Fix home message tools calls
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59
app/core/agent_session_buffer.py
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59
app/core/agent_session_buffer.py
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"""In-process TTL buffer for per-session LangChain message history.
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Stores the full message list (including AIMessage with tool_calls and ToolMessage)
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keyed by (user_id, session_id), so agents can reconstruct tool-call context across
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conversation turns without it being lossy through the wire.
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Single-process only. For multi-worker deployments, replace the _SessionBuffer
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implementation with one backed by Redis (serialize LangChain messages to dicts via
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message_to_dict / messages_from_dict from langchain_core.messages).
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"""
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from __future__ import annotations
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import time
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from threading import Lock
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from langchain_core.messages import BaseMessage
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SESSION_TTL_SECONDS = 1800 # 30-minute idle expiry
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MAX_MESSAGES_PER_SESSION = 80 # cap to avoid unbounded memory growth
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class _SessionBuffer:
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def __init__(self) -> None:
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self._store: dict[tuple[str, str], tuple[float, list[BaseMessage]]] = {}
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self._lock = Lock()
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def _evict_stale(self) -> None:
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now = time.monotonic()
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stale = [k for k, (ts, _) in self._store.items() if now - ts > SESSION_TTL_SECONDS]
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for k in stale:
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del self._store[k]
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def get(self, user_id: str, session_id: str) -> list[BaseMessage] | None:
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key = (user_id, session_id)
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with self._lock:
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entry = self._store.get(key)
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if entry is None:
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return None
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ts, msgs = entry
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if time.monotonic() - ts > SESSION_TTL_SECONDS:
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del self._store[key]
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return None
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self._store[key] = (time.monotonic(), msgs)
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return list(msgs)
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def set(self, user_id: str, session_id: str, messages: list[BaseMessage]) -> None:
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key = (user_id, session_id)
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capped = messages[-MAX_MESSAGES_PER_SESSION:]
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with self._lock:
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self._evict_stale()
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self._store[key] = (time.monotonic(), capped)
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def clear(self, user_id: str, session_id: str) -> None:
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with self._lock:
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self._store.pop((user_id, session_id), None)
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# Module-level singleton — same pattern as _pending_states in api/app/api/routes/auth.py
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session_buffer = _SessionBuffer()
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