Merge refactor/config-deerflow-context into release/2.0-rc

Cherry-pick PR #2271's config refactor onto release/2.0-rc.
Used 'git merge -X theirs' to auto-resolve content conflicts in favor of
the PR's design (frozen AppConfig + explicit-parameter passing).

Limitations:
- Release-only changes that overlapped with PR's refactor in 119 files
  are NOT preserved — those files reflect PR's version. Follow-up commits
  on this branch will need to re-apply release-only modifications where
  meaningful.
- See PR #2271 for design rationale.

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

@@ -25,6 +25,8 @@ from typing import TYPE_CHECKING, Any, Literal
 if TYPE_CHECKING:
     from langchain_core.messages import HumanMessage
 
+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
 
@@ -51,6 +53,8 @@ class RunContext:
     event_store: Any | None = field(default=None)
     run_events_config: Any | None = field(default=None)
     thread_store: Any | None = field(default=None)
+    follow_up_to_run_id: str | None = field(default=None)
+    app_config: AppConfig | None = field(default=None)
 
 
 async def run_agent(
@@ -75,6 +79,7 @@ async def run_agent(
     event_store = ctx.event_store
     run_events_config = ctx.run_events_config
     thread_store = ctx.thread_store
+    follow_up_to_run_id = ctx.follow_up_to_run_id
 
     run_id = record.run_id
     thread_id = record.thread_id
@@ -111,6 +116,22 @@ async def run_agent(
                 track_token_usage=getattr(run_events_config, "track_token_usage", True),
             )
 
+            human_msg = _extract_human_message(graph_input)
+            if human_msg is not None:
+                msg_metadata = {}
+                if follow_up_to_run_id:
+                    msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id
+                await event_store.put(
+                    thread_id=thread_id,
+                    run_id=run_id,
+                    event_type="human_message",
+                    category="message",
+                    content=human_msg.model_dump(),
+                    metadata=msg_metadata or None,
+                )
+                content = human_msg.content
+                journal.set_first_human_message(content if isinstance(content, str) else str(content))
+
         # 1. Mark running
         await run_manager.set_status(run_id, RunStatus.running)
 
@@ -144,18 +165,21 @@ 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, "run_id": run_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["context"].setdefault("run_id", run_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().
+        if ctx.app_config is None:
+            raise RuntimeError("RunContext.app_config is required — Gateway must populate it via get_run_context")
+        deer_flow_context = DeerFlowContext(
+            app_config=ctx.app_config,
+            thread_id=thread_id,
+        )
+
+        # Inject RunJournal as a LangChain callback handler.
+        # on_llm_end captures token usage; on_chain_start/end captures lifecycle.
+        if journal is not None:
+            config.setdefault("callbacks", []).append(journal)
 
         # Inject RunJournal as a LangChain callback handler.
         # on_llm_end captures token usage; on_chain_start/end captures lifecycle.
@@ -207,7 +231,7 @@ 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
@@ -218,6 +242,7 @@ 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,
             ):