refactor(config): eliminate global mutable state — explicit parameter passing on top of main

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).

backend/packages/harness/deerflow/agents/middlewares/memory_middleware.py

@@ -5,12 +5,12 @@ from typing import 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.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 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
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
@@ -43,7 +43,7 @@ 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:
@@ -53,15 +53,11 @@ 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
@@ -86,11 +82,16 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         # Queue the filtered conversation for memory update
         correction_detected = detect_correction(filtered_messages)
         reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
-        queue = get_memory_queue()
+        # Capture user_id at enqueue time while the request context is still alive.
+        # threading.Timer fires on a different thread where ContextVar values are not
+        # propagated, so we must store user_id explicitly in ConversationContext.
+        user_id = get_effective_user_id()
+        queue = get_memory_queue(runtime.context.app_config)
         queue.add(
             thread_id=thread_id,
             messages=filtered_messages,
             agent_name=self._agent_name,
+            user_id=user_id,
             correction_detected=correction_detected,
             reinforcement_detected=reinforcement_detected,
         )