langgraph: expose tags in the metadata for streamed message chunks (#3238)

libs/langgraph/langgraph/pregel/messages.py

@@ -76,11 +76,15 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         chunk: Optional[ChatGenerationChunk] = None,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> Any:
         if not isinstance(chunk, ChatGenerationChunk):
             return
         if meta := self.metadata.get(run_id):
+            filtered_tags = [t for t in (tags or []) if not t.startswith("seq:step")]
+            if filtered_tags:
+                meta[1]["tags"] = filtered_tags
             self._emit(meta, chunk.message)
 
     def on_llm_end(

libs/langgraph/tests/test_pregel.py

@@ -29,6 +29,7 @@ from typing import (
 
 import httpx
 import pytest
+from langchain_core.language_models import GenericFakeChatModel
 from langchain_core.runnables import (
     RunnableConfig,
     RunnableLambda,
@@ -80,6 +81,7 @@ from tests.conftest import (
 from tests.memory_assert import MemorySaverAssertCheckpointMetadata
 from tests.messages import (
     _AnyIdAIMessage,
+    _AnyIdAIMessageChunk,
     _AnyIdHumanMessage,
     _AnyIdToolMessage,
 )
@@ -6288,3 +6290,38 @@ def test_named_tasks_functional() -> None:
         {"qux": "foo|bar|baz|custom_baz|qux"},
         {"workflow": "foo|bar|baz|custom_baz|qux"},
     ]
+
+
+def test_tags_stream_mode_messages() -> None:
+    model = GenericFakeChatModel(messages=iter(["foo"]), tags=["meow"])
+    graph = (
+        StateGraph(MessagesState)
+        .add_node(
+            "call_model", lambda state: {"messages": model.invoke(state["messages"])}
+        )
+        .add_edge(START, "call_model")
+        .compile()
+    )
+    assert list(
+        graph.stream(
+            {
+                "messages": "hi",
+            },
+            stream_mode="messages",
+        )
+    ) == [
+        (
+            _AnyIdAIMessageChunk(content="foo"),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "call_model",
+                "langgraph_triggers": ["start:call_model"],
+                "langgraph_path": ("__pregel_pull", "call_model"),
+                "langgraph_checkpoint_ns": AnyStr("call_model:"),
+                "checkpoint_ns": AnyStr("call_model:"),
+                "ls_provider": "genericfakechatmodel",
+                "ls_model_type": "chat",
+                "tags": ["meow"],
+            },
+        )
+    ]

libs/langgraph/tests/test_pregel_async.py

@@ -26,6 +26,7 @@ from uuid import UUID
 
 import httpx
 import pytest
+from langchain_core.language_models import GenericFakeChatModel
 from langchain_core.runnables import (
     RunnableConfig,
     RunnableLambda,
@@ -82,6 +83,7 @@ from tests.memory_assert import (
 )
 from tests.messages import (
     _AnyIdAIMessage,
+    _AnyIdAIMessageChunk,
     _AnyIdHumanMessage,
     _AnyIdToolMessage,
 )
@@ -7466,3 +7468,41 @@ async def test_overriding_injectable_args_with_async_task() -> None:
         return "OK"
 
     assert await main.ainvoke({}) == "OK"
+
+
+async def test_tags_stream_mode_messages() -> None:
+    model = GenericFakeChatModel(messages=iter(["foo"]), tags=["meow"])
+
+    async def call_model(state, config):
+        return {"messages": await model.ainvoke(state["messages"], config)}
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node(call_model)
+        .add_edge(START, "call_model")
+        .compile()
+    )
+    assert [
+        c
+        async for c in graph.astream(
+            {
+                "messages": "hi",
+            },
+            stream_mode="messages",
+        )
+    ] == [
+        (
+            _AnyIdAIMessageChunk(content="foo"),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "call_model",
+                "langgraph_triggers": ["start:call_model"],
+                "langgraph_path": ("__pregel_pull", "call_model"),
+                "langgraph_checkpoint_ns": AnyStr("call_model:"),
+                "checkpoint_ns": AnyStr("call_model:"),
+                "ls_provider": "genericfakechatmodel",
+                "ls_model_type": "chat",
+                "tags": ["meow"],
+            },
+        )
+    ]