fix(langgraph): only project finalized AIMessages into run.messages

MessagesTransformer routed any whole BaseMessage except AIMessageChunk and
ToolMessage into run.messages. Messages a node writes to state, such as the
HumanMessage summary and RemoveMessage from SummarizationMiddleware, showed
up in stream_events(version="v3") run.messages as if the model produced them.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

libs/langgraph/langgraph/stream/transformers.py

@@ -8,7 +8,7 @@ from langchain_core.language_models.chat_model_stream import (
     AsyncChatModelStream,
     ChatModelStream,
 )
-from langchain_core.messages import AIMessageChunk, BaseMessage, ToolMessage
+from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
 from langchain_protocol.protocol import LifecycleCause, MessagesData
 from typing_extensions import NotRequired, TypedDict
 
@@ -277,11 +277,7 @@ class MessagesTransformer(StreamTransformer):
             self._route_protocol_event(
                 cast("MessagesData", payload), run_id=run_id, node=node
             )
-        elif (
-            isinstance(payload, BaseMessage)
-            and not isinstance(payload, AIMessageChunk)
-            and not isinstance(payload, ToolMessage)
-        ):
+        elif isinstance(payload, AIMessage) and not isinstance(payload, AIMessageChunk):
             self._route_whole_message(payload, node=node)
         # Legacy AIMessageChunk tuples (from on_llm_new_token) are ignored;
         # v1 streaming callers must switch to stream_events(version="v3") to populate this

libs/langgraph/tests/test_stream_messages_transformer.py

@@ -14,7 +14,13 @@ from langchain_core.language_models.chat_model_stream import (
     AsyncChatModelStream,
     ChatModelStream,
 )
-from langchain_core.messages import AIMessage, AIMessageChunk, ToolMessage
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    HumanMessage,
+    RemoveMessage,
+    ToolMessage,
+)
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
 from langchain_core.runnables import RunnableConfig
 from typing_extensions import TypedDict
@@ -317,6 +323,29 @@ class TestWholeMessageFallback:
         log.close()
         assert _unstamped(log._items) == []
 
+    @pytest.mark.parametrize(
+        "message",
+        [
+            HumanMessage(content="summary", id="human-msg-1"),
+            RemoveMessage(id="removed-msg-1"),
+        ],
+    )
+    def test_whole_non_ai_message_is_ignored(self, message: Any) -> None:
+        t, log = _make_sync_transformer()
+        t.process(
+            {
+                "type": "event",
+                "method": "messages",
+                "params": {
+                    "namespace": [],
+                    "timestamp": TS,
+                    "data": (message, {"langgraph_node": "summarize"}),
+                },
+            }
+        )
+        log.close()
+        assert _unstamped(log._items) == []
+
     def test_whole_message_has_full_lifecycle(self) -> None:
         t, log = _make_sync_transformer()
         t.process(_whole_msg("full"))
@@ -586,6 +615,28 @@ class TestEndToEnd:
         (stream,) = list(run.messages)
         assert stream.output.text == "hardcoded"
 
+    def test_human_message_returned_from_node_is_not_projected(self) -> None:
+        def summarize(state: MessagesState) -> dict[str, Any]:
+            return {"messages": HumanMessage(content="summary", id="summary-1")}
+
+        def answer(state: MessagesState) -> dict[str, Any]:
+            return {"messages": AIMessage(content="answer", id="answer-1")}
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("summarize", summarize)
+            .add_node("answer", answer)
+            .add_edge(START, "summarize")
+            .add_edge("summarize", "answer")
+            .add_edge("answer", END)
+            .compile()
+        )
+
+        run = graph.stream_events({"messages": "hi"}, version="v3")
+        (stream,) = list(run.messages)
+        assert stream.node == "answer"
+        assert stream.output.text == "answer"
+
     @pytest.mark.anyio
     async def test_async_node_calling_astream_v2(self) -> None:
         model = GenericFakeChatModel(messages=iter(["async answer"]))