refactor(config): eliminate global mutable state, wire DeerFlowContext into runtime

- Freeze all config models (AppConfig + 15 sub-configs) with frozen=True
- Purify from_file() — remove 9 load_*_from_dict() side-effect calls
- Replace mtime/reload/push/pop machinery with single ContextVar + init_app_config()
- Delete 10 sub-module globals and their getters/setters/loaders
- Migrate 50+ consumers from get_*_config() to get_app_config().xxx

- Expand DeerFlowContext: app_config + thread_id + agent_name (frozen dataclass)
- Wire into Gateway runtime (worker.py) and DeerFlowClient via context= parameter
- Remove sandbox_id from runtime.context — flows through ThreadState.sandbox only
- Middleware/tools access runtime.context directly via Runtime[DeerFlowContext] generic
- resolve_context() retained at server entry points for LangGraph Server fallback
This commit is contained in:
greatmengqi
2026-04-13 23:49:31 +08:00
parent c4d273a68a
commit edf345cd72
111 changed files with 4848 additions and 4079 deletions
@@ -6,11 +6,10 @@ from typing import Any, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.config import get_config
from langgraph.runtime import Runtime
from deerflow.agents.memory.queue import get_memory_queue
from deerflow.config.memory_config import get_memory_config
from deerflow.config.deer_flow_context import DeerFlowContext
logger = logging.getLogger(__name__)
@@ -193,7 +192,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
self._agent_name = agent_name
@override
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
"""Queue conversation for memory update after agent completes.
Args:
@@ -203,15 +202,11 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
Returns:
None (no state changes needed from this middleware).
"""
config = get_memory_config()
if not config.enabled:
memory_config = runtime.context.app_config.memory
if not memory_config.enabled:
return None
# Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
thread_id = runtime.context.get("thread_id") if runtime.context else None
if thread_id is None:
config_data = get_config()
thread_id = config_data.get("configurable", {}).get("thread_id")
thread_id = runtime.context.thread_id
if not thread_id:
logger.debug("No thread_id in context, skipping memory update")
return None