greatmengqi
3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
"""Middleware for memory mechanism."""
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import logging
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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 deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
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from deerflow.agents.memory.queue import get_memory_queue
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from deerflow.config.deer_flow_context import DeerFlowContext
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from deerflow.runtime.user_context import get_effective_user_id
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logger = logging.getLogger(__name__)
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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. Only includes user inputs and final assistant responses (ignores tool calls)
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3. The queue uses debouncing to batch multiple updates together
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4. Memory is updated asynchronously via LLM summarization
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"""
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state_schema = MemoryMiddlewareState
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def __init__(self, agent_name: str | None = None):
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"""Initialize the MemoryMiddleware.
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Args:
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agent_name: If provided, memory is stored per-agent. If None, uses global memory.
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"""
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super().__init__()
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self._agent_name = agent_name
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@override
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def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime[DeerFlowContext]) -> 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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memory_config = runtime.context.app_config.memory
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if not memory_config.enabled:
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return None
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thread_id = runtime.context.thread_id
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if not thread_id:
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logger.debug("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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logger.debug("No messages in state, skipping memory update")
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return None
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# Filter to only keep user inputs and final assistant responses
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filtered_messages = filter_messages_for_memory(messages)
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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 filtered_messages if getattr(m, "type", None) == "human"]
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assistant_messages = [m for m in filtered_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 filtered conversation for memory update
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correction_detected = detect_correction(filtered_messages)
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reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
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# Capture user_id at enqueue time while the request context is still alive.
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# threading.Timer fires on a different thread where ContextVar values are not
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# propagated, so we must store user_id explicitly in ConversationContext.
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user_id = get_effective_user_id()
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queue = get_memory_queue(runtime.context.app_config)
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queue.add(
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thread_id=thread_id,
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messages=filtered_messages,
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agent_name=self._agent_name,
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user_id=user_id,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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return None
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