core, openai[patch]: prefer provider-assigned IDs when aggregating message chunks (#31080)

When aggregating AIMessageChunks in a stream, core prefers the leftmost
non-null ID. This is problematic because:
- Core assigns IDs when they are null to `f"run-{run_manager.run_id}"`
- The desired meaningful ID might not be available until midway through
the stream, as is the case for the OpenAI Responses API.

For the OpenAI Responses API, we assign message IDs to the top-level
`AIMessage.id`. This works in `.(a)invoke`, but during `.(a)stream` the
IDs get overwritten by the defaults assigned in langchain-core. These
IDs
[must](https://community.openai.com/t/how-to-solve-badrequesterror-400-item-rs-of-type-reasoning-was-provided-without-its-required-following-item-error-in-responses-api/1151686/9)
be available on the AIMessage object to support passing reasoning items
back to the API (e.g., if not using OpenAI's `previous_response_id`
feature). We could add them elsewhere, but seeing as we've already made
the decision to store them in `.id` during `.(a)invoke`, addressing the
issue in core lets us fix the problem with no interface changes.

libs/core/langchain_core/language_models/chat_models.py

@@ -58,6 +58,7 @@ from langchain_core.messages import (
     is_data_content_block,
     message_chunk_to_message,
 )
+from langchain_core.messages.ai import _LC_ID_PREFIX
 from langchain_core.outputs import (
     ChatGeneration,
     ChatGenerationChunk,
@@ -493,7 +494,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
                 input_messages = _normalize_messages(messages)
                 for chunk in self._stream(input_messages, stop=stop, **kwargs):
                     if chunk.message.id is None:
-                        chunk.message.id = f"run-{run_manager.run_id}"
+                        chunk.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}"
                     chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
                     run_manager.on_llm_new_token(
                         cast("str", chunk.message.content), chunk=chunk
@@ -583,7 +584,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
                 **kwargs,
             ):
                 if chunk.message.id is None:
-                    chunk.message.id = f"run-{run_manager.run_id}"
+                    chunk.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}"
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
                 await run_manager.on_llm_new_token(
                     cast("str", chunk.message.content), chunk=chunk
@@ -1001,7 +1002,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
                 if run_manager:
                     if chunk.message.id is None:
-                        chunk.message.id = f"run-{run_manager.run_id}"
+                        chunk.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}"
                     run_manager.on_llm_new_token(
                         cast("str", chunk.message.content), chunk=chunk
                     )
@@ -1017,7 +1018,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         # Add response metadata to each generation
         for idx, generation in enumerate(result.generations):
             if run_manager and generation.message.id is None:
-                generation.message.id = f"run-{run_manager.run_id}-{idx}"
+                generation.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
             generation.message.response_metadata = _gen_info_and_msg_metadata(
                 generation
             )
@@ -1073,7 +1074,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
                 if run_manager:
                     if chunk.message.id is None:
-                        chunk.message.id = f"run-{run_manager.run_id}"
+                        chunk.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}"
                     await run_manager.on_llm_new_token(
                         cast("str", chunk.message.content), chunk=chunk
                     )
@@ -1089,7 +1090,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         # Add response metadata to each generation
         for idx, generation in enumerate(result.generations):
             if run_manager and generation.message.id is None:
-                generation.message.id = f"run-{run_manager.run_id}-{idx}"
+                generation.message.id = f"{_LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
             generation.message.response_metadata = _gen_info_and_msg_metadata(
                 generation
             )

libs/core/langchain_core/messages/ai.py

@@ -36,6 +36,9 @@ from langchain_core.utils.usage import _dict_int_op
 logger = logging.getLogger(__name__)
 
 
+_LC_ID_PREFIX = "run-"
+
+
 class InputTokenDetails(TypedDict, total=False):
     """Breakdown of input token counts.
 
@@ -418,10 +421,19 @@ def add_ai_message_chunks(
         usage_metadata = None
 
     id = None
-    for id_ in [left.id] + [o.id for o in others]:
-        if id_:
+    candidates = [left.id] + [o.id for o in others]
+    # first pass: pick the first non‐run-* id
+    for id_ in candidates:
+        if id_ and not id_.startswith(_LC_ID_PREFIX):
             id = id_
             break
+    else:
+        # second pass: no provider-assigned id found, just take the first non‐null
+        for id_ in candidates:
+            if id_:
+                id = id_
+                break
+
     return left.__class__(
         example=left.example,
         content=content,

libs/core/tests/unit_tests/test_messages.py

@@ -178,6 +178,22 @@ def test_message_chunks() -> None:
     assert AIMessageChunk(content="") + left == left
     assert right + AIMessageChunk(content="") == right
 
+    # Test ID order of precedence
+    null_id = AIMessageChunk(content="", id=None)
+    default_id = AIMessageChunk(
+        content="", id="run-abc123"
+    )  # LangChain-assigned run ID
+    meaningful_id = AIMessageChunk(content="", id="msg_def456")  # provider-assigned ID
+
+    assert (null_id + default_id).id == "run-abc123"
+    assert (default_id + null_id).id == "run-abc123"
+
+    assert (null_id + meaningful_id).id == "msg_def456"
+    assert (meaningful_id + null_id).id == "msg_def456"
+
+    assert (default_id + meaningful_id).id == "msg_def456"
+    assert (meaningful_id + default_id).id == "msg_def456"
+
 
 def test_chat_message_chunks() -> None:
     assert ChatMessageChunk(role="User", content="I am", id="ai4") + ChatMessageChunk(

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -3127,6 +3127,7 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
             reasoning_items = []
             if reasoning := lc_msg.additional_kwargs.get("reasoning"):
                 reasoning_items.append(_pop_summary_index_from_reasoning(reasoning))
+            input_.extend(reasoning_items)
             # Function calls
             function_calls = []
             if tool_calls := msg.pop("tool_calls", None):
@@ -3185,13 +3186,11 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
                         pass
                 msg["content"] = new_blocks
             if msg["content"]:
+                if lc_msg.id and lc_msg.id.startswith("msg_"):
+                    msg["id"] = lc_msg.id
                 input_.append(msg)
             input_.extend(function_calls)
-            if computer_calls:
-                # Hack: we only add reasoning items if computer calls are present. See:
-                # https://community.openai.com/t/how-to-solve-badrequesterror-400-item-rs-of-type-reasoning-was-provided-without-its-required-following-item-error-in-responses-api/1151686/5
-                input_.extend(reasoning_items)
-                input_.extend(computer_calls)
+            input_.extend(computer_calls)
         elif msg["role"] == "user":
             if isinstance(msg["content"], list):
                 new_blocks = []

libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py

@@ -271,7 +271,7 @@ def test_function_calling_and_structured_output() -> None:
         """return x * y"""
         return x * y
 
-    llm = ChatOpenAI(model=MODEL_NAME)
+    llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
     bound_llm = llm.bind_tools([multiply], response_format=Foo, strict=True)
     # Test structured output
     response = llm.invoke("how are ya", response_format=Foo)