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

backend/packages/harness/deerflow/runtime/runs/worker.py

@@ -21,6 +21,8 @@ import inspect
 import logging
 from typing import Any, Literal
 
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.runtime.serialization import serialize
 from deerflow.runtime.stream_bridge import StreamBridge
 
@@ -98,17 +100,14 @@ async def run_agent(
 
         # 3. Build the agent
         from langchain_core.runnables import RunnableConfig
-        from langgraph.runtime import Runtime
 
-        # Inject runtime context so middlewares can access thread_id
-        # (langgraph-cli does this automatically; we must do it manually)
-        runtime = Runtime(context={"thread_id": thread_id}, store=store)
-        # If the caller already set a ``context`` key (LangGraph >= 0.6.0
-        # prefers it over ``configurable`` for thread-level data), make
-        # sure ``thread_id`` is available there too.
-        if "context" in config and isinstance(config["context"], dict):
-            config["context"].setdefault("thread_id", thread_id)
-        config.setdefault("configurable", {})["__pregel_runtime"] = runtime
+        # Construct typed context for the agent run.
+        # LangGraph's astream(context=...) injects this into Runtime.context
+        # so middleware/tools can access it via resolve_context().
+        deer_flow_context = DeerFlowContext(
+            app_config=AppConfig.current(),
+            thread_id=thread_id,
+        )
 
         runnable_config = RunnableConfig(**config)
         agent = agent_factory(config=runnable_config)
@@ -155,7 +154,7 @@ async def run_agent(
         if len(lg_modes) == 1 and not stream_subgraphs:
             # Single mode, no subgraphs: astream yields raw chunks
             single_mode = lg_modes[0]
-            async for chunk in agent.astream(graph_input, config=runnable_config, stream_mode=single_mode):
+            async for chunk in agent.astream(graph_input, config=runnable_config, context=deer_flow_context, stream_mode=single_mode):
                 if record.abort_event.is_set():
                     logger.info("Run %s abort requested — stopping", run_id)
                     break
@@ -166,6 +165,7 @@ async def run_agent(
             async for item in agent.astream(
                 graph_input,
                 config=runnable_config,
+                context=deer_flow_context,
                 stream_mode=lg_modes,
                 subgraphs=stream_subgraphs,
             ):