"""Honcho memory plugin — MemoryProvider for Honcho AI-native memory. Provides cross-session user modeling with dialectic Q&A, semantic search, peer cards, and persistent conclusions via the Honcho SDK. Honcho provides AI-native cross-session user modeling with dialectic Q&A, semantic search, peer cards, and conclusions. The 4 tools (profile, search, context, conclude) are exposed through the MemoryProvider interface. Config: Uses the existing Honcho config chain: 1. $HERMES_HOME/honcho.json (profile-scoped) 2. ~/.honcho/config.json (legacy global) 3. Environment variables """ from __future__ import annotations import json import logging import re import threading import time from typing import Any, Callable, Dict, List, Optional from agent.memory_manager import sanitize_context from agent.memory_provider import MemoryProvider from tools.registry import tool_error logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- # Tool schemas (moved from tools/honcho_tools.py) # --------------------------------------------------------------------------- PROFILE_SCHEMA = { "name": "honcho_profile", "description": ( "Read or write a peer's CARD — a short, curated list of standing facts " "about that peer (name, role, preferences, communication style, recurring " "patterns). This is the cheapest, fastest Honcho call: no query, no LLM, " "just the current card. Pass `card` to overwrite it; omit `card` to read. " "An empty read returns a `hint` explaining why (observation disabled, fresh " "peer, representation still warming up) — that is NOT an error; the card " "accumulates over time from observed conversation. " "Related tools: honcho_context for the fuller standing snapshot (card + " "representation + summary + recent messages); honcho_search to find " "specific things that were actually said; honcho_reasoning for a " "synthesized answer to a question." ), "parameters": { "type": "object", "properties": { "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.", }, "card": { "type": "array", "items": {"type": "string"}, "description": "New peer card as a list of fact strings. Omit to read the current card.", }, }, "required": [], }, } SEARCH_SCHEMA = { "name": "honcho_search", "description": ( "Hybrid (semantic + keyword) search over a peer's actual message " "history across ALL past sessions they took part in — not just the " "current one. Returns RRF-ranked raw message excerpts (what was " "literally said, including the assistant's own messages about the " "peer), no LLM synthesis. Cheaper and faster than honcho_reasoning. " "Use this to recall specific past facts — 'what did I say about X', " "'what was the regimen/decision/config we settled on' — and reason " "over the excerpts yourself. For nuanced questions needing synthesis, " "use honcho_reasoning instead." ), "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "What to look for — a topic, keyword, name, or natural-language description of the fact you're trying to recall.", }, "max_tokens": { "type": "integer", "description": "Approximate budget for returned excerpts (default 800, max 2000). Larger budgets return more/longer ranked snippets.", }, "peer": { "type": "string", "description": "Whose history to search. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace. Spans every session that peer took part in.", }, }, "required": ["query"], }, } REASONING_SCHEMA = { "name": "honcho_reasoning", "description": ( "Ask Honcho's dialectic agent a natural-language question about a peer and " "get back a SYNTHESIZED answer. This is the only Honcho tool that runs an " "LLM: it agentically searches both raw messages and derived conclusions, " "reasons over them, and writes a prose answer — so it is the slowest and " "most expensive call (seconds + tokens). Reach for it for nuanced or " "open-ended questions ('how does this person prefer to receive feedback?', " "'what's their relationship to project X?') where you want Honcho to do the " "synthesis. For a specific fact that was stated, prefer honcho_search " "(cheap, raw excerpts, you synthesize). For standing profile facts, prefer " "honcho_profile / honcho_context (no LLM). " "Pass reasoning_level to control depth: minimal (fast/cheap), low (default), " "medium, high, max (deep/expensive). Omit for the configured default." ), "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "A natural language question.", }, "reasoning_level": { "type": "string", "description": ( "Override the default reasoning depth. " "Omit to use the configured default (typically low). " "Guide:\n" "- minimal: quick factual lookups (name, role, simple preference)\n" "- low: straightforward questions with clear answers\n" "- medium: multi-aspect questions requiring synthesis across observations\n" "- high: complex behavioral patterns, contradictions, deep analysis\n" "- max: thorough audit-level analysis, leave no stone unturned" ), "enum": ["minimal", "low", "medium", "high", "max"], }, "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.", }, }, "required": ["query"], }, } CONTEXT_SCHEMA = { "name": "honcho_context", "description": ( "Retrieve the standing SNAPSHOT Honcho holds for the current session — " "session summary, the peer's representation, the peer card, and the most " "recent messages — in one call. No query, no LLM synthesis (cheaper than " "honcho_reasoning). Use it to orient yourself on what Honcho currently " "knows about this conversation and peer. This is a fixed snapshot, not a " "search: to look up a specific past fact use honcho_search; to ask a " "question and get a synthesized answer use honcho_reasoning; for just the " "compact card use honcho_profile." ), "parameters": { "type": "object", "properties": { "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.", }, }, "required": [], }, } CONCLUDE_SCHEMA = { "name": "honcho_conclude", "description": ( "Write, delete, or list CONCLUSIONS — persistent, derived facts about a peer that " "feeds their long-term profile (card + representation). Use this to record " "something durable you've learned about the peer (a stable preference, a " "correction, a standing constraint) so future sessions carry it forward. " "You MUST pass exactly one of `conclusion` (to create), `delete_id` (to " "delete), or `list` (to list/search); any other combination is an error. " "A deletion ID is an opaque server-generated string: first call with `list=true` " "and optionally `query`, then pass the returned ID as `delete_id`. " "Deletion exists only for " "PII removal — for merely wrong facts, write a corrected conclusion instead; " "Honcho self-heals contradictions over time. This is a WRITE tool: to read " "the profile use honcho_profile / honcho_context, and to search what was " "said use honcho_search." ), "parameters": { "type": "object", "properties": { "conclusion": { "type": "string", "description": "A factual statement to persist. Provide this when creating a conclusion. Do not send it together with delete_id or list.", }, "delete_id": { "type": "string", "description": "Conclusion ID to delete for PII removal. Provide this when deleting a conclusion. Do not send it together with conclusion or list. Get this id from a prior `list` call — never guess it.", }, "list": { "type": "boolean", "description": "Set to true to list or search stored conclusions (with their ids) instead of creating or deleting one. Do not send together with conclusion or delete_id.", }, "query": { "type": "string", "description": "Optional semantic search query, used only when `list` is true. Omit to list the most recent conclusions instead of searching.", }, "peer": { "type": "string", "description": "The peer the conclusion is ABOUT. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.", }, }, "required": [], }, } ALL_TOOL_SCHEMAS = [PROFILE_SCHEMA, SEARCH_SCHEMA, REASONING_SCHEMA, CONTEXT_SCHEMA, CONCLUDE_SCHEMA] # --------------------------------------------------------------------------- # MemoryProvider implementation # --------------------------------------------------------------------------- class HonchoMemoryProvider(MemoryProvider): """Honcho AI-native memory with dialectic Q&A and persistent user modeling.""" def backup_paths(self) -> List[str]: """Honcho keeps its peer/session config under ~/.honcho when no profile-local honcho.json exists (see client.resolve_config_path).""" paths: List[str] = [] try: from .client import resolve_global_config_path global_cfg = resolve_global_config_path() # Capture the whole ~/.honcho dir so sibling state travels with it. paths.append(str(global_cfg.parent)) except Exception: pass return paths def __init__(self, query_rewriter: Optional[Callable[[str], str]] = None): self._manager = None # HonchoSessionManager self._config = None # HonchoClientConfig self._session_key = "" self._query_rewriter = query_rewriter self._prefetch_result = "" self._prefetch_lock = threading.Lock() self._prefetch_thread: Optional[threading.Thread] = None self._sync_thread: Optional[threading.Thread] = None # B1: recall_mode — set during initialize from config self._recall_mode = "hybrid" # "context", "tools", or "hybrid" # Base context cache — refreshed on context_cadence, not frozen self._base_context_cache: Optional[str] = None self._base_context_lock = threading.Lock() # B5: Cost-awareness turn counting and cadence self._turn_count = 0 self._injection_frequency = "every-turn" # or "first-turn" self._context_cadence = 1 # minimum turns between context API calls self._dialectic_cadence = 1 # backwards-compat fallback; wizard writes 2 on new configs self._dialectic_depth = 1 # how many .chat() calls per dialectic cycle (1-3) self._dialectic_depth_levels: list[str] | None = None # per-pass reasoning levels self._reasoning_heuristic: bool = True # scale base level by query length self._reasoning_level_cap: str = "high" # ceiling for auto-selected level self._last_context_turn = -999 self._last_dialectic_turn = -999 # Liveness + observability state self._prefetch_thread_started_at: float = 0.0 # monotonic ts of current thread self._prefetch_result_fired_at: int = -999 # turn the pending result was fired at self._dialectic_empty_streak: int = 0 # consecutive empty returns # Port #1957: lazy session init for tools-only mode self._session_initialized = False self._lazy_init_kwargs: Optional[dict] = None self._lazy_init_session_id: Optional[str] = None self._init_thread: Optional[threading.Thread] = None self._init_lock = threading.Lock() self._init_error = "" # Port #4053: cron guard — when True, plugin is fully inactive self._cron_skipped = False @property def name(self) -> str: return "honcho" def is_available(self) -> bool: """Check if Honcho is configured. No network calls.""" try: from plugins.memory.honcho.client import HonchoClientConfig cfg = HonchoClientConfig.from_global_config() # Port #2645: baseUrl-only verification — api_key OR base_url suffices return cfg.enabled and bool(cfg.api_key or cfg.base_url) except Exception: return False def save_config(self, values, hermes_home): """Write config to $HERMES_HOME/honcho.json (Honcho SDK native format).""" import json import os from pathlib import Path config_path = Path(hermes_home) / "honcho.json" existing = {} if config_path.exists(): try: existing = json.loads(config_path.read_text()) except Exception: pass existing.update(values) from utils import atomic_json_write atomic_json_write(config_path, existing, mode=0o600) def get_config_schema(self): return [ {"key": "api_key", "description": "Honcho API key", "secret": True, "env_var": "HONCHO_API_KEY", "url": "https://app.honcho.dev"}, {"key": "baseUrl", "description": "Honcho base URL (for self-hosted)"}, ] def post_setup(self, hermes_home: str, config: dict) -> None: """Run the full Honcho setup wizard after provider selection.""" import types from plugins.memory.honcho.cli import cmd_setup cmd_setup(types.SimpleNamespace()) def initialize(self, session_id: str, **kwargs) -> None: """Initialize Honcho session manager. Handles: cron guard, recall_mode, session name resolution, peer memory mode, SOUL.md ai_peer sync, memory file migration, and pre-warming context at init. """ try: # ----- Port #4053: cron guard ----- agent_context = kwargs.get("agent_context", "") platform = kwargs.get("platform", "cli") if agent_context in {"cron", "flush"} or platform == "cron": logger.debug("Honcho skipped: cron/flush context (agent_context=%s, platform=%s)", agent_context, platform) self._cron_skipped = True return from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client from plugins.memory.honcho.session import HonchoSessionManager cfg = HonchoClientConfig.from_global_config() if not cfg.enabled or not (cfg.api_key or cfg.base_url): logger.debug("Honcho not configured — plugin inactive") return self._config = cfg # ----- B1: recall_mode from config ----- self._recall_mode = cfg.recall_mode # "context", "tools", or "hybrid" logger.debug("Honcho recall_mode: %s", self._recall_mode) # ----- B5: cost-awareness config ----- # All three cadence fields now have proper typed dataclass fields # with host-block-first resolution (client.py). self._injection_frequency = cfg.injection_frequency self._context_cadence = cfg.context_cadence self._dialectic_cadence = cfg.dialectic_cadence self._dialectic_depth = max(1, min(cfg.dialectic_depth, 3)) self._dialectic_depth_levels = cfg.dialectic_depth_levels self._reasoning_heuristic = cfg.reasoning_heuristic if cfg.reasoning_level_cap in self._LEVEL_ORDER: self._reasoning_level_cap = cfg.reasoning_level_cap # aiPeer comes from honcho.json (host block or root) only. # SOUL.md is persona content, not identity config. self._lazy_init_kwargs = dict(kwargs) self._lazy_init_session_id = session_id self._session_key = self._resolve_session_key(cfg, session_id, **kwargs) # Network-backed session creation can block on Honcho service or DB # outages. Startup must fail open for context/hybrid modes, where # Honcho is initialized only to enrich prompts. Tools-only mode has # an explicit contract: init_on_session_start=False stays lazy until # the first tool call, while init_on_session_start=True remains an # eager, ready-on-return initialization path. if self._recall_mode == "tools": if cfg.init_on_session_start: self._ensure_session() return logger.debug("Honcho tools-only mode — deferring session init until first tool call") return self._start_session_init_background(wait_timeout=0.1) except ImportError: logger.debug("honcho-ai package not installed — plugin inactive") except Exception as e: logger.warning("Honcho init failed: %s", e) self._manager = None def _resolve_session_key(self, cfg, session_id: str, **kwargs) -> str: """Resolve the Honcho session key without touching the network.""" session_title = kwargs.get("session_title") gateway_session_key = kwargs.get("gateway_session_key") return ( cfg.resolve_session_name( session_title=session_title, session_id=session_id, gateway_session_key=gateway_session_key, ) or session_id or "hermes-default" ) def _start_session_init_background(self, *, wait_timeout: float = 0.0) -> None: """Start Honcho session initialization in a daemon thread. This keeps Hermes CLI/gateway startup responsive when Honcho is down, slow, or its database is unhealthy. The thread may still take the SDK timeout path, but it cannot block agent construction or first prompt assembly. ``wait_timeout`` lets fast/mock initializations finish before returning while still failing open for slow backends. """ if self._cron_skipped or self._session_initialized: return if not self._config or self._lazy_init_kwargs is None: return with self._init_lock: if self._cron_skipped or self._session_initialized: return if self._init_thread and self._init_thread.is_alive(): return if not self._config or self._lazy_init_kwargs is None: return cfg = self._config init_kwargs = dict(self._lazy_init_kwargs) init_session_id = self._lazy_init_session_id or "hermes-default" def _run() -> None: try: self._do_session_init(cfg, init_session_id, **init_kwargs) self._lazy_init_kwargs = None self._lazy_init_session_id = None self._init_error = "" except Exception as e: self._init_error = str(e) self._manager = None logger.warning("Honcho background session init failed: %s", e) self._init_thread = threading.Thread( target=_run, daemon=True, name="honcho-session-init", ) self._init_thread.start() if wait_timeout > 0: self._init_thread.join(timeout=wait_timeout) def _do_session_init(self, cfg, session_id: str, **kwargs) -> None: """Shared session initialization logic for both eager and lazy paths.""" from plugins.memory.honcho.client import get_honcho_client from plugins.memory.honcho.session import HonchoSessionManager client = get_honcho_client(cfg) self._manager = HonchoSessionManager( honcho=client, config=cfg, context_tokens=cfg.context_tokens, runtime_user_peer_name=kwargs.get("user_id") or None, runtime_user_peer_name_alt=kwargs.get("user_id_alt") or None, ) # ----- B3: resolve_session_name ----- self._session_key = self._resolve_session_key(cfg, session_id, **kwargs) logger.debug("Honcho session key resolved: %s", self._session_key) # Create the remote session before running startup-only migration and # prewarm work. Do not mark the provider ready until this method's # synchronous setup has finished; background startup sets _manager before # get_or_create()/migration/prewarm are complete, and lifecycle hooks must # not treat that partially initialized state as usable. session = self._manager.get_or_create(self._session_key) # ----- B6: Memory file migration (one-time, for new sessions) ----- # Skip under per-session strategy: every Hermes run creates a fresh # Honcho session by design, so uploading MEMORY.md/USER.md/SOUL.md to # each one would flood the backend with short-lived duplicates instead # of performing a one-time migration. try: if not session.messages and cfg.session_strategy != "per-session": from hermes_constants import get_hermes_home mem_dir = str(get_hermes_home() / "memories") self._manager.migrate_memory_files(self._session_key, mem_dir) logger.debug("Honcho memory file migration attempted for new session: %s", self._session_key) elif cfg.session_strategy == "per-session": logger.debug( "Honcho memory file migration skipped: per-session strategy creates a fresh session per run (%s)", self._session_key, ) except Exception as e: logger.debug("Honcho memory file migration skipped: %s", e) # ----- B7: Pre-warming at init ----- # Context prewarm warms peer.context() (base layer), consumed via # pop_context_result() in prefetch(). A generic dialectic prewarm is # retained only for providers without latest-message rewriting; otherwise # it would shadow the real first user message and recreate #36017. if self._recall_mode in {"context", "hybrid"}: try: self._manager.prefetch_context(self._session_key) except Exception as e: logger.debug("Honcho context prewarm failed: %s", e) if self._query_rewriter is None: _prewarm_query = ( "Summarize what you know about this user. " "Focus on preferences, current projects, and working style." ) def _prewarm_dialectic() -> None: try: r = self._run_dialectic_depth( _prewarm_query, use_query_rewrite=False ) except Exception as exc: logger.debug("Honcho dialectic prewarm failed: %s", exc) self._dialectic_empty_streak += 1 return if r and r.strip(): with self._prefetch_lock: self._prefetch_result = r self._prefetch_result_fired_at = 0 # Treat prewarm as turn 0 so cadence gating starts clean. self._last_dialectic_turn = 0 self._dialectic_empty_streak = 0 else: self._dialectic_empty_streak += 1 self._prefetch_thread_started_at = time.monotonic() prewarm_thread = threading.Thread( target=_prewarm_dialectic, daemon=True, name="honcho-prewarm-dialectic", ) prewarm_thread.start() self._prefetch_thread = prewarm_thread else: logger.debug( "Honcho generic dialectic prewarm skipped: awaiting first user message" ) logger.debug("Honcho pre-warm started for session: %s", self._session_key) self._session_initialized = True def _ensure_session(self) -> bool: """Lazily initialize the Honcho session (for tools-only mode). Returns True if the manager is ready, False otherwise. """ if self._manager and self._session_initialized: return True if self._cron_skipped: return False if self._init_thread and self._init_thread.is_alive(): return False if not self._config or self._lazy_init_kwargs is None: return False try: self._do_session_init( self._config, self._lazy_init_session_id or "hermes-default", **self._lazy_init_kwargs, ) # Clear lazy refs self._lazy_init_kwargs = None self._lazy_init_session_id = None return self._manager is not None except Exception as e: self._manager = None self._session_initialized = False logger.warning("Honcho lazy session init failed: %s", e) return False def _session_ready(self) -> bool: """Return whether a manager/session key can be used safely. Background initialization sets ``_manager`` before the blocking get-or-create call completes, so ``_session_initialized`` guards real async startup. Tests and legacy direct construction may inject a ready manager/session key without setting that flag; allow that only when no init thread is currently in flight. """ if not self._manager or not self._session_key: return False if self._session_initialized: return True return not (self._init_thread and self._init_thread.is_alive()) def _format_first_turn_context(self, ctx: dict) -> str: """Format the prefetch context dict into a readable system prompt block.""" parts = [] # Session summary — session-scoped context, placed first for relevance summary = ctx.get("summary", "") if summary: parts.append(f"## Session Summary\n{summary}") rep = ctx.get("representation", "") if rep: parts.append(f"## User Representation\n{rep}") card = ctx.get("card", "") if card: parts.append(f"## User Peer Card\n{card}") ai_rep = ctx.get("ai_representation", "") if ai_rep: parts.append(f"## AI Self-Representation\n{ai_rep}") ai_card = ctx.get("ai_card", "") if ai_card: parts.append(f"## AI Identity Card\n{ai_card}") if not parts: return "" return "\n\n".join(parts) def system_prompt_block(self) -> str: """Return system prompt text, adapted by recall_mode. Returns only the mode header and tool instructions — static text that doesn't change between turns (prompt-cache friendly). Live context (representation, card) is injected via prefetch(). """ if self._cron_skipped: return "" if not self._manager or not self._session_key: if not self._config: return "" # ----- B1: adapt text based on recall_mode ----- if self._recall_mode == "context": 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 (pass reasoning_level " "minimal/low/medium/high/max — you pick the depth per call), " "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 (pass reasoning_level " "minimal/low/medium/high/max — you pick the depth per call), " "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 "" if not self._session_ready(): # Session init runs in a background daemon thread (kept off the # startup path so a slow/unreachable Honcho can't block agent # construction). On turn 1 of a fresh process that thread may not # have finished yet. Rather than return empty immediately — which # denied turn-1 context entirely whenever init crossed the ~100ms # spawn window — wait a bounded time for it to complete so the # peer card can still be injected on the first message. self._start_session_init_background() _init_thread = self._init_thread if _init_thread is not None: _init_thread.join(timeout=self._FIRST_TURN_BASE_TIMEOUT) if not self._session_ready(): return "" # B5: injection_frequency — if "first-turn" and past first turn, skip # the base context layer (static representation + card). The dialectic # supplement has its own cadence and must still be consumed below. _skip_base = ( self._injection_frequency == "first-turn" and self._turn_count > 1 ) # Trivial prompts ("ok", "yes", slash commands) carry no semantic # signal, so we don't fetch base context or fire new dialectic work for # them. But a dialectic result fired at the END of the previous turn was # primed for THIS turn — blindly returning here used to strand it # (consumption happens further down), after which the stale-discard # guard silently dropped a successfully-generated answer. Instead, # drain any ready non-stale pending result and inject just that: # trivial turns still spend no NEW work, but they no longer throw # away work already paid for. if self._is_trivial_prompt(query): ready = self._consume_pending_dialectic() return self._truncate_to_budget(ready) if ready else "" parts = [] # ----- Layer 1: Base context (representation + card) ----- # Skipped when injectionFrequency is "first-turn" past turn 1 — the # representation + card are static within a session and re-injecting # them every turn is pure token waste. if _skip_base: # Still drain any pending dialectic result below; just don't # fetch or inject the base context layer. pass else: # Base context is pure retrieval (no LLM): representation + peer card + # session summary. On the FIRST fetch (cache is None, i.e. turn 1 of a # session) we fetch it SYNCHRONOUSLY with a short bound so the peer card # is injected immediately. Firing it async and popping in the same call # always lost the race on turn 1 (background thread can't finish inside # one synchronous pass), which is why new sessions saw zero context # until turn 2. Subsequent turns consume the background-refreshed result # primed by queue_prefetch() at the end of the previous turn. with self._base_context_lock: _first_base_fetch = self._base_context_cache is None if _first_base_fetch: self._base_context_cache = "" self._last_context_turn = self._turn_count base_context = self._base_context_cache if _first_base_fetch and self._manager: # Synchronous, bounded fetch so turn 1 gets the peer card now. _ctx_holder: dict[str, dict] = {} def _fetch_base() -> None: try: ctx = self._manager.get_prefetch_context( self._session_key, query or None ) or {} _ctx_holder["ctx"] = ctx if ctx: self._manager.set_context_result(self._session_key, ctx) except Exception as e: logger.debug("Honcho first-turn base context failed: %s", e) _bt = threading.Thread( target=_fetch_base, daemon=True, name="honcho-base-first" ) _bt.start() # Bound the synchronous wait by the smaller of the base timeout and # any configured request timeout, so a deliberately tight # config.timeout (fail-fast deployments, tests) is still honored. _base_wait = self._FIRST_TURN_BASE_TIMEOUT if self._config and self._config.timeout: _base_wait = min(_base_wait, self._config.timeout) _bt.join(timeout=_base_wait) _ctx = _ctx_holder.get("ctx") if _ctx: self._manager.pop_context_result(self._session_key) formatted = self._format_first_turn_context(_ctx) if formatted: with self._base_context_lock: self._base_context_cache = formatted base_context = formatted elif _bt.is_alive(): logger.debug( "Honcho first-turn base context still running after %.1fs — " "will surface on next turn", self._FIRST_TURN_BASE_TIMEOUT, ) # Check if a background context prefetch (primed last turn) has a # fresher result. On turn 1 this is normally empty; turn 2+ consumes # the result queued by queue_prefetch(). if not _first_base_fetch and 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 we let the thread keep running and write its result into # _prefetch_result under the lock, so the next turn picks it up. # # Skip if the session-start prewarm already filled _prefetch_result — # firing another .chat() would be duplicate work. with self._prefetch_lock: _prewarm_landed = bool(self._prefetch_result) if _prewarm_landed and self._last_dialectic_turn == -999: self._last_dialectic_turn = self._turn_count _did_first_turn_wait = False if self._last_dialectic_turn == -999 and query: _did_first_turn_wait = True # A dialectic prewarm thread is fired at session init with an # equivalent query. If it's still running, do NOT fire a second # .chat() here — that was duplicate work that also blocked the # first response. Briefly wait for the in-flight prewarm to land; # if it doesn't, let it surface on a later turn via _prefetch_result. # First-turn dialectic wait. Decoupled from a LARGE config.timeout # (a 60s host timeout must not block the first response for 60s), # but still bounded by a TIGHT config.timeout when one is set # (fail-fast deployments / tests). See _FIRST_TURN_DIALECTIC_CAP. _dia_wait = self._FIRST_TURN_DIALECTIC_CAP if self._config and self._config.timeout: _dia_wait = min(_dia_wait, self._config.timeout) if self._thread_is_live(): _live = self._prefetch_thread if _live is not None: _live.join(timeout=_dia_wait) else: _first_turn_timeout = _dia_wait _fired_at = self._turn_count def _run_first_turn() -> None: try: r = self._run_dialectic_depth(query) except Exception as exc: logger.debug("Honcho first-turn dialectic failed: %s", exc) self._dialectic_empty_streak += 1 return if r and r.strip(): with self._prefetch_lock: self._prefetch_result = r self._prefetch_result_fired_at = _fired_at # Advance cadence only on a non-empty result so the next # turn retries when the call returned nothing. self._last_dialectic_turn = _fired_at self._dialectic_empty_streak = 0 else: self._dialectic_empty_streak += 1 self._prefetch_thread_started_at = time.monotonic() first_turn_thread = threading.Thread( target=_run_first_turn, daemon=True, name="honcho-prefetch-first" ) first_turn_thread.start() self._prefetch_thread = first_turn_thread self._prefetch_thread.join(timeout=_first_turn_timeout) if self._prefetch_thread and self._prefetch_thread.is_alive(): logger.debug( "Honcho first-turn dialectic still running after %.1fs — " "will surface on next turn", self._FIRST_TURN_DIALECTIC_CAP, ) # Turn 2+ consumes the result primed by queue_prefetch() last turn — # give a near-finished thread a brief moment. On turn 1 we already # waited above (_did_first_turn_wait); waiting again here just # re-blocks on the same slow in-flight dialectic with no chance of it # landing, so skip the redundant join. if (not _did_first_turn_wait and self._prefetch_thread and self._prefetch_thread.is_alive()): self._prefetch_thread.join(timeout=3.0) # Drain the pending result (applies the stale-discard guard). Shared # with the trivial-prompt path above via _consume_pending_dialectic so # both routes pop + age-check identically. dialectic_result = self._consume_pending_dialectic() 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 _consume_pending_dialectic(self) -> str: """Pop any pending dialectic result, applying the stale-discard guard. Drains _prefetch_result under the lock and returns it unless it is stale (fired more than cadence × multiplier turns ago), in which case it is dropped and "" is returned. Does NOT wait on an in-flight thread — callers that want to give a near-finished thread a moment must join before calling. Idempotent: a second call returns "" until a new result is primed. """ with self._prefetch_lock: dialectic_result = self._prefetch_result fired_at = self._prefetch_result_fired_at self._prefetch_result = "" self._prefetch_result_fired_at = -999 # Discard stale pending results: if the fire happened more than # cadence × multiplier turns ago (e.g. a run of trivial-prompt turns # passed without consumption), the content likely no longer tracks # the current conversational pivot. stale_limit = self._dialectic_cadence * self._STALE_RESULT_MULTIPLIER if dialectic_result and fired_at >= 0 and (self._turn_count - fired_at) > stale_limit: logger.debug( "Honcho pending dialectic discarded as stale: fired_at=%d, " "turn=%d, limit=%d", fired_at, self._turn_count, stale_limit, ) return "" return dialectic_result if (dialectic_result and dialectic_result.strip()) else "" 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 # B1: tools-only mode — no prefetch if self._recall_mode == "tools": return if not self._session_ready() or not query: self._start_session_init_background() return # Trivial prompts don't warrant either a context refresh or a dialectic call. if self._is_trivial_prompt(query): 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) ----- # Thread-alive guard with stale-thread recovery: a hung Honcho call # older than timeout × multiplier is treated as dead so it can't # block subsequent fires. if self._thread_is_live(): logger.debug("Honcho dialectic prefetch skipped: prior thread still running") return # Cadence gate, widened by the empty-streak backoff so a persistently # silent backend doesn't retry every turn forever. effective = self._effective_cadence() if (self._turn_count - self._last_dialectic_turn) < effective: logger.debug( "Honcho dialectic prefetch skipped: effective cadence %d " "(base %d, empty streak %d), turns since last: %d", effective, self._dialectic_cadence, self._dialectic_empty_streak, self._turn_count - self._last_dialectic_turn, ) return # Cadence advances only on a non-empty result so empty returns # (transient API error, sparse representation) retry next turn. _fired_at = self._turn_count def _run(): try: result = self._run_dialectic_depth(query) except Exception as e: logger.debug("Honcho prefetch failed: %s", e) self._dialectic_empty_streak += 1 return if result and result.strip(): with self._prefetch_lock: self._prefetch_result = result self._prefetch_result_fired_at = _fired_at self._last_dialectic_turn = _fired_at self._dialectic_empty_streak = 0 else: self._dialectic_empty_streak += 1 self._prefetch_thread_started_at = time.monotonic() prefetch_thread = threading.Thread( target=_run, daemon=True, name="honcho-prefetch" ) prefetch_thread.start() self._prefetch_thread = prefetch_thread # ----- 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") # Char-count thresholds for the query-length reasoning heuristic. _HEURISTIC_LENGTH_MEDIUM = 120 _HEURISTIC_LENGTH_HIGH = 400 # Liveness constants. A thread older than timeout × multiplier is treated # as dead so a hung Honcho call can't block future retries indefinitely. _STALE_THREAD_MULTIPLIER = 2.0 # Pending result whose fire-turn is older than cadence × multiplier is # discarded on read so we don't inject context for a stale conversational # pivot after a gap of trivial-prompt turns. _STALE_RESULT_MULTIPLIER = 2 # Cap on the empty-streak backoff so a persistently silent backend # eventually settles on a ceiling instead of unbounded widening. _BACKOFF_MAX = 8 # First-turn base-context fetch bound. Base context (representation + # peer card) is pure retrieval with no LLM call (~400-600ms locally), so # turn 1 can afford to wait for it synchronously and inject the peer card # immediately instead of returning an empty block that only fills from # turn 2. A short bound keeps a slow/unreachable backend from blocking the # first response. _FIRST_TURN_BASE_TIMEOUT = 3.0 # First-turn dialectic grace. The dialectic is the slow LLM path # (multi-pass .chat(), often 20s+ at depth 3) and must stay decoupled from # config.timeout: once the host-block timeout is honored (e.g. 60s), # reusing it here would block the first response for the full request # timeout. Turn 1's value is the base context (peer card), fetched # synchronously above; the dialectic only needs an opportunistic grace to # catch a near-finished prewarm. If it doesn't land in this window it # surfaces on turn 2 via _prefetch_result — no benefit to blocking longer. _FIRST_TURN_DIALECTIC_CAP = 2.0 def _thread_is_live(self) -> bool: """Thread-alive guard that treats threads older than the stale threshold as dead, so a hung Honcho request can't block new fires.""" if not self._prefetch_thread or not self._prefetch_thread.is_alive(): return False timeout = (self._config.timeout if self._config and self._config.timeout else 8.0) age = time.monotonic() - self._prefetch_thread_started_at if age > timeout * self._STALE_THREAD_MULTIPLIER: logger.debug( "Honcho prefetch thread age %.1fs exceeds stale threshold " "%.1fs — treating as dead", age, timeout * self._STALE_THREAD_MULTIPLIER, ) return False return True def _effective_cadence(self) -> int: """Cadence plus empty-streak backoff, capped at _BACKOFF_MAX × base.""" if self._dialectic_empty_streak <= 0: return self._dialectic_cadence widened = self._dialectic_cadence + self._dialectic_empty_streak ceiling = self._dialectic_cadence * self._BACKOFF_MAX return min(widened, ceiling) def liveness_snapshot(self) -> dict: """In-process snapshot of dialectic liveness state for diagnostics. Returns current turn, last successful dialectic turn, pending-result fire turn, empty streak, effective cadence, and thread status. """ thread_age = None if self._prefetch_thread and self._prefetch_thread.is_alive(): thread_age = time.monotonic() - self._prefetch_thread_started_at return { "turn_count": self._turn_count, "last_dialectic_turn": self._last_dialectic_turn, "pending_result_fired_at": self._prefetch_result_fired_at, "empty_streak": self._dialectic_empty_streak, "effective_cadence": self._effective_cadence(), "thread_alive": thread_age is not None, "thread_age_seconds": thread_age, } def _apply_reasoning_heuristic(self, base: str, query: str) -> str: """Scale `base` up by query length, clamped at reasoning_level_cap. Char-count heuristic: +1 at >=120 chars, +2 at >=400. """ if not self._reasoning_heuristic or not query: return base if base not in self._LEVEL_ORDER: return base n = len(query) if n < self._HEURISTIC_LENGTH_MEDIUM: bump = 0 elif n < self._HEURISTIC_LENGTH_HIGH: bump = 1 else: bump = 2 base_idx = self._LEVEL_ORDER.index(base) cap_idx = self._LEVEL_ORDER.index(self._reasoning_level_cap) return self._LEVEL_ORDER[min(base_idx + bump, cap_idx)] def _resolve_pass_level(self, pass_idx: int, query: str = "") -> str: """Resolve reasoning level for a given pass index. Precedence: 1. dialecticDepthLevels (explicit per-pass) — wins absolutely 2. _PROPORTIONAL_LEVELS table (depth>1 lighter-early passes) 3. Base level = dialecticReasoningLevel, optionally scaled by the reasoning heuristic when the mapping falls through to 'base' """ 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 self._apply_reasoning_heuristic(base, query) 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, *, use_query_rewrite: bool = True) -> 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] = [] rewritten_query = "" if use_query_rewrite and self._query_rewriter: try: rewritten_query = self._query_rewriter(query).strip() except Exception as exc: logger.debug("Honcho query rewriter failed: %s", exc) for i in range(self._dialectic_depth): # Only non-empty prior results are usable context for dependent # passes. A pass can return "" (e.g. a reasoning model that spends # its whole token budget thinking and emits no content). Feeding # that blank forward produced prompts like "Given this initial # assessment:\n\n\n\nWhat gaps remain..." with an empty body — the # empty-spot symptom seen in Honcho request logs. prior_results = [r for r in results if r and r.strip()] if i == 0: prompt = rewritten_query or self._build_dialectic_prompt( 0, prior_results, is_cold ) else: # Skip further passes if prior pass delivered strong signal if prior_results and self._signal_sufficient(prior_results[-1]): logger.debug("Honcho dialectic depth %d: pass %d skipped, prior signal sufficient", self._dialectic_depth, i) break if not prior_results: # Every prior pass returned empty — a dependent prompt would # carry a blank assessment. Re-issue the pass-0 base prompt # instead so this pass starts fresh rather than referencing # nothing. logger.debug("Honcho dialectic depth %d: pass %d has no non-empty prior — " "falling back to base prompt", self._dialectic_depth, i) prompt = rewritten_query or self._build_dialectic_prompt( 0, prior_results, is_cold ) else: prompt = self._build_dialectic_prompt(i, prior_results, is_cold) level = self._resolve_pass_level(i, query=query) 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 "" # Prompts that carry no semantic signal — trivial acknowledgements, slash # commands, empty input. Skipping injection here saves tokens and prevents # stale user-model context from derailing one-word replies. _TRIVIAL_PROMPT_RE = re.compile( r'^(yes|no|ok|okay|sure|thanks|thank you|y|n|yep|nope|yeah|nah|' r'continue|go ahead|do it|proceed|got it|cool|nice|great|done|next|lgtm|k)$', re.IGNORECASE, ) @classmethod def _is_trivial_prompt(cls, text: str) -> bool: """Return True if the prompt is too trivial to warrant context injection.""" if not text: return True stripped = text.strip() if not stripped: return True if stripped.startswith("/"): return True if cls._TRIVIAL_PROMPT_RE.match(stripped): return True return False 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 _empty_profile_hint(self, peer: str) -> Dict[str, Any]: """Build a diagnostic hint when honcho_profile returns an empty card. A literal "No profile facts available yet." tells the model nothing about WHY. The model then often surfaces it to the user as a cryptic error. This hint enumerates the likely causes so the model can explain the situation (or retry with a different peer). Ordered by likelihood for a typical deployment: 1. Observation is disabled for this peer 2. Card hasn't accumulated yet (fresh peer, not enough dialectic cycles — dialectic cadence runs every N turns) 3. Self-hosted Honcho backend doesn't support peer cards (honcho-ai server < 3.x) """ cfg = self._config reasons: List[str] = [] if cfg is not None: if peer == "user": observe_me = bool(getattr(cfg, "user_observe_me", True)) observe_others = bool(getattr(cfg, "user_observe_others", True)) else: observe_me = bool(getattr(cfg, "ai_observe_me", True)) observe_others = bool(getattr(cfg, "ai_observe_others", True)) if not (observe_me or observe_others): reasons.append( f"observation is disabled for peer '{peer}' " f"(user_observe_me/ai_observe_me in config)" ) cadence = getattr(self, "_dialectic_cadence", 1) turn = getattr(self, "_turn_count", 0) if turn < max(2, cadence): reasons.append( f"this session has only {turn} turn(s); peer cards accumulate " f"as the dialectic layer reasons over conversation history " f"(cadence every {cadence} turn(s))" ) if not reasons: reasons.append( "peer card has no facts yet — Honcho's dialectic layer builds " "this over time from observed turns; self-hosted Honcho < 3.x " "does not support peer cards at all" ) return { "result": "No profile facts available yet.", "hint": ( "This is not an error. " + "; ".join(reasons) + ". Try honcho_reasoning for a synthesized answer, or " "honcho_search to query raw conversation excerpts." ), } 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 self._recall_mode == "tools" and not self._session_ready(): return if not self._session_ready(): self._start_session_init_background() return msg_limit = self._config.message_max_chars if self._config else 25000 clean_user_content = sanitize_context(user_content or "").strip() clean_assistant_content = sanitize_context(assistant_content or "").strip() def _sync(): try: session = self._manager.get_or_create(self._session_key) for chunk in self._chunk_message(clean_user_content, msg_limit): session.add_message("user", chunk) for chunk in self._chunk_message(clean_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, metadata: Optional[Dict[str, Any]] = None, ) -> None: """Mirror built-in user profile writes as Honcho conclusions. ``metadata`` is accepted for compatibility with the write-origin work landed in main (commit 6a957a74); it's not yet threaded into the Honcho conclusion payload. Left as a follow-up so this PR stays focused on the 7-PR consolidation and its review follow-ups. """ if action != "add" or target != "user" or not content: return if self._cron_skipped: return if self._recall_mode == "tools" and not self._session_ready(): return if not self._session_ready(): self._start_session_init_background() 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 if not self._session_initialized and self._init_thread and self._init_thread.is_alive(): 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 self._init_thread and self._init_thread.is_alive(): return tool_error("Honcho session is still initializing; try again shortly.") 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(self._empty_profile_hint(peer)) 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() list_mode = bool(args.get("list")) peer = args.get("peer", "user") has_delete_id = bool(delete_id) has_conclusion = bool(conclusion) if sum([has_delete_id, has_conclusion, list_mode]) != 1: return tool_error("Exactly one of conclusion, delete_id, or list must be provided.") if list_mode: query = (args.get("query") or "").strip() or None conclusions = self._manager.list_conclusions(self._session_key, query=query, peer=peer) return json.dumps({"conclusions": conclusions}) 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 and not (self._init_thread and self._init_thread.is_alive() and not self._session_initialized): try: self._manager.flush_all() except Exception: pass # --------------------------------------------------------------------------- # Plugin entry point # --------------------------------------------------------------------------- def register(ctx) -> None: """Register Honcho as a memory provider plugin.""" from plugins.memory.honcho.query_rewrite import rewrite_dialectic_query ctx.register_memory_provider( HonchoMemoryProvider(query_rewriter=rewrite_dialectic_query) )