Files
hermes-agent/agent/memory_provider.py
T
Teknium 5d278aa31d feat(memory): standardize plugin config + add per-plugin documentation
Config architecture:
- Add save_config(values, hermes_home) to MemoryProvider ABC
- Honcho: writes to $HERMES_HOME/honcho.json (SDK native)
- Mem0: writes to $HERMES_HOME/mem0.json
- Hindsight: writes to $HERMES_HOME/hindsight/config.json
- Holographic: writes to config.yaml under plugins.hermes-memory-store
- OpenViking/RetainDB/ByteRover: env-var only (default no-op)

Setup wizard (hermes memory setup):
- Now calls provider.save_config() for non-secret config
- Secrets still go to .env via env vars
- Only memory.provider activation key goes to config.yaml

Documentation:
- README.md for each of the 7 providers in plugins/memory/<name>/
- Requirements, setup (wizard + manual), config reference, tools table
- Consistent format across all providers

The contract for new memory plugins:
- get_config_schema() declares all fields (REQUIRED)
- save_config() writes native config (REQUIRED if not env-var-only)
- Secrets use env_var field in schema, written to .env by wizard
- README.md in the plugin directory
2026-03-31 00:07:30 -07:00

193 lines
7.6 KiB
Python

"""Abstract base class for pluggable memory providers.
Memory providers give the agent persistent recall across sessions. Multiple
providers can be active simultaneously — the MemoryManager orchestrates them.
Built-in memory (MEMORY.md / USER.md) is always active as the first provider.
External providers (Honcho, Hindsight, Mem0, etc.) are additive — they never
disable the built-in store.
Three registration paths:
1. Built-in: BuiltinMemoryProvider — always present, not removable.
2. First-party: Ship with the repo, activated by config (e.g. Honcho).
3. Plugin: External packages register via ctx.register_memory_provider().
Lifecycle (called by MemoryManager, wired in run_agent.py):
initialize() — connect, create resources, warm up
system_prompt_block() — static text for the system prompt
prefetch(query) — background recall before each turn
sync_turn(user, asst) — async write after each turn
get_tool_schemas() — tool schemas to expose to the model
handle_tool_call() — dispatch a tool call
shutdown() — clean exit
Optional hooks (override to opt in):
on_turn_start(turn, message) — per-turn tick (scope cooling, etc.)
on_session_end(messages) — end-of-session extraction
on_pre_compress(messages) — extract before context compression
on_memory_write(action, target, content) — mirror built-in memory writes
"""
from __future__ import annotations
import logging
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
class MemoryProvider(ABC):
"""Abstract base class for memory providers."""
@property
@abstractmethod
def name(self) -> str:
"""Short identifier for this provider (e.g. 'builtin', 'honcho', 'hindsight')."""
# -- Core lifecycle (implement these) ------------------------------------
@abstractmethod
def is_available(self) -> bool:
"""Return True if this provider is configured, has credentials, and is ready.
Called during agent init to decide whether to activate the provider.
Should not make network calls — just check config and installed deps.
"""
@abstractmethod
def initialize(self, session_id: str, **kwargs) -> None:
"""Initialize for a session.
Called once at agent startup. May create resources (banks, tables),
establish connections, start background threads, etc.
kwargs always include:
- hermes_home (str): The active HERMES_HOME directory path. Use this
for profile-scoped storage instead of hardcoding ``~/.hermes``.
kwargs may also include: platform, model, user_id, and other session context.
"""
def system_prompt_block(self) -> str:
"""Return text to include in the system prompt.
Called during system prompt assembly. Return empty string to skip.
This is for STATIC provider info (instructions, status). Prefetched
recall context is injected separately via prefetch().
"""
return ""
def prefetch(self, query: str) -> str:
"""Recall relevant context for the upcoming turn.
Called before each API call. Return formatted text to inject as
context, or empty string if nothing relevant. Implementations
should be fast — use background threads for the actual recall
and return cached results here.
"""
return ""
def queue_prefetch(self, query: str) -> None:
"""Queue a background recall for the NEXT turn.
Called after each turn completes. The result will be consumed
by prefetch() on the next turn. Default is no-op — providers
that do background prefetching should override this.
"""
def sync_turn(self, user_content: str, assistant_content: str) -> None:
"""Persist a completed turn to the backend.
Called after each turn. Should be non-blocking — queue for
background processing if the backend has latency.
"""
@abstractmethod
def get_tool_schemas(self) -> List[Dict[str, Any]]:
"""Return tool schemas this provider exposes.
Each schema follows the OpenAI function calling format:
{"name": "...", "description": "...", "parameters": {...}}
Return empty list if this provider has no tools (context-only).
"""
def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
"""Handle a tool call for one of this provider's tools.
Must return a JSON string (the tool result).
Only called for tool names returned by get_tool_schemas().
"""
raise NotImplementedError(f"Provider {self.name} does not handle tool {tool_name}")
def shutdown(self) -> None:
"""Clean shutdown — flush queues, close connections."""
# -- Optional hooks (override to opt in) ---------------------------------
def on_turn_start(self, turn_number: int, message: str) -> None:
"""Called at the start of each turn with the user message.
Use for turn-counting, scope management, periodic maintenance.
"""
def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
"""Called when a session ends (explicit exit or timeout).
Use for end-of-session fact extraction, summarization, etc.
messages is the full conversation history.
"""
def on_pre_compress(self, messages: List[Dict[str, Any]]) -> None:
"""Called before context compression discards old messages.
Use to extract insights from messages about to be compressed.
messages is the list that will be summarized/discarded.
"""
def get_config_schema(self) -> List[Dict[str, Any]]:
"""Return config fields this provider needs for setup.
Used by 'hermes memory setup' to walk the user through configuration.
Each field is a dict with:
key: config key name (e.g. 'api_key', 'mode')
description: human-readable description
secret: True if this should go to .env (default: False)
required: True if required (default: False)
default: default value (optional)
choices: list of valid values (optional)
url: URL where user can get this credential (optional)
env_var: explicit env var name for secrets (default: auto-generated)
Return empty list if no config needed (e.g. local-only providers).
"""
return []
def save_config(self, values: Dict[str, Any], hermes_home: str) -> None:
"""Write non-secret config to the provider's native location.
Called by 'hermes memory setup' after collecting user inputs.
``values`` contains only non-secret fields (secrets go to .env).
``hermes_home`` is the active HERMES_HOME directory path.
Providers with native config files (JSON, YAML) should override
this to write to their expected location. Providers that use only
env vars can leave the default (no-op).
All new memory provider plugins MUST implement either:
- save_config() for native config file formats, OR
- use only env vars (in which case get_config_schema() fields
should all have ``env_var`` set and this method stays no-op).
"""
def on_memory_write(self, action: str, target: str, content: str) -> None:
"""Called when the built-in memory tool writes an entry.
action: 'add', 'replace', or 'remove'
target: 'memory' or 'user'
content: the entry content
Use to mirror built-in memory writes to your backend.
"""