout of new paid models).
Returns ``None`` when every candidate is missing, null, or the fetch
fails callers should fall back to their own default (currently
``google/gemini-3-flash-preview``).
"""
base = portal_base_url or _resolve_nous_portal_url()
payload = fetch_nous_recommended_models(base, force_refresh=force_refresh)
if not payload:
return None
if free_tier is None:
try:
free_tier = check_nous_free_tier()
except Exception:
# On any detection error, assume paid — paid users see both fields
# anyway so this is a safe default that maximises model quality.
free_tier = False
if vision:
paid_key, free_key = "paidRecommendedVisionModel", "freeRecommendedVisionModel"
else:
paid_key, free_key = "paidRecommendedCompactionModel", "freeRecommendedCompactionModel"
# Preference order:
# free tier → free only
# paid tier → paid, then free (if paid field is null)
candidates = [free_key] if free_tier else [paid_key, free_key]
for key in candidates:
name = _extract_model_name(payload.get(key))
if name:
return name
return None
# ---------------------------------------------------------------------------
# Canonical provider list — single source of truth for provider identity.
# Every code path that lists, displays, or iterates providers derives from
@@ -705,7 +585,6 @@ CANONICAL_PROVIDERS: list[ProviderEntry] = [
ProviderEntry("zai", "Z.AI / GLM", "Z.AI / GLM (Zhipu AI direct API)"),
ProviderEntry("kimi-coding", "Kimi / Kimi Coding Plan", "Kimi Coding Plan (api.kimi.com) & Moonshot API"),
ProviderEntry("kimi-coding-cn", "Kimi / Moonshot (China)", "Kimi / Moonshot China (Moonshot CN direct API)"),
ProviderEntry("stepfun", "StepFun Step Plan", "StepFun Step Plan (agent/coding models via Step Plan API)"),
ProviderEntry("minimax", "MiniMax", "MiniMax (global direct API)"),
ProviderEntry("minimax-cn", "MiniMax (China)", "MiniMax China (domestic direct API)"),
ProviderEntry("alibaba", "Alibaba Cloud (DashScope)","Alibaba Cloud / DashScope Coding (Qwen + multi-provider)"),
@@ -740,8 +619,6 @@ _PROVIDER_ALIASES = {
"moonshot": "kimi-coding",
"kimi-cn": "kimi-coding-cn",
"moonshot-cn": "kimi-coding-cn",
"step": "stepfun",
"stepfun-coding-plan": "stepfun",
"arcee-ai": "arcee",
"arceeai": "arcee",
"minimax-china": "minimax-cn",
@@ -1589,84 +1466,11 @@ def _resolve_copilot_catalog_api_key() -> str:
return ""
# Providers where models.dev is treated as authoritative: curated static
# lists are kept only as an offline fallback and to capture custom additions
# the registry doesn't publish yet. Adding a provider here causes its
# curated list to be merged with fresh models.dev entries (fresh first, any
# curated-only names appended) for both the CLI and the gateway /model picker.
#
# DELIBERATELY EXCLUDED:
# - "openrouter": curated list is already a hand-picked agentic subset of
# OpenRouter's 400+ catalog. Blindly merging would dump everything.
# - "nous": curated list and Portal /models endpoint are the source of
# truth for the subscription tier.
# Also excluded: providers that already have dedicated live-endpoint
# branches below (copilot, anthropic, ai-gateway, ollama-cloud, custom,
# stepfun, openai-codex) — those paths handle freshness themselves.
_MODELS_DEV_PREFERRED: frozenset[str] = frozenset({
"opencode-go",
"opencode-zen",
"deepseek",
"kilocode",
"fireworks",
"mistral",
"togetherai",
"cohere",
"perplexity",
"groq",
"nvidia",
"huggingface",
"zai",
"gemini",
"google",
})
def _merge_with_models_dev(provider: str, curated: list[str]) -> list[str]:
"""Merge curated list with fresh models.dev entries for a preferred provider.
Returns models.dev entries first (in models.dev order), then any
curated-only entries appended. Preserves case for curated fallbacks
(e.g. ``MiniMax-M2.7``) while trusting models.dev for newer variants.
If models.dev is unreachable or returns nothing, the curated list is
returned unchanged this is the offline/CI fallback path.
"""
try:
from agent.models_dev import list_agentic_models
mdev = list_agentic_models(provider)
except Exception:
mdev = []
if not mdev:
return list(curated)
# Case-insensitive dedup while preserving order and curated casing.
seen_lower: set[str] = set()
merged: list[str] = []
for mid in mdev:
key = str(mid).lower()
if key in seen_lower:
continue
seen_lower.add(key)
merged.append(mid)
for mid in curated:
key = str(mid).lower()
if key in seen_lower:
continue
seen_lower.add(key)
merged.append(mid)
return merged
def provider_model_ids(provider: Optional[str], *, force_refresh: bool = False) -> list[str]:
"""Return the best known model catalog for a provider.
Tries live API endpoints for providers that support them (Codex, Nous),
falling back to static lists. For providers in ``_MODELS_DEV_PREFERRED``
(opencode-go/zen, xiaomi, deepseek, smaller inference providers, etc.),
models.dev entries are merged on top of curated so new models released
on the platform appear in ``/model`` without a Hermes release.
falling back to static lists.
"""
normalized = normalize_provider(provider)
if normalized == "openrouter":
@@ -1695,19 +1499,6 @@ def provider_model_ids(provider: Optional[str], *, force_refresh: bool = False)
return live
except Exception:
pass
if normalized == "stepfun":
try:
from hermes_cli.auth import resolve_api_key_provider_credentials
creds = resolve_api_key_provider_credentials("stepfun")
api_key = str(creds.get("api_key") or "").strip()
base_url = str(creds.get("base_url") or "").strip()
if api_key and base_url:
live = fetch_api_models(api_key, base_url)
if live:
return live
except Exception:
pass
if normalized == "anthropic":
live = _fetch_anthropic_models()
if live:
@@ -1732,10 +1523,7 @@ def provider_model_ids(provider: Optional[str], *, force_refresh: bool = False)
live = fetch_api_models(api_key, base_url)
if live:
return live
curated_static = list(_PROVIDER_MODELS.get(normalized, []))
if normalized in _MODELS_DEV_PREFERRED:
return _merge_with_models_dev(normalized, curated_static)
return curated_static
return list(_PROVIDER_MODELS.get(normalized, []))
def _fetch_anthropic_models(timeout: float = 5.0) -> Optional[list[str]]:
+3 -3
View File
@@ -44,7 +44,7 @@ def _cmd_list(store):
for p in pending:
print(
f" {p['platform']:<12} {p['code']:<10} {p['user_id']:<20} "
f"{(p.get('user_name') or ''):<20} {p['age_minutes']}m ago"
f"{p.get('user_name', ''):<20} {p['age_minutes']}m ago"
)
else:
print("\n No pending pairing requests.")
@@ -54,7 +54,7 @@ def _cmd_list(store):
print(f" {'Platform':<12} {'User ID':<20} {'Name':<20}")
print(f" {'--------':<12} {'-------':<20} {'----':<20}")
for a in approved:
print(f" {a['platform']:<12} {a['user_id']:<20} {(a.get('user_name') or ''):<20}")
print(f" {a['platform']:<12} {a['user_id']:<20} {a.get('user_name', ''):<20}")
else:
print("\n No approved users.")
@@ -69,7 +69,7 @@ def _cmd_approve(store, platform: str, code: str):
result = store.approve_code(platform, code)
if result:
uid = result["user_id"]
name = result.get("user_name") or ""
name = result.get("user_name", "")
display = f"{name} ({uid})" if name else uid
print(f"\n Approved! User {display} on {platform} can now use the bot~")
print(" They'll be recognized automatically on their next message.\n")
+62 -290
View File
@@ -133,9 +133,6 @@ def _get_enabled_plugins() -> Optional[set]:
# Data classes
# ---------------------------------------------------------------------------
_VALID_PLUGIN_KINDS: Set[str] = {"standalone", "backend", "exclusive"}
@dataclass
class PluginManifest:
"""Parsed representation of a plugin.yaml manifest."""
@@ -149,23 +146,6 @@ class PluginManifest:
provides_hooks: List[str] = field(default_factory=list)
source: str = "" # "user", "project", or "entrypoint"
path: Optional[str] = None
# Plugin kind — see plugins.py module docstring for semantics.
# ``standalone`` (default): hooks/tools of its own; opt-in via
# ``plugins.enabled``.
# ``backend``: pluggable backend for an existing core tool (e.g.
# image_gen). Built-in (bundled) backends auto-load;
# user-installed still gated by ``plugins.enabled``.
# ``exclusive``: category with exactly one active provider (memory).
# Selection via ``<category>.provider`` config key; the
# category's own discovery system handles loading and the
# general scanner skips these.
kind: str = "standalone"
# Registry key — path-derived, used by ``plugins.enabled``/``disabled``
# lookups and by ``hermes plugins list``. For a flat plugin at
# ``plugins/disk-cleanup/`` the key is ``disk-cleanup``; for a nested
# category plugin at ``plugins/image_gen/openai/`` the key is
# ``image_gen/openai``. When empty, falls back to ``name``.
key: str = ""
@dataclass
@@ -283,7 +263,6 @@ class PluginContext:
name: str,
handler: Callable,
description: str = "",
args_hint: str = "",
) -> None:
"""Register a slash command (e.g. ``/lcm``) available in CLI and gateway sessions.
@@ -294,13 +273,6 @@ class PluginContext:
terminal commands), this registers in-session slash commands that users
invoke during a conversation.
``args_hint`` is an optional short string (e.g. ``"<file>"`` or
``"dias:7 formato:json"``) used by gateway adapters to surface the
command with an argument field for example Discord's native slash
command picker. Plugin commands without ``args_hint`` register as
parameterless in Discord and still accept trailing text when invoked
as free-form chat.
Names conflicting with built-in commands are rejected with a warning.
"""
clean = name.lower().strip().lstrip("/").replace(" ", "-")
@@ -328,7 +300,6 @@ class PluginContext:
"handler": handler,
"description": description or "Plugin command",
"plugin": self.manifest.name,
"args_hint": (args_hint or "").strip(),
}
logger.debug("Plugin %s registered command: /%s", self.manifest.name, clean)
@@ -395,33 +366,6 @@ class PluginContext:
self.manifest.name, engine.name,
)
# -- image gen provider registration ------------------------------------
def register_image_gen_provider(self, provider) -> None:
"""Register an image generation backend.
``provider`` must be an instance of
:class:`agent.image_gen_provider.ImageGenProvider`. The
``provider.name`` attribute is what ``image_gen.provider`` in
``config.yaml`` matches against when routing ``image_generate``
tool calls.
"""
from agent.image_gen_provider import ImageGenProvider
from agent.image_gen_registry import register_provider
if not isinstance(provider, ImageGenProvider):
logger.warning(
"Plugin '%s' tried to register an image_gen provider that does "
"not inherit from ImageGenProvider. Ignoring.",
self.manifest.name,
)
return
register_provider(provider)
logger.info(
"Plugin '%s' registered image_gen provider: %s",
self.manifest.name, provider.name,
)
# -- hook registration --------------------------------------------------
def register_hook(self, hook_name: str, callback: Callable) -> None:
@@ -512,38 +456,20 @@ class PluginManager:
# Public
# -----------------------------------------------------------------------
def discover_and_load(self, force: bool = False) -> None:
"""Scan all plugin sources and load each plugin found.
When ``force`` is true, clear cached discovery state first so config
changes or newly-added bundled backends become visible in long-lived
sessions without requiring a full agent restart.
"""
if self._discovered and not force:
def discover_and_load(self) -> None:
"""Scan all plugin sources and load each plugin found."""
if self._discovered:
return
if force:
self._plugins.clear()
self._hooks.clear()
self._plugin_tool_names.clear()
self._cli_commands.clear()
self._plugin_commands.clear()
self._plugin_skills.clear()
self._context_engine = None
self._discovered = True
manifests: List[PluginManifest] = []
# 1. Bundled plugins (<repo>/plugins/<name>/)
#
# Repo-shipped plugins live next to hermes_cli/. Two layouts are
# supported (see ``_scan_directory`` for details):
#
# - flat: ``plugins/disk-cleanup/plugin.yaml`` (standalone)
# - category: ``plugins/image_gen/openai/plugin.yaml`` (backend)
#
# ``memory/`` and ``context_engine/`` are skipped at the top level —
# they have their own discovery systems. Porting those to the
# category-namespace ``kind: exclusive`` model is a future PR.
# Repo-shipped generic plugins live next to hermes_cli/. Memory and
# context_engine subdirs are handled by their own discovery paths, so
# skip those names here. Bundled plugins are discovered (so they
# show up in `hermes plugins`) but only loaded when added to
# `plugins.enabled` in config.yaml — opt-in like any other plugin.
repo_plugins = Path(__file__).resolve().parent.parent / "plugins"
manifests.extend(
self._scan_directory(
@@ -566,69 +492,36 @@ class PluginManager:
manifests.extend(self._scan_entry_points())
# Load each manifest (skip user-disabled plugins).
# Later sources override earlier ones on key collision — user
# plugins take precedence over bundled, project plugins take
# precedence over user. Dedup here so we only load the final
# winner. Keys are path-derived (``image_gen/openai``,
# ``disk-cleanup``) so ``tts/openai`` and ``image_gen/openai``
# don't collide even when both manifests say ``name: openai``.
# Later sources override earlier ones on name collision — user plugins
# take precedence over bundled, project plugins take precedence over
# user. Dedup here so we only load the final winner.
disabled = _get_disabled_plugins()
enabled = _get_enabled_plugins() # None = opt-in default (nothing enabled)
winners: Dict[str, PluginManifest] = {}
for manifest in manifests:
winners[manifest.key or manifest.name] = manifest
winners[manifest.name] = manifest
for manifest in winners.values():
lookup_key = manifest.key or manifest.name
# Explicit disable always wins (matches on key or on legacy
# bare name for back-compat with existing user configs).
if lookup_key in disabled or manifest.name in disabled:
# Explicit disable always wins.
if manifest.name in disabled:
loaded = LoadedPlugin(manifest=manifest, enabled=False)
loaded.error = "disabled via config"
self._plugins[lookup_key] = loaded
logger.debug("Skipping disabled plugin '%s'", lookup_key)
self._plugins[manifest.name] = loaded
logger.debug("Skipping disabled plugin '%s'", manifest.name)
continue
# Exclusive plugins (memory providers) have their own
# discovery/activation path. The general loader records the
# manifest for introspection but does not load the module.
if manifest.kind == "exclusive":
# Opt-in gate: plugins must be in the enabled allow-list.
# If the allow-list is missing (None), treat as "nothing enabled"
# — users have to explicitly enable plugins to load them.
# Memory and context_engine providers are excluded from this gate
# since they have their own single-select config (memory.provider
# / context.engine), not the enabled list.
if enabled is None or manifest.name not in enabled:
loaded = LoadedPlugin(manifest=manifest, enabled=False)
loaded.error = (
"exclusive plugin — activate via <category>.provider config"
loaded.error = "not enabled in config (run `hermes plugins enable {}` to activate)".format(
manifest.name
)
self._plugins[lookup_key] = loaded
self._plugins[manifest.name] = loaded
logger.debug(
"Skipping '%s' (exclusive, handled by category discovery)",
lookup_key,
)
continue
# Built-in backends auto-load — they ship with hermes and must
# just work. Selection among them (e.g. which image_gen backend
# services calls) is driven by ``<category>.provider`` config,
# enforced by the tool wrapper.
if manifest.kind == "backend" and manifest.source == "bundled":
self._load_plugin(manifest)
continue
# Everything else (standalone, user-installed backends,
# entry-point plugins) is opt-in via plugins.enabled.
# Accept both the path-derived key and the legacy bare name
# so existing configs keep working.
is_enabled = (
enabled is not None
and (lookup_key in enabled or manifest.name in enabled)
)
if not is_enabled:
loaded = LoadedPlugin(manifest=manifest, enabled=False)
loaded.error = (
"not enabled in config (run `hermes plugins enable {}` to activate)"
.format(lookup_key)
)
self._plugins[lookup_key] = loaded
logger.debug(
"Skipping '%s' (not in plugins.enabled)", lookup_key
"Skipping '%s' (not in plugins.enabled)", manifest.name
)
continue
self._load_plugin(manifest)
@@ -652,37 +545,9 @@ class PluginManager:
) -> List[PluginManifest]:
"""Read ``plugin.yaml`` manifests from subdirectories of *path*.
Supports two layouts, mixed freely:
* **Flat** ``<root>/<plugin-name>/plugin.yaml``. Key is
``<plugin-name>`` (e.g. ``disk-cleanup``).
* **Category** ``<root>/<category>/<plugin-name>/plugin.yaml``,
where the ``<category>`` directory itself has no ``plugin.yaml``.
Key is ``<category>/<plugin-name>`` (e.g. ``image_gen/openai``).
Depth is capped at two segments.
*skip_names* is an optional allow-list of names to ignore at the
top level (kept for back-compat; the current call sites no longer
pass it now that categories are first-class).
"""
return self._scan_directory_level(
path, source, skip_names=skip_names, prefix="", depth=0
)
def _scan_directory_level(
self,
path: Path,
source: str,
*,
skip_names: Optional[Set[str]],
prefix: str,
depth: int,
) -> List[PluginManifest]:
"""Recursive implementation of :meth:`_scan_directory`.
``prefix`` is the category path already accumulated ("" at root,
"image_gen" one level in). ``depth`` is the recursion depth; we
cap at 2 so ``<root>/a/b/c/`` is ignored.
*skip_names* is an optional allow-list of names to ignore (used
for the bundled scan to exclude ``memory`` / ``context_engine``
subdirs that have their own discovery path).
"""
manifests: List[PluginManifest] = []
if not path.is_dir():
@@ -691,112 +556,37 @@ class PluginManager:
for child in sorted(path.iterdir()):
if not child.is_dir():
continue
if depth == 0 and skip_names and child.name in skip_names:
if skip_names and child.name in skip_names:
continue
manifest_file = child / "plugin.yaml"
if not manifest_file.exists():
manifest_file = child / "plugin.yml"
if manifest_file.exists():
manifest = self._parse_manifest(
manifest_file, child, source, prefix
)
if manifest is not None:
manifests.append(manifest)
if not manifest_file.exists():
logger.debug("Skipping %s (no plugin.yaml)", child)
continue
# No manifest at this level. If we're still within the depth
# cap, treat this directory as a category namespace and recurse
# one level in looking for children with manifests.
if depth >= 1:
logger.debug("Skipping %s (no plugin.yaml, depth cap reached)", child)
continue
sub_prefix = f"{prefix}/{child.name}" if prefix else child.name
manifests.extend(
self._scan_directory_level(
child,
source,
skip_names=None,
prefix=sub_prefix,
depth=depth + 1,
try:
if yaml is None:
logger.warning("PyYAML not installed cannot load %s", manifest_file)
continue
data = yaml.safe_load(manifest_file.read_text()) or {}
manifest = PluginManifest(
name=data.get("name", child.name),
version=str(data.get("version", "")),
description=data.get("description", ""),
author=data.get("author", ""),
requires_env=data.get("requires_env", []),
provides_tools=data.get("provides_tools", []),
provides_hooks=data.get("provides_hooks", []),
source=source,
path=str(child),
)
)
manifests.append(manifest)
except Exception as exc:
logger.warning("Failed to parse %s: %s", manifest_file, exc)
return manifests
def _parse_manifest(
self,
manifest_file: Path,
plugin_dir: Path,
source: str,
prefix: str,
) -> Optional[PluginManifest]:
"""Parse a single ``plugin.yaml`` into a :class:`PluginManifest`.
Returns ``None`` on parse failure (logs a warning).
"""
try:
if yaml is None:
logger.warning("PyYAML not installed cannot load %s", manifest_file)
return None
data = yaml.safe_load(manifest_file.read_text()) or {}
name = data.get("name", plugin_dir.name)
key = f"{prefix}/{plugin_dir.name}" if prefix else name
raw_kind = data.get("kind", "standalone")
if not isinstance(raw_kind, str):
raw_kind = "standalone"
kind = raw_kind.strip().lower()
if kind not in _VALID_PLUGIN_KINDS:
logger.warning(
"Plugin %s: unknown kind '%s' (valid: %s); treating as 'standalone'",
key, raw_kind, ", ".join(sorted(_VALID_PLUGIN_KINDS)),
)
kind = "standalone"
# Auto-coerce user-installed memory providers to kind="exclusive"
# so they're routed to plugins/memory discovery instead of being
# loaded by the general PluginManager (which has no
# register_memory_provider on PluginContext). Mirrors the
# heuristic in plugins/memory/__init__.py:_is_memory_provider_dir.
# Bundled memory providers are already skipped via skip_names.
if kind == "standalone" and "kind" not in data:
init_file = plugin_dir / "__init__.py"
if init_file.exists():
try:
source_text = init_file.read_text(errors="replace")[:8192]
if (
"register_memory_provider" in source_text
or "MemoryProvider" in source_text
):
kind = "exclusive"
logger.debug(
"Plugin %s: detected memory provider, "
"treating as kind='exclusive'",
key,
)
except Exception:
pass
return PluginManifest(
name=name,
version=str(data.get("version", "")),
description=data.get("description", ""),
author=data.get("author", ""),
requires_env=data.get("requires_env", []),
provides_tools=data.get("provides_tools", []),
provides_hooks=data.get("provides_hooks", []),
source=source,
path=str(plugin_dir),
kind=kind,
key=key,
)
except Exception as exc:
logger.warning("Failed to parse %s: %s", manifest_file, exc)
return None
# -----------------------------------------------------------------------
# Entry-point scanning
# -----------------------------------------------------------------------
@@ -819,7 +609,6 @@ class PluginManager:
name=ep.name,
source="entrypoint",
path=ep.value,
key=ep.name,
)
manifests.append(manifest)
except Exception as exc:
@@ -881,16 +670,10 @@ class PluginManager:
loaded.error = str(exc)
logger.warning("Failed to load plugin '%s': %s", manifest.name, exc)
self._plugins[manifest.key or manifest.name] = loaded
self._plugins[manifest.name] = loaded
def _load_directory_module(self, manifest: PluginManifest) -> types.ModuleType:
"""Import a directory-based plugin as ``hermes_plugins.<slug>``.
The module slug is derived from ``manifest.key`` so category-namespaced
plugins (``image_gen/openai``) import as
``hermes_plugins.image_gen__openai`` without colliding with any
future ``tts/openai``.
"""
"""Import a directory-based plugin as ``hermes_plugins.<name>``."""
plugin_dir = Path(manifest.path) # type: ignore[arg-type]
init_file = plugin_dir / "__init__.py"
if not init_file.exists():
@@ -903,9 +686,7 @@ class PluginManager:
ns_pkg.__package__ = _NS_PARENT
sys.modules[_NS_PARENT] = ns_pkg
key = manifest.key or manifest.name
slug = key.replace("/", "__").replace("-", "_")
module_name = f"{_NS_PARENT}.{slug}"
module_name = f"{_NS_PARENT}.{manifest.name.replace('-', '_')}"
spec = importlib.util.spec_from_file_location(
module_name,
init_file,
@@ -986,12 +767,10 @@ class PluginManager:
def list_plugins(self) -> List[Dict[str, Any]]:
"""Return a list of info dicts for all discovered plugins."""
result: List[Dict[str, Any]] = []
for key, loaded in sorted(self._plugins.items()):
for name, loaded in sorted(self._plugins.items()):
result.append(
{
"name": loaded.manifest.name,
"key": loaded.manifest.key or loaded.manifest.name,
"kind": loaded.manifest.kind,
"name": name,
"version": loaded.manifest.version,
"description": loaded.manifest.description,
"source": loaded.manifest.source,
@@ -1042,13 +821,9 @@ def get_plugin_manager() -> PluginManager:
return _plugin_manager
def discover_plugins(force: bool = False) -> None:
"""Discover and load all plugins.
Default behavior is idempotent. Pass ``force=True`` to rescan plugin
manifests and reload state in the current process.
"""
get_plugin_manager().discover_and_load(force=force)
def discover_plugins() -> None:
"""Discover and load all plugins (idempotent)."""
get_plugin_manager().discover_and_load()
def invoke_hook(hook_name: str, **kwargs: Any) -> List[Any]:
@@ -1099,13 +874,10 @@ def get_pre_tool_call_block_message(
return None
def _ensure_plugins_discovered(force: bool = False) -> PluginManager:
"""Return the global manager after ensuring plugin discovery has run.
Pass ``force=True`` to rescan in the current process.
"""
def _ensure_plugins_discovered() -> PluginManager:
"""Return the global manager after running idempotent plugin discovery."""
manager = get_plugin_manager()
manager.discover_and_load(force=force)
manager.discover_and_load()
return manager
+15 -41
View File
@@ -863,15 +863,19 @@ def _safe_extract_profile_archive(archive: Path, destination: Path) -> None:
pass
def _inspect_profile_archive_roots(archive: Path) -> set[str]:
"""Return the archive's top-level directory names.
def import_profile(archive_path: str, name: Optional[str] = None) -> Path:
"""Import a profile from a tar.gz archive.
Profile imports expect exactly one root directory. Inspecting the archive
before extraction lets us stage the import safely instead of mutating a
live profile tree first and reconciling names later.
If *name* is not given, infers it from the archive's top-level directory.
Returns the imported profile directory.
"""
import tarfile
archive = Path(archive_path)
if not archive.exists():
raise FileNotFoundError(f"Archive not found: {archive}")
# Peek at the archive to find the top-level directory name
with tarfile.open(archive, "r:gz") as tf:
top_dirs = {
parts[0]
@@ -885,33 +889,13 @@ def _inspect_profile_archive_roots(archive: Path) -> set[str]:
for member in tf.getmembers()
if member.isdir()
}
return top_dirs
def import_profile(archive_path: str, name: Optional[str] = None) -> Path:
"""Import a profile from a tar.gz archive.
If *name* is not given, infers it from the archive's top-level directory.
Returns the imported profile directory.
"""
import tempfile
archive = Path(archive_path)
if not archive.exists():
raise FileNotFoundError(f"Archive not found: {archive}")
top_dirs = _inspect_profile_archive_roots(archive)
archive_root = top_dirs.pop() if len(top_dirs) == 1 else None
inferred_name = name or archive_root
inferred_name = name or (top_dirs.pop() if len(top_dirs) == 1 else None)
if not inferred_name:
raise ValueError(
"Cannot determine profile name from archive. "
"Specify it explicitly: hermes profile import <archive> --name <name>"
)
if archive_root is None:
raise ValueError(
"Profile archive must contain exactly one top-level directory."
)
# Archives exported from the default profile have "default/" as top-level
# dir. Importing as "default" would target ~/.hermes itself — disallow
@@ -930,22 +914,12 @@ def import_profile(archive_path: str, name: Optional[str] = None) -> Path:
profiles_root = _get_profiles_root()
profiles_root.mkdir(parents=True, exist_ok=True)
with tempfile.TemporaryDirectory(prefix="hermes_profile_import_") as tmpdir:
staging_root = Path(tmpdir)
_safe_extract_profile_archive(archive, staging_root)
_safe_extract_profile_archive(archive, profiles_root)
extracted = staging_root / archive_root
if not extracted.is_dir():
raise ValueError(
f"Profile archive root is missing or invalid: {archive_root}"
)
final_source = extracted
if archive_root != inferred_name:
final_source = staging_root / inferred_name
extracted.rename(final_source)
shutil.move(str(final_source), str(profile_dir))
# If the archive extracted under a different name, rename
extracted = profiles_root / (top_dirs.pop() if top_dirs else inferred_name)
if extracted != profile_dir and extracted.exists():
extracted.rename(profile_dir)
return profile_dir
-23
View File
@@ -94,12 +94,6 @@ HERMES_OVERLAYS: Dict[str, HermesOverlay] = {
transport="openai_chat",
base_url_env_var="KIMI_BASE_URL",
),
"stepfun": HermesOverlay(
transport="openai_chat",
extra_env_vars=("STEPFUN_API_KEY",),
base_url_override="https://api.stepfun.ai/step_plan/v1",
base_url_env_var="STEPFUN_BASE_URL",
),
"minimax": HermesOverlay(
transport="anthropic_messages",
base_url_env_var="MINIMAX_BASE_URL",
@@ -216,10 +210,6 @@ ALIASES: Dict[str, str] = {
"kimi-coding-cn": "kimi-for-coding",
"moonshot": "kimi-for-coding",
# stepfun
"step": "stepfun",
"stepfun-coding-plan": "stepfun",
# minimax-cn
"minimax-china": "minimax-cn",
"minimax_cn": "minimax-cn",
@@ -304,7 +294,6 @@ _LABEL_OVERRIDES: Dict[str, str] = {
"nous": "Nous Portal",
"openai-codex": "OpenAI Codex",
"copilot-acp": "GitHub Copilot ACP",
"stepfun": "StepFun Step Plan",
"xiaomi": "Xiaomi MiMo",
"local": "Local endpoint",
"bedrock": "AWS Bedrock",
@@ -438,16 +427,6 @@ def determine_api_mode(provider: str, base_url: str = "") -> str:
"""
pdef = get_provider(provider)
if pdef is not None:
# Even for known providers, check URL heuristics for special endpoints
# (e.g. kimi /coding endpoint needs anthropic_messages even on 'custom')
if base_url:
url_lower = base_url.rstrip("/").lower()
if "api.kimi.com/coding" in url_lower:
return "anthropic_messages"
if url_lower.endswith("/anthropic") or "api.anthropic.com" in url_lower:
return "anthropic_messages"
if "api.openai.com" in url_lower:
return "codex_responses"
return TRANSPORT_TO_API_MODE.get(pdef.transport, "chat_completions")
# Direct provider checks for providers not in HERMES_OVERLAYS
@@ -460,8 +439,6 @@ def determine_api_mode(provider: str, base_url: str = "") -> str:
hostname = base_url_hostname(base_url)
if url_lower.endswith("/anthropic") or hostname == "api.anthropic.com":
return "anthropic_messages"
if hostname == "api.kimi.com" and "/coding" in url_lower:
return "anthropic_messages"
if hostname == "api.openai.com":
return "codex_responses"
if hostname.startswith("bedrock-runtime.") and base_url_host_matches(base_url, "amazonaws.com"):
+2 -9
View File
@@ -46,9 +46,6 @@ def _detect_api_mode_for_url(base_url: str) -> Optional[str]:
protocol under a ``/anthropic`` suffix treat those as
``anthropic_messages`` transport instead of the default
``chat_completions``.
- Kimi Code's ``api.kimi.com/coding`` endpoint also speaks the
Anthropic Messages protocol (the /coding route accepts Claude
Code's native request shape).
"""
normalized = (base_url or "").strip().lower().rstrip("/")
hostname = base_url_hostname(base_url)
@@ -58,8 +55,6 @@ def _detect_api_mode_for_url(base_url: str) -> Optional[str]:
return "codex_responses"
if normalized.endswith("/anthropic"):
return "anthropic_messages"
if hostname == "api.kimi.com" and "/coding" in normalized:
return "anthropic_messages"
return None
@@ -210,8 +205,7 @@ def _resolve_runtime_from_pool_entry(
api_mode = opencode_model_api_mode(provider, model_cfg.get("default", ""))
else:
# Auto-detect Anthropic-compatible endpoints (/anthropic suffix,
# Kimi /coding, api.openai.com → codex_responses, api.x.ai →
# codex_responses).
# api.openai.com → codex_responses, api.x.ai → codex_responses).
detected = _detect_api_mode_for_url(base_url)
if detected:
api_mode = detected
@@ -666,8 +660,7 @@ def _resolve_explicit_runtime(
if configured_mode:
api_mode = configured_mode
else:
# Auto-detect from URL (Anthropic /anthropic suffix,
# api.openai.com → Responses, Kimi /coding, etc.).
# Auto-detect Anthropic-compatible endpoints (/anthropic suffix).
detected = _detect_api_mode_for_url(base_url)
if detected:
api_mode = detected
+2 -40
View File
@@ -96,7 +96,6 @@ _DEFAULT_PROVIDER_MODELS = {
"zai": ["glm-5.1", "glm-5", "glm-4.7", "glm-4.5", "glm-4.5-flash"],
"kimi-coding": ["kimi-k2.6", "kimi-k2.5", "kimi-k2-thinking", "kimi-k2-turbo-preview"],
"kimi-coding-cn": ["kimi-k2.6", "kimi-k2.5", "kimi-k2-thinking", "kimi-k2-turbo-preview"],
"stepfun": ["step-3.5-flash", "step-3.5-flash-2603"],
"arcee": ["trinity-large-thinking", "trinity-large-preview", "trinity-mini"],
"minimax": ["MiniMax-M2.7", "MiniMax-M2.5", "MiniMax-M2.1", "MiniMax-M2"],
"minimax-cn": ["MiniMax-M2.7", "MiniMax-M2.5", "MiniMax-M2.1", "MiniMax-M2"],
@@ -409,36 +408,13 @@ def _print_setup_summary(config: dict, hermes_home):
("Browser Automation", False, missing_browser_hint)
)
# Image generation — FAL (direct or via Nous), or any plugin-registered
# provider (OpenAI, etc.)
# FAL (image generation)
if subscription_features.image_gen.managed_by_nous:
tool_status.append(("Image Generation (Nous subscription)", True, None))
elif subscription_features.image_gen.available:
tool_status.append(("Image Generation", True, None))
else:
# Fall back to probing plugin-registered providers so OpenAI-only
# setups don't show as "missing FAL_KEY".
_img_backend = None
try:
from agent.image_gen_registry import list_providers
from hermes_cli.plugins import _ensure_plugins_discovered
_ensure_plugins_discovered()
for _p in list_providers():
if _p.name == "fal":
continue
try:
if _p.is_available():
_img_backend = _p.display_name
break
except Exception:
continue
except Exception:
pass
if _img_backend:
tool_status.append((f"Image Generation ({_img_backend})", True, None))
else:
tool_status.append(("Image Generation", False, "FAL_KEY or OPENAI_API_KEY"))
tool_status.append(("Image Generation", False, "FAL_KEY"))
# TTS — show configured provider
tts_provider = config.get("tts", {}).get("provider", "edge")
@@ -805,7 +781,6 @@ def setup_model_provider(config: dict, *, quick: bool = False):
"zai": "Z.AI / GLM",
"kimi-coding": "Kimi / Moonshot",
"kimi-coding-cn": "Kimi / Moonshot (China)",
"stepfun": "StepFun Step Plan",
"minimax": "MiniMax",
"minimax-cn": "MiniMax CN",
"anthropic": "Anthropic",
@@ -2334,7 +2309,6 @@ def setup_gateway(config: dict):
launchd_install,
launchd_start,
launchd_restart,
UserSystemdUnavailableError,
)
service_installed = _is_service_installed()
@@ -2358,10 +2332,6 @@ def setup_gateway(config: dict):
systemd_restart()
elif _is_macos:
launchd_restart()
except UserSystemdUnavailableError as e:
print_error(" Restart failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except Exception as e:
print_error(f" Restart failed: {e}")
elif service_installed:
@@ -2371,10 +2341,6 @@ def setup_gateway(config: dict):
systemd_start()
elif _is_macos:
launchd_start()
except UserSystemdUnavailableError as e:
print_error(" Start failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except Exception as e:
print_error(f" Start failed: {e}")
elif supports_service_manager:
@@ -2398,10 +2364,6 @@ def setup_gateway(config: dict):
systemd_start(system=installed_scope == "system")
elif _is_macos:
launchd_start()
except UserSystemdUnavailableError as e:
print_error(" Start failed — user systemd not reachable:")
for line in str(e).splitlines():
print(f" {line}")
except Exception as e:
print_error(f" Start failed: {e}")
except Exception as e:
+7 -71
View File
@@ -30,14 +30,6 @@ All fields are optional. Missing values inherit from the ``default`` skin.
prompt: "#FFF8DC" # Prompt text color
input_rule: "#CD7F32" # Input area horizontal rule
response_border: "#FFD700" # Response box border (ANSI)
status_bar_bg: "#1a1a2e" # Status bar background
status_bar_text: "#C0C0C0" # Status bar default text
status_bar_strong: "#FFD700" # Status bar highlighted text
status_bar_dim: "#8B8682" # Status bar separators/muted text
status_bar_good: "#8FBC8F" # Healthy context usage
status_bar_warn: "#FFD700" # Warning context usage
status_bar_bad: "#FF8C00" # High context usage
status_bar_critical: "#FF6B6B" # Critical context usage
session_label: "#DAA520" # Session label color
session_border: "#8B8682" # Session ID dim color
status_bar_bg: "#1a1a2e" # TUI status/usage bar background
@@ -178,7 +170,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#FFF8DC",
"input_rule": "#CD7F32",
"response_border": "#FFD700",
"status_bar_bg": "#1a1a2e",
"session_label": "#DAA520",
"session_border": "#8B8682",
},
@@ -212,14 +203,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#F1E6CF",
"input_rule": "#9F1C1C",
"response_border": "#C7A96B",
"status_bar_bg": "#2A1212",
"status_bar_text": "#F1E6CF",
"status_bar_strong": "#C7A96B",
"status_bar_dim": "#6E584B",
"status_bar_good": "#7BC96F",
"status_bar_warn": "#C7A96B",
"status_bar_bad": "#DD4A3A",
"status_bar_critical": "#EF5350",
"session_label": "#C7A96B",
"session_border": "#6E584B",
},
@@ -284,14 +267,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#c9d1d9",
"input_rule": "#444444",
"response_border": "#aaaaaa",
"status_bar_bg": "#1F1F1F",
"status_bar_text": "#C9D1D9",
"status_bar_strong": "#E6EDF3",
"status_bar_dim": "#777777",
"status_bar_good": "#B5B5B5",
"status_bar_warn": "#AAAAAA",
"status_bar_bad": "#D0D0D0",
"status_bar_critical": "#F0F0F0",
"session_label": "#888888",
"session_border": "#555555",
},
@@ -323,14 +298,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#c9d1d9",
"input_rule": "#4169e1",
"response_border": "#7eb8f6",
"status_bar_bg": "#151C2F",
"status_bar_text": "#C9D1D9",
"status_bar_strong": "#7EB8F6",
"status_bar_dim": "#4B5563",
"status_bar_good": "#63D0A6",
"status_bar_warn": "#E6A855",
"status_bar_bad": "#F7A072",
"status_bar_critical": "#FF7A7A",
"session_label": "#7eb8f6",
"session_border": "#4b5563",
},
@@ -436,14 +403,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#EAF7FF",
"input_rule": "#2A6FB9",
"response_border": "#5DB8F5",
"status_bar_bg": "#0F2440",
"status_bar_text": "#EAF7FF",
"status_bar_strong": "#A9DFFF",
"status_bar_dim": "#496884",
"status_bar_good": "#6ED7B0",
"status_bar_warn": "#5DB8F5",
"status_bar_bad": "#2A6FB9",
"status_bar_critical": "#D94F4F",
"session_label": "#A9DFFF",
"session_border": "#496884",
},
@@ -508,14 +467,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#F5F5F5",
"input_rule": "#656565",
"response_border": "#B7B7B7",
"status_bar_bg": "#202020",
"status_bar_text": "#D3D3D3",
"status_bar_strong": "#F5F5F5",
"status_bar_dim": "#656565",
"status_bar_good": "#B7B7B7",
"status_bar_warn": "#D3D3D3",
"status_bar_bad": "#E7E7E7",
"status_bar_critical": "#F5F5F5",
"session_label": "#919191",
"session_border": "#656565",
},
@@ -581,14 +532,6 @@ _BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
"prompt": "#FFF0D4",
"input_rule": "#C75B1D",
"response_border": "#F29C38",
"status_bar_bg": "#2B160E",
"status_bar_text": "#FFF0D4",
"status_bar_strong": "#FFD39A",
"status_bar_dim": "#6C4724",
"status_bar_good": "#6BCB77",
"status_bar_warn": "#F29C38",
"status_bar_bad": "#E2832B",
"status_bar_critical": "#EF5350",
"session_label": "#FFD39A",
"session_border": "#6C4724",
},
@@ -827,13 +770,6 @@ def get_prompt_toolkit_style_overrides() -> Dict[str, str]:
warn = skin.get_color("ui_warn", "#FF8C00")
error = skin.get_color("ui_error", "#FF6B6B")
status_bg = skin.get_color("status_bar_bg", "#1a1a2e")
status_text = skin.get_color("status_bar_text", text)
status_strong = skin.get_color("status_bar_strong", title)
status_dim = skin.get_color("status_bar_dim", dim)
status_good = skin.get_color("status_bar_good", skin.get_color("ui_ok", "#8FBC8F"))
status_warn = skin.get_color("status_bar_warn", warn)
status_bad = skin.get_color("status_bar_bad", skin.get_color("banner_accent", warn))
status_critical = skin.get_color("status_bar_critical", error)
voice_bg = skin.get_color("voice_status_bg", status_bg)
menu_bg = skin.get_color("completion_menu_bg", "#1a1a2e")
menu_current_bg = skin.get_color("completion_menu_current_bg", "#333355")
@@ -846,13 +782,13 @@ def get_prompt_toolkit_style_overrides() -> Dict[str, str]:
"prompt": prompt,
"prompt-working": f"{dim} italic",
"hint": f"{dim} italic",
"status-bar": f"bg:{status_bg} {status_text}",
"status-bar-strong": f"bg:{status_bg} {status_strong} bold",
"status-bar-dim": f"bg:{status_bg} {status_dim}",
"status-bar-good": f"bg:{status_bg} {status_good} bold",
"status-bar-warn": f"bg:{status_bg} {status_warn} bold",
"status-bar-bad": f"bg:{status_bg} {status_bad} bold",
"status-bar-critical": f"bg:{status_bg} {status_critical} bold",
"status-bar": f"bg:{status_bg} {text}",
"status-bar-strong": f"bg:{status_bg} {title} bold",
"status-bar-dim": f"bg:{status_bg} {dim}",
"status-bar-good": f"bg:{status_bg} {skin.get_color('ui_ok', '#8FBC8F')} bold",
"status-bar-warn": f"bg:{status_bg} {warn} bold",
"status-bar-bad": f"bg:{status_bg} {skin.get_color('banner_accent', warn)} bold",
"status-bar-critical": f"bg:{status_bg} {error} bold",
"input-rule": input_rule,
"image-badge": f"{label} bold",
"completion-menu": f"bg:{menu_bg} {text}",
-2
View File
@@ -122,7 +122,6 @@ def show_status(args):
"OpenAI": "OPENAI_API_KEY",
"Z.AI/GLM": "GLM_API_KEY",
"Kimi": "KIMI_API_KEY",
"StepFun Step Plan": "STEPFUN_API_KEY",
"MiniMax": "MINIMAX_API_KEY",
"MiniMax-CN": "MINIMAX_CN_API_KEY",
"Firecrawl": "FIRECRAWL_API_KEY",
@@ -253,7 +252,6 @@ def show_status(args):
apikey_providers = {
"Z.AI / GLM": ("GLM_API_KEY", "ZAI_API_KEY", "Z_AI_API_KEY"),
"Kimi / Moonshot": ("KIMI_API_KEY",),
"StepFun Step Plan": ("STEPFUN_API_KEY",),
"MiniMax": ("MINIMAX_API_KEY",),
"MiniMax (China)": ("MINIMAX_CN_API_KEY",),
}
+1 -237
View File
@@ -847,51 +847,6 @@ def _configure_toolset(ts_key: str, config: dict):
_configure_simple_requirements(ts_key)
def _plugin_image_gen_providers() -> list[dict]:
"""Build picker-row dicts from plugin-registered image gen providers.
Each returned dict looks like a regular ``TOOL_CATEGORIES`` provider
row but carries an ``image_gen_plugin_name`` marker so downstream
code (config writing, model picker) knows to route through the
plugin registry instead of the in-tree FAL backend.
FAL is skipped it's already exposed by the hardcoded
``TOOL_CATEGORIES["image_gen"]`` entries. When FAL gets ported to
a plugin in a follow-up PR, the hardcoded entries go away and this
function surfaces it alongside OpenAI automatically.
"""
try:
from agent.image_gen_registry import list_providers
from hermes_cli.plugins import _ensure_plugins_discovered
_ensure_plugins_discovered()
providers = list_providers()
except Exception:
return []
rows: list[dict] = []
for provider in providers:
if getattr(provider, "name", None) == "fal":
# FAL has its own hardcoded rows today.
continue
try:
schema = provider.get_setup_schema()
except Exception:
continue
if not isinstance(schema, dict):
continue
rows.append(
{
"name": schema.get("name", provider.display_name),
"badge": schema.get("badge", ""),
"tag": schema.get("tag", ""),
"env_vars": schema.get("env_vars", []),
"image_gen_plugin_name": provider.name,
}
)
return rows
def _visible_providers(cat: dict, config: dict) -> list[dict]:
"""Return provider entries visible for the current auth/config state."""
features = get_nous_subscription_features(config)
@@ -902,12 +857,6 @@ def _visible_providers(cat: dict, config: dict) -> list[dict]:
if provider.get("requires_nous_auth") and not features.nous_auth_present:
continue
visible.append(provider)
# Inject plugin-registered image_gen backends (OpenAI today, more
# later) so the picker lists them alongside FAL / Nous Subscription.
if cat.get("name") == "Image Generation":
visible.extend(_plugin_image_gen_providers())
return visible
@@ -927,24 +876,7 @@ def _toolset_needs_configuration_prompt(ts_key: str, config: dict) -> bool:
browser_cfg = config.get("browser", {})
return not isinstance(browser_cfg, dict) or "cloud_provider" not in browser_cfg
if ts_key == "image_gen":
# Satisfied when the in-tree FAL backend is configured OR any
# plugin-registered image gen provider is available.
if fal_key_is_configured():
return False
try:
from agent.image_gen_registry import list_providers
from hermes_cli.plugins import _ensure_plugins_discovered
_ensure_plugins_discovered()
for provider in list_providers():
try:
if provider.is_available():
return False
except Exception:
continue
except Exception:
pass
return True
return not fal_key_is_configured()
return not _toolset_has_keys(ts_key, config)
@@ -1019,11 +951,6 @@ def _configure_tool_category(ts_key: str, cat: dict, config: dict):
def _is_provider_active(provider: dict, config: dict) -> bool:
"""Check if a provider entry matches the currently active config."""
plugin_name = provider.get("image_gen_plugin_name")
if plugin_name:
image_cfg = config.get("image_gen", {})
return isinstance(image_cfg, dict) and image_cfg.get("provider") == plugin_name
managed_feature = provider.get("managed_nous_feature")
if managed_feature:
features = get_nous_subscription_features(config)
@@ -1031,13 +958,6 @@ def _is_provider_active(provider: dict, config: dict) -> bool:
if feature is None:
return False
if managed_feature == "image_gen":
image_cfg = config.get("image_gen", {})
if isinstance(image_cfg, dict):
configured_provider = image_cfg.get("provider")
if configured_provider not in (None, "", "fal"):
return False
if image_cfg.get("use_gateway") is False:
return False
return feature.managed_by_nous
if provider.get("tts_provider"):
return (
@@ -1060,16 +980,6 @@ def _is_provider_active(provider: dict, config: dict) -> bool:
if provider.get("web_backend"):
current = config.get("web", {}).get("backend")
return current == provider["web_backend"]
if provider.get("imagegen_backend"):
image_cfg = config.get("image_gen", {})
if not isinstance(image_cfg, dict):
return False
configured_provider = image_cfg.get("provider")
return (
provider["imagegen_backend"] == "fal"
and configured_provider in (None, "", "fal")
and not image_cfg.get("use_gateway")
)
return False
@@ -1185,100 +1095,6 @@ def _configure_imagegen_model(backend_name: str, config: dict) -> None:
_print_success(f" Model set to: {chosen}")
def _plugin_image_gen_catalog(plugin_name: str):
"""Return ``(catalog_dict, default_model_id)`` for a plugin provider.
``catalog_dict`` is shaped like the legacy ``FAL_MODELS`` table
``{model_id: {"display", "speed", "strengths", "price", ...}}``
so the existing picker code paths work without change. Returns
``({}, None)`` if the provider isn't registered or has no models.
"""
try:
from agent.image_gen_registry import get_provider
from hermes_cli.plugins import _ensure_plugins_discovered
_ensure_plugins_discovered()
provider = get_provider(plugin_name)
except Exception:
return {}, None
if provider is None:
return {}, None
try:
models = provider.list_models() or []
default = provider.default_model()
except Exception:
return {}, None
catalog = {m["id"]: m for m in models if isinstance(m, dict) and "id" in m}
return catalog, default
def _configure_imagegen_model_for_plugin(plugin_name: str, config: dict) -> None:
"""Prompt the user to pick a model for a plugin-registered backend.
Writes selection to ``image_gen.model``. Mirrors
:func:`_configure_imagegen_model` but sources its catalog from the
plugin registry instead of :data:`IMAGEGEN_BACKENDS`.
"""
catalog, default_model = _plugin_image_gen_catalog(plugin_name)
if not catalog:
return
cur_cfg = config.setdefault("image_gen", {})
if not isinstance(cur_cfg, dict):
cur_cfg = {}
config["image_gen"] = cur_cfg
current_model = cur_cfg.get("model") or default_model
if current_model not in catalog:
current_model = default_model
model_ids = list(catalog.keys())
ordered = [current_model] + [m for m in model_ids if m != current_model]
widths = {
"model": max(len(m) for m in model_ids),
"speed": max((len(catalog[m].get("speed", "")) for m in model_ids), default=6),
"strengths": max((len(catalog[m].get("strengths", "")) for m in model_ids), default=0),
}
print()
header = (
f" {'Model':<{widths['model']}} "
f"{'Speed':<{widths['speed']}} "
f"{'Strengths':<{widths['strengths']}} "
f"Price"
)
print(color(header, Colors.CYAN))
rows = []
for mid in ordered:
row = _format_imagegen_model_row(mid, catalog[mid], widths)
if mid == current_model:
row += " ← currently in use"
rows.append(row)
idx = _prompt_choice(
f" Choose {plugin_name} model:",
rows,
default=0,
)
chosen = ordered[idx]
cur_cfg["model"] = chosen
_print_success(f" Model set to: {chosen}")
def _select_plugin_image_gen_provider(plugin_name: str, config: dict) -> None:
"""Persist a plugin-backed image generation provider selection."""
img_cfg = config.setdefault("image_gen", {})
if not isinstance(img_cfg, dict):
img_cfg = {}
config["image_gen"] = img_cfg
img_cfg["provider"] = plugin_name
img_cfg["use_gateway"] = False
_print_success(f" image_gen.provider set to: {plugin_name}")
_configure_imagegen_model_for_plugin(plugin_name, config)
def _configure_provider(provider: dict, config: dict):
"""Configure a single provider - prompt for API keys and set config."""
env_vars = provider.get("env_vars", [])
@@ -1335,22 +1151,10 @@ def _configure_provider(provider: dict, config: dict):
_print_success(f" {provider['name']} - no configuration needed!")
if managed_feature:
_print_info(" Requests for this tool will be billed to your Nous subscription.")
# Plugin-registered image_gen provider: write image_gen.provider
# and route model selection to the plugin's own catalog.
plugin_name = provider.get("image_gen_plugin_name")
if plugin_name:
_select_plugin_image_gen_provider(plugin_name, config)
return
# Imagegen backends prompt for model selection after backend pick.
backend = provider.get("imagegen_backend")
if backend:
_configure_imagegen_model(backend, config)
# In-tree FAL is the only non-plugin backend today. Keep
# image_gen.provider clear so the dispatch shim falls through
# to the legacy FAL path.
img_cfg = config.setdefault("image_gen", {})
if isinstance(img_cfg, dict) and img_cfg.get("provider") not in (None, "", "fal"):
img_cfg["provider"] = "fal"
return
# Prompt for each required env var
@@ -1385,17 +1189,10 @@ def _configure_provider(provider: dict, config: dict):
if all_configured:
_print_success(f" {provider['name']} configured!")
plugin_name = provider.get("image_gen_plugin_name")
if plugin_name:
_select_plugin_image_gen_provider(plugin_name, config)
return
# Imagegen backends prompt for model selection after env vars are in.
backend = provider.get("imagegen_backend")
if backend:
_configure_imagegen_model(backend, config)
img_cfg = config.setdefault("image_gen", {})
if isinstance(img_cfg, dict) and img_cfg.get("provider") not in (None, "", "fal"):
img_cfg["provider"] = "fal"
def _configure_simple_requirements(ts_key: str):
@@ -1561,39 +1358,16 @@ def _reconfigure_provider(provider: dict, config: dict):
config.setdefault("web", {})["backend"] = provider["web_backend"]
_print_success(f" Web backend set to: {provider['web_backend']}")
if managed_feature and managed_feature not in ("web", "tts", "browser"):
section = config.setdefault(managed_feature, {})
if not isinstance(section, dict):
section = {}
config[managed_feature] = section
section["use_gateway"] = True
elif not managed_feature:
for cat_key, cat in TOOL_CATEGORIES.items():
if provider in cat.get("providers", []):
section = config.get(cat_key)
if isinstance(section, dict) and section.get("use_gateway"):
section["use_gateway"] = False
break
if not env_vars:
if provider.get("post_setup"):
_run_post_setup(provider["post_setup"])
_print_success(f" {provider['name']} - no configuration needed!")
if managed_feature:
_print_info(" Requests for this tool will be billed to your Nous subscription.")
plugin_name = provider.get("image_gen_plugin_name")
if plugin_name:
_select_plugin_image_gen_provider(plugin_name, config)
return
# Imagegen backends prompt for model selection on reconfig too.
backend = provider.get("imagegen_backend")
if backend:
_configure_imagegen_model(backend, config)
if backend == "fal":
img_cfg = config.setdefault("image_gen", {})
if isinstance(img_cfg, dict):
img_cfg["provider"] = "fal"
img_cfg["use_gateway"] = False
return
for var in env_vars:
@@ -1612,19 +1386,9 @@ def _reconfigure_provider(provider: dict, config: dict):
_print_info(" Kept current")
# Imagegen backends prompt for model selection on reconfig too.
plugin_name = provider.get("image_gen_plugin_name")
if plugin_name:
_select_plugin_image_gen_provider(plugin_name, config)
return
backend = provider.get("imagegen_backend")
if backend:
_configure_imagegen_model(backend, config)
if backend == "fal":
img_cfg = config.setdefault("image_gen", {})
if isinstance(img_cfg, dict):
img_cfg["provider"] = "fal"
img_cfg["use_gateway"] = False
def _reconfigure_simple_requirements(ts_key: str):
+3 -6
View File
@@ -2189,8 +2189,7 @@ async def get_usage_analytics(days: int = 30):
SUM(reasoning_tokens) as reasoning_tokens,
COALESCE(SUM(estimated_cost_usd), 0) as estimated_cost,
COALESCE(SUM(actual_cost_usd), 0) as actual_cost,
COUNT(*) as sessions,
SUM(COALESCE(api_call_count, 0)) as api_calls
COUNT(*) as sessions
FROM sessions WHERE started_at > ?
GROUP BY day ORDER BY day
""", (cutoff,))
@@ -2201,8 +2200,7 @@ async def get_usage_analytics(days: int = 30):
SUM(input_tokens) as input_tokens,
SUM(output_tokens) as output_tokens,
COALESCE(SUM(estimated_cost_usd), 0) as estimated_cost,
COUNT(*) as sessions,
SUM(COALESCE(api_call_count, 0)) as api_calls
COUNT(*) as sessions
FROM sessions WHERE started_at > ? AND model IS NOT NULL
GROUP BY model ORDER BY SUM(input_tokens) + SUM(output_tokens) DESC
""", (cutoff,))
@@ -2215,8 +2213,7 @@ async def get_usage_analytics(days: int = 30):
SUM(reasoning_tokens) as total_reasoning,
COALESCE(SUM(estimated_cost_usd), 0) as total_estimated_cost,
COALESCE(SUM(actual_cost_usd), 0) as total_actual_cost,
COUNT(*) as total_sessions,
SUM(COALESCE(api_call_count, 0)) as total_api_calls
COUNT(*) as total_sessions
FROM sessions WHERE started_at > ?
""", (cutoff,))
totals = dict(cur3.fetchone())
+6 -154
View File
@@ -31,7 +31,7 @@ T = TypeVar("T")
DEFAULT_DB_PATH = get_hermes_home() / "state.db"
SCHEMA_VERSION = 8
SCHEMA_VERSION = 6
SCHEMA_SQL = """
CREATE TABLE IF NOT EXISTS schema_version (
@@ -65,7 +65,6 @@ CREATE TABLE IF NOT EXISTS sessions (
cost_source TEXT,
pricing_version TEXT,
title TEXT,
api_call_count INTEGER DEFAULT 0,
FOREIGN KEY (parent_session_id) REFERENCES sessions(id)
);
@@ -81,16 +80,10 @@ CREATE TABLE IF NOT EXISTS messages (
token_count INTEGER,
finish_reason TEXT,
reasoning TEXT,
reasoning_content TEXT,
reasoning_details TEXT,
codex_reasoning_items TEXT
);
CREATE TABLE IF NOT EXISTS state_meta (
key TEXT PRIMARY KEY,
value TEXT
);
CREATE INDEX IF NOT EXISTS idx_sessions_source ON sessions(source);
CREATE INDEX IF NOT EXISTS idx_sessions_parent ON sessions(parent_session_id);
CREATE INDEX IF NOT EXISTS idx_sessions_started ON sessions(started_at DESC);
@@ -336,26 +329,6 @@ class SessionDB:
except sqlite3.OperationalError:
pass # Column already exists
cursor.execute("UPDATE schema_version SET version = 6")
if current_version < 7:
# v7: preserve provider-native reasoning_content separately from
# normalized reasoning text. Kimi/Moonshot replay can require
# this field on assistant tool-call messages when thinking is on.
try:
cursor.execute('ALTER TABLE messages ADD COLUMN "reasoning_content" TEXT')
except sqlite3.OperationalError:
pass # Column already exists
cursor.execute("UPDATE schema_version SET version = 7")
if current_version < 8:
# v8: add api_call_count column to sessions — tracks the number
# of individual LLM API calls made within a session (as opposed
# to the session count itself).
try:
cursor.execute(
'ALTER TABLE sessions ADD COLUMN "api_call_count" INTEGER DEFAULT 0'
)
except sqlite3.OperationalError:
pass # Column already exists
cursor.execute("UPDATE schema_version SET version = 8")
# Unique title index — always ensure it exists (safe to run after migrations
# since the title column is guaranteed to exist at this point)
@@ -462,7 +435,6 @@ class SessionDB:
billing_provider: Optional[str] = None,
billing_base_url: Optional[str] = None,
billing_mode: Optional[str] = None,
api_call_count: int = 0,
absolute: bool = False,
) -> None:
"""Update token counters and backfill model if not already set.
@@ -492,8 +464,7 @@ class SessionDB:
billing_provider = COALESCE(billing_provider, ?),
billing_base_url = COALESCE(billing_base_url, ?),
billing_mode = COALESCE(billing_mode, ?),
model = COALESCE(model, ?),
api_call_count = ?
model = COALESCE(model, ?)
WHERE id = ?"""
else:
sql = """UPDATE sessions SET
@@ -513,8 +484,7 @@ class SessionDB:
billing_provider = COALESCE(billing_provider, ?),
billing_base_url = COALESCE(billing_base_url, ?),
billing_mode = COALESCE(billing_mode, ?),
model = COALESCE(model, ?),
api_call_count = COALESCE(api_call_count, 0) + ?
model = COALESCE(model, ?)
WHERE id = ?"""
params = (
input_tokens,
@@ -532,7 +502,6 @@ class SessionDB:
billing_base_url,
billing_mode,
model,
api_call_count,
session_id,
)
def _do(conn):
@@ -953,7 +922,6 @@ class SessionDB:
token_count: int = None,
finish_reason: str = None,
reasoning: str = None,
reasoning_content: str = None,
reasoning_details: Any = None,
codex_reasoning_items: Any = None,
) -> int:
@@ -983,8 +951,8 @@ class SessionDB:
cursor = conn.execute(
"""INSERT INTO messages (session_id, role, content, tool_call_id,
tool_calls, tool_name, timestamp, token_count, finish_reason,
reasoning, reasoning_content, reasoning_details, codex_reasoning_items)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
reasoning, reasoning_details, codex_reasoning_items)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
(
session_id,
role,
@@ -996,7 +964,6 @@ class SessionDB:
token_count,
finish_reason,
reasoning,
reasoning_content,
reasoning_details_json,
codex_items_json,
),
@@ -1047,7 +1014,7 @@ class SessionDB:
with self._lock:
cursor = self._conn.execute(
"SELECT role, content, tool_call_id, tool_calls, tool_name, "
"reasoning, reasoning_content, reasoning_details, codex_reasoning_items "
"reasoning, reasoning_details, codex_reasoning_items "
"FROM messages WHERE session_id = ? ORDER BY timestamp, id",
(session_id,),
)
@@ -1071,8 +1038,6 @@ class SessionDB:
if row["role"] == "assistant":
if row["reasoning"]:
msg["reasoning"] = row["reasoning"]
if row["reasoning_content"] is not None:
msg["reasoning_content"] = row["reasoning_content"]
if row["reasoning_details"]:
try:
msg["reasoning_details"] = json.loads(row["reasoning_details"])
@@ -1476,116 +1441,3 @@ class SessionDB:
return len(session_ids)
return self._execute_write(_do)
# ── Meta key/value (for scheduler bookkeeping) ──
def get_meta(self, key: str) -> Optional[str]:
"""Read a value from the state_meta key/value store."""
with self._lock:
row = self._conn.execute(
"SELECT value FROM state_meta WHERE key = ?", (key,)
).fetchone()
if row is None:
return None
return row["value"] if isinstance(row, sqlite3.Row) else row[0]
def set_meta(self, key: str, value: str) -> None:
"""Write a value to the state_meta key/value store."""
def _do(conn):
conn.execute(
"INSERT INTO state_meta (key, value) VALUES (?, ?) "
"ON CONFLICT(key) DO UPDATE SET value = excluded.value",
(key, value),
)
self._execute_write(_do)
# ── Space reclamation ──
def vacuum(self) -> None:
"""Run VACUUM to reclaim disk space after large deletes.
SQLite does not shrink the database file when rows are deleted
freed pages just get reused on the next insert. After a prune that
removed hundreds of sessions, the file stays bloated unless we
explicitly VACUUM.
VACUUM rewrites the entire DB, so it's expensive (seconds per
100MB) and cannot run inside a transaction. It also acquires an
exclusive lock, so callers must ensure no other writers are
active. Safe to call at startup before the gateway/CLI starts
serving traffic.
"""
# VACUUM cannot be executed inside a transaction.
with self._lock:
# Best-effort WAL checkpoint first, then VACUUM.
try:
self._conn.execute("PRAGMA wal_checkpoint(TRUNCATE)")
except Exception:
pass
self._conn.execute("VACUUM")
def maybe_auto_prune_and_vacuum(
self,
retention_days: int = 90,
min_interval_hours: int = 24,
vacuum: bool = True,
) -> Dict[str, Any]:
"""Idempotent auto-maintenance: prune old sessions + optional VACUUM.
Records the last run timestamp in state_meta so subsequent calls
within ``min_interval_hours`` no-op. Designed to be called once at
startup from long-lived entrypoints (CLI, gateway, cron scheduler).
Never raises. On any failure, logs a warning and returns a dict
with ``"error"`` set.
Returns a dict with keys:
- ``"skipped"`` (bool) true if within min_interval_hours of last run
- ``"pruned"`` (int) number of sessions deleted
- ``"vacuumed"`` (bool) true if VACUUM ran
- ``"error"`` (str, optional) present only on failure
"""
result: Dict[str, Any] = {"skipped": False, "pruned": 0, "vacuumed": False}
try:
# Skip if another process/call did maintenance recently.
last_raw = self.get_meta("last_auto_prune")
now = time.time()
if last_raw:
try:
last_ts = float(last_raw)
if now - last_ts < min_interval_hours * 3600:
result["skipped"] = True
return result
except (TypeError, ValueError):
pass # corrupt meta; treat as no prior run
pruned = self.prune_sessions(older_than_days=retention_days)
result["pruned"] = pruned
# Only VACUUM if we actually freed rows — VACUUM on a tight DB
# is wasted I/O. Threshold keeps small DBs from paying the cost.
if vacuum and pruned > 0:
try:
self.vacuum()
result["vacuumed"] = True
except Exception as exc:
logger.warning("state.db VACUUM failed: %s", exc)
# Record the attempt even if pruned == 0, so we don't retry
# every startup within the min_interval_hours window.
self.set_meta("last_auto_prune", str(now))
if pruned > 0:
logger.info(
"state.db auto-maintenance: pruned %d session(s) older than %d days%s",
pruned,
retention_days,
" + VACUUM" if result["vacuumed"] else "",
)
except Exception as exc:
# Maintenance must never block startup. Log and return error marker.
logger.warning("state.db auto-maintenance failed: %s", exc)
result["error"] = str(exc)
return result
+2 -8
View File
@@ -108,15 +108,9 @@ def _run_async(coro):
if loop and loop.is_running():
# Inside an async context (gateway, RL env) — run in a fresh thread.
import concurrent.futures
pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
future = pool.submit(asyncio.run, coro)
try:
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
future = pool.submit(asyncio.run, coro)
return future.result(timeout=300)
except concurrent.futures.TimeoutError:
future.cancel()
raise
finally:
pool.shutdown(wait=False, cancel_futures=True)
# If we're on a worker thread (e.g., parallel tool execution in
# delegate_task), use a per-thread persistent loop. This avoids
+2 -5
View File
@@ -28,7 +28,7 @@
let
cfg = config.services.hermes-agent;
hermes-agent = inputs.self.packages.${pkgs.stdenv.hostPlatform.system}.default;
hermes-agent = inputs.self.packages.${pkgs.system}.default;
# Deep-merge config type (from 0xrsydn/nix-hermes-agent)
deepConfigType = lib.types.mkOptionType {
@@ -777,10 +777,7 @@ HERMES_NIX_ENV_EOF
NoNewPrivileges = true;
ProtectSystem = "strict";
ProtectHome = false;
ReadWritePaths = [
cfg.stateDir
cfg.workingDirectory
];
ReadWritePaths = [ cfg.stateDir ];
PrivateTmp = true;
};
@@ -1,5 +0,0 @@
# Web Development
Optional skills for client-side web development workflows — embedding agents, copilots, and AI-native UX patterns into user-facing web apps.
These are distinct from Hermes' own browser automation (Browserbase, Camofox), which operate *on* websites from outside. Web-development skills here help users build *into* their own websites.
@@ -1,189 +0,0 @@
---
name: page-agent
description: Embed alibaba/page-agent into your own web application — a pure-JavaScript in-page GUI agent that ships as a single <script> tag or npm package and lets end-users of your site drive the UI with natural language ("click login, fill username as John"). No Python, no headless browser, no extension required. Use this skill when the user is a web developer who wants to add an AI copilot to their SaaS / admin panel / B2B tool, make a legacy web app accessible via natural language, or evaluate page-agent against a local (Ollama) or cloud (Qwen / OpenAI / OpenRouter) LLM. NOT for server-side browser automation — point those users to Hermes' built-in browser tool instead.
version: 1.0.0
author: Hermes Agent
license: MIT
metadata:
hermes:
tags: [web, javascript, agent, browser, gui, alibaba, embed, copilot, saas]
category: web-development
---
# page-agent
alibaba/page-agent (https://github.com/alibaba/page-agent, 17k+ stars, MIT) is an in-page GUI agent written in TypeScript. It lives inside a webpage, reads the DOM as text (no screenshots, no multi-modal LLM), and executes natural-language instructions like "click the login button, then fill username as John" against the current page. Pure client-side — the host site just includes a script and passes an OpenAI-compatible LLM endpoint.
## When to use this skill
Load this skill when a user wants to:
- **Ship an AI copilot inside their own web app** (SaaS, admin panel, B2B tool, ERP, CRM) — "users on my dashboard should be able to type 'create invoice for Acme Corp and email it' instead of clicking through five screens"
- **Modernize a legacy web app** without rewriting the frontend — page-agent drops on top of existing DOM
- **Add accessibility via natural language** — voice / screen-reader users drive the UI by describing what they want
- **Demo or evaluate page-agent** against a local (Ollama) or hosted (Qwen, OpenAI, OpenRouter) LLM
- **Build interactive training / product demos** — let an AI walk a user through "how to submit an expense report" live in the real UI
## When NOT to use this skill
- User wants **Hermes itself to drive a browser** → use Hermes' built-in browser tool (Browserbase / Camofox). page-agent is the *opposite* direction.
- User wants **cross-tab automation without embedding** → use Playwright, browser-use, or the page-agent Chrome extension
- User needs **visual grounding / screenshots** → page-agent is text-DOM only; use a multimodal browser agent instead
## Prerequisites
- Node 22.13+ or 24+, npm 10+ (docs claim 11+ but 10.9 works fine)
- An OpenAI-compatible LLM endpoint: Qwen (DashScope), OpenAI, Ollama, OpenRouter, or anything speaking `/v1/chat/completions`
- Browser with devtools (for debugging)
## Path 1 — 30-second demo via CDN (no install)
Fastest way to see it work. Uses alibaba's free testing LLM proxy — **for evaluation only**, subject to their terms.
Add to any HTML page (or paste into the devtools console as a bookmarklet):
```html
<script src="https://cdn.jsdelivr.net/npm/page-agent@1.8.0/dist/iife/page-agent.demo.js" crossorigin="true"></script>
```
A panel appears. Type an instruction. Done.
Bookmarklet form (drop into bookmarks bar, click on any page):
```javascript
javascript:(function(){var s=document.createElement('script');s.src='https://cdn.jsdelivr.net/npm/page-agent@1.8.0/dist/iife/page-agent.demo.js';document.head.appendChild(s);})();
```
## Path 2 — npm install into your own web app (production use)
Inside an existing web project (React / Vue / Svelte / plain):
```bash
npm install page-agent
```
Wire it up with your own LLM endpoint — **never ship the demo CDN to real users**:
```javascript
import { PageAgent } from 'page-agent'
const agent = new PageAgent({
model: 'qwen3.5-plus',
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1',
apiKey: process.env.LLM_API_KEY, // never hardcode
language: 'en-US',
})
// Show the panel for end users:
agent.panel.show()
// Or drive it programmatically:
await agent.execute('Click submit button, then fill username as John')
```
Provider examples (any OpenAI-compatible endpoint works):
| Provider | `baseURL` | `model` |
|----------|-----------|---------|
| Qwen / DashScope | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `qwen3.5-plus` |
| OpenAI | `https://api.openai.com/v1` | `gpt-4o-mini` |
| Ollama (local) | `http://localhost:11434/v1` | `qwen3:14b` |
| OpenRouter | `https://openrouter.ai/api/v1` | `anthropic/claude-sonnet-4.6` |
**Key config fields** (passed to `new PageAgent({...})`):
- `model`, `baseURL`, `apiKey` — LLM connection
- `language` — UI language (`en-US`, `zh-CN`, etc.)
- Allowlist and data-masking hooks exist for locking down what the agent can touch — see https://alibaba.github.io/page-agent/ for the full option list
**Security.** Don't put your `apiKey` in client-side code for a real deployment — proxy LLM calls through your backend and point `baseURL` at your proxy. The demo CDN exists because alibaba runs that proxy for evaluation.
## Path 3 — clone the source repo (contributing, or hacking on it)
Use this when the user wants to modify page-agent itself, test it against arbitrary sites via a local IIFE bundle, or develop the browser extension.
```bash
git clone https://github.com/alibaba/page-agent.git
cd page-agent
npm ci # exact lockfile install (or `npm i` to allow updates)
```
Create `.env` in the repo root with an LLM endpoint. Example:
```
LLM_MODEL_NAME=gpt-4o-mini
LLM_API_KEY=sk-...
LLM_BASE_URL=https://api.openai.com/v1
```
Ollama flavor:
```
LLM_BASE_URL=http://localhost:11434/v1
LLM_API_KEY=NA
LLM_MODEL_NAME=qwen3:14b
```
Common commands:
```bash
npm start # docs/website dev server
npm run build # build every package
npm run dev:demo # serve IIFE bundle at http://localhost:5174/page-agent.demo.js
npm run dev:ext # develop the browser extension (WXT + React)
npm run build:ext # build the extension
```
**Test on any website** using the local IIFE bundle. Add this bookmarklet:
```javascript
javascript:(function(){var s=document.createElement('script');s.src=`http://localhost:5174/page-agent.demo.js?t=${Math.random()}`;s.onload=()=>console.log('PageAgent ready!');document.head.appendChild(s);})();
```
Then: `npm run dev:demo`, click the bookmarklet on any page, and the local build injects. Auto-rebuilds on save.
**Warning:** your `.env` `LLM_API_KEY` is inlined into the IIFE bundle during dev builds. Don't share the bundle. Don't commit it. Don't paste the URL into Slack. (Verified: grepping the public dev bundle returns the literal values from `.env`.)
## Repo layout (Path 3)
Monorepo with npm workspaces. Key packages:
| Package | Path | Purpose |
|---------|------|---------|
| `page-agent` | `packages/page-agent/` | Main entry with UI panel |
| `@page-agent/core` | `packages/core/` | Core agent logic, no UI |
| `@page-agent/mcp` | `packages/mcp/` | MCP server (beta) |
| — | `packages/llms/` | LLM client |
| — | `packages/page-controller/` | DOM ops + visual feedback |
| — | `packages/ui/` | Panel + i18n |
| — | `packages/extension/` | Chrome/Firefox extension |
| — | `packages/website/` | Docs + landing site |
## Verifying it works
After Path 1 or Path 2:
1. Open the page in a browser with devtools open
2. You should see a floating panel. If not, check the console for errors (most common: CORS on the LLM endpoint, wrong `baseURL`, or a bad API key)
3. Type a simple instruction matching something visible on the page ("click the Login link")
4. Watch the Network tab — you should see a request to your `baseURL`
After Path 3:
1. `npm run dev:demo` prints `Accepting connections at http://localhost:5174`
2. `curl -I http://localhost:5174/page-agent.demo.js` returns `HTTP/1.1 200 OK` with `Content-Type: application/javascript`
3. Click the bookmarklet on any site; panel appears
## Pitfalls
- **Demo CDN in production** — don't. It's rate-limited, uses alibaba's free proxy, and their terms forbid production use.
- **API key exposure** — any key passed to `new PageAgent({apiKey: ...})` ships in your JS bundle. Always proxy through your own backend for real deployments.
- **Non-OpenAI-compatible endpoints** fail silently or with cryptic errors. If your provider needs native Anthropic/Gemini formatting, use an OpenAI-compatibility proxy (LiteLLM, OpenRouter) in front.
- **CSP blocks** — sites with strict Content-Security-Policy may refuse to load the CDN script or disallow inline eval. In that case, self-host from your origin.
- **Restart dev server** after editing `.env` in Path 3 — Vite only reads env at startup.
- **Node version** — the repo declares `^22.13.0 || >=24`. Node 20 will fail `npm ci` with engine errors.
- **npm 10 vs 11** — docs say npm 11+; npm 10.9 actually works fine.
## Reference
- Repo: https://github.com/alibaba/page-agent
- Docs: https://alibaba.github.io/page-agent/
- License: MIT (built on browser-use's DOM processing internals, Copyright 2024 Gregor Zunic)
+3347 -4
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+2 -2
View File
@@ -16,8 +16,8 @@
},
"homepage": "https://github.com/NousResearch/Hermes-Agent#readme",
"dependencies": {
"@askjo/camofox-browser": "^1.5.2",
"agent-browser": "^0.26.0"
"agent-browser": "^0.13.0",
"@askjo/camofox-browser": "^1.5.2"
},
"overrides": {
"lodash": "4.18.1"
-378
View File
@@ -1,378 +0,0 @@
"""OpenAI image generation backend — ChatGPT/Codex OAuth variant.
Identical model catalog and tier semantics to the ``openai`` image-gen plugin
(``gpt-image-2`` at low/medium/high quality), but routes the request through
the Codex Responses API ``image_generation`` tool instead of the
``images.generate`` REST endpoint. This lets users who are already
authenticated with Codex/ChatGPT generate images without configuring a
separate ``OPENAI_API_KEY``.
Selection precedence for the tier (first hit wins):
1. ``OPENAI_IMAGE_MODEL`` env var (escape hatch for scripts / tests)
2. ``image_gen.openai-codex.model`` in ``config.yaml``
3. ``image_gen.model`` in ``config.yaml`` (when it's one of our tier IDs)
4. :data:`DEFAULT_MODEL` ``gpt-image-2-medium``
Output is saved as PNG under ``$HERMES_HOME/cache/images/``.
"""
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional, Tuple
from agent.image_gen_provider import (
DEFAULT_ASPECT_RATIO,
ImageGenProvider,
error_response,
resolve_aspect_ratio,
save_b64_image,
success_response,
)
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Model catalog — mirrors the ``openai`` plugin so the picker UX is identical.
# ---------------------------------------------------------------------------
API_MODEL = "gpt-image-2"
_MODELS: Dict[str, Dict[str, Any]] = {
"gpt-image-2-low": {
"display": "GPT Image 2 (Low)",
"speed": "~15s",
"strengths": "Fast iteration, lowest cost",
"quality": "low",
},
"gpt-image-2-medium": {
"display": "GPT Image 2 (Medium)",
"speed": "~40s",
"strengths": "Balanced — default",
"quality": "medium",
},
"gpt-image-2-high": {
"display": "GPT Image 2 (High)",
"speed": "~2min",
"strengths": "Highest fidelity, strongest prompt adherence",
"quality": "high",
},
}
DEFAULT_MODEL = "gpt-image-2-medium"
_SIZES = {
"landscape": "1536x1024",
"square": "1024x1024",
"portrait": "1024x1536",
}
# Codex Responses surface used for the request. The chat model itself is only
# the host that calls the ``image_generation`` tool; the actual image work is
# done by ``API_MODEL``.
_CODEX_CHAT_MODEL = "gpt-5.4"
_CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
_CODEX_INSTRUCTIONS = (
"You are an assistant that must fulfill image generation requests by "
"using the image_generation tool when provided."
)
# ---------------------------------------------------------------------------
# Config + auth helpers
# ---------------------------------------------------------------------------
def _load_image_gen_config() -> Dict[str, Any]:
"""Read ``image_gen`` from config.yaml (returns {} on any failure)."""
try:
from hermes_cli.config import load_config
cfg = load_config()
section = cfg.get("image_gen") if isinstance(cfg, dict) else None
return section if isinstance(section, dict) else {}
except Exception as exc:
logger.debug("Could not load image_gen config: %s", exc)
return {}
def _resolve_model() -> Tuple[str, Dict[str, Any]]:
"""Decide which tier to use and return ``(model_id, meta)``."""
import os
env_override = os.environ.get("OPENAI_IMAGE_MODEL")
if env_override and env_override in _MODELS:
return env_override, _MODELS[env_override]
cfg = _load_image_gen_config()
sub = cfg.get("openai-codex") if isinstance(cfg.get("openai-codex"), dict) else {}
candidate: Optional[str] = None
if isinstance(sub, dict):
value = sub.get("model")
if isinstance(value, str) and value in _MODELS:
candidate = value
if candidate is None:
top = cfg.get("model")
if isinstance(top, str) and top in _MODELS:
candidate = top
if candidate is not None:
return candidate, _MODELS[candidate]
return DEFAULT_MODEL, _MODELS[DEFAULT_MODEL]
def _read_codex_access_token() -> Optional[str]:
"""Return a usable Codex OAuth token, or None.
Delegates to the canonical reader in ``agent.auxiliary_client`` so token
expiry, credential pool selection, and JWT decoding stay in one place.
"""
try:
from agent.auxiliary_client import _read_codex_access_token as _reader
token = _reader()
if isinstance(token, str) and token.strip():
return token.strip()
return None
except Exception as exc:
logger.debug("Could not resolve Codex access token: %s", exc)
return None
def _build_codex_client():
"""Return an OpenAI client pointed at the ChatGPT/Codex backend, or None."""
token = _read_codex_access_token()
if not token:
return None
try:
import openai
from agent.auxiliary_client import _codex_cloudflare_headers
return openai.OpenAI(
api_key=token,
base_url=_CODEX_BASE_URL,
default_headers=_codex_cloudflare_headers(token),
)
except Exception as exc:
logger.debug("Could not build Codex image client: %s", exc)
return None
def _collect_image_b64(client: Any, *, prompt: str, size: str, quality: str) -> Optional[str]:
"""Stream a Codex Responses image_generation call and return the b64 image."""
image_b64: Optional[str] = None
with client.responses.stream(
model=_CODEX_CHAT_MODEL,
store=False,
instructions=_CODEX_INSTRUCTIONS,
input=[{
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": prompt}],
}],
tools=[{
"type": "image_generation",
"model": API_MODEL,
"size": size,
"quality": quality,
"output_format": "png",
"background": "opaque",
"partial_images": 1,
}],
tool_choice={
"type": "allowed_tools",
"mode": "required",
"tools": [{"type": "image_generation"}],
},
) as stream:
for event in stream:
event_type = getattr(event, "type", "")
if event_type == "response.output_item.done":
item = getattr(event, "item", None)
if getattr(item, "type", None) == "image_generation_call":
result = getattr(item, "result", None)
if isinstance(result, str) and result:
image_b64 = result
elif event_type == "response.image_generation_call.partial_image":
partial = getattr(event, "partial_image_b64", None)
if isinstance(partial, str) and partial:
image_b64 = partial
final = stream.get_final_response()
# Final-response sweep covers the case where the stream finished before
# we observed the ``output_item.done`` event for the image call.
for item in getattr(final, "output", None) or []:
if getattr(item, "type", None) == "image_generation_call":
result = getattr(item, "result", None)
if isinstance(result, str) and result:
image_b64 = result
return image_b64
# ---------------------------------------------------------------------------
# Provider
# ---------------------------------------------------------------------------
class OpenAICodexImageGenProvider(ImageGenProvider):
"""gpt-image-2 routed through ChatGPT/Codex OAuth instead of an API key."""
@property
def name(self) -> str:
return "openai-codex"
@property
def display_name(self) -> str:
return "OpenAI (Codex auth)"
def is_available(self) -> bool:
if not _read_codex_access_token():
return False
try:
import openai # noqa: F401
except ImportError:
return False
return True
def list_models(self) -> List[Dict[str, Any]]:
return [
{
"id": model_id,
"display": meta["display"],
"speed": meta["speed"],
"strengths": meta["strengths"],
"price": "varies",
}
for model_id, meta in _MODELS.items()
]
def default_model(self) -> Optional[str]:
return DEFAULT_MODEL
def get_setup_schema(self) -> Dict[str, Any]:
return {
"name": "OpenAI (Codex auth)",
"badge": "free",
"tag": "gpt-image-2 via ChatGPT/Codex OAuth — no API key required",
"env_vars": [],
"post_setup_hint": (
"Sign in with `hermes auth codex` (or `hermes setup` → Codex) "
"if you haven't already. No API key needed."
),
}
def generate(
self,
prompt: str,
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
**kwargs: Any,
) -> Dict[str, Any]:
prompt = (prompt or "").strip()
aspect = resolve_aspect_ratio(aspect_ratio)
if not prompt:
return error_response(
error="Prompt is required and must be a non-empty string",
error_type="invalid_argument",
provider="openai-codex",
aspect_ratio=aspect,
)
if not _read_codex_access_token():
return error_response(
error=(
"No Codex/ChatGPT OAuth credentials available. Run "
"`hermes auth codex` (or `hermes setup` → Codex) to sign in."
),
error_type="auth_required",
provider="openai-codex",
aspect_ratio=aspect,
)
try:
import openai # noqa: F401
except ImportError:
return error_response(
error="openai Python package not installed (pip install openai)",
error_type="missing_dependency",
provider="openai-codex",
aspect_ratio=aspect,
)
tier_id, meta = _resolve_model()
size = _SIZES.get(aspect, _SIZES["square"])
client = _build_codex_client()
if client is None:
return error_response(
error="Could not initialize Codex image client",
error_type="auth_required",
provider="openai-codex",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
try:
b64 = _collect_image_b64(
client,
prompt=prompt,
size=size,
quality=meta["quality"],
)
except Exception as exc:
logger.debug("Codex image generation failed", exc_info=True)
return error_response(
error=f"OpenAI image generation via Codex auth failed: {exc}",
error_type="api_error",
provider="openai-codex",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
if not b64:
return error_response(
error="Codex response contained no image_generation_call result",
error_type="empty_response",
provider="openai-codex",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
try:
saved_path = save_b64_image(b64, prefix=f"openai_codex_{tier_id}")
except Exception as exc:
return error_response(
error=f"Could not save image to cache: {exc}",
error_type="io_error",
provider="openai-codex",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
return success_response(
image=str(saved_path),
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
provider="openai-codex",
extra={"size": size, "quality": meta["quality"]},
)
# ---------------------------------------------------------------------------
# Plugin entry point
# ---------------------------------------------------------------------------
def register(ctx) -> None:
"""Plugin entry point — register the Codex-backed image-gen provider."""
ctx.register_image_gen_provider(OpenAICodexImageGenProvider())
@@ -1,5 +0,0 @@
name: openai-codex
version: 1.0.0
description: "OpenAI image generation backed by ChatGPT/Codex OAuth (gpt-image-2 via the Responses image_generation tool). Saves generated images to $HERMES_HOME/cache/images/."
author: NousResearch
kind: backend
-303
View File
@@ -1,303 +0,0 @@
"""OpenAI image generation backend.
Exposes OpenAI's ``gpt-image-2`` model at three quality tiers as an
:class:`ImageGenProvider` implementation. The tiers are implemented as
three virtual model IDs so the ``hermes tools`` model picker and the
``image_gen.model`` config key behave like any other multi-model backend:
gpt-image-2-low ~15s fastest, good for iteration
gpt-image-2-medium ~40s default balanced
gpt-image-2-high ~2min slowest, highest fidelity
All three hit the same underlying API model (``gpt-image-2``) with a
different ``quality`` parameter. Output is base64 JSON saved under
``$HERMES_HOME/cache/images/``.
Selection precedence (first hit wins):
1. ``OPENAI_IMAGE_MODEL`` env var (escape hatch for scripts / tests)
2. ``image_gen.openai.model`` in ``config.yaml``
3. ``image_gen.model`` in ``config.yaml`` (when it's one of our tier IDs)
4. :data:`DEFAULT_MODEL` ``gpt-image-2-medium``
"""
from __future__ import annotations
import logging
import os
from typing import Any, Dict, List, Optional, Tuple
from agent.image_gen_provider import (
DEFAULT_ASPECT_RATIO,
ImageGenProvider,
error_response,
resolve_aspect_ratio,
save_b64_image,
success_response,
)
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Model catalog
# ---------------------------------------------------------------------------
#
# All three IDs resolve to the same underlying API model with a different
# ``quality`` setting. ``api_model`` is what gets sent to OpenAI;
# ``quality`` is the knob that changes generation time and output fidelity.
API_MODEL = "gpt-image-2"
_MODELS: Dict[str, Dict[str, Any]] = {
"gpt-image-2-low": {
"display": "GPT Image 2 (Low)",
"speed": "~15s",
"strengths": "Fast iteration, lowest cost",
"quality": "low",
},
"gpt-image-2-medium": {
"display": "GPT Image 2 (Medium)",
"speed": "~40s",
"strengths": "Balanced — default",
"quality": "medium",
},
"gpt-image-2-high": {
"display": "GPT Image 2 (High)",
"speed": "~2min",
"strengths": "Highest fidelity, strongest prompt adherence",
"quality": "high",
},
}
DEFAULT_MODEL = "gpt-image-2-medium"
_SIZES = {
"landscape": "1536x1024",
"square": "1024x1024",
"portrait": "1024x1536",
}
def _load_openai_config() -> Dict[str, Any]:
"""Read ``image_gen`` from config.yaml (returns {} on any failure)."""
try:
from hermes_cli.config import load_config
cfg = load_config()
section = cfg.get("image_gen") if isinstance(cfg, dict) else None
return section if isinstance(section, dict) else {}
except Exception as exc:
logger.debug("Could not load image_gen config: %s", exc)
return {}
def _resolve_model() -> Tuple[str, Dict[str, Any]]:
"""Decide which tier to use and return ``(model_id, meta)``."""
env_override = os.environ.get("OPENAI_IMAGE_MODEL")
if env_override and env_override in _MODELS:
return env_override, _MODELS[env_override]
cfg = _load_openai_config()
openai_cfg = cfg.get("openai") if isinstance(cfg.get("openai"), dict) else {}
candidate: Optional[str] = None
if isinstance(openai_cfg, dict):
value = openai_cfg.get("model")
if isinstance(value, str) and value in _MODELS:
candidate = value
if candidate is None:
top = cfg.get("model")
if isinstance(top, str) and top in _MODELS:
candidate = top
if candidate is not None:
return candidate, _MODELS[candidate]
return DEFAULT_MODEL, _MODELS[DEFAULT_MODEL]
# ---------------------------------------------------------------------------
# Provider
# ---------------------------------------------------------------------------
class OpenAIImageGenProvider(ImageGenProvider):
"""OpenAI ``images.generate`` backend — gpt-image-2 at low/medium/high."""
@property
def name(self) -> str:
return "openai"
@property
def display_name(self) -> str:
return "OpenAI"
def is_available(self) -> bool:
if not os.environ.get("OPENAI_API_KEY"):
return False
try:
import openai # noqa: F401
except ImportError:
return False
return True
def list_models(self) -> List[Dict[str, Any]]:
return [
{
"id": model_id,
"display": meta["display"],
"speed": meta["speed"],
"strengths": meta["strengths"],
"price": "varies",
}
for model_id, meta in _MODELS.items()
]
def default_model(self) -> Optional[str]:
return DEFAULT_MODEL
def get_setup_schema(self) -> Dict[str, Any]:
return {
"name": "OpenAI",
"badge": "paid",
"tag": "gpt-image-2 at low/medium/high quality tiers",
"env_vars": [
{
"key": "OPENAI_API_KEY",
"prompt": "OpenAI API key",
"url": "https://platform.openai.com/api-keys",
},
],
}
def generate(
self,
prompt: str,
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
**kwargs: Any,
) -> Dict[str, Any]:
prompt = (prompt or "").strip()
aspect = resolve_aspect_ratio(aspect_ratio)
if not prompt:
return error_response(
error="Prompt is required and must be a non-empty string",
error_type="invalid_argument",
provider="openai",
aspect_ratio=aspect,
)
if not os.environ.get("OPENAI_API_KEY"):
return error_response(
error=(
"OPENAI_API_KEY not set. Run `hermes tools` → Image "
"Generation → OpenAI to configure, or `hermes setup` "
"to add the key."
),
error_type="auth_required",
provider="openai",
aspect_ratio=aspect,
)
try:
import openai
except ImportError:
return error_response(
error="openai Python package not installed (pip install openai)",
error_type="missing_dependency",
provider="openai",
aspect_ratio=aspect,
)
tier_id, meta = _resolve_model()
size = _SIZES.get(aspect, _SIZES["square"])
# gpt-image-2 returns b64_json unconditionally and REJECTS
# ``response_format`` as an unknown parameter. Don't send it.
payload: Dict[str, Any] = {
"model": API_MODEL,
"prompt": prompt,
"size": size,
"n": 1,
"quality": meta["quality"],
}
try:
client = openai.OpenAI()
response = client.images.generate(**payload)
except Exception as exc:
logger.debug("OpenAI image generation failed", exc_info=True)
return error_response(
error=f"OpenAI image generation failed: {exc}",
error_type="api_error",
provider="openai",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
data = getattr(response, "data", None) or []
if not data:
return error_response(
error="OpenAI returned no image data",
error_type="empty_response",
provider="openai",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
first = data[0]
b64 = getattr(first, "b64_json", None)
url = getattr(first, "url", None)
revised_prompt = getattr(first, "revised_prompt", None)
if b64:
try:
saved_path = save_b64_image(b64, prefix=f"openai_{tier_id}")
except Exception as exc:
return error_response(
error=f"Could not save image to cache: {exc}",
error_type="io_error",
provider="openai",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
image_ref = str(saved_path)
elif url:
# Defensive — gpt-image-2 returns b64 today, but fall back
# gracefully if the API ever changes.
image_ref = url
else:
return error_response(
error="OpenAI response contained neither b64_json nor URL",
error_type="empty_response",
provider="openai",
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
)
extra: Dict[str, Any] = {"size": size, "quality": meta["quality"]}
if revised_prompt:
extra["revised_prompt"] = revised_prompt
return success_response(
image=image_ref,
model=tier_id,
prompt=prompt,
aspect_ratio=aspect,
provider="openai",
extra=extra,
)
# ---------------------------------------------------------------------------
# Plugin entry point
# ---------------------------------------------------------------------------
def register(ctx) -> None:
"""Plugin entry point — wire ``OpenAIImageGenProvider`` into the registry."""
ctx.register_image_gen_provider(OpenAIImageGenProvider())
-7
View File
@@ -1,7 +0,0 @@
name: openai
version: 1.0.0
description: "OpenAI image generation backend (gpt-image-2). Saves generated images to $HERMES_HOME/cache/images/."
author: NousResearch
kind: backend
requires_env:
- OPENAI_API_KEY
+2 -5
View File
@@ -84,10 +84,7 @@ Config file: `~/.hermes/hindsight/config.json`
| `retain_async` | `true` | Process retain asynchronously on the Hindsight server |
| `retain_every_n_turns` | `1` | Retain every N turns (1 = every turn) |
| `retain_context` | `conversation between Hermes Agent and the User` | Context label for retained memories |
| `retain_tags` | — | Default tags applied to retained memories; merged with per-call tool tags |
| `retain_source` | — | Optional `metadata.source` attached to retained memories |
| `retain_user_prefix` | `User` | Label used before user turns in auto-retained transcripts |
| `retain_assistant_prefix` | `Assistant` | Label used before assistant turns in auto-retained transcripts |
| `tags` | — | Tags applied when storing memories |
### Integration
@@ -116,7 +113,7 @@ Available in `hybrid` and `tools` memory modes:
| Tool | Description |
|------|-------------|
| `hindsight_retain` | Store information with auto entity extraction; supports optional per-call `tags` |
| `hindsight_retain` | Store information with auto entity extraction |
| `hindsight_recall` | Multi-strategy search (semantic + entity graph) |
| `hindsight_reflect` | Cross-memory synthesis (LLM-powered) |
+37 -198
View File
@@ -6,15 +6,11 @@ retrieval. Supports cloud (API key) and local modes.
Original PR #1811 by benfrank241, adapted to MemoryProvider ABC.
Config via environment variables:
HINDSIGHT_API_KEY API key for Hindsight Cloud
HINDSIGHT_BANK_ID memory bank identifier (default: hermes)
HINDSIGHT_BUDGET recall budget: low/mid/high (default: mid)
HINDSIGHT_API_URL API endpoint
HINDSIGHT_MODE cloud or local (default: cloud)
HINDSIGHT_RETAIN_TAGS comma-separated tags attached to retained memories
HINDSIGHT_RETAIN_SOURCE metadata source value attached to retained memories
HINDSIGHT_RETAIN_USER_PREFIX label used before user turns in retained transcripts
HINDSIGHT_RETAIN_ASSISTANT_PREFIX label used before assistant turns in retained transcripts
HINDSIGHT_API_KEY API key for Hindsight Cloud
HINDSIGHT_BANK_ID memory bank identifier (default: hermes)
HINDSIGHT_BUDGET recall budget: low/mid/high (default: mid)
HINDSIGHT_API_URL API endpoint
HINDSIGHT_MODE cloud or local (default: cloud)
Or via $HERMES_HOME/hindsight/config.json (profile-scoped), falling back to
~/.hindsight/config.json (legacy, shared) for backward compatibility.
@@ -28,7 +24,7 @@ import logging
import os
import threading
from datetime import datetime, timezone
from hermes_constants import get_hermes_home
from typing import Any, Dict, List
from agent.memory_provider import MemoryProvider
@@ -103,11 +99,6 @@ RETAIN_SCHEMA = {
"properties": {
"content": {"type": "string", "description": "The information to store."},
"context": {"type": "string", "description": "Short label (e.g. 'user preference', 'project decision')."},
"tags": {
"type": "array",
"items": {"type": "string"},
"description": "Optional per-call tags to merge with configured default retain tags.",
},
},
"required": ["content"],
},
@@ -177,10 +168,6 @@ def _load_config() -> dict:
return {
"mode": os.environ.get("HINDSIGHT_MODE", "cloud"),
"apiKey": os.environ.get("HINDSIGHT_API_KEY", ""),
"retain_tags": os.environ.get("HINDSIGHT_RETAIN_TAGS", ""),
"retain_source": os.environ.get("HINDSIGHT_RETAIN_SOURCE", ""),
"retain_user_prefix": os.environ.get("HINDSIGHT_RETAIN_USER_PREFIX", "User"),
"retain_assistant_prefix": os.environ.get("HINDSIGHT_RETAIN_ASSISTANT_PREFIX", "Assistant"),
"banks": {
"hermes": {
"bankId": os.environ.get("HINDSIGHT_BANK_ID", "hermes"),
@@ -191,48 +178,6 @@ def _load_config() -> dict:
}
def _normalize_retain_tags(value: Any) -> List[str]:
"""Normalize tag config/tool values to a deduplicated list of strings."""
if value is None:
return []
raw_items: list[Any]
if isinstance(value, list):
raw_items = value
elif isinstance(value, str):
text = value.strip()
if not text:
return []
if text.startswith("["):
try:
parsed = json.loads(text)
except Exception:
parsed = None
if isinstance(parsed, list):
raw_items = parsed
else:
raw_items = text.split(",")
else:
raw_items = text.split(",")
else:
raw_items = [value]
normalized = []
seen = set()
for item in raw_items:
tag = str(item).strip()
if not tag or tag in seen:
continue
seen.add(tag)
normalized.append(tag)
return normalized
def _utc_timestamp() -> str:
"""Return current UTC timestamp in ISO-8601 with milliseconds and Z suffix."""
return datetime.now(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z")
# ---------------------------------------------------------------------------
# MemoryProvider implementation
# ---------------------------------------------------------------------------
@@ -250,19 +195,6 @@ class HindsightMemoryProvider(MemoryProvider):
self._llm_base_url = ""
self._memory_mode = "hybrid" # "context", "tools", or "hybrid"
self._prefetch_method = "recall" # "recall" or "reflect"
self._retain_tags: List[str] = []
self._retain_source = ""
self._retain_user_prefix = "User"
self._retain_assistant_prefix = "Assistant"
self._platform = ""
self._user_id = ""
self._user_name = ""
self._chat_id = ""
self._chat_name = ""
self._chat_type = ""
self._thread_id = ""
self._agent_identity = ""
self._turn_index = 0
self._client = None
self._prefetch_result = ""
self._prefetch_lock = threading.Lock()
@@ -278,7 +210,6 @@ class HindsightMemoryProvider(MemoryProvider):
# Retain controls
self._auto_retain = True
self._retain_every_n_turns = 1
self._retain_async = True
self._retain_context = "conversation between Hermes Agent and the User"
self._turn_counter = 0
self._session_turns: list[str] = [] # accumulates ALL turns for the session
@@ -293,6 +224,7 @@ class HindsightMemoryProvider(MemoryProvider):
# Bank
self._bank_mission = ""
self._bank_retain_mission: str | None = None
self._retain_async = True
@property
def name(self) -> str:
@@ -491,10 +423,7 @@ class HindsightMemoryProvider(MemoryProvider):
{"key": "recall_budget", "description": "Recall thoroughness", "default": "mid", "choices": ["low", "mid", "high"]},
{"key": "memory_mode", "description": "Memory integration mode", "default": "hybrid", "choices": ["hybrid", "context", "tools"]},
{"key": "recall_prefetch_method", "description": "Auto-recall method", "default": "recall", "choices": ["recall", "reflect"]},
{"key": "retain_tags", "description": "Default tags applied to retained memories (comma-separated)", "default": ""},
{"key": "retain_source", "description": "Metadata source value attached to retained memories", "default": ""},
{"key": "retain_user_prefix", "description": "Label used before user turns in retained transcripts", "default": "User"},
{"key": "retain_assistant_prefix", "description": "Label used before assistant turns in retained transcripts", "default": "Assistant"},
{"key": "tags", "description": "Tags applied when storing memories (comma-separated)", "default": ""},
{"key": "recall_tags", "description": "Tags to filter when searching memories (comma-separated)", "default": ""},
{"key": "recall_tags_match", "description": "Tag matching mode for recall", "default": "any", "choices": ["any", "all", "any_strict", "all_strict"]},
{"key": "auto_recall", "description": "Automatically recall memories before each turn", "default": True},
@@ -538,7 +467,7 @@ class HindsightMemoryProvider(MemoryProvider):
return self._client
def initialize(self, session_id: str, **kwargs) -> None:
self._session_id = str(session_id or "").strip()
self._session_id = session_id
# Check client version and auto-upgrade if needed
try:
@@ -567,16 +496,6 @@ class HindsightMemoryProvider(MemoryProvider):
pass # packaging not available or other issue — proceed anyway
self._config = _load_config()
self._platform = str(kwargs.get("platform") or "").strip()
self._user_id = str(kwargs.get("user_id") or "").strip()
self._user_name = str(kwargs.get("user_name") or "").strip()
self._chat_id = str(kwargs.get("chat_id") or "").strip()
self._chat_name = str(kwargs.get("chat_name") or "").strip()
self._chat_type = str(kwargs.get("chat_type") or "").strip()
self._thread_id = str(kwargs.get("thread_id") or "").strip()
self._agent_identity = str(kwargs.get("agent_identity") or "").strip()
self._turn_index = 0
self._session_turns = []
self._mode = self._config.get("mode", "cloud")
# "local" is a legacy alias for "local_embedded"
if self._mode == "local":
@@ -594,7 +513,7 @@ class HindsightMemoryProvider(MemoryProvider):
memory_mode = self._config.get("memory_mode", "hybrid")
self._memory_mode = memory_mode if memory_mode in ("context", "tools", "hybrid") else "hybrid"
prefetch_method = self._config.get("recall_prefetch_method") or self._config.get("prefetch_method", "recall")
prefetch_method = self._config.get("recall_prefetch_method", "recall")
self._prefetch_method = prefetch_method if prefetch_method in ("recall", "reflect") else "recall"
# Bank options
@@ -602,22 +521,9 @@ class HindsightMemoryProvider(MemoryProvider):
self._bank_retain_mission = self._config.get("bank_retain_mission") or None
# Tags
self._retain_tags = _normalize_retain_tags(
self._config.get("retain_tags")
or os.environ.get("HINDSIGHT_RETAIN_TAGS", "")
)
self._tags = self._retain_tags or None
self._tags = self._config.get("tags") or None
self._recall_tags = self._config.get("recall_tags") or None
self._recall_tags_match = self._config.get("recall_tags_match", "any")
self._retain_source = str(
self._config.get("retain_source") or os.environ.get("HINDSIGHT_RETAIN_SOURCE", "")
).strip()
self._retain_user_prefix = str(
self._config.get("retain_user_prefix") or os.environ.get("HINDSIGHT_RETAIN_USER_PREFIX", "User")
).strip() or "User"
self._retain_assistant_prefix = str(
self._config.get("retain_assistant_prefix") or os.environ.get("HINDSIGHT_RETAIN_ASSISTANT_PREFIX", "Assistant")
).strip() or "Assistant"
# Retain controls
self._auto_retain = self._config.get("auto_retain", True)
@@ -641,9 +547,11 @@ class HindsightMemoryProvider(MemoryProvider):
logger.info("Hindsight initialized: mode=%s, api_url=%s, bank=%s, budget=%s, memory_mode=%s, prefetch_method=%s, client=%s",
self._mode, self._api_url, self._bank_id, self._budget, self._memory_mode, self._prefetch_method, _client_version)
logger.debug("Hindsight config: auto_retain=%s, auto_recall=%s, retain_every_n=%d, "
"retain_async=%s, retain_context=%s, recall_max_tokens=%d, recall_max_input_chars=%d, tags=%s, recall_tags=%s",
"retain_async=%s, retain_context=%s, "
"recall_max_tokens=%d, recall_max_input_chars=%d, tags=%s, recall_tags=%s",
self._auto_retain, self._auto_recall, self._retain_every_n_turns,
self._retain_async, self._retain_context, self._recall_max_tokens, self._recall_max_input_chars,
self._retain_async, self._retain_context,
self._recall_max_tokens, self._recall_max_input_chars,
self._tags, self._recall_tags)
# For local mode, start the embedded daemon in the background so it
@@ -804,78 +712,6 @@ class HindsightMemoryProvider(MemoryProvider):
self._prefetch_thread = threading.Thread(target=_run, daemon=True, name="hindsight-prefetch")
self._prefetch_thread.start()
def _build_turn_messages(self, user_content: str, assistant_content: str) -> List[Dict[str, str]]:
now = datetime.now(timezone.utc).isoformat()
return [
{
"role": "user",
"content": f"{self._retain_user_prefix}: {user_content}",
"timestamp": now,
},
{
"role": "assistant",
"content": f"{self._retain_assistant_prefix}: {assistant_content}",
"timestamp": now,
},
]
def _build_metadata(self, *, message_count: int, turn_index: int) -> Dict[str, str]:
metadata: Dict[str, str] = {
"retained_at": _utc_timestamp(),
"message_count": str(message_count),
"turn_index": str(turn_index),
}
if self._retain_source:
metadata["source"] = self._retain_source
if self._session_id:
metadata["session_id"] = self._session_id
if self._platform:
metadata["platform"] = self._platform
if self._user_id:
metadata["user_id"] = self._user_id
if self._user_name:
metadata["user_name"] = self._user_name
if self._chat_id:
metadata["chat_id"] = self._chat_id
if self._chat_name:
metadata["chat_name"] = self._chat_name
if self._chat_type:
metadata["chat_type"] = self._chat_type
if self._thread_id:
metadata["thread_id"] = self._thread_id
if self._agent_identity:
metadata["agent_identity"] = self._agent_identity
return metadata
def _build_retain_kwargs(
self,
content: str,
*,
context: str | None = None,
document_id: str | None = None,
metadata: Dict[str, str] | None = None,
tags: List[str] | None = None,
retain_async: bool | None = None,
) -> Dict[str, Any]:
kwargs: Dict[str, Any] = {
"bank_id": self._bank_id,
"content": content,
"metadata": metadata or self._build_metadata(message_count=1, turn_index=self._turn_index),
}
if context is not None:
kwargs["context"] = context
if document_id:
kwargs["document_id"] = document_id
if retain_async is not None:
kwargs["retain_async"] = retain_async
merged_tags = _normalize_retain_tags(self._retain_tags)
for tag in _normalize_retain_tags(tags):
if tag not in merged_tags:
merged_tags.append(tag)
if merged_tags:
kwargs["tags"] = merged_tags
return kwargs
def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
"""Retain conversation turn in background (non-blocking).
@@ -885,14 +721,19 @@ class HindsightMemoryProvider(MemoryProvider):
logger.debug("sync_turn: skipped (auto_retain disabled)")
return
if session_id:
self._session_id = str(session_id).strip()
from datetime import datetime, timezone
now = datetime.now(timezone.utc).isoformat()
turn = json.dumps(self._build_turn_messages(user_content, assistant_content))
messages = [
{"role": "user", "content": user_content, "timestamp": now},
{"role": "assistant", "content": assistant_content, "timestamp": now},
]
turn = json.dumps(messages)
self._session_turns.append(turn)
self._turn_counter += 1
self._turn_index = self._turn_counter
# Only retain every N turns
if self._turn_counter % self._retain_every_n_turns != 0:
logger.debug("sync_turn: buffered turn %d (will retain at turn %d)",
self._turn_counter, self._turn_counter + (self._retain_every_n_turns - self._turn_counter % self._retain_every_n_turns))
@@ -900,21 +741,19 @@ class HindsightMemoryProvider(MemoryProvider):
logger.debug("sync_turn: retaining %d turns, total session content %d chars",
len(self._session_turns), sum(len(t) for t in self._session_turns))
# Send the ENTIRE session as a single JSON array (document_id deduplicates).
# Each element in _session_turns is a JSON string of that turn's messages.
content = "[" + ",".join(self._session_turns) + "]"
def _sync():
try:
client = self._get_client()
item = self._build_retain_kwargs(
content,
context=self._retain_context,
metadata=self._build_metadata(
message_count=len(self._session_turns) * 2,
turn_index=self._turn_index,
),
)
item.pop("bank_id", None)
item.pop("retain_async", None)
item: dict = {
"content": content,
"context": self._retain_context,
}
if self._tags:
item["tags"] = self._tags
logger.debug("Hindsight retain: bank=%s, doc=%s, async=%s, content_len=%d, num_turns=%d",
self._bank_id, self._session_id, self._retain_async, len(content), len(self._session_turns))
_run_sync(client.aretain_batch(
@@ -950,11 +789,11 @@ class HindsightMemoryProvider(MemoryProvider):
return tool_error("Missing required parameter: content")
context = args.get("context")
try:
retain_kwargs = self._build_retain_kwargs(
content,
context=context,
tags=args.get("tags"),
)
retain_kwargs: dict = {
"bank_id": self._bank_id, "content": content, "context": context,
}
if self._tags:
retain_kwargs["tags"] = self._tags
logger.debug("Tool hindsight_retain: bank=%s, content_len=%d, context=%s",
self._bank_id, len(content), context)
_run_sync(client.aretain(**retain_kwargs))
+3 -28
View File
@@ -39,7 +39,7 @@ dependencies = [
[project.optional-dependencies]
modal = ["modal>=1.0.0,<2"]
daytona = ["daytona>=0.148.0,<1"]
dev = ["debugpy>=1.8.0,<2", "pytest>=9.0.2,<10", "pytest-asyncio>=1.3.0,<2", "pytest-xdist>=3.0,<4", "mcp>=1.2.0,<2", "ty>=0.0.1a29,<0.0.22", "ruff"]
dev = ["debugpy>=1.8.0,<2", "pytest>=9.0.2,<10", "pytest-asyncio>=1.3.0,<2", "pytest-xdist>=3.0,<4", "mcp>=1.2.0,<2"]
messaging = ["python-telegram-bot[webhooks]>=22.6,<23", "discord.py[voice]>=2.7.1,<3", "aiohttp>=3.13.3,<4", "slack-bolt>=1.18.0,<2", "slack-sdk>=3.27.0,<4", "qrcode>=7.0,<8"]
cron = ["croniter>=6.0.0,<7"]
slack = ["slack-bolt>=1.18.0,<2", "slack-sdk>=3.27.0,<4"]
@@ -117,7 +117,7 @@ all = [
[project.scripts]
hermes = "hermes_cli.main:main"
hermes-agent = "run_agent:main"
hermes-acp = "hermes_agent.acp.entry:main"
hermes-acp = "acp_adapter.entry:main"
[tool.setuptools]
py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajectory_compressor", "toolset_distributions", "cli", "hermes_constants", "hermes_state", "hermes_time", "hermes_logging", "rl_cli", "utils"]
@@ -126,7 +126,7 @@ py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajector
hermes_cli = ["web_dist/**/*"]
[tool.setuptools.packages.find]
include = ["agent", "agent.*", "tools", "tools.*", "hermes_cli", "gateway", "gateway.*", "tui_gateway", "tui_gateway.*", "cron", "hermes_agent", "hermes_agent.*", "plugins", "plugins.*"]
include = ["agent", "tools", "tools.*", "hermes_cli", "gateway", "gateway.*", "tui_gateway", "tui_gateway.*", "cron", "acp_adapter", "plugins", "plugins.*"]
[tool.pytest.ini_options]
testpaths = ["tests"]
@@ -134,28 +134,3 @@ markers = [
"integration: marks tests requiring external services (API keys, Modal, etc.)",
]
addopts = "-m 'not integration' -n auto"
[tool.ty.environment]
python-version = "3.13"
[tool.ty.rules]
unknown-argument = "warn"
redundant-cast = "ignore"
[tool.ty.src]
exclude = ["**"]
[[tool.ty.overrides]]
include = ["**"]
[tool.ty.overrides.rules]
unresolved-import = "ignore"
invalid-method-override = "ignore"
invalid-assignment = "ignore"
not-iterable = "ignore"
[tool.ruff]
exclude = ["*"]
[tool.uv]
exclude-newer = "7 days"
+511 -502
View File
File diff suppressed because it is too large Load Diff
+1 -1
View File
@@ -265,7 +265,7 @@ def check_config(groq_key, eleven_key):
if voice_mode_path.exists():
try:
import json
modes = json.loads(voice_mode_path.read_text(encoding="utf-8"))
modes = json.loads(voice_mode_path.read_text())
off_count = sum(1 for v in modes.values() if v == "off")
all_count = sum(1 for v in modes.values() if v == "all")
check("Voice mode state", True, f"{all_count} on, {off_count} off, {len(modes)} total")
-76
View File
@@ -43,24 +43,17 @@ AUTHOR_MAP = {
"teknium1@gmail.com": "teknium1",
"teknium@nousresearch.com": "teknium1",
"127238744+teknium1@users.noreply.github.com": "teknium1",
"343873859@qq.com": "DrStrangerUJN",
# contributors (from noreply pattern)
"david.vv@icloud.com": "davidvv",
"wangqiang@wangqiangdeMac-mini.local": "xiaoqiang243",
"snreynolds2506@gmail.com": "snreynolds",
"35742124+0xbyt4@users.noreply.github.com": "0xbyt4",
"71184274+MassiveMassimo@users.noreply.github.com": "MassiveMassimo",
"massivemassimo@users.noreply.github.com": "MassiveMassimo",
"82637225+kshitijk4poor@users.noreply.github.com": "kshitijk4poor",
"keifergu@tencent.com": "keifergu",
"kshitijk4poor@users.noreply.github.com": "kshitijk4poor",
"abner.the.foreman@agentmail.to": "Abnertheforeman",
"harryykyle1@gmail.com": "hharry11",
"kshitijk4poor@gmail.com": "kshitijk4poor",
"16443023+stablegenius49@users.noreply.github.com": "stablegenius49",
"185121704+stablegenius49@users.noreply.github.com": "stablegenius49",
"101283333+batuhankocyigit@users.noreply.github.com": "batuhankocyigit",
"255305877+ismell0992-afk@users.noreply.github.com": "ismell0992-afk",
"valdi.jorge@gmail.com": "jvcl",
"francip@gmail.com": "francip",
"omni@comelse.com": "omnissiah-comelse",
@@ -98,9 +91,6 @@ AUTHOR_MAP = {
"135070653+sgaofen@users.noreply.github.com": "sgaofen",
"nocoo@users.noreply.github.com": "nocoo",
"30841158+n-WN@users.noreply.github.com": "n-WN",
"tsuijinglei@gmail.com": "hiddenpuppy",
"jerome@clawwork.ai": "HiddenPuppy",
"wysie@users.noreply.github.com": "Wysie",
"leoyuan0099@gmail.com": "keyuyuan",
"bxzt2006@163.com": "Only-Code-A",
"i@troy-y.org": "TroyMitchell911",
@@ -108,12 +98,8 @@ AUTHOR_MAP = {
"hansnow@users.noreply.github.com": "hansnow",
"134848055+UNLINEARITY@users.noreply.github.com": "UNLINEARITY",
"ben.burtenshaw@gmail.com": "burtenshaw",
"roopaknijhara@gmail.com": "rnijhara",
"josephzcan@gmail.com": "j0sephz",
# contributors (manual mapping from git names)
"ahmedsherif95@gmail.com": "asheriif",
"dyxushuai@gmail.com": "dyxushuai",
"33860762+etcircle@users.noreply.github.com": "etcircle",
"liujinkun@bytedance.com": "liujinkun2025",
"dmayhem93@gmail.com": "dmahan93",
"fr@tecompanytea.com": "ifrederico",
@@ -147,7 +133,6 @@ AUTHOR_MAP = {
"331214+counterposition@users.noreply.github.com": "counterposition",
"blspear@gmail.com": "BrennerSpear",
"akhater@gmail.com": "akhater",
"Cos_Admin@PTG-COS.lodluvup4uaudnm3ycd14giyug.xx.internal.cloudapp.net": "akhater",
"239876380+handsdiff@users.noreply.github.com": "handsdiff",
"hesapacicam112@gmail.com": "etherman-os",
"mark.ramsell@rivermounts.com": "mark-ramsell",
@@ -189,7 +174,6 @@ AUTHOR_MAP = {
"adavyasharma@gmail.com": "adavyas",
"acaayush1111@gmail.com": "aayushchaudhary",
"jason@outland.art": "jasonoutland",
"73175452+Magaav@users.noreply.github.com": "Magaav",
"mrflu1918@proton.me": "SPANISHFLU",
"morganemoss@gmai.com": "mormio",
"kopjop926@gmail.com": "cesareth",
@@ -294,7 +278,6 @@ AUTHOR_MAP = {
"srhtsrht17@gmail.com": "Sertug17",
"stephenschoettler@gmail.com": "stephenschoettler",
"tanishq231003@gmail.com": "yyovil",
"taosiyuan163@153.com": "taosiyuan163",
"tesseracttars@gmail.com": "tesseracttars-creator",
"tianliangjay@gmail.com": "xingkongliang",
"tranquil_flow@protonmail.com": "Tranquil-Flow",
@@ -350,65 +333,6 @@ AUTHOR_MAP = {
"asslaenn5@gmail.com": "Aslaaen",
"shalompmc0505@naver.com": "pinion05",
"105142614+VTRiot@users.noreply.github.com": "VTRiot",
"vivien000812@gmail.com": "iamagenius00",
"89228157+Feranmi10@users.noreply.github.com": "Feranmi10",
"simon@gtcl.us": "simon-gtcl",
"suzukaze.haduki@gmail.com": "houko",
"cliff@cigii.com": "cgarwood82",
"anna@oa.ke": "anna-oake",
"jaffarkeikei@gmail.com": "jaffarkeikei",
"hxp@hxp.plus": "hxp-plus",
"3580442280@qq.com": "Tianworld",
"wujianxu91@gmail.com": "wujhsu",
"zhrh120@gmail.com": "niyoh120",
"vrinek@hey.com": "vrinek",
"268198004+xandersbell@users.noreply.github.com": "xandersbell",
"somme4096@gmail.com": "Somme4096",
"brian@tiuxo.com": "brianclemens",
"25944632+yudaiyan@users.noreply.github.com": "yudaiyan",
"chayton@sina.com": "ycbai",
"longsizhuo@gmail.com": "longsizhuo",
"chenb19870707@gmail.com": "ms-alan",
"276886827+WuTianyi123@users.noreply.github.com": "WuTianyi123",
"22549957+li0near@users.noreply.github.com": "li0near",
"23434080+sicnuyudidi@users.noreply.github.com": "sicnuyudidi",
"haimu0x0@proton.me": "haimu0x",
"abdelmajidnidnasser1@gmail.com": "NIDNASSER-Abdelmajid",
"projectadmin@wit.id": "projectadmin-dev",
"mrigankamondal10@gmail.com": "Dev-Mriganka",
"132275809+shushuzn@users.noreply.github.com": "shushuzn",
"ibrahimozsarac@gmail.com": "iborazzi",
"130149563+A-afflatus@users.noreply.github.com": "A-afflatus",
"huangkwell@163.com": "huangke19",
"tanishq@exa.ai": "10ishq",
"363708+christopherwoodall@users.noreply.github.com": "christopherwoodall",
"zhang9w0v5@qq.com": "zhang9w0v5",
"fuleinist@outlook.com": "fuleinist",
"43494187+Llugaes@users.noreply.github.com": "Llugaes",
"fengtianyu88@users.noreply.github.com": "fengtianyu88",
"l.moncany@gmail.com": "lmoncany",
"fatinghenji@users.noreply.github.com": "fatinghenji",
"xin.peng.dr@gmail.com": "xinpengdr",
"mike@mikewaters.net": "mikewaters",
"65117428+WadydX@users.noreply.github.com": "WadydX",
"216480837+isaachuangGMICLOUD@users.noreply.github.com": "isaachuangGMICLOUD",
"nukuom976228@gmail.com": "hsy5571616",
"11462216+Nan93@users.noreply.github.com": "Nan93",
"l973401489@126.com": "zhouxiaoya12",
"373119611@qq.com": "roytian1217",
"brett@brettbrewer.com": "minorgod",
"67779267+wenhao7@users.noreply.github.com": "wenhao7",
"git@yzx9.xyz": "yzx9",
"nilesh@cloudgeni.us": "lvnilesh",
"63502660+azhengbot@users.noreply.github.com": "azhengbot",
"sharvil.saxena@gmail.com": "sharziki",
"yuanhe@minimaxi.com": "RyanLee-Dev",
"curtis992250@gmail.com": "TaroballzChen",
"92638503+Lind3ey@users.noreply.github.com": "Lind3ey",
"1352808998@qq.com": "phpoh",
"caliberoviv@gmail.com": "vivganes",
"michaelfackerell@gmail.com": "MikeFac",
"18024642@qq.com": "GuyCui",
}
+1 -1
View File
@@ -8,7 +8,7 @@
"name": "hermes-whatsapp-bridge",
"version": "1.0.0",
"dependencies": {
"@whiskeysockets/baileys": "WhiskeySockets/Baileys#01047debd81beb20da7b7779b08edcb06aa03770",
"@whiskeysockets/baileys": "WhiskeySockets/Baileys#fix/abprops-abt-fetch",
"express": "^4.21.0",
"pino": "^9.0.0",
"qrcode-terminal": "^0.12.0"
-77
View File
@@ -1,77 +0,0 @@
# Port Notes — baoyu-comic
Ported from [JimLiu/baoyu-skills](https://github.com/JimLiu/baoyu-skills) v1.56.1.
## Changes from upstream
### SKILL.md adaptations
| Change | Upstream | Hermes |
|--------|----------|--------|
| Metadata namespace | `openclaw` | `hermes` (with `tags` + `homepage`) |
| Trigger | Slash commands / CLI flags | Natural language skill matching |
| User config | EXTEND.md file (project/user/XDG paths) | Removed — not part of Hermes infra |
| User prompts | `AskUserQuestion` (batched) | `clarify` tool (one question at a time) |
| Image generation | baoyu-imagine (Bun/TypeScript, supports `--ref`) | `image_generate`**prompt-only**, returns a URL; no reference image input; agent must download the URL to the output directory |
| PDF assembly | `scripts/merge-to-pdf.ts` (Bun + `pdf-lib`) | Removed — the PDF merge step is out of scope for this port; pages are delivered as PNGs only |
| Platform support | Linux/macOS/Windows/WSL/PowerShell | Linux/macOS only |
| File operations | Generic instructions | Hermes file tools (`write_file`, `read_file`) |
### Structural removals
- **`references/config/` directory** (removed entirely):
- `first-time-setup.md` — blocking first-time setup flow for EXTEND.md
- `preferences-schema.md` — EXTEND.md YAML schema
- `watermark-guide.md` — watermark config (tied to EXTEND.md)
- **`scripts/` directory** (removed entirely): upstream's `merge-to-pdf.ts` depended on `pdf-lib`, which is not declared anywhere in the Hermes repo. Rather than add a new dependency, the port drops PDF assembly and delivers per-page PNGs.
- **Workflow Step 8 (Merge to PDF)** removed from `workflow.md`; Step 9 (Completion report) renumbered to Step 8.
- **Workflow Step 1.1** — "Load Preferences (EXTEND.md)" section removed from `workflow.md`; steps 1.2/1.3 renumbered to 1.1/1.2.
- **Generic "User Input Tools" and "Image Generation Tools" preambles** — SKILL.md no longer lists fallback rules for multiple possible tools; it references `clarify` and `image_generate` directly.
### Image generation strategy changes
`image_generate`'s schema accepts only `prompt` and `aspect_ratio` (`landscape` | `portrait` | `square`). Upstream's reference-image flow (`--ref characters.png` for character consistency, plus user-supplied refs for style/palette/scene) does not map to this tool, so the workflow was restructured:
- **Character sheet PNG** is still generated for multi-page comics, but it is repositioned as a **human-facing review artifact** (for visual verification) and a reference for later regenerations / manual prompt edits. Page prompts themselves are built from the **text descriptions** in `characters/characters.md` (embedded inline during Step 5). `image_generate` never sees the PNG as a visual input.
- **User-supplied reference images** are reduced to `style` / `palette` / `scene` trait extraction — traits are embedded in the prompt body; the image files themselves are kept only for provenance under `refs/`.
- **Page prompts** now mandate that character descriptions are embedded inline (copied from `characters/characters.md`) — this is the only mechanism left to enforce cross-page character consistency.
- **Download step** — after every `image_generate` call, the returned URL is fetched to disk (e.g., `curl -fsSL "<url>" -o <target>.png`) and verified before the workflow advances.
### SKILL.md reductions
- CLI option columns (`--art`, `--tone`, `--layout`, `--aspect`, `--lang`, `--ref`, `--storyboard-only`, `--prompts-only`, `--images-only`, `--regenerate`) converted to plain-English option descriptions.
- Preset files (`presets/*.md`) and `ohmsha-guide.md`: `` `--style X` `` / `` `--art X --tone Y` `` shorthand rewritten to `art=X, tone=Y` + natural-language references.
- `partial-workflows.md`: per-skill slash command invocations rewritten as user-intent cues; PDF-related outputs removed.
- `auto-selection.md`: priority order dropped the EXTEND.md tier.
- `analysis-framework.md`: language-priority comment updated (user option → conversation → source).
### File naming convention
Source content pasted by the user is saved as `source-{slug}.md`, where `{slug}` is the kebab-case topic slug used for the output directory. Backups follow the same pattern with a `-backup-YYYYMMDD-HHMMSS` suffix. SKILL.md and `workflow.md` now agree on this single convention.
### What was preserved verbatim
- All 6 art-style definitions (`references/art-styles/`)
- All 7 tone definitions (`references/tones/`)
- All 7 layout definitions (`references/layouts/`)
- Core templates: `character-template.md`, `storyboard-template.md`, `base-prompt.md`
- Preset bodies (only the first few intro lines adapted; special rules unchanged)
- Author, version, homepage attribution
## Syncing with upstream
To pull upstream updates:
```bash
# Compare versions
curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/SKILL.md | head -5
# Look for the version: line
# Diff a reference file
diff <(curl -sL https://raw.githubusercontent.com/JimLiu/baoyu-skills/main/skills/baoyu-comic/references/art-styles/manga.md) \
references/art-styles/manga.md
```
Art-style, tone, and layout reference files can usually be overwritten directly (they're upstream-verbatim). `SKILL.md`, `references/workflow.md`, `references/partial-workflows.md`, `references/auto-selection.md`, `references/analysis-framework.md`, `references/ohmsha-guide.md`, and `references/presets/*.md` must be manually merged since they contain Hermes-specific adaptations.
If upstream adds a Hermes-compatible PDF merge step (no extra npm deps), restore `scripts/` and reintroduce Step 8 in `workflow.md`.
-246
View File
@@ -1,246 +0,0 @@
---
name: baoyu-comic
description: Knowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and sequential image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".
version: 1.56.1
author: 宝玉 (JimLiu)
license: MIT
metadata:
hermes:
tags: [comic, knowledge-comic, creative, image-generation]
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-comic
---
# Knowledge Comic Creator
Adapted from [baoyu-comic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.
Create original knowledge comics with flexible art style × tone combinations.
## When to Use
Trigger this skill when the user asks to create a knowledge/educational comic, biography comic, tutorial comic, or uses terms like "知识漫画", "教育漫画", or "Logicomix-style". The user provides content (text, file path, URL, or topic) and optionally specifies art style, tone, layout, aspect ratio, or language.
## Reference Images
Hermes' `image_generate` tool is **prompt-only** — it accepts a text prompt and an aspect ratio, and returns an image URL. It does **NOT** accept reference images. When the user supplies a reference image, use it to **extract traits in text** that get embedded in every page prompt:
**Intake**: Accept file paths when the user provides them (or pastes images in conversation).
- File path(s) → copy to `refs/NN-ref-{slug}.{ext}` alongside the comic output for provenance
- Pasted image with no path → ask the user for the path via `clarify`, or extract style traits verbally as a text fallback
- No reference → skip this section
**Usage modes** (per reference):
| Usage | Effect |
|-------|--------|
| `style` | Extract style traits (line treatment, texture, mood) and append to every page's prompt body |
| `palette` | Extract hex colors and append to every page's prompt body |
| `scene` | Extract scene composition or subject notes and append to the relevant page(s) |
**Record in each page's prompt frontmatter** when refs exist:
```yaml
references:
- ref_id: 01
filename: 01-ref-scene.png
usage: style
traits: "muted earth tones, soft-edged ink wash, low-contrast backgrounds"
```
Character consistency is driven by **text descriptions** in `characters/characters.md` (written in Step 3) that get embedded inline in every page prompt (Step 5). The optional PNG character sheet generated in Step 7.1 is a human-facing review artifact, not an input to `image_generate`.
## Options
### Visual Dimensions
| Option | Values | Description |
|--------|--------|-------------|
| Art | ligne-claire (default), manga, realistic, ink-brush, chalk, minimalist | Art style / rendering technique |
| Tone | neutral (default), warm, dramatic, romantic, energetic, vintage, action | Mood / atmosphere |
| Layout | standard (default), cinematic, dense, splash, mixed, webtoon, four-panel | Panel arrangement |
| Aspect | 3:4 (default, portrait), 4:3 (landscape), 16:9 (widescreen) | Page aspect ratio |
| Language | auto (default), zh, en, ja, etc. | Output language |
| Refs | File paths | Reference images used for style / palette trait extraction (not passed to the image model). See [Reference Images](#reference-images) above. |
### Partial Workflow Options
| Option | Description |
|--------|-------------|
| Storyboard only | Generate storyboard only, skip prompts and images |
| Prompts only | Generate storyboard + prompts, skip images |
| Images only | Generate images from existing prompts directory |
| Regenerate N | Regenerate specific page(s) only (e.g., `3` or `2,5,8`) |
Details: [references/partial-workflows.md](references/partial-workflows.md)
### Art, Tone & Preset Catalogue
- **Art styles** (6): `ligne-claire`, `manga`, `realistic`, `ink-brush`, `chalk`, `minimalist`. Full definitions at `references/art-styles/<style>.md`.
- **Tones** (7): `neutral`, `warm`, `dramatic`, `romantic`, `energetic`, `vintage`, `action`. Full definitions at `references/tones/<tone>.md`.
- **Presets** (5) with special rules beyond plain art+tone:
| Preset | Equivalent | Hook |
|--------|-----------|------|
| `ohmsha` | manga + neutral | Visual metaphors, no talking heads, gadget reveals |
| `wuxia` | ink-brush + action | Qi effects, combat visuals, atmospheric |
| `shoujo` | manga + romantic | Decorative elements, eye details, romantic beats |
| `concept-story` | manga + warm | Visual symbol system, growth arc, dialogue+action balance |
| `four-panel` | minimalist + neutral + four-panel layout | 起承转合 structure, B&W + spot color, stick-figure characters |
Full rules at `references/presets/<preset>.md` — load the file when a preset is picked.
- **Compatibility matrix** and **content-signal → preset** table live in [references/auto-selection.md](references/auto-selection.md). Read it before recommending combinations in Step 2.
## File Structure
Output directory: `comic/{topic-slug}/`
- Slug: 2-4 words kebab-case from topic (e.g., `alan-turing-bio`)
- Conflict: append timestamp (e.g., `turing-story-20260118-143052`)
**Contents**:
| File | Description |
|------|-------------|
| `source-{slug}.md` | Saved source content (kebab-case slug matches the output directory) |
| `analysis.md` | Content analysis |
| `storyboard.md` | Storyboard with panel breakdown |
| `characters/characters.md` | Character definitions |
| `characters/characters.png` | Character reference sheet (downloaded from `image_generate`) |
| `prompts/NN-{cover\|page}-[slug].md` | Generation prompts |
| `NN-{cover\|page}-[slug].png` | Generated images (downloaded from `image_generate`) |
| `refs/NN-ref-{slug}.{ext}` | User-supplied reference images (optional, for provenance) |
## Language Handling
**Detection Priority**:
1. User-specified language (explicit option)
2. User's conversation language
3. Source content language
**Rule**: Use user's input language for ALL interactions:
- Storyboard outlines and scene descriptions
- Image generation prompts
- User selection options and confirmations
- Progress updates, questions, errors, summaries
Technical terms remain in English.
## Workflow
### Progress Checklist
```
Comic Progress:
- [ ] Step 1: Setup & Analyze
- [ ] 1.1 Analyze content
- [ ] 1.2 Check existing directory
- [ ] Step 2: Confirmation - Style & options ⚠️ REQUIRED
- [ ] Step 3: Generate storyboard + characters
- [ ] Step 4: Review outline (conditional)
- [ ] Step 5: Generate prompts
- [ ] Step 6: Review prompts (conditional)
- [ ] Step 7: Generate images
- [ ] 7.1 Generate character sheet (if needed) → characters/characters.png
- [ ] 7.2 Generate pages (with character descriptions embedded in prompt)
- [ ] Step 8: Completion report
```
### Flow
```
Input → Analyze → [Check Existing?] → [Confirm: Style + Reviews] → Storyboard → [Review?] → Prompts → [Review?] → Images → Complete
```
### Step Summary
| Step | Action | Key Output |
|------|--------|------------|
| 1.1 | Analyze content | `analysis.md`, `source-{slug}.md` |
| 1.2 | Check existing directory | Handle conflicts |
| 2 | Confirm style, focus, audience, reviews | User preferences |
| 3 | Generate storyboard + characters | `storyboard.md`, `characters/` |
| 4 | Review outline (if requested) | User approval |
| 5 | Generate prompts | `prompts/*.md` |
| 6 | Review prompts (if requested) | User approval |
| 7.1 | Generate character sheet (if needed) | `characters/characters.png` |
| 7.2 | Generate pages | `*.png` files |
| 8 | Completion report | Summary |
### User Questions
Use the `clarify` tool to confirm options. Since `clarify` handles one question at a time, ask the most important question first and proceed sequentially. See [references/workflow.md](references/workflow.md) for the full Step 2 question set.
**Timeout handling (CRITICAL)**: `clarify` can return `"The user did not provide a response within the time limit. Use your best judgement to make the choice and proceed."` — this is NOT user consent to default everything.
- Treat it as a default **for that one question only**. Continue asking the remaining Step 2 questions in sequence; each question is an independent consent point.
- **Surface the default to the user visibly** in your next message so they have a chance to correct it: e.g. `"Style: defaulted to ohmsha preset (clarify timed out). Say the word to switch."` — an unreported default is indistinguishable from never having asked.
- Do NOT collapse Step 2 into a single "use all defaults" pass after one timeout. If the user is genuinely absent, they will be equally absent for all five questions — but they can correct visible defaults when they return, and cannot correct invisible ones.
### Step 7: Image Generation
Use Hermes' built-in `image_generate` tool for all image rendering. Its schema accepts only `prompt` and `aspect_ratio` (`landscape` | `portrait` | `square`); it **returns a URL**, not a local file. Every generated page or character sheet must therefore be downloaded to the output directory.
**Prompt file requirement (hard)**: write each image's full, final prompt to a standalone file under `prompts/` (naming: `NN-{type}-[slug].md`) BEFORE calling `image_generate`. The prompt file is the reproducibility record.
**Aspect ratio mapping** — the storyboard's `aspect_ratio` field maps to `image_generate`'s format as follows:
| Storyboard ratio | `image_generate` format |
|------------------|-------------------------|
| `3:4`, `9:16`, `2:3` | `portrait` |
| `4:3`, `16:9`, `3:2` | `landscape` |
| `1:1` | `square` |
**Download step** — after every `image_generate` call:
1. Read the URL from the tool result
2. Fetch the image bytes using an **absolute** output path, e.g.
`curl -fsSL "<url>" -o /abs/path/to/comic/<slug>/NN-page-<slug>.png`
3. Verify the file exists and is non-empty at that exact path before proceeding to the next page
**Never rely on shell CWD persistence for `-o` paths.** The terminal tool's persistent-shell CWD can change between batches (session expiry, `TERMINAL_LIFETIME_SECONDS`, a failed `cd` that leaves you in the wrong directory). `curl -o relative/path.png` is a silent footgun: if CWD has drifted, the file lands somewhere else with no error. **Always pass a fully-qualified absolute path to `-o`**, or pass `workdir=<abs path>` to the terminal tool. Incident Apr 2026: pages 06-09 of a 10-page comic landed at the repo root instead of `comic/<slug>/` because batch 3 inherited a stale CWD from batch 2 and `curl -o 06-page-skills.png` wrote to the wrong directory. The agent then spent several turns claiming the files existed where they didn't.
**7.1 Character sheet** — generate it (to `characters/characters.png`, aspect `landscape`) when the comic is multi-page with recurring characters. Skip for simple presets (e.g., four-panel minimalist) or single-page comics. The prompt file at `characters/characters.md` must exist before invoking `image_generate`. The rendered PNG is a **human-facing review artifact** (so the user can visually verify character design) and a reference for later regenerations or manual prompt edits — it does **not** drive Step 7.2. Page prompts are already written in Step 5 from the **text descriptions** in `characters/characters.md`; `image_generate` cannot accept images as visual input.
**7.2 Pages** — each page's prompt MUST already be at `prompts/NN-{cover|page}-[slug].md` before invoking `image_generate`. Because `image_generate` is prompt-only, character consistency is enforced by **embedding character descriptions (sourced from `characters/characters.md`) inline in every page prompt during Step 5**. The embedding is done uniformly whether or not a PNG sheet is produced in 7.1; the PNG is only a review/regeneration aid.
**Backup rule**: existing `prompts/…md` and `…png` files → rename with `-backup-YYYYMMDD-HHMMSS` suffix before regenerating.
Full step-by-step workflow (analysis, storyboard, review gates, regeneration variants): [references/workflow.md](references/workflow.md).
## References
**Core Templates**:
- [analysis-framework.md](references/analysis-framework.md) - Deep content analysis
- [character-template.md](references/character-template.md) - Character definition format
- [storyboard-template.md](references/storyboard-template.md) - Storyboard structure
- [ohmsha-guide.md](references/ohmsha-guide.md) - Ohmsha manga specifics
**Style Definitions**:
- `references/art-styles/` - Art styles (ligne-claire, manga, realistic, ink-brush, chalk, minimalist)
- `references/tones/` - Tones (neutral, warm, dramatic, romantic, energetic, vintage, action)
- `references/presets/` - Presets with special rules (ohmsha, wuxia, shoujo, concept-story, four-panel)
- `references/layouts/` - Layouts (standard, cinematic, dense, splash, mixed, webtoon, four-panel)
**Workflow**:
- [workflow.md](references/workflow.md) - Full workflow details
- [auto-selection.md](references/auto-selection.md) - Content signal analysis
- [partial-workflows.md](references/partial-workflows.md) - Partial workflow options
## Page Modification
| Action | Steps |
|--------|-------|
| **Edit** | **Update prompt file FIRST** → regenerate image → download new PNG |
| **Add** | Create prompt at position → generate with character descriptions embedded → renumber subsequent → update storyboard |
| **Delete** | Remove files → renumber subsequent → update storyboard |
**IMPORTANT**: When updating pages, ALWAYS update the prompt file (`prompts/NN-{cover|page}-[slug].md`) FIRST before regenerating. This ensures changes are documented and reproducible.
## Pitfalls
- Image generation: 10-30 seconds per page; auto-retry once on failure
- **Always download** the URL returned by `image_generate` to a local PNG — downstream tooling (and the user's review) expects files in the output directory, not ephemeral URLs
- **Use absolute paths for `curl -o`** — never rely on persistent-shell CWD across batches. Silent footgun: files land in the wrong directory and subsequent `ls` on the intended path shows nothing. See Step 7 "Download step".
- Use stylized alternatives for sensitive public figures
- **Step 2 confirmation required** - do not skip
- **Steps 4/6 conditional** - only if user requested in Step 2
- **Step 7.1 character sheet** - recommended for multi-page comics, optional for simple presets. The PNG is a review/regeneration aid; page prompts (written in Step 5) use the text descriptions in `characters/characters.md`, not the PNG. `image_generate` does not accept images as visual input
- **Strip secrets** — scan source content for API keys, tokens, or credentials before writing any output file
@@ -1,176 +0,0 @@
# Comic Content Analysis Framework
Deep analysis framework for transforming source content into effective visual storytelling.
## Purpose
Before creating a comic, thoroughly analyze the source material to:
- Identify the target audience and their needs
- Determine what value the comic will deliver
- Extract narrative potential for visual storytelling
- Plan character arcs and key moments
## Analysis Dimensions
### 1. Core Content (Understanding "What")
**Central Message**
- What is the single most important idea readers should take away?
- Can you express it in one sentence?
**Key Concepts**
- What are the essential concepts readers must understand?
- How should these concepts be visualized?
- Which concepts need simplified explanations?
**Content Structure**
- How is the source material organized?
- What is the natural narrative arc?
- Where are the climax and turning points?
**Evidence & Examples**
- What concrete examples, data, or stories support the main ideas?
- Which examples translate well to visual panels?
- What can be shown rather than told?
### 2. Context & Background (Understanding "Why")
**Source Origin**
- Who created this content? What is their perspective?
- What was the original purpose?
- Is there bias to be aware of?
**Historical/Cultural Context**
- When and where does the story take place?
- What background knowledge do readers need?
- What period-specific visual elements are required?
**Underlying Assumptions**
- What does the source assume readers already know?
- What implicit beliefs or values are present?
- Should the comic challenge or reinforce these?
### 3. Audience Analysis
**Primary Audience**
- Who will read this comic?
- What is their existing knowledge level?
- What are their interests and motivations?
**Secondary Audiences**
- Who else might benefit from this comic?
- How might their needs differ?
**Reader Questions**
- What questions will readers have?
- What misconceptions might they bring?
- What "aha moments" can we create?
### 4. Value Proposition
**Knowledge Value**
- What will readers learn?
- What new perspectives will they gain?
- How will this change their understanding?
**Emotional Value**
- What emotions should readers feel?
- What connections will they make with characters?
- What will make this memorable?
**Practical Value**
- Can readers apply what they learn?
- What actions might this inspire?
- What conversations might it spark?
### 5. Narrative Potential
**Story Arc Candidates**
- What natural narratives exist in the content?
- Where is the conflict or tension?
- What transformations occur?
**Character Potential**
- Who are the key figures?
- What are their motivations and obstacles?
- How do they change throughout?
**Visual Opportunities**
- What scenes have strong visual potential?
- Where can abstract concepts become concrete images?
- What metaphors can be visualized?
**Dramatic Moments**
- What are the breakthrough/revelation moments?
- Where are the emotional peaks?
- What creates tension and release?
### 6. Adaptation Considerations
**What to Keep**
- Essential facts and ideas
- Key quotes or moments
- Core emotional beats
**What to Simplify**
- Complex explanations
- Dense technical details
- Lengthy descriptions
**What to Expand**
- Brief mentions that deserve more attention
- Implied emotions or relationships
- Visual details not in source
**What to Omit**
- Tangential information
- Redundant examples
- Content that doesn't serve the narrative
## Output Format
Analysis results should be saved to `analysis.md` with:
1. **YAML Front Matter**: Metadata (title, topic, time_span, source_language, user_language, aspect_ratio, recommended_page_count, recommended_art, recommended_tone, recommended_layout)
2. **Target Audience**: Primary, secondary, tertiary audiences with their needs
3. **Value Proposition**: What readers will gain (knowledge, emotional, practical)
4. **Core Themes**: Table with theme, narrative potential, visual opportunity
5. **Key Figures & Story Arcs**: Character profiles with arcs, visual identity, key moments
6. **Content Signals**: Style and layout recommendations based on content type
7. **Recommended Approaches**: Narrative approaches ranked by suitability
### YAML Front Matter Example
```yaml
---
title: "Alan Turing: The Father of Computing"
topic: alan-turing-biography
time_span: 1912-1954
source_language: en
user_language: zh # User-specified or detected from conversation
aspect_ratio: "3:4"
recommended_page_count: 16
recommended_art: ligne-claire # ligne-claire|manga|realistic|ink-brush|chalk
recommended_tone: neutral # neutral|warm|dramatic|romantic|energetic|vintage|action
recommended_layout: mixed # standard|cinematic|dense|splash|mixed|webtoon
---
```
### Language Fields
| Field | Description |
|-------|-------------|
| `source_language` | Detected language of source content |
| `user_language` | Output language for comic (user-specified option > conversation language > source_language) |
## Analysis Checklist
Before proceeding to storyboard:
- [ ] Can I state the core message in one sentence?
- [ ] Do I know exactly who will read this comic?
- [ ] Have I identified at least 3 ways this comic provides value?
- [ ] Are there clear protagonists with compelling arcs?
- [ ] Have I found at least 5 visually powerful moments?
- [ ] Do I understand what to keep, simplify, expand, and omit?
- [ ] Have I identified the emotional peaks and valleys?
@@ -1,101 +0,0 @@
# chalk
粉笔画风 - Chalkboard aesthetic with hand-drawn warmth
## Overview
Classic classroom chalkboard aesthetic with hand-drawn chalk illustrations. Nostalgic educational feel with imperfect, sketchy lines that capture the warmth of traditional teaching.
## Line Work
- Sketchy, imperfect hand-drawn lines
- Chalk texture on all strokes
- Varying line weight from chalk pressure
- Soft edges, no sharp digital lines
- Visible chalk dust effects
## Character Design
- Simplified, friendly character designs
- Stick figures to semi-detailed range
- Expressive through simple gestures
- Approachable, non-intimidating
- Educational presenter style
## Background
- Chalkboard Black (#1A1A1A) or Dark Green-Black (#1C2B1C)
- Realistic chalkboard texture
- Subtle scratches and dust particles
- Faint eraser marks for authenticity
- Wooden frame border optional
## Typography
- Hand-drawn chalk lettering style
- Visible chalk texture on text
- Imperfect baseline adds authenticity
- White or bright colored chalk for emphasis
## Visual Elements
- Hand-drawn chalk illustrations
- Chalk dust effects around elements
- Doodles: stars, arrows, underlines, circles
- Mathematical formulas and diagrams
- Eraser smudges and chalk residue
- Stick figures and simple icons
- Connection lines with hand-drawn feel
## Default Color Palette
| Role | Color | Hex |
|------|-------|-----|
| Background | Chalkboard Black | #1A1A1A |
| Alt Background | Green-Black | #1C2B1C |
| Primary Text | Chalk White | #F5F5F5 |
| Accent 1 | Chalk Yellow | #FFE566 |
| Accent 2 | Chalk Pink | #FF9999 |
| Accent 3 | Chalk Blue | #66B3FF |
| Accent 4 | Chalk Green | #90EE90 |
| Accent 5 | Chalk Orange | #FFB366 |
## Style Rules
### Do
- Maintain authentic chalk texture on all elements
- Use imperfect, hand-drawn quality throughout
- Add subtle chalk dust and smudge effects
- Create visual hierarchy with color variety
- Include playful doodles and annotations
### Don't
- Use perfect geometric shapes
- Create clean digital-looking lines
- Add photorealistic elements
- Use gradients or glossy effects
## Quality Markers
- ✓ Authentic chalk texture throughout
- ✓ Imperfect, hand-drawn quality
- ✓ Readable despite sketchy style
- ✓ Nostalgic classroom feel
- ✓ Effective color hierarchy
- ✓ Playful educational aesthetic
## Compatibility
| Tone | Fit | Notes |
|------|-----|-------|
| neutral | ✓✓ | Classic educational |
| warm | ✓✓ | Nostalgic feel |
| dramatic | ✗ | Style mismatch |
| vintage | ✓ | Old school feel |
| romantic | ✗ | Style mismatch |
| energetic | ✓✓ | Fun learning |
| action | ✗ | Style mismatch |
## Best For
Educational content, tutorials, classroom themes, teaching materials, workshops, informal learning, knowledge sharing
@@ -1,97 +0,0 @@
# ink-brush
水墨画风 - Chinese ink brush aesthetics with dynamic strokes
## Overview
Traditional Chinese ink brush painting style adapted for comics. Combines calligraphic brush strokes with ink wash effects. Creates atmospheric, artistic visuals rooted in East Asian aesthetics.
## Line Work
- 2-3px dynamic brush strokes with varying weight
- Ink wash effects, traditional Chinese brush feel
- Bold, confident strokes with sharp edges
- Flowing lines for fabric and hair
- Pressure-sensitive stroke variation
## Character Design
- Realistic human proportions (7.5-8 head heights)
- Defined features with ink brush definition
- Dynamic poses capturing movement
- Flowing hair and clothing in motion
- Traditional attire options (robes, hanfu)
- Intense, expressive faces
## Brush Techniques
| Technique | Usage |
|-----------|-------|
| Bold strokes | Character outlines |
| Fine lines | Details, hair |
| Ink wash | Atmosphere, shadows |
| Dry brush | Texture, aging |
| Splatter | Impact, drama |
## Background Treatment
- Dramatic landscapes: mountains, waterfalls, temples
- Ink wash atmospheric effects
- Misty, layered depth
- Traditional architecture elements
- High contrast silhouettes
- Negative space as design element
## Color Approach
- Ink gradients as primary
- Limited accent colors
- Traditional Chinese palette
- Atmospheric color washes
- High contrast compositions
## Default Color Palette
| Role | Color | Hex |
|------|-------|-----|
| Primary | Deep black ink | #1A1A1A |
| Accent | Crimson red | #8B0000 |
| Accent | Imperial gold | #D4AF37 |
| Skin | Natural tan | #D4A574 |
| Background | Misty gray | #9CA3AF |
| Background | Earth tone | #8B7355 |
| Wash | Ink gradient | #2D3748 |
## Visual Elements
- Calligraphic text integration
- Seal stamps (optional)
- Ink splatter effects
- Flowing fabric trails
- Atmospheric mist
- Mountain silhouettes
## Quality Markers
- ✓ Dynamic brush stroke quality
- ✓ Authentic ink wash atmosphere
- ✓ High contrast compositions
- ✓ Flowing movement in fabric/hair
- ✓ Traditional aesthetic elements
- ✓ Atmospheric depth
## Compatibility
| Tone | Fit | Notes |
|------|-----|-------|
| neutral | ✓ | Contemplative stories |
| warm | ✓ | Nostalgic, gentle |
| dramatic | ✓✓ | High contrast |
| vintage | ✓✓ | Historical pieces |
| romantic | ✗ | Style mismatch |
| energetic | ✗ | Too refined |
| action | ✓✓ | Martial arts |
## Best For
Chinese historical stories, martial arts, traditional tales, contemplative narratives, artistic adaptations
@@ -1,75 +0,0 @@
# ligne-claire
清线画风 - Uniform lines, flat colors, European comic tradition
## Overview
Classic European comic style originating from Hergé's Tintin. Characterized by clean, uniform outlines and flat color fills without gradients. Creates a timeless, accessible aesthetic suitable for educational and narrative content.
## Line Work
- Uniform, clean outlines with consistent weight (2px)
- No hatching or cross-hatching for shading
- Sharp, precise edges on all elements
- Black ink outlines on all figures and objects
- Shadows indicated through flat color areas, not line techniques
## Character Design
- Slightly stylized/cartoonish characters with realistic proportions
- Distinctive, recognizable facial features
- Expressive faces with clear emotions
- Period-appropriate clothing with attention to detail
- Consistent character appearance across panels
- 6-7 head height proportions
## Background Treatment
- Detailed, realistic backgrounds with architectural accuracy
- Period-specific props and technology
- Clear spatial depth and perspective
- Environmental storytelling through details
- Contrast between simplified characters and detailed backgrounds
## Color Approach
- Flat colors without gradients (true to Ligne Claire tradition)
- Limited palette per page for cohesion
- Colors support narrative mood
- Consistent lighting logic within scenes
## Default Color Palette
| Role | Color | Hex |
|------|-------|-----|
| Primary Blue | Clean blue | #3182CE |
| Primary Red | Classic red | #E53E3E |
| Primary Yellow | Warm yellow | #ECC94B |
| Skin | Warm tan | #F7CFAE |
| Background Light | Light cream | #FFFAF0 |
| Background Sky | Sky blue | #BEE3F8 |
## Quality Markers
- ✓ Clean, uniform line weight throughout
- ✓ Flat colors without gradients
- ✓ Detailed backgrounds, stylized characters
- ✓ Clear panel borders and reading flow
- ✓ Hand-drawn text style
- ✓ Proper perspective in environments
## Compatibility
| Tone | Fit | Notes |
|------|-----|-------|
| neutral | ✓✓ | Classic combination |
| warm | ✓✓ | Nostalgic stories |
| dramatic | ✓ | Works with high contrast |
| vintage | ✓ | Period pieces |
| romantic | ✗ | Style mismatch |
| energetic | ✓ | Lighter stories |
| action | ✗ | Lacks dynamic lines |
## Best For
Educational content, balanced narratives, biography comics, historical stories
@@ -1,93 +0,0 @@
# manga
日漫画风 - Anime/manga aesthetics with expressive characters
## Overview
Japanese manga art style characterized by large expressive eyes, dynamic poses, and visual emotion indicators. Versatile style that works across genres from educational to romantic to action.
## Line Work
- Clean, smooth lines (1.5-2px)
- Expressive weight variation for emphasis
- Smooth curves, dynamic strokes
- Speed lines and motion effects available
- Screen tone effects for atmosphere
## Character Design
- Anime/manga proportions: larger eyes, expressive faces
- 5-7 head height proportions (varies by sub-style)
- Clear emotional indicators (, , sweat drops, sparkles)
- Dynamic poses and gestures
- Detailed hair with individual strands
- Fashionable clothing with natural folds
## Eye Styles
| Type | Description |
|------|-------------|
| Standard | Medium-large, 2-3 highlights |
| Educational | Friendly, approachable eyes |
| Dramatic | Intense, detailed irises |
| Cute | Very large, sparkly eyes |
## Background Treatment
- Simplified during dialogue/explanation
- Detailed for establishing shots
- Screen tone gradients for mood
- Abstract backgrounds for emotional moments
- Technical diagrams styled as displays
## Color Approach
- Clean, bright anime colors
- Soft gradients on skin
- Vibrant palette options
- Light and shadow with soft transitions
- Color coding for character identification
## Default Color Palette
| Role | Color | Hex |
|------|-------|-----|
| Primary Blue | Bright blue | #4299E1 |
| Primary Orange | Warm orange | #ED8936 |
| Primary Green | Soft green | #68D391 |
| Skin | Anime warm | #FEEBC8 |
| Background | Clean white | #FFFFFF |
| Highlight | Golden | #FFD700 |
## Visual Elements
- Speech bubbles: rounded (normal), spiky (excitement)
- Sound effects integrated visually
- Emotion symbols (sweat drops, anger marks, hearts)
- Speed lines and motion blur
- Sparkle and glow effects
## Quality Markers
- ✓ Expressive character faces
- ✓ Clean, consistent line work
- ✓ Dynamic poses and compositions
- ✓ Appropriate use of manga conventions
- ✓ Readable panel flow
- ✓ Consistent character designs
## Compatibility
| Tone | Fit | Notes |
|------|-----|-------|
| neutral | ✓✓ | Educational manga |
| warm | ✓ | Slice of life |
| dramatic | ✓ | Intense moments |
| romantic | ✓✓ | Shoujo style |
| energetic | ✓✓ | Shonen style |
| vintage | ✗ | Style mismatch |
| action | ✓✓ | Battle manga |
## Best For
Educational tutorials, romance, action, coming-of-age, technical explanations, youth-oriented content
@@ -1,84 +0,0 @@
# minimalist
极简画风 - Clean black line art, limited spot color, simplified stick-figure characters
## Overview
Minimalist cartoon illustration characterized by clean black line art on white background with very limited spot color for emphasis. Characters are simplified to near-stick-figure abstraction, focusing on gesture and concept rather than anatomical detail. Designed for business allegory, quick-read educational content, and concept illustration.
## Line Work
- Clean, uniform black lines (1.5-2px)
- No hatching, cross-hatching, or shading techniques
- Minimal detail — every line serves a purpose
- Bold outlines for characters, thinner lines for props/labels
- No decorative flourishes or ornamental lines
## Character Design
- Highly simplified, stick-figure-like business characters
- Circle or oval heads with minimal facial features (dot eyes, simple line mouth)
- Body as simple geometric shapes or line constructions
- Distinguishing features through props only (tie, hat, briefcase, glasses)
- No anatomical detail — expressive through posture and gesture
- 4-5 head height proportions (squat, iconic)
## Background Treatment
- Mostly blank/white — negative space is a design element
- Minimal environmental cues (a line for ground, simple desk outline)
- Concept labels and text annotations replace detailed environments
- Icons and symbols over realistic rendering
- No perspective or spatial depth
## Color Approach
- Primarily black and white (90%+ of the image)
- 1-2 spot accent colors for emphasis on key concepts
- Accent color used sparingly: highlighting key objects, text labels, concept indicators
- No gradients, no shading, no color fills on backgrounds
- Color draws the eye to the most important element in each panel
## Default Color Palette
| Role | Color | Hex |
|------|-------|-----|
| Primary | Black ink | `#1A1A1A` |
| Background | Clean white | `#FFFFFF` |
| Accent 1 | Spot orange | `#FF6B35` |
| Accent 2 | Spot blue (optional) | `#3182CE` |
| Text labels | Dark gray | `#4A4A4A` |
| Panel border | Medium gray | `#666666` |
## Visual Elements
- Text labels with accent-color backgrounds or underlines for key terms
- Simple icons: arrows, circles, checkmarks, crosses
- Concept highlight boxes with spot color
- Minimal speech bubbles (simple oval or rectangle, thin black outline)
- No sound effects, no motion lines, no screen tones
## Quality Markers
- ✓ Clean, purposeful line work with no unnecessary detail
- ✓ 90%+ black-and-white with strategic spot color
- ✓ Simplified characters readable at small sizes
- ✓ Text labels integrated naturally into panels
- ✓ Strong negative space usage
- ✓ Every element serves the narrative point
## Compatibility
| Tone | Fit | Notes |
|------|-----|-------|
| neutral | ✓✓ | Ideal for business/educational content |
| warm | ✓ | Works for gentle stories, slight warmth in accent |
| energetic | ✓ | Works for punchy, high-energy content |
| dramatic | ✗ | Style too stripped down for dramatic intensity |
| vintage | ✗ | Minimalist aesthetic conflicts with aged/textured look |
| romantic | ✗ | No capacity for decorative/soft elements |
| action | ✗ | No dynamic line capability for speed/impact |
## Best For
Business allegory, management fables, short concept illustration, four-panel comic strips, quick-insight education, social media content

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