fix: post_model_hook should inject state + store into tool calls if necessary (#4842)

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -727,6 +727,10 @@ def create_react_agent(
             ]
 
             if pending_tool_calls:
+                pending_tool_calls = [
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
+                    for call in pending_tool_calls
+                ]
                 return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
             elif isinstance(messages[-1], ToolMessage):
                 return entrypoint

libs/prebuilt/tests/test_react_agent.py

@@ -1540,3 +1540,63 @@ def test_post_model_hook_with_structured_output() -> None:
             }
         },
     ]
+
+
+@pytest.mark.parametrize(
+    "state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
+)
+def test_create_react_agent_inject_vars_with_post_model_hook(
+    state_schema: StateSchemaType,
+) -> None:
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"bar": 3})
+
+    if issubclass(state_schema, AgentStatePydantic):
+
+        def tool1(
+            some_val: int,
+            state: Annotated[AgentStateExtraKeyPydantic, InjectedState],
+            store: Annotated[BaseStore, InjectedStore()],
+        ) -> str:
+            """Tool 1 docstring."""
+            store_val = store.get(namespace, "test_key").value["bar"]
+            return some_val + state.foo + store_val
+    else:
+
+        def tool1(
+            some_val: int,
+            state: Annotated[dict, InjectedState],
+            store: Annotated[BaseStore, InjectedStore()],
+        ) -> str:
+            """Tool 1 docstring."""
+            store_val = store.get(namespace, "test_key").value["bar"]
+            return some_val + state["foo"] + store_val
+
+    tool_call = {
+        "name": "tool1",
+        "args": {"some_val": 1},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+
+    def post_model_hook(state: dict) -> None:
+        return
+
+    model = FakeToolCallingModel(tool_calls=[[tool_call], []])
+    agent = create_react_agent(
+        model,
+        [tool1],
+        state_schema=state_schema,
+        store=store,
+        post_model_hook=post_model_hook,
+    )
+    input_message = HumanMessage("hi")
+    result = agent.invoke({"messages": [input_message], "foo": 2})
+    assert result["messages"] == [
+        input_message,
+        AIMessage(content="hi", tool_calls=[tool_call], id="0"),
+        _AnyIdToolMessage(content="6", name="tool1", tool_call_id="some 0"),
+        AIMessage("hi-hi-6", id="1"),
+    ]
+    assert result["foo"] == 2