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docs/docs/how-tos/multi_agent.md

@@ -201,7 +201,7 @@ def create_task_description_handoff_tool(
             "Description of what the next agent should do, including all of the relevant context.",
         ],
         # these parameters are ignored by the LLM
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
     ) -> Command:
         task_description_message = {"role": "user", "content": task_description}
         agent_input = {**state, "messages": [task_description_message]}
@@ -1251,3 +1251,4 @@ LangGraph comes with prebuilt implementations of two of the most popular multi-a
 - [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-js) library to create a swarm multi-agent systems.
 :::
 
+