hetaoBackend
ffd07bbafe
Implement a memory system that stores user context and conversation history in memory.json, uses LLM to summarize conversations, and injects relevant context into system prompts for personalized responses. Key components: - MemoryConfig for configuration management - MemoryUpdateQueue with debounce for batch processing - MemoryUpdater for LLM-based memory extraction - MemoryMiddleware to queue conversations after agent execution - Memory injection into lead agent system prompt Note: Add memory section to config.yaml to enable (see config.example.yaml) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
"""Middleware for memory mechanism."""
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from typing import override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langgraph.runtime import Runtime
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from src.agents.memory.queue import get_memory_queue
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from src.config.memory_config import get_memory_config
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class MemoryMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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pass
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class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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"""Middleware that queues conversation for memory update after agent execution.
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This middleware:
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1. After each agent execution, queues the conversation for memory update
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2. The queue uses debouncing to batch multiple updates together
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3. Memory is updated asynchronously via LLM summarization
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"""
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state_schema = MemoryMiddlewareState
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@override
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def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
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"""Queue conversation for memory update after agent completes.
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Args:
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state: The current agent state.
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runtime: The runtime context.
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Returns:
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None (no state changes needed from this middleware).
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"""
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config = get_memory_config()
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if not config.enabled:
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return None
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# Get thread ID from runtime context
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thread_id = runtime.context.get("thread_id")
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if not thread_id:
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print("MemoryMiddleware: No thread_id in context, skipping memory update")
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return None
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# Get messages from state
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messages = state.get("messages", [])
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if not messages:
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print("MemoryMiddleware: No messages in state, skipping memory update")
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return None
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# Only queue if there's meaningful conversation
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# At minimum need one user message and one assistant response
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user_messages = [m for m in messages if getattr(m, "type", None) == "human"]
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assistant_messages = [m for m in messages if getattr(m, "type", None) == "ai"]
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if not user_messages or not assistant_messages:
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return None
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# Queue the conversation for memory update
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queue = get_memory_queue()
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queue.add(thread_id=thread_id, messages=list(messages))
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return None
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