mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-25 00:51:20 +00:00
fe3e68f572
Models may send whitespace-only strings like {"conclusion": " "} which
pass bool() but create meaningless conclusions. Strip both inputs so
whitespace-only values are treated as empty.
Adds tests for whitespace-only conclusion and delete_id.
Reviewed-by: @erosika
1054 lines
44 KiB
Python
1054 lines
44 KiB
Python
"""Honcho memory plugin — MemoryProvider for Honcho AI-native memory.
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Provides cross-session user modeling with dialectic Q&A, semantic search,
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peer cards, and persistent conclusions via the Honcho SDK. Honcho provides AI-native cross-session user
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modeling with dialectic Q&A, semantic search, peer cards, and conclusions.
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The 4 tools (profile, search, context, conclude) are exposed through
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the MemoryProvider interface.
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Config: Uses the existing Honcho config chain:
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1. $HERMES_HOME/honcho.json (profile-scoped)
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2. ~/.honcho/config.json (legacy global)
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3. Environment variables
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import threading
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from typing import Any, Dict, List, Optional
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from agent.memory_provider import MemoryProvider
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from tools.registry import tool_error
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Tool schemas (moved from tools/honcho_tools.py)
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# ---------------------------------------------------------------------------
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PROFILE_SCHEMA = {
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"name": "honcho_profile",
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"description": (
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"Retrieve or update a peer card from Honcho — a curated list of key facts "
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"about that peer (name, role, preferences, communication style, patterns). "
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"Pass `card` to update; omit `card` to read."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"peer": {
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"type": "string",
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"description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.",
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},
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"card": {
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"type": "array",
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"items": {"type": "string"},
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"description": "New peer card as a list of fact strings. Omit to read the current card.",
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},
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},
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"required": [],
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},
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}
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SEARCH_SCHEMA = {
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"name": "honcho_search",
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"description": (
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"Semantic search over Honcho's stored context about a peer. "
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"Returns raw excerpts ranked by relevance — no LLM synthesis. "
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"Cheaper and faster than honcho_reasoning. "
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"Good when you want to find specific past facts and reason over them yourself."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "What to search for in Honcho's memory.",
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},
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"max_tokens": {
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"type": "integer",
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"description": "Token budget for returned context (default 800, max 2000).",
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},
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"peer": {
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"type": "string",
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"description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.",
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},
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},
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"required": ["query"],
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},
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}
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REASONING_SCHEMA = {
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"name": "honcho_reasoning",
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"description": (
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"Ask Honcho a natural language question and get a synthesized answer. "
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"Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. "
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"Can query about any peer via alias or explicit peer ID. "
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"Pass reasoning_level to control depth: minimal (fast/cheap), low (default), "
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"medium, high, max (deep/expensive). Omit for configured default."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "A natural language question.",
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},
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"reasoning_level": {
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"type": "string",
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"description": (
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"Override the default reasoning depth. "
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"Omit to use the configured default (typically low). "
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"Guide:\n"
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"- minimal: quick factual lookups (name, role, simple preference)\n"
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"- low: straightforward questions with clear answers\n"
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"- medium: multi-aspect questions requiring synthesis across observations\n"
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"- high: complex behavioral patterns, contradictions, deep analysis\n"
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"- max: thorough audit-level analysis, leave no stone unturned"
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),
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"enum": ["minimal", "low", "medium", "high", "max"],
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},
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"peer": {
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"type": "string",
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"description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.",
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},
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},
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"required": ["query"],
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},
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}
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CONTEXT_SCHEMA = {
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"name": "honcho_context",
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"description": (
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"Retrieve full session context from Honcho — summary, peer representation, "
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"peer card, and recent messages. No LLM synthesis. "
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"Cheaper than honcho_reasoning. Use this to see what Honcho knows about "
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"the current conversation and the specified peer."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Optional focus query to filter context. Omit for full session context snapshot.",
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},
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"peer": {
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"type": "string",
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"description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.",
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},
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},
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"required": [],
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},
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}
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CONCLUDE_SCHEMA = {
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"name": "honcho_conclude",
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"description": (
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"Write or delete a conclusion about a peer in Honcho's memory. "
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"Conclusions are persistent facts that build a peer's profile. "
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"You MUST pass exactly one of: `conclusion` (to create) or `delete_id` (to delete). "
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"Passing neither is an error. "
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"Deletion is only for PII removal — Honcho self-heals incorrect conclusions over time."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"conclusion": {
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"type": "string",
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"description": "A factual statement to persist. Provide this when creating a conclusion. Do not send it together with delete_id.",
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},
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"delete_id": {
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"type": "string",
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"description": "Conclusion ID to delete for PII removal. Provide this when deleting a conclusion. Do not send it together with conclusion.",
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},
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"peer": {
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"type": "string",
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"description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.",
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},
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},
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"required": [],
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},
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}
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ALL_TOOL_SCHEMAS = [PROFILE_SCHEMA, SEARCH_SCHEMA, REASONING_SCHEMA, CONTEXT_SCHEMA, CONCLUDE_SCHEMA]
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# ---------------------------------------------------------------------------
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# MemoryProvider implementation
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# ---------------------------------------------------------------------------
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class HonchoMemoryProvider(MemoryProvider):
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"""Honcho AI-native memory with dialectic Q&A and persistent user modeling."""
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def __init__(self):
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self._manager = None # HonchoSessionManager
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self._config = None # HonchoClientConfig
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self._session_key = ""
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self._prefetch_result = ""
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self._prefetch_lock = threading.Lock()
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self._prefetch_thread: Optional[threading.Thread] = None
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self._sync_thread: Optional[threading.Thread] = None
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# B1: recall_mode — set during initialize from config
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self._recall_mode = "hybrid" # "context", "tools", or "hybrid"
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# Base context cache — refreshed on context_cadence, not frozen
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self._base_context_cache: Optional[str] = None
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self._base_context_lock = threading.Lock()
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# B5: Cost-awareness turn counting and cadence
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self._turn_count = 0
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self._injection_frequency = "every-turn" # or "first-turn"
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self._context_cadence = 1 # minimum turns between context API calls
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self._dialectic_cadence = 3 # minimum turns between dialectic API calls
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self._dialectic_depth = 1 # how many .chat() calls per dialectic cycle (1-3)
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self._dialectic_depth_levels: list[str] | None = None # per-pass reasoning levels
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self._reasoning_level_cap: Optional[str] = None # "minimal", "low", "medium", "high"
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self._last_context_turn = -999
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self._last_dialectic_turn = -999
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# Port #1957: lazy session init for tools-only mode
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self._session_initialized = False
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self._lazy_init_kwargs: Optional[dict] = None
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self._lazy_init_session_id: Optional[str] = None
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# Port #4053: cron guard — when True, plugin is fully inactive
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self._cron_skipped = False
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@property
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def name(self) -> str:
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return "honcho"
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def is_available(self) -> bool:
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"""Check if Honcho is configured. No network calls."""
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try:
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from plugins.memory.honcho.client import HonchoClientConfig
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cfg = HonchoClientConfig.from_global_config()
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# Port #2645: baseUrl-only verification — api_key OR base_url suffices
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return cfg.enabled and bool(cfg.api_key or cfg.base_url)
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except Exception:
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return False
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def save_config(self, values, hermes_home):
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"""Write config to $HERMES_HOME/honcho.json (Honcho SDK native format)."""
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import json
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from pathlib import Path
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config_path = Path(hermes_home) / "honcho.json"
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existing = {}
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if config_path.exists():
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try:
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existing = json.loads(config_path.read_text())
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except Exception:
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pass
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existing.update(values)
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config_path.write_text(json.dumps(existing, indent=2))
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def get_config_schema(self):
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return [
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{"key": "api_key", "description": "Honcho API key", "secret": True, "env_var": "HONCHO_API_KEY", "url": "https://app.honcho.dev"},
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{"key": "baseUrl", "description": "Honcho base URL (for self-hosted)"},
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]
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def post_setup(self, hermes_home: str, config: dict) -> None:
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"""Run the full Honcho setup wizard after provider selection."""
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import types
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from plugins.memory.honcho.cli import cmd_setup
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cmd_setup(types.SimpleNamespace())
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def initialize(self, session_id: str, **kwargs) -> None:
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"""Initialize Honcho session manager.
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Handles: cron guard, recall_mode, session name resolution,
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peer memory mode, SOUL.md ai_peer sync, memory file migration,
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and pre-warming context at init.
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"""
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try:
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# ----- Port #4053: cron guard -----
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agent_context = kwargs.get("agent_context", "")
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platform = kwargs.get("platform", "cli")
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if agent_context in ("cron", "flush") or platform == "cron":
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logger.debug("Honcho skipped: cron/flush context (agent_context=%s, platform=%s)",
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agent_context, platform)
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self._cron_skipped = True
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return
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from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client
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from plugins.memory.honcho.session import HonchoSessionManager
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cfg = HonchoClientConfig.from_global_config()
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if not cfg.enabled or not (cfg.api_key or cfg.base_url):
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logger.debug("Honcho not configured — plugin inactive")
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return
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# Override peer_name with gateway user_id for per-user memory scoping.
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# Only when no explicit peerName was configured — an explicit peerName
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# means the user chose their identity; a raw user_id (e.g. Telegram
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# chat ID) should not silently replace it.
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_gw_user_id = kwargs.get("user_id")
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if _gw_user_id and not cfg.peer_name:
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cfg.peer_name = _gw_user_id
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self._config = cfg
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# ----- B1: recall_mode from config -----
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self._recall_mode = cfg.recall_mode # "context", "tools", or "hybrid"
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logger.debug("Honcho recall_mode: %s", self._recall_mode)
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# ----- B5: cost-awareness config -----
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try:
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raw = cfg.raw or {}
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self._injection_frequency = raw.get("injectionFrequency", "every-turn")
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self._context_cadence = int(raw.get("contextCadence", 1))
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self._dialectic_cadence = int(raw.get("dialecticCadence", 3))
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self._dialectic_depth = max(1, min(cfg.dialectic_depth, 3))
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self._dialectic_depth_levels = cfg.dialectic_depth_levels
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cap = raw.get("reasoningLevelCap")
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if cap and cap in ("minimal", "low", "medium", "high"):
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self._reasoning_level_cap = cap
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except Exception as e:
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logger.debug("Honcho cost-awareness config parse error: %s", e)
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# ----- Port #1969: aiPeer sync from SOUL.md — REMOVED -----
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# SOUL.md is persona content, not identity config. aiPeer should
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# only come from honcho.json (host block or root) or the default.
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# See scratch/memory-plugin-ux-specs.md #10 for rationale.
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# ----- Port #1957: lazy session init for tools-only mode -----
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if self._recall_mode == "tools":
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if cfg.init_on_session_start:
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# Eager init even in tools mode (opt-in)
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self._do_session_init(cfg, session_id, **kwargs)
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return
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# Defer actual session creation until first tool call
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self._lazy_init_kwargs = kwargs
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self._lazy_init_session_id = session_id
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# Still need a client reference for _ensure_session
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self._config = cfg
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logger.debug("Honcho tools-only mode — deferring session init until first tool call")
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return
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# ----- Eager init (context or hybrid mode) -----
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self._do_session_init(cfg, session_id, **kwargs)
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except ImportError:
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logger.debug("honcho-ai package not installed — plugin inactive")
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except Exception as e:
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logger.warning("Honcho init failed: %s", e)
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self._manager = None
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def _do_session_init(self, cfg, session_id: str, **kwargs) -> None:
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"""Shared session initialization logic for both eager and lazy paths."""
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from plugins.memory.honcho.client import get_honcho_client
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from plugins.memory.honcho.session import HonchoSessionManager
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client = get_honcho_client(cfg)
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self._manager = HonchoSessionManager(
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honcho=client,
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config=cfg,
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context_tokens=cfg.context_tokens,
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)
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# ----- B3: resolve_session_name -----
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session_title = kwargs.get("session_title")
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gateway_session_key = kwargs.get("gateway_session_key")
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self._session_key = (
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cfg.resolve_session_name(
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session_title=session_title,
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session_id=session_id,
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gateway_session_key=gateway_session_key,
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)
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or session_id
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or "hermes-default"
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)
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logger.debug("Honcho session key resolved: %s", self._session_key)
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# Create session eagerly
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session = self._manager.get_or_create(self._session_key)
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self._session_initialized = True
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# ----- B6: Memory file migration (one-time, for new sessions) -----
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# Skip under per-session strategy: every Hermes run creates a fresh
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# Honcho session by design, so uploading MEMORY.md/USER.md/SOUL.md to
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# each one would flood the backend with short-lived duplicates instead
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# of performing a one-time migration.
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try:
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if not session.messages and cfg.session_strategy != "per-session":
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from hermes_constants import get_hermes_home
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mem_dir = str(get_hermes_home() / "memories")
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self._manager.migrate_memory_files(self._session_key, mem_dir)
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logger.debug("Honcho memory file migration attempted for new session: %s", self._session_key)
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elif cfg.session_strategy == "per-session":
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logger.debug(
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"Honcho memory file migration skipped: per-session strategy creates a fresh session per run (%s)",
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self._session_key,
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)
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except Exception as e:
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logger.debug("Honcho memory file migration skipped: %s", e)
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# ----- B7: Pre-warming context at init -----
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if self._recall_mode in ("context", "hybrid"):
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try:
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self._manager.prefetch_context(self._session_key)
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self._manager.prefetch_dialectic(self._session_key, "What should I know about this user?")
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logger.debug("Honcho pre-warm threads started for session: %s", self._session_key)
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except Exception as e:
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logger.debug("Honcho pre-warm failed: %s", e)
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def _ensure_session(self) -> bool:
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"""Lazily initialize the Honcho session (for tools-only mode).
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Returns True if the manager is ready, False otherwise.
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"""
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if self._manager and self._session_initialized:
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return True
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if self._cron_skipped:
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return False
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if not self._config or not self._lazy_init_kwargs:
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return False
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try:
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self._do_session_init(
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self._config,
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self._lazy_init_session_id or "hermes-default",
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**self._lazy_init_kwargs,
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)
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# Clear lazy refs
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self._lazy_init_kwargs = None
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self._lazy_init_session_id = None
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return self._manager is not None
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except Exception as e:
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logger.warning("Honcho lazy session init failed: %s", e)
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return False
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def _format_first_turn_context(self, ctx: dict) -> str:
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"""Format the prefetch context dict into a readable system prompt block."""
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parts = []
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# Session summary — session-scoped context, placed first for relevance
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summary = ctx.get("summary", "")
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if summary:
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parts.append(f"## Session Summary\n{summary}")
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rep = ctx.get("representation", "")
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if rep:
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parts.append(f"## User Representation\n{rep}")
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card = ctx.get("card", "")
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if card:
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parts.append(f"## User Peer Card\n{card}")
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ai_rep = ctx.get("ai_representation", "")
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if ai_rep:
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parts.append(f"## AI Self-Representation\n{ai_rep}")
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ai_card = ctx.get("ai_card", "")
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if ai_card:
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parts.append(f"## AI Identity Card\n{ai_card}")
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if not parts:
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return ""
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return "\n\n".join(parts)
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def system_prompt_block(self) -> str:
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"""Return system prompt text, adapted by recall_mode.
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Returns only the mode header and tool instructions — static text
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that doesn't change between turns (prompt-cache friendly).
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Live context (representation, card) is injected via prefetch().
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"""
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if self._cron_skipped:
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return ""
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if not self._manager or not self._session_key:
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# tools-only mode without session yet still returns a minimal block
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if self._recall_mode == "tools" and self._config:
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return (
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"# Honcho Memory\n"
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"Active (tools-only mode). Use honcho_profile, honcho_search, "
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"honcho_reasoning, honcho_context, and honcho_conclude tools to access user memory."
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)
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return ""
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# ----- B1: adapt text based on recall_mode -----
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if self._recall_mode == "context":
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|
header = (
|
|
"# Honcho Memory\n"
|
|
"Active (context-injection mode). Relevant user context is automatically "
|
|
"injected before each turn. No memory tools are available — context is "
|
|
"managed automatically."
|
|
)
|
|
elif self._recall_mode == "tools":
|
|
header = (
|
|
"# Honcho Memory\n"
|
|
"Active (tools-only mode). Use honcho_profile for a quick factual snapshot, "
|
|
"honcho_search for raw excerpts, honcho_context for raw peer context, "
|
|
"honcho_reasoning for synthesized answers, "
|
|
"honcho_conclude to save facts about the user. "
|
|
"No automatic context injection — you must use tools to access memory."
|
|
)
|
|
else: # hybrid
|
|
header = (
|
|
"# Honcho Memory\n"
|
|
"Active (hybrid mode). Relevant context is auto-injected AND memory tools are available. "
|
|
"Use honcho_profile for a quick factual snapshot, "
|
|
"honcho_search for raw excerpts, honcho_context for raw peer context, "
|
|
"honcho_reasoning for synthesized answers, "
|
|
"honcho_conclude to save facts about the user."
|
|
)
|
|
|
|
return header
|
|
|
|
def prefetch(self, query: str, *, session_id: str = "") -> str:
|
|
"""Return base context (representation + card) plus dialectic supplement.
|
|
|
|
Assembles two layers:
|
|
1. Base context from peer.context() — cached, refreshed on context_cadence
|
|
2. Dialectic supplement — cached, refreshed on dialectic_cadence
|
|
|
|
B1: Returns empty when recall_mode is "tools" (no injection).
|
|
B5: Respects injection_frequency — "first-turn" returns cached/empty after turn 0.
|
|
Port #3265: Truncates to context_tokens budget.
|
|
"""
|
|
if self._cron_skipped:
|
|
return ""
|
|
|
|
# B1: tools-only mode — no auto-injection
|
|
if self._recall_mode == "tools":
|
|
return ""
|
|
|
|
# B5: injection_frequency — if "first-turn" and past first turn, return empty.
|
|
# _turn_count is 1-indexed (first user message = 1), so > 1 means "past first".
|
|
if self._injection_frequency == "first-turn" and self._turn_count > 1:
|
|
return ""
|
|
|
|
parts = []
|
|
|
|
# ----- Layer 1: Base context (representation + card) -----
|
|
# On first call, fetch synchronously so turn 1 isn't empty.
|
|
# After that, serve from cache and refresh in background on cadence.
|
|
with self._base_context_lock:
|
|
if self._base_context_cache is None:
|
|
# First call — synchronous fetch
|
|
try:
|
|
ctx = self._manager.get_prefetch_context(self._session_key)
|
|
self._base_context_cache = self._format_first_turn_context(ctx) if ctx else ""
|
|
self._last_context_turn = self._turn_count
|
|
except Exception as e:
|
|
logger.debug("Honcho base context fetch failed: %s", e)
|
|
self._base_context_cache = ""
|
|
base_context = self._base_context_cache
|
|
|
|
# Check if background context prefetch has a fresher result
|
|
if self._manager:
|
|
fresh_ctx = self._manager.pop_context_result(self._session_key)
|
|
if fresh_ctx:
|
|
formatted = self._format_first_turn_context(fresh_ctx)
|
|
if formatted:
|
|
with self._base_context_lock:
|
|
self._base_context_cache = formatted
|
|
base_context = formatted
|
|
|
|
if base_context:
|
|
parts.append(base_context)
|
|
|
|
# ----- Layer 2: Dialectic supplement -----
|
|
# On the very first turn, no queue_prefetch() has run yet so the
|
|
# dialectic result is empty. Run with a bounded timeout so a slow
|
|
# Honcho connection doesn't block the first response indefinitely.
|
|
# On timeout the result is skipped and queue_prefetch() will pick it
|
|
# up at the next cadence-allowed turn.
|
|
if self._last_dialectic_turn == -999 and query:
|
|
_first_turn_timeout = (
|
|
self._config.timeout if self._config and self._config.timeout else 8.0
|
|
)
|
|
_result_holder: list[str] = []
|
|
|
|
def _run_first_turn() -> None:
|
|
try:
|
|
_result_holder.append(self._run_dialectic_depth(query))
|
|
except Exception as exc:
|
|
logger.debug("Honcho first-turn dialectic failed: %s", exc)
|
|
|
|
_t = threading.Thread(target=_run_first_turn, daemon=True)
|
|
_t.start()
|
|
_t.join(timeout=_first_turn_timeout)
|
|
if not _t.is_alive():
|
|
first_turn_dialectic = _result_holder[0] if _result_holder else ""
|
|
if first_turn_dialectic and first_turn_dialectic.strip():
|
|
with self._prefetch_lock:
|
|
self._prefetch_result = first_turn_dialectic
|
|
self._last_dialectic_turn = self._turn_count
|
|
else:
|
|
logger.debug(
|
|
"Honcho first-turn dialectic timed out (%.1fs) — "
|
|
"will inject at next cadence-allowed turn",
|
|
_first_turn_timeout,
|
|
)
|
|
# Don't update _last_dialectic_turn: queue_prefetch() will
|
|
# retry at the next cadence-allowed turn via the async path.
|
|
|
|
if self._prefetch_thread and self._prefetch_thread.is_alive():
|
|
self._prefetch_thread.join(timeout=3.0)
|
|
with self._prefetch_lock:
|
|
dialectic_result = self._prefetch_result
|
|
self._prefetch_result = ""
|
|
|
|
if dialectic_result and dialectic_result.strip():
|
|
parts.append(dialectic_result)
|
|
|
|
if not parts:
|
|
return ""
|
|
|
|
result = "\n\n".join(parts)
|
|
|
|
# ----- Port #3265: token budget enforcement -----
|
|
result = self._truncate_to_budget(result)
|
|
|
|
return result
|
|
|
|
def _truncate_to_budget(self, text: str) -> str:
|
|
"""Truncate text to fit within context_tokens budget if set."""
|
|
if not self._config or not self._config.context_tokens:
|
|
return text
|
|
budget_chars = self._config.context_tokens * 4 # conservative char estimate
|
|
if len(text) <= budget_chars:
|
|
return text
|
|
# Truncate at word boundary
|
|
truncated = text[:budget_chars]
|
|
last_space = truncated.rfind(" ")
|
|
if last_space > budget_chars * 0.8:
|
|
truncated = truncated[:last_space]
|
|
return truncated + " …"
|
|
|
|
def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
|
|
"""Fire background prefetch threads for the upcoming turn.
|
|
|
|
B5: Checks cadence independently for dialectic and context refresh.
|
|
Context refresh updates the base layer (representation + card).
|
|
Dialectic fires the LLM reasoning supplement.
|
|
"""
|
|
if self._cron_skipped:
|
|
return
|
|
if not self._manager or not self._session_key or not query:
|
|
return
|
|
|
|
# B1: tools-only mode — no prefetch
|
|
if self._recall_mode == "tools":
|
|
return
|
|
|
|
# ----- Context refresh (base layer) — independent cadence -----
|
|
if self._context_cadence <= 1 or (self._turn_count - self._last_context_turn) >= self._context_cadence:
|
|
self._last_context_turn = self._turn_count
|
|
try:
|
|
self._manager.prefetch_context(self._session_key, query)
|
|
except Exception as e:
|
|
logger.debug("Honcho context prefetch failed: %s", e)
|
|
|
|
# ----- Dialectic prefetch (supplement layer) -----
|
|
# B5: cadence check — skip if too soon since last dialectic call
|
|
if self._dialectic_cadence > 1:
|
|
if (self._turn_count - self._last_dialectic_turn) < self._dialectic_cadence:
|
|
logger.debug("Honcho dialectic prefetch skipped: cadence %d, turns since last: %d",
|
|
self._dialectic_cadence, self._turn_count - self._last_dialectic_turn)
|
|
return
|
|
|
|
self._last_dialectic_turn = self._turn_count
|
|
|
|
def _run():
|
|
try:
|
|
result = self._run_dialectic_depth(query)
|
|
if result and result.strip():
|
|
with self._prefetch_lock:
|
|
self._prefetch_result = result
|
|
except Exception as e:
|
|
logger.debug("Honcho prefetch failed: %s", e)
|
|
|
|
self._prefetch_thread = threading.Thread(
|
|
target=_run, daemon=True, name="honcho-prefetch"
|
|
)
|
|
self._prefetch_thread.start()
|
|
|
|
# ----- Dialectic depth: multi-pass .chat() with cold/warm prompts -----
|
|
|
|
# Proportional reasoning levels per depth/pass when dialecticDepthLevels
|
|
# is not configured. The base level is dialecticReasoningLevel.
|
|
# Index: (depth, pass) → level relative to base.
|
|
_PROPORTIONAL_LEVELS: dict[tuple[int, int], str] = {
|
|
# depth 1: single pass at base level
|
|
(1, 0): "base",
|
|
# depth 2: pass 0 lighter, pass 1 at base
|
|
(2, 0): "minimal",
|
|
(2, 1): "base",
|
|
# depth 3: pass 0 lighter, pass 1 at base, pass 2 one above minimal
|
|
(3, 0): "minimal",
|
|
(3, 1): "base",
|
|
(3, 2): "low",
|
|
}
|
|
|
|
_LEVEL_ORDER = ("minimal", "low", "medium", "high", "max")
|
|
|
|
def _resolve_pass_level(self, pass_idx: int) -> str:
|
|
"""Resolve reasoning level for a given pass index.
|
|
|
|
Uses dialecticDepthLevels if configured, otherwise proportional
|
|
defaults relative to dialecticReasoningLevel.
|
|
"""
|
|
if self._dialectic_depth_levels and pass_idx < len(self._dialectic_depth_levels):
|
|
return self._dialectic_depth_levels[pass_idx]
|
|
|
|
base = (self._config.dialectic_reasoning_level if self._config else "low")
|
|
mapping = self._PROPORTIONAL_LEVELS.get((self._dialectic_depth, pass_idx))
|
|
if mapping is None or mapping == "base":
|
|
return base
|
|
return mapping
|
|
|
|
def _build_dialectic_prompt(self, pass_idx: int, prior_results: list[str], is_cold: bool) -> str:
|
|
"""Build the prompt for a given dialectic pass.
|
|
|
|
Pass 0: cold start (general user query) or warm (session-scoped).
|
|
Pass 1: self-audit / targeted synthesis against gaps from pass 0.
|
|
Pass 2: reconciliation / contradiction check across prior passes.
|
|
"""
|
|
if pass_idx == 0:
|
|
if is_cold:
|
|
return (
|
|
"Who is this person? What are their preferences, goals, "
|
|
"and working style? Focus on facts that would help an AI "
|
|
"assistant be immediately useful."
|
|
)
|
|
return (
|
|
"Given what's been discussed in this session so far, what "
|
|
"context about this user is most relevant to the current "
|
|
"conversation? Prioritize active context over biographical facts."
|
|
)
|
|
elif pass_idx == 1:
|
|
prior = prior_results[-1] if prior_results else ""
|
|
return (
|
|
f"Given this initial assessment:\n\n{prior}\n\n"
|
|
"What gaps remain in your understanding that would help "
|
|
"going forward? Synthesize what you actually know about "
|
|
"the user's current state and immediate needs, grounded "
|
|
"in evidence from recent sessions."
|
|
)
|
|
else:
|
|
# pass 2: reconciliation
|
|
return (
|
|
f"Prior passes produced:\n\n"
|
|
f"Pass 1:\n{prior_results[0] if len(prior_results) > 0 else '(empty)'}\n\n"
|
|
f"Pass 2:\n{prior_results[1] if len(prior_results) > 1 else '(empty)'}\n\n"
|
|
"Do these assessments cohere? Reconcile any contradictions "
|
|
"and produce a final, concise synthesis of what matters most "
|
|
"for the current conversation."
|
|
)
|
|
|
|
@staticmethod
|
|
def _signal_sufficient(result: str) -> bool:
|
|
"""Check if a dialectic pass returned enough signal to skip further passes.
|
|
|
|
Heuristic: a response longer than 100 chars with some structure
|
|
(section headers, bullets, or an ordered list) is considered sufficient.
|
|
"""
|
|
if not result or len(result.strip()) < 100:
|
|
return False
|
|
# Structured output with sections/bullets is strong signal
|
|
if "\n" in result and (
|
|
"##" in result
|
|
or "•" in result
|
|
or re.search(r"^[*-] ", result, re.MULTILINE)
|
|
or re.search(r"^\s*\d+\. ", result, re.MULTILINE)
|
|
):
|
|
return True
|
|
# Long enough even without structure
|
|
return len(result.strip()) > 300
|
|
|
|
def _run_dialectic_depth(self, query: str) -> str:
|
|
"""Execute up to dialecticDepth .chat() calls with conditional bail-out.
|
|
|
|
Cold start (no base context): general user-oriented query.
|
|
Warm session (base context exists): session-scoped query.
|
|
Each pass is conditional — bails early if prior pass returned strong signal.
|
|
Returns the best (usually last) result.
|
|
"""
|
|
if not self._manager or not self._session_key:
|
|
return ""
|
|
|
|
is_cold = not self._base_context_cache
|
|
results: list[str] = []
|
|
|
|
for i in range(self._dialectic_depth):
|
|
if i == 0:
|
|
prompt = self._build_dialectic_prompt(0, results, is_cold)
|
|
else:
|
|
# Skip further passes if prior pass delivered strong signal
|
|
if results and self._signal_sufficient(results[-1]):
|
|
logger.debug("Honcho dialectic depth %d: pass %d skipped, prior signal sufficient",
|
|
self._dialectic_depth, i)
|
|
break
|
|
prompt = self._build_dialectic_prompt(i, results, is_cold)
|
|
|
|
level = self._resolve_pass_level(i)
|
|
logger.debug("Honcho dialectic depth %d: pass %d, level=%s, cold=%s",
|
|
self._dialectic_depth, i, level, is_cold)
|
|
|
|
result = self._manager.dialectic_query(
|
|
self._session_key, prompt,
|
|
reasoning_level=level,
|
|
peer="user",
|
|
)
|
|
results.append(result or "")
|
|
|
|
# Return the last non-empty result (deepest pass that ran)
|
|
for r in reversed(results):
|
|
if r and r.strip():
|
|
return r
|
|
return ""
|
|
|
|
def on_turn_start(self, turn_number: int, message: str, **kwargs) -> None:
|
|
"""Track turn count for cadence and injection_frequency logic."""
|
|
self._turn_count = turn_number
|
|
|
|
@staticmethod
|
|
def _chunk_message(content: str, limit: int) -> list[str]:
|
|
"""Split content into chunks that fit within the Honcho message limit.
|
|
|
|
Splits at paragraph boundaries when possible, falling back to
|
|
sentence boundaries, then word boundaries. Each continuation
|
|
chunk is prefixed with "[continued] " so Honcho's representation
|
|
engine can reconstruct the full message.
|
|
"""
|
|
if len(content) <= limit:
|
|
return [content]
|
|
|
|
prefix = "[continued] "
|
|
prefix_len = len(prefix)
|
|
chunks = []
|
|
remaining = content
|
|
first = True
|
|
while remaining:
|
|
effective = limit if first else limit - prefix_len
|
|
if len(remaining) <= effective:
|
|
chunks.append(remaining if first else prefix + remaining)
|
|
break
|
|
|
|
segment = remaining[:effective]
|
|
|
|
# Try paragraph break, then sentence, then word
|
|
cut = segment.rfind("\n\n")
|
|
if cut < effective * 0.3:
|
|
cut = segment.rfind(". ")
|
|
if cut >= 0:
|
|
cut += 2 # include the period and space
|
|
if cut < effective * 0.3:
|
|
cut = segment.rfind(" ")
|
|
if cut < effective * 0.3:
|
|
cut = effective # hard cut
|
|
|
|
chunk = remaining[:cut].rstrip()
|
|
remaining = remaining[cut:].lstrip()
|
|
if not first:
|
|
chunk = prefix + chunk
|
|
chunks.append(chunk)
|
|
first = False
|
|
|
|
return chunks
|
|
|
|
def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
|
|
"""Record the conversation turn in Honcho (non-blocking).
|
|
|
|
Messages exceeding the Honcho API limit (default 25k chars) are
|
|
split into multiple messages with continuation markers.
|
|
"""
|
|
if self._cron_skipped:
|
|
return
|
|
if not self._manager or not self._session_key:
|
|
return
|
|
|
|
msg_limit = self._config.message_max_chars if self._config else 25000
|
|
|
|
def _sync():
|
|
try:
|
|
session = self._manager.get_or_create(self._session_key)
|
|
for chunk in self._chunk_message(user_content, msg_limit):
|
|
session.add_message("user", chunk)
|
|
for chunk in self._chunk_message(assistant_content, msg_limit):
|
|
session.add_message("assistant", chunk)
|
|
self._manager._flush_session(session)
|
|
except Exception as e:
|
|
logger.debug("Honcho sync_turn failed: %s", e)
|
|
|
|
if self._sync_thread and self._sync_thread.is_alive():
|
|
self._sync_thread.join(timeout=5.0)
|
|
self._sync_thread = threading.Thread(
|
|
target=_sync, daemon=True, name="honcho-sync"
|
|
)
|
|
self._sync_thread.start()
|
|
|
|
def on_memory_write(self, action: str, target: str, content: str) -> None:
|
|
"""Mirror built-in user profile writes as Honcho conclusions."""
|
|
if action != "add" or target != "user" or not content:
|
|
return
|
|
if self._cron_skipped:
|
|
return
|
|
if not self._manager or not self._session_key:
|
|
return
|
|
|
|
def _write():
|
|
try:
|
|
self._manager.create_conclusion(self._session_key, content)
|
|
except Exception as e:
|
|
logger.debug("Honcho memory mirror failed: %s", e)
|
|
|
|
t = threading.Thread(target=_write, daemon=True, name="honcho-memwrite")
|
|
t.start()
|
|
|
|
def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
|
|
"""Flush all pending messages to Honcho on session end."""
|
|
if self._cron_skipped:
|
|
return
|
|
if not self._manager:
|
|
return
|
|
# Wait for pending sync
|
|
if self._sync_thread and self._sync_thread.is_alive():
|
|
self._sync_thread.join(timeout=10.0)
|
|
try:
|
|
self._manager.flush_all()
|
|
except Exception as e:
|
|
logger.debug("Honcho session-end flush failed: %s", e)
|
|
|
|
def get_tool_schemas(self) -> List[Dict[str, Any]]:
|
|
"""Return tool schemas, respecting recall_mode.
|
|
|
|
B1: context-only mode hides all tools.
|
|
"""
|
|
if self._cron_skipped:
|
|
return []
|
|
if self._recall_mode == "context":
|
|
return []
|
|
return list(ALL_TOOL_SCHEMAS)
|
|
|
|
def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
|
|
"""Handle a Honcho tool call, with lazy session init for tools-only mode."""
|
|
if self._cron_skipped:
|
|
return tool_error("Honcho is not active (cron context).")
|
|
|
|
# Port #1957: ensure session is initialized for tools-only mode
|
|
if not self._session_initialized:
|
|
if not self._ensure_session():
|
|
return tool_error("Honcho session could not be initialized.")
|
|
|
|
if not self._manager or not self._session_key:
|
|
return tool_error("Honcho is not active for this session.")
|
|
|
|
try:
|
|
if tool_name == "honcho_profile":
|
|
peer = args.get("peer", "user")
|
|
card_update = args.get("card")
|
|
if card_update:
|
|
result = self._manager.set_peer_card(self._session_key, card_update, peer=peer)
|
|
if result is None:
|
|
return tool_error("Failed to update peer card.")
|
|
return json.dumps({"result": f"Peer card updated ({len(result)} facts).", "card": result})
|
|
card = self._manager.get_peer_card(self._session_key, peer=peer)
|
|
if not card:
|
|
return json.dumps({"result": "No profile facts available yet."})
|
|
return json.dumps({"result": card})
|
|
|
|
elif tool_name == "honcho_search":
|
|
query = args.get("query", "")
|
|
if not query:
|
|
return tool_error("Missing required parameter: query")
|
|
max_tokens = min(int(args.get("max_tokens", 800)), 2000)
|
|
peer = args.get("peer", "user")
|
|
result = self._manager.search_context(
|
|
self._session_key, query, max_tokens=max_tokens, peer=peer
|
|
)
|
|
if not result:
|
|
return json.dumps({"result": "No relevant context found."})
|
|
return json.dumps({"result": result})
|
|
|
|
elif tool_name == "honcho_reasoning":
|
|
query = args.get("query", "")
|
|
if not query:
|
|
return tool_error("Missing required parameter: query")
|
|
peer = args.get("peer", "user")
|
|
reasoning_level = args.get("reasoning_level")
|
|
result = self._manager.dialectic_query(
|
|
self._session_key, query,
|
|
reasoning_level=reasoning_level,
|
|
peer=peer,
|
|
)
|
|
# Update cadence tracker so auto-injection respects the gap after an explicit call
|
|
self._last_dialectic_turn = self._turn_count
|
|
return json.dumps({"result": result or "No result from Honcho."})
|
|
|
|
elif tool_name == "honcho_context":
|
|
peer = args.get("peer", "user")
|
|
ctx = self._manager.get_session_context(self._session_key, peer=peer)
|
|
if not ctx:
|
|
return json.dumps({"result": "No context available yet."})
|
|
parts = []
|
|
if ctx.get("summary"):
|
|
parts.append(f"## Summary\n{ctx['summary']}")
|
|
if ctx.get("representation"):
|
|
parts.append(f"## Representation\n{ctx['representation']}")
|
|
if ctx.get("card"):
|
|
parts.append(f"## Card\n{ctx['card']}")
|
|
if ctx.get("recent_messages"):
|
|
msgs = ctx["recent_messages"]
|
|
msg_str = "\n".join(
|
|
f" [{m['role']}] {m['content'][:200]}"
|
|
for m in msgs[-5:] # last 5 for brevity
|
|
)
|
|
parts.append(f"## Recent messages\n{msg_str}")
|
|
return json.dumps({"result": "\n\n".join(parts) or "No context available."})
|
|
|
|
elif tool_name == "honcho_conclude":
|
|
delete_id = (args.get("delete_id") or "").strip()
|
|
conclusion = args.get("conclusion", "").strip()
|
|
peer = args.get("peer", "user")
|
|
|
|
has_delete_id = bool(delete_id)
|
|
has_conclusion = bool(conclusion)
|
|
if has_delete_id == has_conclusion:
|
|
return tool_error("Exactly one of conclusion or delete_id must be provided.")
|
|
|
|
if has_delete_id:
|
|
ok = self._manager.delete_conclusion(self._session_key, delete_id, peer=peer)
|
|
if ok:
|
|
return json.dumps({"result": f"Conclusion {delete_id} deleted."})
|
|
return tool_error(f"Failed to delete conclusion {delete_id}.")
|
|
ok = self._manager.create_conclusion(self._session_key, conclusion, peer=peer)
|
|
if ok:
|
|
return json.dumps({"result": f"Conclusion saved for {peer}: {conclusion}"})
|
|
return tool_error("Failed to save conclusion.")
|
|
|
|
return tool_error(f"Unknown tool: {tool_name}")
|
|
|
|
except Exception as e:
|
|
logger.error("Honcho tool %s failed: %s", tool_name, e)
|
|
return tool_error(f"Honcho {tool_name} failed: {e}")
|
|
|
|
def shutdown(self) -> None:
|
|
for t in (self._prefetch_thread, self._sync_thread):
|
|
if t and t.is_alive():
|
|
t.join(timeout=5.0)
|
|
# Flush any remaining messages
|
|
if self._manager:
|
|
try:
|
|
self._manager.flush_all()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Plugin entry point
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def register(ctx) -> None:
|
|
"""Register Honcho as a memory provider plugin."""
|
|
ctx.register_memory_provider(HonchoMemoryProvider())
|