hermes-agent/honcho_integration/session.py

"""Honcho-based session management for conversation history."""

from __future__ import annotations

import queue
import re
import logging
import threading
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, TYPE_CHECKING

from honcho_integration.client import get_honcho_client

if TYPE_CHECKING:
    from honcho import Honcho

logger = logging.getLogger(__name__)

# Sentinel to signal the async writer thread to shut down
_ASYNC_SHUTDOWN = object()


@dataclass
class HonchoSession:
    """
    A conversation session backed by Honcho.

    Provides a local message cache that syncs to Honcho's
    AI-native memory system for user modeling.
    """

    key: str  # channel:chat_id
    user_peer_id: str  # Honcho peer ID for the user
    assistant_peer_id: str  # Honcho peer ID for the assistant
    honcho_session_id: str  # Honcho session ID
    messages: list[dict[str, Any]] = field(default_factory=list)
    created_at: datetime = field(default_factory=datetime.now)
    updated_at: datetime = field(default_factory=datetime.now)
    metadata: dict[str, Any] = field(default_factory=dict)

    def add_message(self, role: str, content: str, **kwargs: Any) -> None:
        """Add a message to the local cache."""
        msg = {
            "role": role,
            "content": content,
            "timestamp": datetime.now().isoformat(),
            **kwargs,
        }
        self.messages.append(msg)
        self.updated_at = datetime.now()

    def get_history(self, max_messages: int = 50) -> list[dict[str, Any]]:
        """Get message history for LLM context."""
        recent = (
            self.messages[-max_messages:]
            if len(self.messages) > max_messages
            else self.messages
        )
        return [{"role": m["role"], "content": m["content"]} for m in recent]

    def clear(self) -> None:
        """Clear all messages in the session."""
        self.messages = []
        self.updated_at = datetime.now()


class HonchoSessionManager:
    """
    Manages conversation sessions using Honcho.

    Runs alongside hermes' existing SQLite state and file-based memory,
    adding persistent cross-session user modeling via Honcho's AI-native memory.
    """

    def __init__(
        self,
        honcho: Honcho | None = None,
        context_tokens: int | None = None,
        config: Any | None = None,
    ):
        """
        Initialize the session manager.

        Args:
            honcho: Optional Honcho client. If not provided, uses the singleton.
            context_tokens: Max tokens for context() calls (None = Honcho default).
            config: HonchoClientConfig from global config (provides peer_name, ai_peer,
                    write_frequency, memory_mode, etc.).
        """
        self._honcho = honcho
        self._context_tokens = context_tokens
        self._config = config
        self._cache: dict[str, HonchoSession] = {}
        self._peers_cache: dict[str, Any] = {}
        self._sessions_cache: dict[str, Any] = {}

        # Write frequency state
        write_frequency = (config.write_frequency if config else "async")
        self._write_frequency = write_frequency
        self._turn_counter: int = 0

        # Prefetch caches: session_key → last result (consumed once per turn)
        self._context_cache: dict[str, dict] = {}
        self._dialectic_cache: dict[str, str] = {}
        self._prefetch_cache_lock = threading.Lock()
        self._dialectic_reasoning_level: str = (
            config.dialectic_reasoning_level if config else "low"
        )
        self._dialectic_max_chars: int = (
            config.dialectic_max_chars if config else 600
        )

        # Async write queue — started lazily on first enqueue
        self._async_queue: queue.Queue | None = None
        self._async_thread: threading.Thread | None = None
        if write_frequency == "async":
            self._async_queue = queue.Queue()
            self._async_thread = threading.Thread(
                target=self._async_writer_loop,
                name="honcho-async-writer",
                daemon=True,
            )
            self._async_thread.start()

    @property
    def honcho(self) -> Honcho:
        """Get the Honcho client, initializing if needed."""
        if self._honcho is None:
            self._honcho = get_honcho_client()
        return self._honcho

    def _get_or_create_peer(self, peer_id: str) -> Any:
        """
        Get or create a Honcho peer.

        Peers are lazy -- no API call until first use.
        Observation settings are controlled per-session via SessionPeerConfig.
        """
        if peer_id in self._peers_cache:
            return self._peers_cache[peer_id]

        peer = self.honcho.peer(peer_id)
        self._peers_cache[peer_id] = peer
        return peer

    def _get_or_create_honcho_session(
        self, session_id: str, user_peer: Any, assistant_peer: Any
    ) -> tuple[Any, list]:
        """
        Get or create a Honcho session with peers configured.

        Returns:
            Tuple of (honcho_session, existing_messages).
        """
        if session_id in self._sessions_cache:
            logger.debug("Honcho session '%s' retrieved from cache", session_id)
            return self._sessions_cache[session_id], []

        session = self.honcho.session(session_id)

        # Configure peer observation settings.
        # observe_me=True for AI peer so Honcho watches what the agent says
        # and builds its representation over time — enabling identity formation.
        from honcho.session import SessionPeerConfig
        user_config = SessionPeerConfig(observe_me=True, observe_others=True)
        ai_config = SessionPeerConfig(observe_me=True, observe_others=True)

        session.add_peers([(user_peer, user_config), (assistant_peer, ai_config)])

        # Load existing messages via context() - single call for messages + metadata
        existing_messages = []
        try:
            ctx = session.context(summary=True, tokens=self._context_tokens)
            existing_messages = ctx.messages or []

            # Verify chronological ordering
            if existing_messages and len(existing_messages) > 1:
                timestamps = [m.created_at for m in existing_messages if m.created_at]
                if timestamps and timestamps != sorted(timestamps):
                    logger.warning(
                        "Honcho messages not chronologically ordered for session '%s', sorting",
                        session_id,
                    )
                    existing_messages = sorted(
                        existing_messages,
                        key=lambda m: m.created_at or datetime.min,
                    )

            if existing_messages:
                logger.info(
                    "Honcho session '%s' retrieved (%d existing messages)",
                    session_id, len(existing_messages),
                )
            else:
                logger.info("Honcho session '%s' created (new)", session_id)
        except Exception as e:
            logger.warning(
                "Honcho session '%s' loaded (failed to fetch context: %s)",
                session_id, e,
            )

        self._sessions_cache[session_id] = session
        return session, existing_messages

    def _sanitize_id(self, id_str: str) -> str:
        """Sanitize an ID to match Honcho's pattern: ^[a-zA-Z0-9_-]+"""
        return re.sub(r'[^a-zA-Z0-9_-]', '-', id_str)

    def get_or_create(self, key: str) -> HonchoSession:
        """
        Get an existing session or create a new one.

        Args:
            key: Session key (usually channel:chat_id).

        Returns:
            The session.
        """
        if key in self._cache:
            logger.debug("Local session cache hit: %s", key)
            return self._cache[key]

        # Use peer names from global config when available
        if self._config and self._config.peer_name:
            user_peer_id = self._sanitize_id(self._config.peer_name)
        else:
            # Fallback: derive from session key
            parts = key.split(":", 1)
            channel = parts[0] if len(parts) > 1 else "default"
            chat_id = parts[1] if len(parts) > 1 else key
            user_peer_id = self._sanitize_id(f"user-{channel}-{chat_id}")

        assistant_peer_id = (
            self._config.ai_peer if self._config else "hermes-assistant"
        )

        # Sanitize session ID for Honcho
        honcho_session_id = self._sanitize_id(key)

        # Get or create peers
        user_peer = self._get_or_create_peer(user_peer_id)
        assistant_peer = self._get_or_create_peer(assistant_peer_id)

        # Get or create Honcho session
        honcho_session, existing_messages = self._get_or_create_honcho_session(
            honcho_session_id, user_peer, assistant_peer
        )

        # Convert Honcho messages to local format
        local_messages = []
        for msg in existing_messages:
            role = "assistant" if msg.peer_id == assistant_peer_id else "user"
            local_messages.append({
                "role": role,
                "content": msg.content,
                "timestamp": msg.created_at.isoformat() if msg.created_at else "",
                "_synced": True,  # Already in Honcho
            })

        # Create local session wrapper with existing messages
        session = HonchoSession(
            key=key,
            user_peer_id=user_peer_id,
            assistant_peer_id=assistant_peer_id,
            honcho_session_id=honcho_session_id,
            messages=local_messages,
        )

        self._cache[key] = session
        return session

    def _flush_session(self, session: HonchoSession) -> None:
        """Internal: write unsynced messages to Honcho synchronously."""
        if not session.messages:
            return

        user_peer = self._get_or_create_peer(session.user_peer_id)
        assistant_peer = self._get_or_create_peer(session.assistant_peer_id)
        honcho_session = self._sessions_cache.get(session.honcho_session_id)

        if not honcho_session:
            honcho_session, _ = self._get_or_create_honcho_session(
                session.honcho_session_id, user_peer, assistant_peer
            )

        new_messages = [m for m in session.messages if not m.get("_synced")]
        if not new_messages:
            return

        honcho_messages = []
        for msg in new_messages:
            peer = user_peer if msg["role"] == "user" else assistant_peer
            honcho_messages.append(peer.message(msg["content"]))

        try:
            honcho_session.add_messages(honcho_messages)
            for msg in new_messages:
                msg["_synced"] = True
            logger.debug("Synced %d messages to Honcho for %s", len(honcho_messages), session.key)
        except Exception as e:
            for msg in new_messages:
                msg["_synced"] = False
            logger.error("Failed to sync messages to Honcho: %s", e)

        self._cache[session.key] = session

    def _async_writer_loop(self) -> None:
        """Background daemon thread: drains the async write queue."""
        while True:
            try:
                item = self._async_queue.get(timeout=5)
                if item is _ASYNC_SHUTDOWN:
                    break
                try:
                    self._flush_session(item)
                except Exception as e:
                    logger.warning("Honcho async write failed, retrying once: %s", e)
                    import time as _time
                    _time.sleep(2)
                    try:
                        self._flush_session(item)
                    except Exception as e2:
                        logger.error("Honcho async write retry failed, dropping batch: %s", e2)
            except queue.Empty:
                continue
            except Exception as e:
                logger.error("Honcho async writer error: %s", e)

    def save(self, session: HonchoSession) -> None:
        """Save messages to Honcho, respecting write_frequency.

        write_frequency modes:
          "async"   — enqueue for background thread (zero blocking, zero token cost)
          "turn"    — flush synchronously every turn
          "session" — defer until flush_session() is called explicitly
          N (int)   — flush every N turns
        """
        self._turn_counter += 1
        wf = self._write_frequency

        if wf == "async":
            if self._async_queue is not None:
                self._async_queue.put(session)
        elif wf == "turn":
            self._flush_session(session)
        elif wf == "session":
            # Accumulate; caller must call flush_all() at session end
            pass
        elif isinstance(wf, int) and wf > 0:
            if self._turn_counter % wf == 0:
                self._flush_session(session)

    def flush_all(self) -> None:
        """Flush all pending unsynced messages for all cached sessions.

        Called at session end for "session" write_frequency, or to force
        a sync before process exit regardless of mode.
        """
        for session in list(self._cache.values()):
            try:
                self._flush_session(session)
            except Exception as e:
                logger.error("Honcho flush_all error for %s: %s", session.key, e)

        # Drain async queue synchronously if it exists
        if self._async_queue is not None:
            while not self._async_queue.empty():
                try:
                    item = self._async_queue.get_nowait()
                    if item is not _ASYNC_SHUTDOWN:
                        self._flush_session(item)
                except queue.Empty:
                    break

    def shutdown(self) -> None:
        """Gracefully shut down the async writer thread."""
        if self._async_queue is not None and self._async_thread is not None:
            self.flush_all()
            self._async_queue.put(_ASYNC_SHUTDOWN)
            self._async_thread.join(timeout=10)

    def delete(self, key: str) -> bool:
        """Delete a session from local cache."""
        if key in self._cache:
            del self._cache[key]
            return True
        return False

    def new_session(self, key: str) -> HonchoSession:
        """
        Create a new session, preserving the old one for user modeling.

        Creates a fresh session with a new ID while keeping the old
        session's data in Honcho for continued user modeling.
        """
        import time

        # Remove old session from caches (but don't delete from Honcho)
        old_session = self._cache.pop(key, None)
        if old_session:
            self._sessions_cache.pop(old_session.honcho_session_id, None)

        # Create new session with timestamp suffix
        timestamp = int(time.time())
        new_key = f"{key}:{timestamp}"

        # get_or_create will create a fresh session
        session = self.get_or_create(new_key)

        # Cache under the original key so callers find it by the expected name
        self._cache[key] = session

        logger.info("Created new session for %s (honcho: %s)", key, session.honcho_session_id)
        return session

    _REASONING_LEVELS = ("minimal", "low", "medium", "high", "max")

    def _dynamic_reasoning_level(self, query: str) -> str:
        """
        Pick a reasoning level based on message complexity.

        Uses the configured default as a floor; bumps up for longer or
        more complex messages so Honcho applies more inference where it matters.

          < 120 chars  → default (typically "low")
          120–400 chars → one level above default (cap at "high")
          > 400 chars  → two levels above default (cap at "high")

        "max" is never selected automatically — reserve it for explicit config.
        """
        levels = self._REASONING_LEVELS
        default_idx = levels.index(self._dialectic_reasoning_level) if self._dialectic_reasoning_level in levels else 1
        n = len(query)
        if n < 120:
            bump = 0
        elif n < 400:
            bump = 1
        else:
            bump = 2
        # Cap at "high" (index 3) for auto-selection
        idx = min(default_idx + bump, 3)
        return levels[idx]

    def dialectic_query(
        self, session_key: str, query: str,
        reasoning_level: str | None = None,
        peer: str = "user",
    ) -> str:
        """
        Query Honcho's dialectic endpoint about a peer.

        Runs an LLM on Honcho's backend against the target peer's full
        representation. Higher latency than context() — call async via
        prefetch_dialectic() to avoid blocking the response.

        Args:
            session_key: The session key to query against.
            query: Natural language question.
            reasoning_level: Override the config default. If None, uses
                             _dynamic_reasoning_level(query).
            peer: Which peer to query — "user" (default) or "ai".

        Returns:
            Honcho's synthesized answer, or empty string on failure.
        """
        session = self._cache.get(session_key)
        if not session:
            return ""

        peer_id = session.assistant_peer_id if peer == "ai" else session.user_peer_id
        target_peer = self._get_or_create_peer(peer_id)
        level = reasoning_level or self._dynamic_reasoning_level(query)

        try:
            result = target_peer.chat(query, reasoning_level=level) or ""
            # Apply Hermes-side char cap before caching
            if result and self._dialectic_max_chars and len(result) > self._dialectic_max_chars:
                result = result[:self._dialectic_max_chars].rsplit(" ", 1)[0] + " …"
            return result
        except Exception as e:
            logger.warning("Honcho dialectic query failed: %s", e)
            return ""

    def prefetch_dialectic(self, session_key: str, query: str) -> None:
        """
        Fire a dialectic_query in a background thread, caching the result.

        Non-blocking. The result is available via pop_dialectic_result()
        on the next call (typically the following turn). Reasoning level
        is selected dynamically based on query complexity.

        Args:
            session_key: The session key to query against.
            query: The user's current message, used as the query.
        """
        def _run():
            result = self.dialectic_query(session_key, query)
            if result:
                self.set_dialectic_result(session_key, result)

        t = threading.Thread(target=_run, name="honcho-dialectic-prefetch", daemon=True)
        t.start()

    def set_dialectic_result(self, session_key: str, result: str) -> None:
        """Store a prefetched dialectic result in a thread-safe way."""
        if not result:
            return
        with self._prefetch_cache_lock:
            self._dialectic_cache[session_key] = result

    def pop_dialectic_result(self, session_key: str) -> str:
        """
        Return and clear the cached dialectic result for this session.

        Returns empty string if no result is ready yet.
        """
        with self._prefetch_cache_lock:
            return self._dialectic_cache.pop(session_key, "")

    def prefetch_context(self, session_key: str, user_message: str | None = None) -> None:
        """
        Fire get_prefetch_context in a background thread, caching the result.

        Non-blocking. Consumed next turn via pop_context_result(). This avoids
        a synchronous HTTP round-trip blocking every response.
        """
        def _run():
            result = self.get_prefetch_context(session_key, user_message)
            if result:
                self.set_context_result(session_key, result)

        t = threading.Thread(target=_run, name="honcho-context-prefetch", daemon=True)
        t.start()

    def set_context_result(self, session_key: str, result: dict[str, str]) -> None:
        """Store a prefetched context result in a thread-safe way."""
        if not result:
            return
        with self._prefetch_cache_lock:
            self._context_cache[session_key] = result

    def pop_context_result(self, session_key: str) -> dict[str, str]:
        """
        Return and clear the cached context result for this session.

        Returns empty dict if no result is ready yet (first turn).
        """
        with self._prefetch_cache_lock:
            return self._context_cache.pop(session_key, {})

    def get_prefetch_context(self, session_key: str, user_message: str | None = None) -> dict[str, str]:
        """
        Pre-fetch user and AI peer context from Honcho.

        Fetches peer_representation and peer_card for both peers. search_query
        is intentionally omitted — it would only affect additional excerpts
        that this code does not consume, and passing the raw message exposes
        conversation content in server access logs.

        Args:
            session_key: The session key to get context for.
            user_message: Unused; kept for call-site compatibility.

        Returns:
            Dictionary with 'representation', 'card', 'ai_representation',
            and 'ai_card' keys.
        """
        session = self._cache.get(session_key)
        if not session:
            return {}

        honcho_session = self._sessions_cache.get(session.honcho_session_id)
        if not honcho_session:
            return {}

        result: dict[str, str] = {}
        try:
            ctx = honcho_session.context(
                summary=False,
                tokens=self._context_tokens,
                peer_target=session.user_peer_id,
                peer_perspective=session.assistant_peer_id,
            )
            card = ctx.peer_card or []
            result["representation"] = ctx.peer_representation or ""
            result["card"] = "\n".join(card) if isinstance(card, list) else str(card)
        except Exception as e:
            logger.warning("Failed to fetch user context from Honcho: %s", e)

        # Also fetch AI peer's own representation so Hermes knows itself.
        try:
            ai_ctx = honcho_session.context(
                summary=False,
                tokens=self._context_tokens,
                peer_target=session.assistant_peer_id,
                peer_perspective=session.user_peer_id,
            )
            ai_card = ai_ctx.peer_card or []
            result["ai_representation"] = ai_ctx.peer_representation or ""
            result["ai_card"] = "\n".join(ai_card) if isinstance(ai_card, list) else str(ai_card)
        except Exception as e:
            logger.debug("Failed to fetch AI peer context from Honcho: %s", e)

        return result

    def migrate_local_history(self, session_key: str, messages: list[dict[str, Any]]) -> bool:
        """
        Upload local session history to Honcho as a file.

        Used when Honcho activates mid-conversation to preserve prior context.

        Args:
            session_key: The session key (e.g., "telegram:123456").
            messages: Local messages (dicts with role, content, timestamp).

        Returns:
            True if upload succeeded, False otherwise.
        """
        sanitized = self._sanitize_id(session_key)
        honcho_session = self._sessions_cache.get(sanitized)
        if not honcho_session:
            logger.warning("No Honcho session cached for '%s', skipping migration", session_key)
            return False

        # Resolve user peer for attribution
        parts = session_key.split(":", 1)
        channel = parts[0] if len(parts) > 1 else "default"
        chat_id = parts[1] if len(parts) > 1 else session_key
        user_peer_id = self._sanitize_id(f"user-{channel}-{chat_id}")
        user_peer = self._peers_cache.get(user_peer_id)
        if not user_peer:
            logger.warning("No user peer cached for '%s', skipping migration", user_peer_id)
            return False

        content_bytes = self._format_migration_transcript(session_key, messages)
        first_ts = messages[0].get("timestamp") if messages else None

        try:
            honcho_session.upload_file(
                file=("prior_history.txt", content_bytes, "text/plain"),
                peer=user_peer,
                metadata={"source": "local_jsonl", "count": len(messages)},
                created_at=first_ts,
            )
            logger.info("Migrated %d local messages to Honcho for %s", len(messages), session_key)
            return True
        except Exception as e:
            logger.error("Failed to upload local history to Honcho for %s: %s", session_key, e)
            return False

    @staticmethod
    def _format_migration_transcript(session_key: str, messages: list[dict[str, Any]]) -> bytes:
        """Format local messages as an XML transcript for Honcho file upload."""
        timestamps = [m.get("timestamp", "") for m in messages]
        time_range = f"{timestamps[0]} to {timestamps[-1]}" if timestamps else "unknown"

        lines = [
            "<prior_conversation_history>",
            "<context>",
            "This conversation history occurred BEFORE the Honcho memory system was activated.",
            "These messages are the preceding elements of this conversation session and should",
            "be treated as foundational context for all subsequent interactions. The user and",
            "assistant have already established rapport through these exchanges.",
            "</context>",
            "",
            f'<transcript session_key="{session_key}" message_count="{len(messages)}"',
            f'           time_range="{time_range}">',
            "",
        ]
        for msg in messages:
            ts = msg.get("timestamp", "?")
            role = msg.get("role", "unknown")
            content = msg.get("content") or ""
            lines.append(f"[{ts}] {role}: {content}")

        lines.append("")
        lines.append("</transcript>")
        lines.append("</prior_conversation_history>")

        return "\n".join(lines).encode("utf-8")

    def migrate_memory_files(self, session_key: str, memory_dir: str) -> bool:
        """
        Upload MEMORY.md and USER.md to Honcho as files.

        Used when Honcho activates on an instance that already has locally
        consolidated memory. Backwards compatible -- skips if files don't exist.

        Args:
            session_key: The session key to associate files with.
            memory_dir: Path to the memories directory (~/.hermes/memories/).

        Returns:
            True if at least one file was uploaded, False otherwise.
        """
        from pathlib import Path
        memory_path = Path(memory_dir)

        if not memory_path.exists():
            return False

        sanitized = self._sanitize_id(session_key)
        honcho_session = self._sessions_cache.get(sanitized)
        if not honcho_session:
            logger.warning("No Honcho session cached for '%s', skipping memory migration", session_key)
            return False

        # Resolve user peer for attribution
        parts = session_key.split(":", 1)
        channel = parts[0] if len(parts) > 1 else "default"
        chat_id = parts[1] if len(parts) > 1 else session_key
        user_peer_id = self._sanitize_id(f"user-{channel}-{chat_id}")
        user_peer = self._peers_cache.get(user_peer_id)
        if not user_peer:
            logger.warning("No user peer cached for '%s', skipping memory migration", user_peer_id)
            return False

        uploaded = False
        files = [
            ("MEMORY.md", "consolidated_memory.md", "Long-term agent notes and preferences"),
            ("USER.md", "user_profile.md", "User profile and preferences"),
            ("SOUL.md", "agent_soul.md", "Agent persona and identity configuration"),
        ]

        for filename, upload_name, description in files:
            filepath = memory_path / filename
            if not filepath.exists():
                continue
            content = filepath.read_text(encoding="utf-8").strip()
            if not content:
                continue

            wrapped = (
                f"<prior_memory_file>\n"
                f"<context>\n"
                f"This file was consolidated from local conversations BEFORE Honcho was activated.\n"
                f"{description}. Treat as foundational context for this user.\n"
                f"</context>\n"
                f"\n"
                f"{content}\n"
                f"</prior_memory_file>\n"
            )

            try:
                honcho_session.upload_file(
                    file=(upload_name, wrapped.encode("utf-8"), "text/plain"),
                    peer=user_peer,
                    metadata={"source": "local_memory", "original_file": filename},
                )
                logger.info("Uploaded %s to Honcho for %s", filename, session_key)
                uploaded = True
            except Exception as e:
                logger.error("Failed to upload %s to Honcho: %s", filename, e)

        return uploaded

    def get_peer_card(self, session_key: str) -> list[str]:
        """
        Fetch the user peer's card — a curated list of key facts.

        Fast, no LLM reasoning. Returns raw structured facts Honcho has
        inferred about the user (name, role, preferences, patterns).
        Empty list if unavailable.
        """
        session = self._cache.get(session_key)
        if not session:
            return []

        honcho_session = self._sessions_cache.get(session.honcho_session_id)
        if not honcho_session:
            return []

        try:
            ctx = honcho_session.context(
                summary=False,
                tokens=200,
                peer_target=session.user_peer_id,
                peer_perspective=session.assistant_peer_id,
            )
            card = ctx.peer_card or []
            return card if isinstance(card, list) else [str(card)]
        except Exception as e:
            logger.debug("Failed to fetch peer card from Honcho: %s", e)
            return []

    def search_context(self, session_key: str, query: str, max_tokens: int = 800) -> str:
        """
        Semantic search over Honcho session context.

        Returns raw excerpts ranked by relevance to the query. No LLM
        reasoning — cheaper and faster than dialectic_query. Good for
        factual lookups where the model will do its own synthesis.

        Args:
            session_key: Session to search against.
            query: Search query for semantic matching.
            max_tokens: Token budget for returned content.

        Returns:
            Relevant context excerpts as a string, or empty string if none.
        """
        session = self._cache.get(session_key)
        if not session:
            return ""

        honcho_session = self._sessions_cache.get(session.honcho_session_id)
        if not honcho_session:
            return ""

        try:
            ctx = honcho_session.context(
                summary=False,
                tokens=max_tokens,
                peer_target=session.user_peer_id,
                peer_perspective=session.assistant_peer_id,
                search_query=query,
            )
            parts = []
            if ctx.peer_representation:
                parts.append(ctx.peer_representation)
            card = ctx.peer_card or []
            if card:
                facts = card if isinstance(card, list) else [str(card)]
                parts.append("\n".join(f"- {f}" for f in facts))
            return "\n\n".join(parts)
        except Exception as e:
            logger.debug("Honcho search_context failed: %s", e)
            return ""

    def create_conclusion(self, session_key: str, content: str) -> bool:
        """Write a conclusion about the user back to Honcho.

        Conclusions are facts the AI peer observes about the user —
        preferences, corrections, clarifications, project context.
        They feed into the user's peer card and representation.

        Args:
            session_key: Session to associate the conclusion with.
            content: The conclusion text (e.g. "User prefers dark mode").

        Returns:
            True on success, False on failure.
        """
        if not content or not content.strip():
            return False

        session = self._cache.get(session_key)
        if not session:
            logger.warning("No session cached for '%s', skipping conclusion", session_key)
            return False

        assistant_peer = self._get_or_create_peer(session.assistant_peer_id)
        try:
            conclusions_scope = assistant_peer.conclusions_of(session.user_peer_id)
            conclusions_scope.create([{
                "content": content.strip(),
                "session_id": session.honcho_session_id,
            }])
            logger.info("Created conclusion for %s: %s", session_key, content[:80])
            return True
        except Exception as e:
            logger.error("Failed to create conclusion: %s", e)
            return False

    def seed_ai_identity(self, session_key: str, content: str, source: str = "manual") -> bool:
        """
        Seed the AI peer's Honcho representation from text content.

        Useful for priming AI identity from SOUL.md, exported chats, or
        any structured description. The content is sent as an assistant
        peer message so Honcho's reasoning model can incorporate it.

        Args:
            session_key: The session key to associate with.
            content: The identity/persona content to seed.
            source: Metadata tag for the source (e.g. "soul_md", "export").

        Returns:
            True on success, False on failure.
        """
        if not content or not content.strip():
            return False

        session = self._cache.get(session_key)
        if not session:
            logger.warning("No session cached for '%s', skipping AI seed", session_key)
            return False

        assistant_peer = self._get_or_create_peer(session.assistant_peer_id)
        try:
            wrapped = (
                f"<ai_identity_seed>\n"
                f"<source>{source}</source>\n"
                f"\n"
                f"{content.strip()}\n"
                f"</ai_identity_seed>"
            )
            assistant_peer.add_message("assistant", wrapped)
            logger.info("Seeded AI identity from '%s' into %s", source, session_key)
            return True
        except Exception as e:
            logger.error("Failed to seed AI identity: %s", e)
            return False

    def get_ai_representation(self, session_key: str) -> dict[str, str]:
        """
        Fetch the AI peer's current Honcho representation.

        Returns:
            Dict with 'representation' and 'card' keys, empty strings if unavailable.
        """
        session = self._cache.get(session_key)
        if not session:
            return {"representation": "", "card": ""}

        honcho_session = self._sessions_cache.get(session.honcho_session_id)
        if not honcho_session:
            return {"representation": "", "card": ""}

        try:
            ctx = honcho_session.context(
                summary=False,
                tokens=self._context_tokens,
                peer_target=session.assistant_peer_id,
                peer_perspective=session.user_peer_id,
            )
            ai_card = ctx.peer_card or []
            return {
                "representation": ctx.peer_representation or "",
                "card": "\n".join(ai_card) if isinstance(ai_card, list) else str(ai_card),
            }
        except Exception as e:
            logger.debug("Failed to fetch AI representation: %s", e)
            return {"representation": "", "card": ""}

    def list_sessions(self) -> list[dict[str, Any]]:
        """List all cached sessions."""
        return [
            {
                "key": s.key,
                "created_at": s.created_at.isoformat(),
                "updated_at": s.updated_at.isoformat(),
                "message_count": len(s.messages),
            }
            for s in self._cache.values()
        ]