Merge branch 'cc/1.0/standard_content' into mdrxy/invocation-version

Extensible registry system for translating AI message content blocks
from various model providers. Refactors the way provider-specific
content is handled, moving from hardcoded logic to a plugin-like
architecture.

libs/core/langchain_core/language_models/chat_models.py

@@ -36,16 +36,17 @@ from langchain_core.language_models.base import (
 from langchain_core.load import dumpd, dumps
 from langchain_core.messages import (
     AIMessage,
+    AIMessageChunk,
     AnyMessage,
     BaseMessage,
     BaseMessageChunk,
     HumanMessage,
     convert_to_messages,
+    convert_to_openai_data_block,
     convert_to_openai_image_block,
     is_data_content_block,
     message_chunk_to_message,
 )
-from langchain_core.messages.ai import _LC_ID_PREFIX
 from langchain_core.outputs import (
     ChatGeneration,
     ChatGenerationChunk,
@@ -65,6 +66,7 @@ from langchain_core.utils.function_calling import (
     convert_to_openai_tool,
 )
 from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
+from langchain_core.utils.utils import LC_ID_PREFIX
 
 if TYPE_CHECKING:
     import uuid
@@ -130,6 +132,19 @@ def _format_for_tracing(messages: list[BaseMessage]) -> list[BaseMessage]:
                         message_to_trace.content[idx] = (  # type: ignore[index]  # mypy confused by .model_copy
                             convert_to_openai_image_block(block)
                         )
+                    elif (
+                        block.get("type") == "file"
+                        and is_data_content_block(block)
+                        and "base64" in block
+                    ):
+                        if message_to_trace is message:
+                            # Shallow copy
+                            message_to_trace = message.model_copy()
+                            message_to_trace.content = list(message_to_trace.content)
+
+                        message_to_trace.content[idx] = convert_to_openai_data_block(  # type: ignore[index]
+                            block
+                        )
                     elif len(block) == 1 and "type" not in block:
                         # Tracing assumes all content blocks have a "type" key. Here
                         # we add this key if it is missing, and there's an obvious
@@ -320,6 +335,21 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
 
     """
 
+    output_version: str = "v0"
+    """Version of ``AIMessage`` output format to use.
+
+    This field is used to roll-out new output formats for chat model ``AIMessage``s
+    in a backwards-compatible way.
+
+    ``'v1'`` standardizes output format using a list of typed ContentBlock dicts. We
+    recommend this for new applications.
+
+    All chat models currently support the default of ``'v0'``.
+
+    .. versionadded:: 1.0
+
+    """
+
     @model_validator(mode="before")
     @classmethod
     def raise_deprecation(cls, values: dict) -> Any:
@@ -380,9 +410,29 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         config: Optional[RunnableConfig] = None,
         *,
         stop: Optional[list[str]] = None,
+        output_version: Optional[str] = None,
         **kwargs: Any,
     ) -> BaseMessage:
+        """Invoke the chat model.
+
+        Args:
+            input: The input to the chat model.
+            config: The config to use for this run.
+            stop: Stop words to use when generating.
+            output_version: Override the model's ``output_version`` for this invocation.
+                If None, uses the model's configured ``output_version``.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            The model's response message.
+        """
         config = ensure_config(config)
+
+        effective_output_version = (
+            output_version if output_version is not None else self.output_version
+        )
+        kwargs["_output_version"] = effective_output_version
+
         return cast(
             "ChatGeneration",
             self.generate_prompt(
@@ -404,9 +454,29 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         config: Optional[RunnableConfig] = None,
         *,
         stop: Optional[list[str]] = None,
+        output_version: Optional[str] = None,
         **kwargs: Any,
     ) -> BaseMessage:
+        """Asynchronously invoke the chat model.
+
+        Args:
+            input: The input to the chat model.
+            config: The config to use for this run.
+            stop: Stop words to use when generating.
+            output_version: Override the model's ``output_version`` for this invocation.
+                If None, uses the model's configured ``output_version``.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            The model's response message.
+        """
         config = ensure_config(config)
+
+        effective_output_version = (
+            output_version if output_version is not None else self.output_version
+        )
+        kwargs["_output_version"] = effective_output_version
+
         llm_result = await self.agenerate_prompt(
             [self._convert_input(input)],
             stop=stop,
@@ -461,13 +531,38 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         config: Optional[RunnableConfig] = None,
         *,
         stop: Optional[list[str]] = None,
+        output_version: Optional[str] = None,
         **kwargs: Any,
     ) -> Iterator[BaseMessageChunk]:
+        """Stream responses from the chat model.
+
+        Args:
+            input: The input to the chat model.
+            config: The config to use for this run.
+            stop: Stop words to use when generating.
+            output_version: Override the model's ``output_version`` for this invocation.
+                If None, uses the model's configured ``output_version``.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Iterator of message chunks.
+        """
+        effective_output_version = (
+            output_version if output_version is not None else self.output_version
+        )
+        kwargs["_output_version"] = effective_output_version
+
         if not self._should_stream(async_api=False, **{**kwargs, "stream": True}):
             # model doesn't implement streaming, so use default implementation
             yield cast(
                 "BaseMessageChunk",
-                self.invoke(input, config=config, stop=stop, **kwargs),
+                self.invoke(
+                    input,
+                    config=config,
+                    stop=stop,
+                    output_version=effective_output_version,
+                    **kwargs,
+                ),
             )
         else:
             config = ensure_config(config)
@@ -511,11 +606,19 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
 
             try:
                 input_messages = _normalize_messages(messages)
-                run_id = "-".join((_LC_ID_PREFIX, str(run_manager.run_id)))
-                for chunk in self._stream(input_messages, stop=stop, **kwargs):
+                run_id = "-".join((LC_ID_PREFIX, str(run_manager.run_id)))
+                for chunk in self._stream(
+                    input_messages,
+                    stop=stop,
+                    output_version=kwargs["_output_version"],
+                    **kwargs,
+                ):
                     if chunk.message.id is None:
                         chunk.message.id = run_id
                     chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                    output_version = kwargs["_output_version"]
+                if isinstance(chunk.message, (AIMessage, AIMessageChunk)):
+                    chunk.message.additional_kwargs["output_version"] = output_version
                     run_manager.on_llm_new_token(
                         cast("str", chunk.message.content), chunk=chunk
                     )
@@ -552,13 +655,38 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         config: Optional[RunnableConfig] = None,
         *,
         stop: Optional[list[str]] = None,
+        output_version: Optional[str] = None,
         **kwargs: Any,
     ) -> AsyncIterator[BaseMessageChunk]:
+        """Asynchronously stream responses from the chat model.
+
+        Args:
+            input: The input to the chat model.
+            config: The config to use for this run.
+            stop: Stop words to use when generating.
+            output_version: Override the model's ``output_version`` for this invocation.
+                If None, uses the model's configured ``output_version``.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Async iterator of message chunks.
+        """
+        effective_output_version = (
+            output_version if output_version is not None else self.output_version
+        )
+        kwargs["_output_version"] = effective_output_version
+
         if not self._should_stream(async_api=True, **{**kwargs, "stream": True}):
             # No async or sync stream is implemented, so fall back to ainvoke
             yield cast(
                 "BaseMessageChunk",
-                await self.ainvoke(input, config=config, stop=stop, **kwargs),
+                await self.ainvoke(
+                    input,
+                    config=config,
+                    stop=stop,
+                    output_version=effective_output_version,
+                    **kwargs,
+                ),
             )
             return
 
@@ -604,15 +732,19 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
 
         try:
             input_messages = _normalize_messages(messages)
-            run_id = "-".join((_LC_ID_PREFIX, str(run_manager.run_id)))
+            run_id = "-".join((LC_ID_PREFIX, str(run_manager.run_id)))
             async for chunk in self._astream(
                 input_messages,
                 stop=stop,
+                output_version=kwargs["_output_version"],
                 **kwargs,
             ):
                 if chunk.message.id is None:
                     chunk.message.id = run_id
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                output_version = kwargs["_output_version"]
+                if isinstance(chunk.message, (AIMessage, AIMessageChunk)):
+                    chunk.message.additional_kwargs["output_version"] = output_version
                 await run_manager.on_llm_new_token(
                     cast("str", chunk.message.content), chunk=chunk
                 )
@@ -622,7 +754,10 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             generations_with_error_metadata = _generate_response_from_error(e)
             chat_generation_chunk = merge_chat_generation_chunks(chunks)
             if chat_generation_chunk:
-                generations = [[chat_generation_chunk], generations_with_error_metadata]
+                generations = [
+                    [chat_generation_chunk],
+                    generations_with_error_metadata,
+                ]
             else:
                 generations = [generations_with_error_metadata]
             await run_manager.on_llm_error(
@@ -1058,6 +1193,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         if self.rate_limiter:
             self.rate_limiter.acquire(blocking=True)
 
+        output_version = kwargs.pop("_output_version", self.output_version)
+
         # If stream is not explicitly set, check if implicitly requested by
         # astream_events() or astream_log(). Bail out if _stream not implemented
         if self._should_stream(
@@ -1066,11 +1203,15 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             **kwargs,
         ):
             chunks: list[ChatGenerationChunk] = []
-            for chunk in self._stream(messages, stop=stop, **kwargs):
+            for chunk in self._stream(
+                messages, stop=stop, output_version=output_version, **kwargs
+            ):
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                if isinstance(chunk.message, (AIMessage, AIMessageChunk)):
+                    chunk.message.additional_kwargs["output_version"] = output_version
                 if run_manager:
                     if chunk.message.id is None:
-                        chunk.message.id = f"{_LC_ID_PREFIX}-{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
                     )
@@ -1078,18 +1219,26 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             result = generate_from_stream(iter(chunks))
         elif inspect.signature(self._generate).parameters.get("run_manager"):
             result = self._generate(
-                messages, stop=stop, run_manager=run_manager, **kwargs
+                messages,
+                stop=stop,
+                run_manager=run_manager,
+                output_version=output_version,
+                **kwargs,
             )
         else:
-            result = self._generate(messages, stop=stop, **kwargs)
+            result = self._generate(
+                messages, stop=stop, output_version=output_version, **kwargs
+            )
 
         # 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"{_LC_ID_PREFIX}-{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
             )
+            if isinstance(generation.message, (AIMessage, AIMessageChunk)):
+                generation.message.additional_kwargs["output_version"] = output_version
         if len(result.generations) == 1 and result.llm_output is not None:
             result.generations[0].message.response_metadata = {
                 **result.llm_output,
@@ -1131,6 +1280,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         if self.rate_limiter:
             await self.rate_limiter.aacquire(blocking=True)
 
+        output_version = kwargs.pop("_output_version", self.output_version)
+
         # If stream is not explicitly set, check if implicitly requested by
         # astream_events() or astream_log(). Bail out if _astream not implemented
         if self._should_stream(
@@ -1139,11 +1290,15 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             **kwargs,
         ):
             chunks: list[ChatGenerationChunk] = []
-            async for chunk in self._astream(messages, stop=stop, **kwargs):
+            async for chunk in self._astream(
+                messages, stop=stop, output_version=output_version, **kwargs
+            ):
                 chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                if isinstance(chunk.message, (AIMessage, AIMessageChunk)):
+                    chunk.message.additional_kwargs["output_version"] = output_version
                 if run_manager:
                     if chunk.message.id is None:
-                        chunk.message.id = f"{_LC_ID_PREFIX}-{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
                     )
@@ -1151,18 +1306,26 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             result = generate_from_stream(iter(chunks))
         elif inspect.signature(self._agenerate).parameters.get("run_manager"):
             result = await self._agenerate(
-                messages, stop=stop, run_manager=run_manager, **kwargs
+                messages,
+                stop=stop,
+                run_manager=run_manager,
+                output_version=output_version,
+                **kwargs,
             )
         else:
-            result = await self._agenerate(messages, stop=stop, **kwargs)
+            result = await self._agenerate(
+                messages, stop=stop, output_version=output_version, **kwargs
+            )
 
         # 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"{_LC_ID_PREFIX}-{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
             )
+            if isinstance(generation.message, (AIMessage, AIMessageChunk)):
+                generation.message.additional_kwargs["output_version"] = output_version
         if len(result.generations) == 1 and result.llm_output is not None:
             result.generations[0].message.response_metadata = {
                 **result.llm_output,
@@ -1178,15 +1341,20 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
         **kwargs: Any,
     ) -> ChatResult:
         """Top Level call."""
+        # Concrete implementations should override this method and use the same params
 
     async def _agenerate(
         self,
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
         **kwargs: Any,
     ) -> ChatResult:
         """Top Level call."""
@@ -1196,6 +1364,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             messages,
             stop,
             run_manager.get_sync() if run_manager else None,
+            output_version=output_version,
             **kwargs,
         )
 
@@ -1204,6 +1373,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
         raise NotImplementedError
@@ -1213,6 +1384,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
         iterator = await run_in_executor(
@@ -1221,6 +1394,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             messages,
             stop,
             run_manager.get_sync() if run_manager else None,
+            output_version=output_version,
             **kwargs,
         )
         done = object()
@@ -1567,6 +1741,9 @@ class SimpleChatModel(BaseChatModel):
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
+        *,
+        # For backward compatibility
+        output_version: str = "v0",  # noqa: ARG002
         **kwargs: Any,
     ) -> ChatResult:
         output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
@@ -1589,6 +1766,8 @@ class SimpleChatModel(BaseChatModel):
         messages: list[BaseMessage],
         stop: Optional[list[str]] = None,
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
         **kwargs: Any,
     ) -> ChatResult:
         return await run_in_executor(
@@ -1597,6 +1776,7 @@ class SimpleChatModel(BaseChatModel):
             messages,
             stop=stop,
             run_manager=run_manager.get_sync() if run_manager else None,
+            output_version=output_version,
             **kwargs,
         )
 

libs/core/langchain_core/messages/__init__.py

@@ -18,6 +18,7 @@
 from typing import TYPE_CHECKING
 
 from langchain_core._import_utils import import_attr
+from langchain_core.utils.utils import LC_AUTO_PREFIX, LC_ID_PREFIX, ensure_id
 
 if TYPE_CHECKING:
     from langchain_core.messages.ai import (
@@ -32,10 +33,32 @@ if TYPE_CHECKING:
         messages_to_dict,
     )
     from langchain_core.messages.chat import ChatMessage, ChatMessageChunk
-    from langchain_core.messages.content_blocks import (
+    from langchain_core.messages.content import (
+        Annotation,
+        AudioContentBlock,
+        Citation,
+        CodeInterpreterCall,
+        CodeInterpreterOutput,
+        CodeInterpreterResult,
+        ContentBlock,
+        DataContentBlock,
+        FileContentBlock,
+        ImageContentBlock,
+        NonStandardAnnotation,
+        NonStandardContentBlock,
+        PlainTextContentBlock,
+        ReasoningContentBlock,
+        TextContentBlock,
+        VideoContentBlock,
+        WebSearchCall,
+        WebSearchResult,
         convert_to_openai_data_block,
         convert_to_openai_image_block,
         is_data_content_block,
+        is_reasoning_block,
+        is_text_block,
+        is_tool_call_block,
+        is_tool_call_chunk,
     )
     from langchain_core.messages.function import FunctionMessage, FunctionMessageChunk
     from langchain_core.messages.human import HumanMessage, HumanMessageChunk
@@ -63,34 +86,59 @@ if TYPE_CHECKING:
     )
 
 __all__ = (
+    "LC_AUTO_PREFIX",
+    "LC_ID_PREFIX",
     "AIMessage",
     "AIMessageChunk",
+    "Annotation",
     "AnyMessage",
+    "AudioContentBlock",
     "BaseMessage",
     "BaseMessageChunk",
     "ChatMessage",
     "ChatMessageChunk",
+    "Citation",
+    "CodeInterpreterCall",
+    "CodeInterpreterOutput",
+    "CodeInterpreterResult",
+    "ContentBlock",
+    "DataContentBlock",
+    "FileContentBlock",
     "FunctionMessage",
     "FunctionMessageChunk",
     "HumanMessage",
     "HumanMessageChunk",
+    "ImageContentBlock",
     "InvalidToolCall",
     "MessageLikeRepresentation",
+    "NonStandardAnnotation",
+    "NonStandardContentBlock",
+    "PlainTextContentBlock",
+    "ReasoningContentBlock",
     "RemoveMessage",
     "SystemMessage",
     "SystemMessageChunk",
+    "TextContentBlock",
     "ToolCall",
     "ToolCallChunk",
     "ToolMessage",
     "ToolMessageChunk",
+    "VideoContentBlock",
+    "WebSearchCall",
+    "WebSearchResult",
     "_message_from_dict",
     "convert_to_messages",
     "convert_to_openai_data_block",
     "convert_to_openai_image_block",
     "convert_to_openai_messages",
+    "ensure_id",
     "filter_messages",
     "get_buffer_string",
     "is_data_content_block",
+    "is_reasoning_block",
+    "is_text_block",
+    "is_tool_call_block",
+    "is_tool_call_chunk",
     "merge_content",
     "merge_message_runs",
     "message_chunk_to_message",
@@ -103,35 +151,57 @@ __all__ = (
 _dynamic_imports = {
     "AIMessage": "ai",
     "AIMessageChunk": "ai",
+    "Annotation": "content",
+    "AudioContentBlock": "content",
     "BaseMessage": "base",
     "BaseMessageChunk": "base",
     "merge_content": "base",
     "message_to_dict": "base",
     "messages_to_dict": "base",
+    "Citation": "content",
+    "ContentBlock": "content",
     "ChatMessage": "chat",
     "ChatMessageChunk": "chat",
+    "CodeInterpreterCall": "content",
+    "CodeInterpreterOutput": "content",
+    "CodeInterpreterResult": "content",
+    "DataContentBlock": "content",
+    "FileContentBlock": "content",
     "FunctionMessage": "function",
     "FunctionMessageChunk": "function",
     "HumanMessage": "human",
     "HumanMessageChunk": "human",
+    "NonStandardAnnotation": "content",
+    "NonStandardContentBlock": "content",
+    "PlainTextContentBlock": "content",
+    "ReasoningContentBlock": "content",
     "RemoveMessage": "modifier",
     "SystemMessage": "system",
     "SystemMessageChunk": "system",
+    "WebSearchCall": "content",
+    "WebSearchResult": "content",
+    "ImageContentBlock": "content",
     "InvalidToolCall": "tool",
+    "TextContentBlock": "content",
     "ToolCall": "tool",
     "ToolCallChunk": "tool",
     "ToolMessage": "tool",
     "ToolMessageChunk": "tool",
+    "VideoContentBlock": "content",
     "AnyMessage": "utils",
     "MessageLikeRepresentation": "utils",
     "_message_from_dict": "utils",
     "convert_to_messages": "utils",
-    "convert_to_openai_data_block": "content_blocks",
-    "convert_to_openai_image_block": "content_blocks",
+    "convert_to_openai_data_block": "content",
+    "convert_to_openai_image_block": "content",
     "convert_to_openai_messages": "utils",
     "filter_messages": "utils",
     "get_buffer_string": "utils",
-    "is_data_content_block": "content_blocks",
+    "is_data_content_block": "content",
+    "is_reasoning_block": "content",
+    "is_text_block": "content",
+    "is_tool_call_block": "content",
+    "is_tool_call_chunk": "content",
     "merge_message_runs": "utils",
     "message_chunk_to_message": "utils",
     "messages_from_dict": "utils",

libs/core/langchain_core/messages/ai.py

@@ -3,11 +3,12 @@
 import json
 import logging
 import operator
-from typing import Any, Literal, Optional, Union, cast
+from typing import Any, Literal, Optional, Union, cast, overload
 
 from pydantic import model_validator
 from typing_extensions import NotRequired, Self, TypedDict, override
 
+from langchain_core.messages import content as types
 from langchain_core.messages.base import (
     BaseMessage,
     BaseMessageChunk,
@@ -20,25 +21,17 @@ from langchain_core.messages.tool import (
     default_tool_chunk_parser,
     default_tool_parser,
 )
-from langchain_core.messages.tool import (
-    invalid_tool_call as create_invalid_tool_call,
-)
-from langchain_core.messages.tool import (
-    tool_call as create_tool_call,
-)
-from langchain_core.messages.tool import (
-    tool_call_chunk as create_tool_call_chunk,
-)
+from langchain_core.messages.tool import invalid_tool_call as create_invalid_tool_call
+from langchain_core.messages.tool import tool_call as create_tool_call
+from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
 from langchain_core.utils._merge import merge_dicts, merge_lists
 from langchain_core.utils.json import parse_partial_json
 from langchain_core.utils.usage import _dict_int_op
+from langchain_core.utils.utils import LC_AUTO_PREFIX, LC_ID_PREFIX
 
 logger = logging.getLogger(__name__)
 
 
-_LC_ID_PREFIX = "run-"
-
-
 class InputTokenDetails(TypedDict, total=False):
     """Breakdown of input token counts.
 
@@ -180,16 +173,42 @@ class AIMessage(BaseMessage):
     type: Literal["ai"] = "ai"
     """The type of the message (used for deserialization). Defaults to "ai"."""
 
+    @overload
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
-    ) -> None:
-        """Pass in content as positional arg.
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
+    ) -> None: ...
 
-        Args:
-            content: The content of the message.
-            kwargs: Additional arguments to pass to the parent class.
-        """
-        super().__init__(content=content, **kwargs)
+    @overload
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        if content_blocks is not None:
+            # If there are tool calls in content_blocks, but not in tool_calls, add them
+            content_tool_calls = [
+                block for block in content_blocks if block.get("type") == "tool_call"
+            ]
+            if content_tool_calls and "tool_calls" not in kwargs:
+                kwargs["tool_calls"] = content_tool_calls
+
+            super().__init__(
+                content=cast("Union[str, list[Union[str, dict]]]", content_blocks),
+                **kwargs,
+            )
+        else:
+            super().__init__(content=content, **kwargs)
 
     @property
     def lc_attributes(self) -> dict:
@@ -199,6 +218,46 @@ class AIMessage(BaseMessage):
             "invalid_tool_calls": self.invalid_tool_calls,
         }
 
+    @property
+    def content_blocks(self) -> list[types.ContentBlock]:
+        """Return content blocks of the message."""
+        if self.response_metadata.get("output_version") == "v1":
+            return cast("list[types.ContentBlock]", self.content)
+
+        model_provider = self.response_metadata.get("model_provider")
+        if model_provider:
+            from langchain_core.messages.block_translators import get_translator
+
+            translator = get_translator(model_provider)
+            if translator:
+                return translator["translate_content"](self)
+
+        # Otherwise, use best-effort parsing
+        blocks = super().content_blocks
+
+        if self.tool_calls:
+            # Add from tool_calls if missing from content
+            content_tool_call_ids = {
+                block.get("id")
+                for block in self.content
+                if isinstance(block, dict) and block.get("type") == "tool_call"
+            }
+            for tool_call in self.tool_calls:
+                if (id_ := tool_call.get("id")) and id_ not in content_tool_call_ids:
+                    tool_call_block: types.ToolCall = {
+                        "type": "tool_call",
+                        "id": id_,
+                        "name": tool_call["name"],
+                        "args": tool_call["args"],
+                    }
+                    if "index" in tool_call:
+                        tool_call_block["index"] = tool_call["index"]
+                    if "extras" in tool_call:
+                        tool_call_block["extras"] = tool_call["extras"]
+                    blocks.append(tool_call_block)
+
+        return blocks
+
     # TODO: remove this logic if possible, reducing breaking nature of changes
     @model_validator(mode="before")
     @classmethod
@@ -227,7 +286,9 @@ class AIMessage(BaseMessage):
         # Ensure "type" is properly set on all tool call-like dicts.
         if tool_calls := values.get("tool_calls"):
             values["tool_calls"] = [
-                create_tool_call(**{k: v for k, v in tc.items() if k != "type"})
+                create_tool_call(
+                    **{k: v for k, v in tc.items() if k not in ("type", "extras")}
+                )
                 for tc in tool_calls
             ]
         if invalid_tool_calls := values.get("invalid_tool_calls"):
@@ -306,6 +367,42 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
             "invalid_tool_calls": self.invalid_tool_calls,
         }
 
+    @property
+    def content_blocks(self) -> list[types.ContentBlock]:
+        """Return content blocks of the message."""
+        if self.response_metadata.get("output_version") == "v1":
+            return cast("list[types.ContentBlock]", self.content)
+
+        model_provider = self.response_metadata.get("model_provider")
+        if model_provider:
+            from langchain_core.messages.block_translators import get_translator
+
+            translator = get_translator(model_provider)
+            if translator:
+                return translator["translate_content_chunk"](self)
+
+        # Otherwise, use best-effort parsing
+        blocks = super().content_blocks
+
+        if self.tool_call_chunks and not self.content:
+            blocks = [
+                block
+                for block in blocks
+                if block["type"] not in ("tool_call", "invalid_tool_call")
+            ]
+            for tool_call_chunk in self.tool_call_chunks:
+                tc: types.ToolCallChunk = {
+                    "type": "tool_call_chunk",
+                    "id": tool_call_chunk.get("id"),
+                    "name": tool_call_chunk.get("name"),
+                    "args": tool_call_chunk.get("args"),
+                }
+                if (idx := tool_call_chunk.get("index")) is not None:
+                    tc["index"] = idx
+                blocks.append(tc)
+
+        return blocks
+
     @model_validator(mode="after")
     def init_tool_calls(self) -> Self:
         """Initialize tool calls from tool call chunks.
@@ -431,17 +528,27 @@ def add_ai_message_chunks(
 
     chunk_id = None
     candidates = [left.id] + [o.id for o in others]
-    # first pass: pick the first non-run-* id
+    # first pass: pick the first provider-assigned id (non-run-* and non-lc_*)
     for id_ in candidates:
-        if id_ and not id_.startswith(_LC_ID_PREFIX):
+        if (
+            id_
+            and not id_.startswith(LC_ID_PREFIX)
+            and not id_.startswith(LC_AUTO_PREFIX)
+        ):
             chunk_id = id_
             break
     else:
-        # second pass: no provider-assigned id found, just take the first non-null
+        # second pass: prefer lc_run-* ids over lc_* ids
         for id_ in candidates:
-            if id_:
+            if id_ and id_.startswith(LC_ID_PREFIX):
                 chunk_id = id_
                 break
+        else:
+            # third pass: take any remaining id (auto-generated lc_* ids)
+            for id_ in candidates:
+                if id_:
+                    chunk_id = id_
+                    break
 
     return left.__class__(
         example=left.example,

libs/core/langchain_core/messages/base.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Optional, Union, cast
+from typing import TYPE_CHECKING, Any, Optional, Union, cast, overload
 
 from pydantic import ConfigDict, Field
 
@@ -14,6 +14,7 @@ from langchain_core.utils.interactive_env import is_interactive_env
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
+    from langchain_core.messages import content as types
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 
@@ -61,15 +62,32 @@ class BaseMessage(Serializable):
         extra="allow",
     )
 
+    @overload
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
-    ) -> None:
-        """Pass in content as positional arg.
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
+    ) -> None: ...
 
-        Args:
-            content: The string contents of the message.
-        """
-        super().__init__(content=content, **kwargs)
+    @overload
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        if content_blocks is not None:
+            super().__init__(content=content_blocks, **kwargs)
+        else:
+            super().__init__(content=content, **kwargs)
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
@@ -88,6 +106,47 @@ class BaseMessage(Serializable):
         """
         return ["langchain", "schema", "messages"]
 
+    @property
+    def content_blocks(self) -> list[types.ContentBlock]:
+        """Return the content as a list of standard ``ContentBlock``s.
+
+        To use this property, the corresponding chat model must support
+        ``message_version='v1'`` or higher:
+
+        .. code-block:: python
+
+            from langchain.chat_models import init_chat_model
+            llm = init_chat_model("...", message_version="v1")
+
+        Otherwise, does best-effort parsing to standard types.
+
+        """
+        from langchain_core.messages import content as types
+
+        blocks: list[types.ContentBlock] = []
+        content = (
+            [self.content]
+            if isinstance(self.content, str) and self.content
+            else self.content
+        )
+        for item in content:
+            if isinstance(item, str):
+                blocks.append({"type": "text", "text": item})
+            elif isinstance(item, dict):
+                item_type = item.get("type")
+                if item_type not in types.KNOWN_BLOCK_TYPES:
+                    msg = (
+                        f"Non-standard content block type '{item_type}'. Ensure "
+                        "the model supports `output_version='v1'` or higher and "
+                        "that this attribute is set on initialization."
+                    )
+                    raise ValueError(msg)
+                blocks.append(cast("types.ContentBlock", item))
+            else:
+                pass
+
+        return blocks
+
     def text(self) -> str:
         """Get the text content of the message.
 

libs/core/langchain_core/messages/block_translators/__init__.py

@@ -0,0 +1,81 @@
+"""Derivations of standard content blocks from provider content."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Callable
+
+if TYPE_CHECKING:
+    from langchain_core.messages import AIMessage, AIMessageChunk
+    from langchain_core.messages import content as types
+
+# Provider to translator mapping
+PROVIDER_TRANSLATORS: dict[str, dict[str, Callable[..., list[types.ContentBlock]]]] = {}
+
+
+def register_translator(
+    provider: str,
+    translate_content: Callable[[AIMessage], list[types.ContentBlock]],
+    translate_content_chunk: Callable[[AIMessageChunk], list[types.ContentBlock]],
+) -> None:
+    """Register content translators for a provider.
+
+    Args:
+        provider: The model provider name (e.g. ``'openai'``, ``'anthropic'``).
+        translate_content: Function to translate ``AIMessage`` content.
+        translate_content_chunk: Function to translate ``AIMessageChunk`` content.
+    """
+    PROVIDER_TRANSLATORS[provider] = {
+        "translate_content": translate_content,
+        "translate_content_chunk": translate_content_chunk,
+    }
+
+
+def get_translator(
+    provider: str,
+) -> dict[str, Callable[..., list[types.ContentBlock]]] | None:
+    """Get the translator functions for a provider.
+
+    Args:
+        provider: The model provider name.
+
+    Returns:
+        Dictionary with ``'translate_content'`` and ``'translate_content_chunk'``
+        functions, or None if no translator is registered for the provider.
+    """
+    return PROVIDER_TRANSLATORS.get(provider)
+
+
+def _auto_register_translators() -> None:
+    """Automatically register all available block translators."""
+    import contextlib
+    import importlib
+    import pkgutil
+    from pathlib import Path
+
+    package_path = Path(__file__).parent
+
+    # Discover all sub-modules
+    for module_info in pkgutil.iter_modules([str(package_path)]):
+        module_name = module_info.name
+
+        # Skip the __init__ module and any private modules
+        if module_name.startswith("_"):
+            continue
+
+        if module_info.ispkg:
+            # For subpackages, discover their submodules
+            subpackage_path = package_path / module_name
+            for submodule_info in pkgutil.iter_modules([str(subpackage_path)]):
+                submodule_name = submodule_info.name
+                if not submodule_name.startswith("_"):
+                    with contextlib.suppress(ImportError, AttributeError):
+                        importlib.import_module(
+                            f".{module_name}.{submodule_name}", package=__name__
+                        )
+        else:
+            # Import top-level translator modules
+            with contextlib.suppress(ImportError, AttributeError):
+                importlib.import_module(f".{module_name}", package=__name__)
+
+
+_auto_register_translators()

libs/core/langchain_core/messages/block_translators/amazon/__init__.py

@@ -0,0 +1 @@
+"""Derivations of standard content blocks from Amazon content."""

libs/core/langchain_core/messages/block_translators/amazon/bedrock.py

@@ -0,0 +1,29 @@
+"""Derivations of standard content blocks from Amazon (Bedrock) content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Bedrock content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a chunk with Bedrock content."""
+    raise NotImplementedError
+
+
+def _register_bedrock_translator() -> None:
+    """Register the Bedrock translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator(
+        "amazon_bedrock_chat", translate_content, translate_content_chunk
+    )
+
+
+_register_bedrock_translator()

libs/core/langchain_core/messages/block_translators/amazon/bedrock_converse.py

@@ -0,0 +1,29 @@
+"""Derivations of standard content blocks from Amazon (Bedrock Converse) content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Bedrock Converse content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a chunk with Bedrock Converse content."""
+    raise NotImplementedError
+
+
+def _register_bedrock_converse_translator() -> None:
+    """Register the Bedrock Converse translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator(
+        "amazon_bedrock_converse_chat", translate_content, translate_content_chunk
+    )
+
+
+_register_bedrock_converse_translator()

libs/core/langchain_core/messages/block_translators/anthropic.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Anthropic content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Anthropic content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with Anthropic content."""
+    raise NotImplementedError
+
+
+def _register_anthropic_translator() -> None:
+    """Register the Anthropic translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("anthropic", translate_content, translate_content_chunk)
+
+
+_register_anthropic_translator()

libs/core/langchain_core/messages/block_translators/chroma.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Chroma content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Chroma content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with Chroma content."""
+    raise NotImplementedError
+
+
+def _register_chroma_translator() -> None:
+    """Register the Chroma translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("chroma", translate_content, translate_content_chunk)
+
+
+_register_chroma_translator()

libs/core/langchain_core/messages/block_translators/google/__init__.py

@@ -0,0 +1 @@
+"""Derivations of standard content blocks from Google content."""

libs/core/langchain_core/messages/block_translators/google/genai.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Google (GenAI) content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Google (GenAI) content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a chunk with Google (GenAI) content."""
+    raise NotImplementedError
+
+
+def _register_google_genai_translator() -> None:
+    """Register the Google (GenAI) translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("google_genai", translate_content, translate_content_chunk)
+
+
+_register_google_genai_translator()

libs/core/langchain_core/messages/block_translators/google/vertexai.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Google (VertexAI) content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Google (VertexAI) content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a chunk with Google (VertexAI) content."""
+    raise NotImplementedError
+
+
+def _register_google_vertexai_translator() -> None:
+    """Register the Google (VertexAI) translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("google_vertexai", translate_content, translate_content_chunk)
+
+
+_register_google_vertexai_translator()

libs/core/langchain_core/messages/block_translators/groq.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Groq content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Groq content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with Groq content."""
+    raise NotImplementedError
+
+
+def _register_groq_translator() -> None:
+    """Register the Groq translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("groq", translate_content, translate_content_chunk)
+
+
+_register_groq_translator()

libs/core/langchain_core/messages/block_translators/ollama.py

@@ -0,0 +1,27 @@
+"""Derivations of standard content blocks from Ollama content."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with Ollama content."""
+    raise NotImplementedError
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with Ollama content."""
+    raise NotImplementedError
+
+
+def _register_ollama_translator() -> None:
+    """Register the Ollama translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("ollama", translate_content, translate_content_chunk)
+
+
+_register_ollama_translator()

libs/core/langchain_core/messages/block_translators/openai.py

@@ -0,0 +1,358 @@
+"""Derivations of standard content blocks from OpenAI content."""
+
+from collections.abc import Iterable
+from typing import Any, Optional, Union, cast
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+# v1 / Chat Completions
+def _convert_to_v1_from_chat_completions(
+    message: AIMessage,
+) -> list[types.ContentBlock]:
+    """Mutate a Chat Completions message to v1 format."""
+    content_blocks: list[types.ContentBlock] = []
+    if isinstance(message.content, str):
+        if message.content:
+            content_blocks = [{"type": "text", "text": message.content}]
+        else:
+            content_blocks = []
+
+    for tool_call in message.tool_calls:
+        content_blocks.append(tool_call)
+
+    return content_blocks
+
+
+def _convert_to_v1_from_chat_completions_chunk(
+    chunk: AIMessageChunk,
+) -> list[types.ContentBlock]:
+    """Mutate a Chat Completions chunk to v1 format."""
+    content_blocks: list[types.ContentBlock] = []
+    if isinstance(chunk.content, str):
+        if chunk.content:
+            content_blocks = [{"type": "text", "text": chunk.content}]
+        else:
+            content_blocks = []
+
+    for tool_call_chunk in chunk.tool_call_chunks:
+        tc: types.ToolCallChunk = {
+            "type": "tool_call_chunk",
+            "id": tool_call_chunk.get("id"),
+            "name": tool_call_chunk.get("name"),
+            "args": tool_call_chunk.get("args"),
+        }
+        if (idx := tool_call_chunk.get("index")) is not None:
+            tc["index"] = idx
+        content_blocks.append(tc)
+
+    return content_blocks
+
+
+def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
+    """Convert a v1 message to the Chat Completions format."""
+    if isinstance(message.content, list):
+        new_content: list = []
+        for block in message.content:
+            if isinstance(block, dict):
+                block_type = block.get("type")
+                if block_type == "text":
+                    # Strip annotations
+                    new_content.append({"type": "text", "text": block["text"]})
+                elif block_type in ("reasoning", "tool_call"):
+                    pass
+                else:
+                    new_content.append(block)
+            else:
+                new_content.append(block)
+        return message.model_copy(update={"content": new_content})
+
+    return message
+
+
+# v1 / Responses
+def _convert_annotation_to_v1(annotation: dict[str, Any]) -> types.Annotation:
+    annotation_type = annotation.get("type")
+
+    if annotation_type == "url_citation":
+        known_fields = {
+            "type",
+            "url",
+            "title",
+            "cited_text",
+            "start_index",
+            "end_index",
+        }
+        url_citation = cast("types.Citation", {})
+        for field in ("end_index", "start_index", "title"):
+            if field in annotation:
+                url_citation[field] = annotation[field]
+        url_citation["type"] = "citation"
+        url_citation["url"] = annotation["url"]
+        for field, value in annotation.items():
+            if field not in known_fields:
+                if "extras" not in url_citation:
+                    url_citation["extras"] = {}
+                url_citation["extras"][field] = value
+        return url_citation
+
+    if annotation_type == "file_citation":
+        known_fields = {
+            "type",
+            "title",
+            "cited_text",
+            "start_index",
+            "end_index",
+            "filename",
+        }
+        document_citation: types.Citation = {"type": "citation"}
+        if "filename" in annotation:
+            document_citation["title"] = annotation["filename"]
+        for field, value in annotation.items():
+            if field not in known_fields:
+                if "extras" not in document_citation:
+                    document_citation["extras"] = {}
+                document_citation["extras"][field] = value
+
+        return document_citation
+
+    # TODO: standardise container_file_citation?
+    non_standard_annotation: types.NonStandardAnnotation = {
+        "type": "non_standard_annotation",
+        "value": annotation,
+    }
+    return non_standard_annotation
+
+
+def _explode_reasoning(block: dict[str, Any]) -> Iterable[types.ReasoningContentBlock]:
+    if "summary" not in block:
+        yield cast("types.ReasoningContentBlock", block)
+        return
+
+    known_fields = {"type", "reasoning", "id", "index"}
+    unknown_fields = [
+        field for field in block if field != "summary" and field not in known_fields
+    ]
+    if unknown_fields:
+        block["extras"] = {}
+    for field in unknown_fields:
+        block["extras"][field] = block.pop(field)
+
+    if not block["summary"]:
+        # [{'id': 'rs_...', 'summary': [], 'type': 'reasoning', 'index': 0}]
+        block = {k: v for k, v in block.items() if k != "summary"}
+        if "index" in block:
+            meaningful_idx = f"{block['index']}_0"
+            block["index"] = f"lc_rs_{meaningful_idx.encode().hex()}"
+        yield cast("types.ReasoningContentBlock", block)
+        return
+
+    # Common part for every exploded line, except 'summary'
+    common = {k: v for k, v in block.items() if k in known_fields}
+
+    # Optional keys that must appear only in the first exploded item
+    first_only = block.pop("extras", None)
+
+    for idx, part in enumerate(block["summary"]):
+        new_block = dict(common)
+        new_block["reasoning"] = part.get("text", "")
+        if idx == 0 and first_only:
+            new_block.update(first_only)
+        if "index" in new_block:
+            summary_index = part.get("index", 0)
+            meaningful_idx = f"{new_block['index']}_{summary_index}"
+            new_block["index"] = f"lc_rs_{meaningful_idx.encode().hex()}"
+
+        yield cast("types.ReasoningContentBlock", new_block)
+
+
+def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock]:
+    """Convert a Responses message to v1 format."""
+
+    def _iter_blocks() -> Iterable[types.ContentBlock]:
+        for raw_block in message.content:
+            if not isinstance(raw_block, dict):
+                continue
+            block = raw_block.copy()
+            block_type = block.get("type")
+
+            if block_type == "text":
+                if "text" not in block:
+                    block["text"] = ""
+                if "annotations" in block:
+                    block["annotations"] = [
+                        _convert_annotation_to_v1(a) for a in block["annotations"]
+                    ]
+                if "index" in block:
+                    block["index"] = f"lc_txt_{block['index']}"
+                yield cast("types.TextContentBlock", block)
+
+            elif block_type == "reasoning":
+                yield from _explode_reasoning(block)
+
+            elif block_type == "image_generation_call" and (
+                result := block.get("result")
+            ):
+                new_block = {"type": "image", "base64": result}
+                if output_format := block.get("output_format"):
+                    new_block["mime_type"] = f"image/{output_format}"
+                if "id" in block:
+                    new_block["id"] = block["id"]
+                if "index" in block:
+                    new_block["index"] = f"lc_img_{block['index']}"
+                for extra_key in (
+                    "status",
+                    "background",
+                    "output_format",
+                    "quality",
+                    "revised_prompt",
+                    "size",
+                ):
+                    if extra_key in block:
+                        if "extras" not in new_block:
+                            new_block["extras"] = {}
+                        new_block["extras"][extra_key] = block[extra_key]
+                yield cast("types.ImageContentBlock", new_block)
+
+            elif block_type == "function_call":
+                tool_call_block: Optional[
+                    Union[types.ToolCall, types.InvalidToolCall, types.ToolCallChunk]
+                ] = None
+                call_id = block.get("call_id", "")
+                if (
+                    isinstance(message, AIMessageChunk)
+                    and len(message.tool_call_chunks) == 1
+                ):
+                    tool_call_block = message.tool_call_chunks[0].copy()  # type: ignore[assignment]
+                elif call_id:
+                    for tool_call in message.tool_calls or []:
+                        if tool_call.get("id") == call_id:
+                            tool_call_block = tool_call.copy()
+                            break
+                    else:
+                        for invalid_tool_call in message.invalid_tool_calls or []:
+                            if invalid_tool_call.get("id") == call_id:
+                                tool_call_block = invalid_tool_call.copy()
+                                break
+                else:
+                    pass
+                if tool_call_block:
+                    if "id" in block:
+                        if "extras" not in tool_call_block:
+                            tool_call_block["extras"] = {}
+                        tool_call_block["extras"]["item_id"] = block["id"]
+                    if "index" in block:
+                        tool_call_block["index"] = f"lc_tc_{block['index']}"
+                    yield tool_call_block
+
+            elif block_type == "web_search_call":
+                web_search_call = {"type": "web_search_call", "id": block["id"]}
+                if "index" in block:
+                    web_search_call["index"] = f"lc_wsc_{block['index']}"
+                if (
+                    "action" in block
+                    and isinstance(block["action"], dict)
+                    and block["action"].get("type") == "search"
+                    and "query" in block["action"]
+                ):
+                    web_search_call["query"] = block["action"]["query"]
+                for key in block:
+                    if key not in ("type", "id", "index"):
+                        web_search_call[key] = block[key]
+
+                yield cast("types.WebSearchCall", web_search_call)
+
+                # If .content already has web_search_result, don't add
+                if not any(
+                    isinstance(other_block, dict)
+                    and other_block.get("type") == "web_search_result"
+                    and other_block.get("id") == block["id"]
+                    for other_block in message.content
+                ):
+                    web_search_result = {"type": "web_search_result", "id": block["id"]}
+                    if "index" in block and isinstance(block["index"], int):
+                        web_search_result["index"] = f"lc_wsr_{block['index'] + 1}"
+                    yield cast("types.WebSearchResult", web_search_result)
+
+            elif block_type == "code_interpreter_call":
+                code_interpreter_call = {
+                    "type": "code_interpreter_call",
+                    "id": block["id"],
+                }
+                if "code" in block:
+                    code_interpreter_call["code"] = block["code"]
+                if "index" in block:
+                    code_interpreter_call["index"] = f"lc_cic_{block['index']}"
+                known_fields = {"type", "id", "language", "code", "extras", "index"}
+                for key in block:
+                    if key not in known_fields:
+                        if "extras" not in code_interpreter_call:
+                            code_interpreter_call["extras"] = {}
+                        code_interpreter_call["extras"][key] = block[key]
+
+                code_interpreter_result = {
+                    "type": "code_interpreter_result",
+                    "id": block["id"],
+                }
+                if "outputs" in block:
+                    code_interpreter_result["outputs"] = block["outputs"]
+                    for output in block["outputs"]:
+                        if (
+                            isinstance(output, dict)
+                            and (output_type := output.get("type"))
+                            and output_type == "logs"
+                        ):
+                            if "output" not in code_interpreter_result:
+                                code_interpreter_result["output"] = []
+                            code_interpreter_result["output"].append(
+                                {
+                                    "type": "code_interpreter_output",
+                                    "stdout": output.get("logs", ""),
+                                }
+                            )
+
+                if "status" in block:
+                    code_interpreter_result["status"] = block["status"]
+                if "index" in block and isinstance(block["index"], int):
+                    code_interpreter_result["index"] = f"lc_cir_{block['index'] + 1}"
+
+                yield cast("types.CodeInterpreterCall", code_interpreter_call)
+                yield cast("types.CodeInterpreterResult", code_interpreter_result)
+
+            elif block_type in types.KNOWN_BLOCK_TYPES:
+                yield cast("types.ContentBlock", block)
+            else:
+                new_block = {"type": "non_standard", "value": block}
+                if "index" in new_block["value"]:
+                    new_block["index"] = f"lc_ns_{new_block['value'].pop('index')}"
+                yield cast("types.NonStandardContentBlock", new_block)
+
+    return list(_iter_blocks())
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with OpenAI content."""
+    if isinstance(message.content, str):
+        return _convert_to_v1_from_chat_completions(message)
+    return _convert_to_v1_from_responses(message)
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with OpenAI content."""
+    if isinstance(message.content, str):
+        return _convert_to_v1_from_chat_completions_chunk(message)
+    return _convert_to_v1_from_responses(message)
+
+
+def _register_openai_translator() -> None:
+    """Register the OpenAI translator with the central registry.
+
+    Run automatically when the module is imported.
+    """
+    from langchain_core.messages.block_translators import register_translator
+
+    register_translator("openai", translate_content, translate_content_chunk)
+
+
+_register_openai_translator()

libs/core/langchain_core/messages/content_blocks.py

@@ -1,155 +0,0 @@
-"""Types for content blocks."""
-
-import warnings
-from typing import Any, Literal, Union
-
-from pydantic import TypeAdapter, ValidationError
-from typing_extensions import NotRequired, TypedDict
-
-
-class BaseDataContentBlock(TypedDict, total=False):
-    """Base class for data content blocks."""
-
-    mime_type: NotRequired[str]
-    """MIME type of the content block (if needed)."""
-
-
-class URLContentBlock(BaseDataContentBlock):
-    """Content block for data from a URL."""
-
-    type: Literal["image", "audio", "file"]
-    """Type of the content block."""
-    source_type: Literal["url"]
-    """Source type (url)."""
-    url: str
-    """URL for data."""
-
-
-class Base64ContentBlock(BaseDataContentBlock):
-    """Content block for inline data from a base64 string."""
-
-    type: Literal["image", "audio", "file"]
-    """Type of the content block."""
-    source_type: Literal["base64"]
-    """Source type (base64)."""
-    data: str
-    """Data as a base64 string."""
-
-
-class PlainTextContentBlock(BaseDataContentBlock):
-    """Content block for plain text data (e.g., from a document)."""
-
-    type: Literal["file"]
-    """Type of the content block."""
-    source_type: Literal["text"]
-    """Source type (text)."""
-    text: str
-    """Text data."""
-
-
-class IDContentBlock(TypedDict):
-    """Content block for data specified by an identifier."""
-
-    type: Literal["image", "audio", "file"]
-    """Type of the content block."""
-    source_type: Literal["id"]
-    """Source type (id)."""
-    id: str
-    """Identifier for data source."""
-
-
-DataContentBlock = Union[
-    URLContentBlock,
-    Base64ContentBlock,
-    PlainTextContentBlock,
-    IDContentBlock,
-]
-
-_DataContentBlockAdapter: TypeAdapter[DataContentBlock] = TypeAdapter(DataContentBlock)
-
-
-def is_data_content_block(
-    content_block: dict,
-) -> bool:
-    """Check if the content block is a standard data content block.
-
-    Args:
-        content_block: The content block to check.
-
-    Returns:
-        True if the content block is a data content block, False otherwise.
-    """
-    try:
-        _ = _DataContentBlockAdapter.validate_python(content_block)
-    except ValidationError:
-        return False
-    else:
-        return True
-
-
-def convert_to_openai_image_block(content_block: dict[str, Any]) -> dict:
-    """Convert image content block to format expected by OpenAI Chat Completions API."""
-    if content_block["source_type"] == "url":
-        return {
-            "type": "image_url",
-            "image_url": {
-                "url": content_block["url"],
-            },
-        }
-    if content_block["source_type"] == "base64":
-        if "mime_type" not in content_block:
-            error_message = "mime_type key is required for base64 data."
-            raise ValueError(error_message)
-        mime_type = content_block["mime_type"]
-        return {
-            "type": "image_url",
-            "image_url": {
-                "url": f"data:{mime_type};base64,{content_block['data']}",
-            },
-        }
-    error_message = "Unsupported source type. Only 'url' and 'base64' are supported."
-    raise ValueError(error_message)
-
-
-def convert_to_openai_data_block(block: dict) -> dict:
-    """Format standard data content block to format expected by OpenAI."""
-    if block["type"] == "image":
-        formatted_block = convert_to_openai_image_block(block)
-
-    elif block["type"] == "file":
-        if block["source_type"] == "base64":
-            file = {"file_data": f"data:{block['mime_type']};base64,{block['data']}"}
-            if filename := block.get("filename"):
-                file["filename"] = filename
-            elif (metadata := block.get("metadata")) and ("filename" in metadata):
-                file["filename"] = metadata["filename"]
-            else:
-                warnings.warn(
-                    "OpenAI may require a filename for file inputs. Specify a filename "
-                    "in the content block: {'type': 'file', 'source_type': 'base64', "
-                    "'mime_type': 'application/pdf', 'data': '...', "
-                    "'filename': 'my-pdf'}",
-                    stacklevel=1,
-                )
-            formatted_block = {"type": "file", "file": file}
-        elif block["source_type"] == "id":
-            formatted_block = {"type": "file", "file": {"file_id": block["id"]}}
-        else:
-            error_msg = "source_type base64 or id is required for file blocks."
-            raise ValueError(error_msg)
-
-    elif block["type"] == "audio":
-        if block["source_type"] == "base64":
-            audio_format = block["mime_type"].split("/")[-1]
-            formatted_block = {
-                "type": "input_audio",
-                "input_audio": {"data": block["data"], "format": audio_format},
-            }
-        else:
-            error_msg = "source_type base64 is required for audio blocks."
-            raise ValueError(error_msg)
-    else:
-        error_msg = f"Block of type {block['type']} is not supported."
-        raise ValueError(error_msg)
-
-    return formatted_block

libs/core/langchain_core/messages/human.py

@@ -1,7 +1,8 @@
 """Human message."""
 
-from typing import Any, Literal, Union
+from typing import Any, Literal, Optional, Union, cast, overload
 
+from langchain_core.messages import content as types
 from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 
 
@@ -41,16 +42,35 @@ class HumanMessage(BaseMessage):
     type: Literal["human"] = "human"
     """The type of the message (used for serialization). Defaults to "human"."""
 
+    @overload
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
-    ) -> None:
-        """Pass in content as positional arg.
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
+    ) -> None: ...
 
-        Args:
-            content: The string contents of the message.
-            kwargs: Additional fields to pass to the message.
-        """
-        super().__init__(content=content, **kwargs)
+    @overload
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        if content_blocks is not None:
+            super().__init__(
+                content=cast("Union[str, list[Union[str, dict]]]", content_blocks),
+                **kwargs,
+            )
+        else:
+            super().__init__(content=content, **kwargs)
 
 
 class HumanMessageChunk(HumanMessage, BaseMessageChunk):

libs/core/langchain_core/messages/system.py

@@ -1,7 +1,8 @@
 """System message."""
 
-from typing import Any, Literal, Union
+from typing import Any, Literal, Optional, Union, cast, overload
 
+from langchain_core.messages import content as types
 from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 
 
@@ -34,16 +35,35 @@ class SystemMessage(BaseMessage):
     type: Literal["system"] = "system"
     """The type of the message (used for serialization). Defaults to "system"."""
 
+    @overload
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
-    ) -> None:
-        """Pass in content as positional arg.
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
+    ) -> None: ...
 
-        Args:
-               content: The string contents of the message.
-               kwargs: Additional fields to pass to the message.
-        """
-        super().__init__(content=content, **kwargs)
+    @overload
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        if content_blocks is not None:
+            super().__init__(
+                content=cast("Union[str, list[Union[str, dict]]]", content_blocks),
+                **kwargs,
+            )
+        else:
+            super().__init__(content=content, **kwargs)
 
 
 class SystemMessageChunk(SystemMessage, BaseMessageChunk):

libs/core/langchain_core/messages/tool.py

@@ -1,13 +1,16 @@
 """Messages for tools."""
 
 import json
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal, Optional, Union, cast, overload
 from uuid import UUID
 
 from pydantic import Field, model_validator
 from typing_extensions import NotRequired, TypedDict, override
 
+from langchain_core.messages import content as types
 from langchain_core.messages.base import BaseMessage, BaseMessageChunk, merge_content
+from langchain_core.messages.content import InvalidToolCall as InvalidToolCall
+from langchain_core.messages.content import ToolCall as ToolCall
 from langchain_core.utils._merge import merge_dicts, merge_obj
 
 
@@ -133,16 +136,35 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
             values["tool_call_id"] = str(tool_call_id)
         return values
 
+    @overload
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
-    ) -> None:
-        """Create a ToolMessage.
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
+    ) -> None: ...
 
-        Args:
-            content: The string contents of the message.
-            **kwargs: Additional fields.
-        """
-        super().__init__(content=content, **kwargs)
+    @overload
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        content: Optional[Union[str, list[Union[str, dict]]]] = None,
+        content_blocks: Optional[list[types.ContentBlock]] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        if content_blocks is not None:
+            super().__init__(
+                content=cast("Union[str, list[Union[str, dict]]]", content_blocks),
+                **kwargs,
+            )
+        else:
+            super().__init__(content=content, **kwargs)
 
 
 class ToolMessageChunk(ToolMessage, BaseMessageChunk):
@@ -177,37 +199,6 @@ class ToolMessageChunk(ToolMessage, BaseMessageChunk):
         return super().__add__(other)
 
 
-class ToolCall(TypedDict):
-    """Represents a request to call a tool.
-
-    Example:
-
-        .. code-block:: python
-
-            {
-                "name": "foo",
-                "args": {"a": 1},
-                "id": "123"
-            }
-
-        This represents a request to call the tool named "foo" with arguments {"a": 1}
-        and an identifier of "123".
-
-    """
-
-    name: str
-    """The name of the tool to be called."""
-    args: dict[str, Any]
-    """The arguments to the tool call."""
-    id: Optional[str]
-    """An identifier associated with the tool call.
-
-    An identifier is needed to associate a tool call request with a tool
-    call result in events when multiple concurrent tool calls are made.
-    """
-    type: NotRequired[Literal["tool_call"]]
-
-
 def tool_call(
     *,
     name: str,
@@ -276,24 +267,6 @@ def tool_call_chunk(
     )
 
 
-class InvalidToolCall(TypedDict):
-    """Allowance for errors made by LLM.
-
-    Here we add an `error` key to surface errors made during generation
-    (e.g., invalid JSON arguments.)
-    """
-
-    name: Optional[str]
-    """The name of the tool to be called."""
-    args: Optional[str]
-    """The arguments to the tool call."""
-    id: Optional[str]
-    """An identifier associated with the tool call."""
-    error: Optional[str]
-    """An error message associated with the tool call."""
-    type: NotRequired[Literal["invalid_tool_call"]]
-
-
 def invalid_tool_call(
     *,
     name: Optional[str] = None,

libs/core/langchain_core/messages/utils.py

@@ -31,10 +31,13 @@ from typing import (
 from pydantic import Discriminator, Field, Tag
 
 from langchain_core.exceptions import ErrorCode, create_message
-from langchain_core.messages import convert_to_openai_data_block, is_data_content_block
 from langchain_core.messages.ai import AIMessage, AIMessageChunk
 from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 from langchain_core.messages.chat import ChatMessage, ChatMessageChunk
+from langchain_core.messages.content import (
+    convert_to_openai_data_block,
+    is_data_content_block,
+)
 from langchain_core.messages.function import FunctionMessage, FunctionMessageChunk
 from langchain_core.messages.human import HumanMessage, HumanMessageChunk
 from langchain_core.messages.modifier import RemoveMessage

libs/core/langchain_core/prompt_values.py

@@ -123,7 +123,7 @@ class ImagePromptValue(PromptValue):
 
     def to_string(self) -> str:
         """Return prompt (image URL) as string."""
-        return self.image_url["url"]
+        return self.image_url.get("url", "")
 
     def to_messages(self) -> list[BaseMessage]:
         """Return prompt (image URL) as messages."""

libs/core/langchain_core/runnables/base.py

@@ -2399,7 +2399,7 @@ class Runnable(ABC, Generic[Input, Output]):
             description: The description of the tool. Defaults to None.
             arg_types: A dictionary of argument names to types. Defaults to None.
             message_version: Version of ``ToolMessage`` to return given
-            :class:`~langchain_core.messages.content_blocks.ToolCall` input.
+            :class:`~langchain_core.messages.content.ToolCall` input.
 
         Returns:
             A ``BaseTool`` instance.

libs/core/langchain_core/utils/_merge.py

@@ -44,6 +44,17 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
                 )
                 raise TypeError(msg)
             elif isinstance(merged[right_k], str):
+                # Special handling for output_version - it should be consistent
+                if right_k == "output_version":
+                    if merged[right_k] == right_v:
+                        continue
+                    msg = (
+                        "Unable to merge. Two different values seen for "
+                        f"'output_version': {merged[right_k]} and {right_v}. "
+                        "'output_version' should have the same value across "
+                        "all chunks in a generation."
+                    )
+                    raise ValueError(msg)
                 # TODO: Add below special handling for 'type' key in 0.3 and remove
                 # merge_lists 'type' logic.
                 #
@@ -57,6 +68,11 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
                 #             "should either occur once or have the same value across "
                 #             "all dicts."
                 #         )
+                if (right_k == "index" and merged[right_k].startswith("lc_")) or (
+                    right_k in ("id", "output_version", "model_provider")
+                    and merged[right_k] == right_v
+                ):
+                    continue
                 merged[right_k] += right_v
             elif isinstance(merged[right_k], dict):
                 merged[right_k] = merge_dicts(merged[right_k], right_v)
@@ -93,7 +109,16 @@ def merge_lists(left: Optional[list], *others: Optional[list]) -> Optional[list]
             merged = other.copy()
         else:
             for e in other:
-                if isinstance(e, dict) and "index" in e and isinstance(e["index"], int):
+                if (
+                    isinstance(e, dict)
+                    and "index" in e
+                    and (
+                        isinstance(e["index"], int)
+                        or (
+                            isinstance(e["index"], str) and e["index"].startswith("lc_")
+                        )
+                    )
+                ):
                     to_merge = [
                         i
                         for i, e_left in enumerate(merged)

libs/core/langchain_core/utils/utils.py

@@ -9,6 +9,7 @@ import warnings
 from collections.abc import Iterator, Sequence
 from importlib.metadata import version
 from typing import Any, Callable, Optional, Union, overload
+from uuid import uuid4
 
 from packaging.version import parse
 from pydantic import SecretStr
@@ -466,3 +467,31 @@ def secret_from_env(
         raise ValueError(msg)
 
     return get_secret_from_env
+
+
+LC_AUTO_PREFIX = "lc_"
+"""LangChain auto-generated ID prefix for messages and content blocks."""
+
+LC_ID_PREFIX = "lc_run-"
+"""Internal tracing/callback system identifier.
+
+Used for:
+- Tracing. Every LangChain operation (LLM call, chain execution, tool use, etc.)
+  gets a unique run_id (UUID)
+- Enables tracking parent-child relationships between operations
+"""
+
+
+def ensure_id(id_val: Optional[str]) -> str:
+    """Ensure the ID is a valid string, generating a new UUID if not provided.
+
+    Auto-generated UUIDs are prefixed by ``'lc_'`` to indicate they are
+    LangChain-generated IDs.
+
+    Args:
+        id_val: Optional string ID value to validate.
+
+    Returns:
+        A string ID, either the validated provided value or a newly generated UUID4.
+    """
+    return id_val or str(f"{LC_AUTO_PREFIX}{uuid4()}")

libs/core/tests/unit_tests/fake/test_fake_chat_model.py

@@ -77,8 +77,12 @@ async def test_generic_fake_chat_model_stream() -> None:
     model = GenericFakeChatModel(messages=cycle([message]))
     chunks = [chunk async for chunk in model.astream("meow")]
     assert chunks == [
-        _any_id_ai_message_chunk(content="", additional_kwargs={"foo": 42}),
-        _any_id_ai_message_chunk(content="", additional_kwargs={"bar": 24}),
+        _any_id_ai_message_chunk(
+            content="", additional_kwargs={"foo": 42, "output_version": "v0"}
+        ),
+        _any_id_ai_message_chunk(
+            content="", additional_kwargs={"bar": 24, "output_version": "v0"}
+        ),
     ]
     assert len({chunk.id for chunk in chunks}) == 1
 
@@ -97,21 +101,31 @@ async def test_generic_fake_chat_model_stream() -> None:
 
     assert chunks == [
         _any_id_ai_message_chunk(
-            content="", additional_kwargs={"function_call": {"name": "move_file"}}
+            content="",
+            additional_kwargs={
+                "function_call": {"name": "move_file"},
+                "output_version": "v0",
+            },
         ),
         _any_id_ai_message_chunk(
             content="",
             additional_kwargs={
                 "function_call": {"arguments": '{\n  "source_path": "foo"'},
+                "output_version": "v0",
             },
         ),
         _any_id_ai_message_chunk(
-            content="", additional_kwargs={"function_call": {"arguments": ","}}
+            content="",
+            additional_kwargs={
+                "function_call": {"arguments": ","},
+                "output_version": "v0",
+            },
         ),
         _any_id_ai_message_chunk(
             content="",
             additional_kwargs={
                 "function_call": {"arguments": '\n  "destination_path": "bar"\n}'},
+                "output_version": "v0",
             },
         ),
     ]
@@ -131,7 +145,8 @@ async def test_generic_fake_chat_model_stream() -> None:
                 "name": "move_file",
                 "arguments": '{\n  "source_path": "foo",\n  "'
                 'destination_path": "bar"\n}',
-            }
+            },
+            "output_version": "v0",
         },
         id=chunks[0].id,
     )

libs/core/tests/unit_tests/language_models/chat_models/test_base.py

@@ -8,6 +8,7 @@ import pytest
 from typing_extensions import override
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
 from langchain_core.language_models import (
     BaseChatModel,
     FakeListChatModel,
@@ -239,7 +240,9 @@ async def test_astream_implementation_uses_astream() -> None:
             self,
             messages: list[BaseMessage],
             stop: Optional[list[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,  # type: ignore[override]
+            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+            *,
+            output_version: Optional[str] = "v0",
             **kwargs: Any,
         ) -> AsyncIterator[ChatGenerationChunk]:
             """Stream the output of the model."""

libs/core/tests/unit_tests/language_models/chat_models/test_cache.py

@@ -301,8 +301,9 @@ def test_llm_representation_for_serializable() -> None:
     assert chat._get_llm_string() == (
         '{"id": ["tests", "unit_tests", "language_models", "chat_models", '
         '"test_cache", "CustomChat"], "kwargs": {"messages": {"id": '
-        '["builtins", "list_iterator"], "lc": 1, "type": "not_implemented"}}, "lc": '
-        '1, "name": "CustomChat", "type": "constructor"}---[(\'stop\', None)]'
+        '["builtins", "list_iterator"], "lc": 1, "type": "not_implemented"}, '
+        '"output_version": "v0"}, "lc": 1, "name": "CustomChat", "type": '
+        "\"constructor\"}---[('stop', None)]"
     )
 
 

libs/core/tests/unit_tests/language_models/chat_models/test_output_version.py

@@ -0,0 +1,570 @@
+"""Test output_version functionality in BaseChatModel."""
+
+from collections.abc import AsyncIterator, Iterator
+from typing import Any, Optional, Union
+from unittest.mock import patch
+
+import pytest
+from pydantic import ConfigDict
+from typing_extensions import override
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, HumanMessage
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+
+
+class MockChatModel(BaseChatModel):
+    """Mock chat model to test output_version functionality."""
+
+    model_config = ConfigDict(extra="allow")
+
+    @override
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Store the output_version that was passed."""
+        self.last_output_version = output_version
+        message = AIMessage(content="test response")
+        generation = ChatGeneration(message=message)
+        return ChatResult(generations=[generation])
+
+    @override
+    def _stream(
+        self,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        *,
+        output_version: str = "v0",
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        """Store the output_version that was passed."""
+        self.last_output_version = output_version
+        yield ChatGenerationChunk(message=AIMessageChunk(content="test"))
+        yield ChatGenerationChunk(message=AIMessageChunk(content=" stream"))
+
+    @override
+    async def _astream(
+        self,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,  # type: ignore[override]
+        *,
+        output_version: str = "v0",
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        """Store the output_version that was passed."""
+        self.last_output_version = output_version
+        yield ChatGenerationChunk(message=AIMessageChunk(content="async"))
+        yield ChatGenerationChunk(message=AIMessageChunk(content=" stream"))
+
+    @property
+    def _llm_type(self) -> str:
+        return "mock-chat-model"
+
+
+@pytest.fixture
+def messages() -> list[BaseMessage]:
+    return [HumanMessage("Hello")]
+
+
+def test_invoke_uses_default_output_version(messages: list[BaseMessage]) -> None:
+    """Test `invoke()` uses the model's default `output_version` when not specified."""
+    model = MockChatModel(output_version="v1")
+    model.invoke(messages)
+    assert model.last_output_version == "v1"
+
+
+def test_invoke_uses_provided_output_version(messages: list[BaseMessage]) -> None:
+    """Test that `invoke()` uses the provided `output_version` parameter."""
+    model = MockChatModel(output_version="v0")
+    model.invoke(messages, output_version="v1")
+    assert model.last_output_version == "v1"
+
+
+def test_invoke_output_version_none_uses_default(messages: list[BaseMessage]) -> None:
+    """Test that passing `output_version=None` uses the model's default."""
+    model = MockChatModel(output_version="v1")
+    model.invoke(messages, output_version=None)
+    assert model.last_output_version == "v1"
+
+
+async def test_ainvoke_uses_default_output_version(messages: list[BaseMessage]) -> None:
+    """Test `ainvoke()` uses the model's default `output_version` when not specified."""
+    model = MockChatModel(output_version="v1")
+    await model.ainvoke(messages)
+    assert model.last_output_version == "v1"
+
+
+async def test_ainvoke_uses_provided_output_version(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that `ainvoke()` uses the provided `output_version` parameter."""
+    model = MockChatModel(output_version="v0")
+    await model.ainvoke(messages, output_version="v1")
+    assert model.last_output_version == "v1"
+
+
+async def test_ainvoke_output_version_none_uses_default(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that passing `output_version=None` uses the model's default."""
+    model = MockChatModel(output_version="v1")
+    await model.ainvoke(messages, output_version=None)
+    assert model.last_output_version == "v1"
+
+
+def test_stream_uses_default_output_version(messages: list[BaseMessage]) -> None:
+    """Test `stream()` uses the model's default `output_version` when not specified."""
+    model = MockChatModel(output_version="v1")
+    list(model.stream(messages))
+    assert model.last_output_version == "v1"
+
+
+def test_stream_uses_provided_output_version(messages: list[BaseMessage]) -> None:
+    """Test that `stream()` uses the provided `output_version` parameter."""
+    model = MockChatModel(output_version="v1")
+    list(model.stream(messages, output_version="v2"))
+    assert model.last_output_version == "v2"
+
+
+def test_stream_output_version_none_uses_default(messages: list[BaseMessage]) -> None:
+    """Test that passing `output_version=None` uses the model's default."""
+    model = MockChatModel(output_version="v1")
+    list(model.stream(messages, output_version=None))
+    assert model.last_output_version == "v1"
+
+
+async def test_astream_uses_default_output_version(messages: list[BaseMessage]) -> None:
+    """Test `astream()` uses the model's default `output_version` when not specified."""
+    model = MockChatModel(output_version="v1")
+    async for _ in model.astream(messages):
+        pass
+    assert model.last_output_version == "v1"
+
+
+async def test_astream_uses_provided_output_version(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that `astream()` uses the provided `output_version` parameter."""
+    model = MockChatModel(output_version="v1")
+    async for _ in model.astream(messages, output_version="v0"):
+        pass
+    assert model.last_output_version == "v0"
+
+
+async def test_astream_output_version_none_uses_default(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that passing `output_version=None` uses the model's default."""
+    model = MockChatModel(output_version="v1")
+    async for _ in model.astream(messages, output_version=None):
+        pass
+    assert model.last_output_version == "v1"
+
+
+def test_stream_fallback_to_invoke_passes_output_version(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that `stream()` fallback to invoke passes the `output_version` correctly."""
+
+    class NoStreamModel(BaseChatModel):
+        model_config = ConfigDict(extra="allow")
+
+        @override
+        def _generate(
+            self,
+            messages: list[BaseMessage],
+            stop: Optional[list[str]] = None,
+            run_manager: Optional[CallbackManagerForLLMRun] = None,
+            *,
+            output_version: str = "v0",
+            **kwargs: Any,
+        ) -> ChatResult:
+            self.last_output_version = output_version
+            message = AIMessage(content="test response")
+            generation = ChatGeneration(message=message)
+            return ChatResult(generations=[generation])
+
+        @property
+        def _llm_type(self) -> str:
+            return "no-stream-model"
+
+    model = NoStreamModel(output_version="v1")
+    # Stream should fallback to invoke and pass the output_version
+    list(model.stream(messages, output_version="v2"))
+    assert model.last_output_version == "v2"
+
+
+async def test_astream_fallback_to_ainvoke_passes_output_version(
+    messages: list[BaseMessage],
+) -> None:
+    """Test `astream()` fallback to ainvoke passes the `output_version` correctly."""
+
+    class NoStreamModel(BaseChatModel):
+        model_config = ConfigDict(extra="allow")
+
+        @override
+        def _generate(
+            self,
+            messages: list[BaseMessage],
+            stop: Optional[list[str]] = None,
+            run_manager: Optional[CallbackManagerForLLMRun] = None,
+            *,
+            output_version: str = "v0",
+            **kwargs: Any,
+        ) -> ChatResult:
+            self.last_output_version = output_version
+            message = AIMessage(content="test response")
+            generation = ChatGeneration(message=message)
+            return ChatResult(generations=[generation])
+
+        @property
+        def _llm_type(self) -> str:
+            return "no-stream-model"
+
+    model = NoStreamModel(output_version="v1")
+    # Astream should fallback to ainvoke and pass the output_version
+    async for _ in model.astream(messages, output_version="v2"):
+        pass
+    assert model.last_output_version == "v2"
+
+
+def test_generate_prompt_passes_output_version_to_internal_methods(
+    messages: list[BaseMessage],
+) -> None:
+    """Test `generate_prompt()` passes `output_version` to internal `_generate()`."""
+    model = MockChatModel(output_version="v1")
+
+    # Mock the _generate method to verify it receives the output_version
+    with patch.object(model, "_generate", wraps=model._generate) as mock_generate:
+        model.invoke(messages, output_version="v2")
+        mock_generate.assert_called_once()
+        # Verify that _generate was called with output_version="v2"
+        call_kwargs = mock_generate.call_args.kwargs
+        assert call_kwargs.get("output_version") == "v2"
+
+
+async def test_agenerate_prompt_passes_output_version_to_internal_methods(
+    messages: list[BaseMessage],
+) -> None:
+    """Test `agenerate_prompt()` passes output_version to internal `_agenerate()`."""
+    model = MockChatModel(output_version="v1")
+
+    # Mock the _agenerate method to verify it receives the output_version
+    with patch.object(model, "_agenerate", wraps=model._agenerate) as mock_agenerate:
+        await model.ainvoke(messages, output_version="v2")
+        mock_agenerate.assert_called_once()
+        # Verify that _agenerate was called with output_version="v2"
+        call_kwargs = mock_agenerate.call_args.kwargs
+        assert call_kwargs.get("output_version") == "v2"
+
+
+def test_different_output_versions() -> None:
+    """Test that different `output_version` values are handled correctly."""
+    messages = [HumanMessage(content="Hello")]
+    model = MockChatModel(output_version="v0")
+
+    # Test with various output version strings
+    test_versions = ["v0", "v1", "v2", "beta", "experimental", "1.0", "2025-01-01"]
+
+    for version in test_versions:
+        model.invoke(messages, output_version=version)
+        assert model.last_output_version == version
+
+
+def test_output_version_is_keyword_only() -> None:
+    """Test that `output_version` parameter is keyword-only in public methods."""
+    messages = [HumanMessage(content="Hello")]
+    model = MockChatModel()
+
+    # These should work (keyword argument)
+    model.invoke(messages, output_version="v1")
+    list(model.stream(messages, output_version="v1"))
+
+    # These should fail if output_version were positional (but they don't because
+    # it's after the * in the signature, making it keyword-only)
+    with pytest.raises(TypeError):
+        model.invoke(messages, None, "v1")  # type: ignore[arg-type,misc]
+
+    with pytest.raises(TypeError):
+        list(model.stream(messages, None, "v1"))  # type: ignore[arg-type,misc]
+
+
+async def test_async_output_version_is_keyword_only() -> None:
+    """Test that `output_version` parameter is keyword-only in async public methods."""
+    messages = [HumanMessage(content="Hello")]
+    model = MockChatModel()
+
+    # These should work (keyword argument)
+    await model.ainvoke(messages, output_version="v1")
+    async for _ in model.astream(messages, output_version="v1"):
+        pass
+
+    # These should fail if output_version were positional
+    with pytest.raises(TypeError):
+        await model.ainvoke(messages, None, "v1")  # type: ignore[arg-type,misc]
+
+    with pytest.raises(TypeError):
+        async for _ in model.astream(messages, None, "v1"):  # type: ignore[arg-type,misc]
+            pass
+
+
+def test_output_version_inheritance() -> None:
+    """Test that subclasses properly inherit `output_version` functionality."""
+
+    class CustomChatModel(BaseChatModel):
+        model_config = ConfigDict(extra="allow")
+
+        def __init__(self, **kwargs: Any):
+            super().__init__(**kwargs)
+            self.received_versions: list[str] = []
+
+        @override
+        def _generate(
+            self,
+            messages: list[BaseMessage],
+            stop: Optional[list[str]] = None,
+            run_manager: Optional[CallbackManagerForLLMRun] = None,
+            *,
+            output_version: str = "v0",
+            **kwargs: Any,
+        ) -> ChatResult:
+            self.received_versions.append(output_version)
+            message = AIMessage(content="response")
+            generation = ChatGeneration(message=message)
+            return ChatResult(generations=[generation])
+
+        @property
+        def _llm_type(self) -> str:
+            return "custom-model"
+
+    messages = [HumanMessage(content="Hello")]
+    model = CustomChatModel(output_version="default_v1")
+
+    # Test that default is used
+    model.invoke(messages)
+    assert model.received_versions[-1] == "default_v1"
+
+    # Test that override is used
+    model.invoke(messages, output_version="override_v2")
+    assert model.received_versions[-1] == "override_v2"
+
+
+def test_internal_output_version_parameter_in_signature() -> None:
+    """Test that internal methods have `output_version` in their signatures."""
+    import inspect
+
+    model = MockChatModel()
+
+    # Check that the internal methods have output_version parameters
+    generate_sig = inspect.signature(model._generate)
+    assert "output_version" in generate_sig.parameters
+    assert generate_sig.parameters["output_version"].default == "v0"
+
+    agenerate_sig = inspect.signature(model._agenerate)
+    assert "output_version" in agenerate_sig.parameters
+    assert agenerate_sig.parameters["output_version"].default == "v0"
+
+    stream_sig = inspect.signature(model._stream)
+    assert "output_version" in stream_sig.parameters
+    assert stream_sig.parameters["output_version"].default == "v0"
+
+    astream_sig = inspect.signature(model._astream)
+    assert "output_version" in astream_sig.parameters
+    assert astream_sig.parameters["output_version"].default == "v0"
+
+
+@pytest.mark.parametrize(
+    ("model_version", "expected"),
+    [
+        ("v0", "v0"),
+        ("v1", "v1"),
+        ("responses/v1", "responses/v1"),
+    ],
+)
+def test_output_version_stored_in_additional_kwargs_invoke(
+    messages: list[BaseMessage], model_version: str, expected: str
+) -> None:
+    """Test that output_version is stored in message additional_kwargs for invoke."""
+    model = MockChatModel(output_version=model_version)
+    response = model.invoke(messages)
+
+    assert "output_version" in response.additional_kwargs
+    assert response.additional_kwargs["output_version"] == expected
+
+
+@pytest.mark.parametrize(
+    ("model_version", "override_version", "expected"),
+    [
+        ("v0", None, "v0"),
+        ("v1", None, "v1"),
+        ("v0", "v2", "v2"),
+        ("v1", "v0", "v0"),
+    ],
+)
+async def test_output_version_ainvoke_with_override(
+    messages: list[BaseMessage],
+    model_version: str,
+    override_version: str,
+    expected: str,
+) -> None:
+    """Test ainvoke with output_version override."""
+    model = MockChatModel(output_version=model_version)
+    response = await model.ainvoke(messages, output_version=override_version)
+
+    assert "output_version" in response.additional_kwargs
+    assert response.additional_kwargs["output_version"] == expected
+
+
+@pytest.mark.parametrize(
+    ("model_version", "override_version", "expected"),
+    [
+        ("v0", None, "v0"),
+        ("v1", None, "v1"),
+        ("v0", "v2", "v2"),
+    ],
+)
+def test_output_version_stored_in_stream_chunks(
+    messages: list[BaseMessage],
+    model_version: str,
+    override_version: str,
+    expected: str,
+) -> None:
+    """Test that output_version is stored in streaming chunk additional_kwargs."""
+    model = MockChatModel(output_version=model_version)
+    chunks = list(model.stream(messages, output_version=override_version))
+
+    for chunk in chunks:
+        assert "output_version" in chunk.additional_kwargs
+        assert chunk.additional_kwargs["output_version"] == expected
+
+
+@pytest.mark.parametrize(
+    ("model_version", "override_version", "expected"),
+    [
+        ("v0", None, "v0"),
+        ("v1", None, "v1"),
+        ("v0", "v2", "v2"),
+    ],
+)
+async def test_output_version_stored_in_astream_chunks(
+    messages: list[BaseMessage],
+    model_version: str,
+    override_version: str,
+    expected: str,
+) -> None:
+    """Test that output_version is stored in async streaming chunk additional_kwargs."""
+    model = MockChatModel(output_version=model_version)
+    chunks = [
+        chunk
+        async for chunk in model.astream(messages, output_version=override_version)
+    ]
+
+    for chunk in chunks:
+        assert "output_version" in chunk.additional_kwargs
+        assert chunk.additional_kwargs["output_version"] == expected
+
+
+@pytest.mark.parametrize("version", ["v0", "v1", "v2", "beta", "responses/v1"])
+def test_output_version_preserved_through_serialization(
+    messages: list[BaseMessage], version: str
+) -> None:
+    """Test that output_version in additional_kwargs persists through serialization."""
+    import json
+
+    model = MockChatModel(output_version="v0")
+    response = model.invoke(messages, output_version=version)
+    assert response.additional_kwargs["output_version"] == version
+
+    # Verify serialization preserves version
+    message_dict = {"additional_kwargs": response.additional_kwargs}
+    serialized = json.dumps(message_dict)
+    deserialized = json.loads(serialized)
+    assert deserialized["additional_kwargs"]["output_version"] == version
+
+
+@pytest.mark.parametrize(
+    ("output_version", "content_type"),
+    [
+        ("v0", str),
+        ("v1", list),
+    ],
+)
+def test_output_version_with_different_content_formats(
+    messages: list[BaseMessage], output_version: str, content_type: type
+) -> None:
+    """Test output_version storage works with different content formats."""
+
+    class CustomChatModel(BaseChatModel):
+        model_config = ConfigDict(extra="allow")
+
+        def _generate(
+            self,
+            messages: list[BaseMessage],  # noqa: ARG002
+            stop: Optional[list[str]] = None,  # noqa: ARG002
+            run_manager: Optional[CallbackManagerForLLMRun] = None,  # noqa: ARG002
+            *,
+            output_version: str = "v0",
+            **kwargs: Any,  # noqa: ARG002
+        ) -> ChatResult:
+            if output_version == "v0":
+                content: Union[str, list[dict[str, Any]]] = "test response"
+            else:
+                content = [{"type": "text", "text": "test response"}]
+            message = AIMessage(content=content)  # type: ignore[arg-type]
+            return ChatResult(generations=[ChatGeneration(message=message)])
+
+        @property
+        def _llm_type(self) -> str:
+            return "custom-test-model"
+
+    model = CustomChatModel()
+    response = model.invoke(messages, output_version=output_version)
+
+    assert response.additional_kwargs["output_version"] == output_version
+    assert isinstance(response.content, content_type)
+
+
+def test_output_version_preserves_existing_additional_kwargs(
+    messages: list[BaseMessage],
+) -> None:
+    """Test that output_version doesn't overwrite existing additional_kwargs."""
+
+    class ModelWithExistingKwargs(BaseChatModel):
+        model_config = ConfigDict(extra="allow")
+
+        def _generate(
+            self,
+            messages: list[BaseMessage],  # noqa: ARG002
+            stop: Optional[list[str]] = None,  # noqa: ARG002
+            run_manager: Optional[CallbackManagerForLLMRun] = None,  # noqa: ARG002
+            *,
+            output_version: str = "v0",  # noqa: ARG002
+            **kwargs: Any,  # noqa: ARG002
+        ) -> ChatResult:
+            message = AIMessage(
+                content="test response",
+                additional_kwargs={"model": "test-model", "temperature": 0.7},
+            )
+            return ChatResult(generations=[ChatGeneration(message=message)])
+
+        @property
+        def _llm_type(self) -> str:
+            return "kwargs-test-model"
+
+    model = ModelWithExistingKwargs(output_version="v1")
+    response = model.invoke(messages)
+
+    # Verify output_version was added and existing kwargs preserved
+    assert response.additional_kwargs["output_version"] == "v1"
+    assert response.additional_kwargs["model"] == "test-model"
+    assert response.additional_kwargs["temperature"] == 0.7
+    assert len(response.additional_kwargs) == 3

libs/core/tests/unit_tests/language_models/chat_models/test_rate_limiting.py

@@ -216,7 +216,7 @@ def test_rate_limit_skips_cache() -> None:
             '[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", '
             '"messages", '
             '"HumanMessage"], "kwargs": {"content": "foo", "type": "human"}}]',
-            "[('_type', 'generic-fake-chat-model'), ('stop', None)]",
+            "[('_output_version', 'v0'), ('_type', 'generic-fake-chat-model'), ('stop', None)]",  # noqa: E501
         )
     ]
 

libs/core/tests/unit_tests/messages/block_translators/test_openai.py

@@ -0,0 +1,231 @@
+from typing import Optional
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
+
+
+def test_convert_to_v1_from_responses() -> None:
+    message = AIMessage(
+        [
+            {"type": "reasoning", "id": "abc123", "summary": []},
+            {
+                "type": "reasoning",
+                "id": "abc234",
+                "summary": [
+                    {"type": "summary_text", "text": "foo bar"},
+                    {"type": "summary_text", "text": "baz"},
+                ],
+            },
+            {
+                "type": "function_call",
+                "call_id": "call_123",
+                "name": "get_weather",
+                "arguments": '{"location": "San Francisco"}',
+            },
+            {
+                "type": "function_call",
+                "call_id": "call_234",
+                "name": "get_weather_2",
+                "arguments": '{"location": "New York"}',
+                "id": "fc_123",
+            },
+            {"type": "text", "text": "Hello "},
+            {
+                "type": "text",
+                "text": "world",
+                "annotations": [
+                    {"type": "url_citation", "url": "https://example.com"},
+                    {
+                        "type": "file_citation",
+                        "filename": "my doc",
+                        "index": 1,
+                        "file_id": "file_123",
+                    },
+                    {"bar": "baz"},
+                ],
+            },
+            {"type": "image_generation_call", "id": "ig_123", "result": "..."},
+            {"type": "something_else", "foo": "bar"},
+        ],
+        tool_calls=[
+            {
+                "type": "tool_call",
+                "id": "call_123",
+                "name": "get_weather",
+                "args": {"location": "San Francisco"},
+            },
+            {
+                "type": "tool_call",
+                "id": "call_234",
+                "name": "get_weather_2",
+                "args": {"location": "New York"},
+            },
+        ],
+        response_metadata={"model_provider": "openai"},
+    )
+    expected_content: list[types.ContentBlock] = [
+        {"type": "reasoning", "id": "abc123"},
+        {"type": "reasoning", "id": "abc234", "reasoning": "foo bar"},
+        {"type": "reasoning", "id": "abc234", "reasoning": "baz"},
+        {
+            "type": "tool_call",
+            "id": "call_123",
+            "name": "get_weather",
+            "args": {"location": "San Francisco"},
+        },
+        {
+            "type": "tool_call",
+            "id": "call_234",
+            "name": "get_weather_2",
+            "args": {"location": "New York"},
+            "extras": {"item_id": "fc_123"},
+        },
+        {"type": "text", "text": "Hello "},
+        {
+            "type": "text",
+            "text": "world",
+            "annotations": [
+                {"type": "citation", "url": "https://example.com"},
+                {
+                    "type": "citation",
+                    "title": "my doc",
+                    "extras": {"file_id": "file_123", "index": 1},
+                },
+                {"type": "non_standard_annotation", "value": {"bar": "baz"}},
+            ],
+        },
+        {"type": "image", "base64": "...", "id": "ig_123"},
+        {
+            "type": "non_standard",
+            "value": {"type": "something_else", "foo": "bar"},
+        },
+    ]
+    assert message.content_blocks == expected_content
+
+    # Check no mutation
+    assert message.content != expected_content
+
+
+def test_convert_to_v1_from_responses_chunk() -> None:
+    chunks = [
+        AIMessageChunk(
+            content=[{"type": "reasoning", "id": "abc123", "summary": [], "index": 0}],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "summary": [
+                        {"type": "summary_text", "text": "foo ", "index": 0},
+                    ],
+                    "index": 1,
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "summary": [
+                        {"type": "summary_text", "text": "bar", "index": 0},
+                    ],
+                    "index": 1,
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "summary": [
+                        {"type": "summary_text", "text": "baz", "index": 1},
+                    ],
+                    "index": 1,
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+    ]
+    expected_chunks = [
+        AIMessageChunk(
+            content=[{"type": "reasoning", "id": "abc123", "index": "lc_rs_305f30"}],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "reasoning": "foo ",
+                    "index": "lc_rs_315f30",
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "reasoning": "bar",
+                    "index": "lc_rs_315f30",
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+        AIMessageChunk(
+            content=[
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "reasoning": "baz",
+                    "index": "lc_rs_315f31",
+                }
+            ],
+            response_metadata={"model_provider": "openai"},
+        ),
+    ]
+    for chunk, expected in zip(chunks, expected_chunks):
+        assert chunk.content_blocks == expected.content_blocks
+
+    full: Optional[AIMessageChunk] = None
+    for chunk in chunks:
+        full = chunk if full is None else full + chunk  # type: ignore[assignment]
+    assert isinstance(full, AIMessageChunk)
+
+    expected_content = [
+        {"type": "reasoning", "id": "abc123", "summary": [], "index": 0},
+        {
+            "type": "reasoning",
+            "id": "abc234",
+            "summary": [
+                {"type": "summary_text", "text": "foo bar", "index": 0},
+                {"type": "summary_text", "text": "baz", "index": 1},
+            ],
+            "index": 1,
+        },
+    ]
+    assert full.content == expected_content
+
+    expected_content_blocks = [
+        {"type": "reasoning", "id": "abc123", "index": "lc_rs_305f30"},
+        {
+            "type": "reasoning",
+            "id": "abc234",
+            "reasoning": "foo bar",
+            "index": "lc_rs_315f30",
+        },
+        {
+            "type": "reasoning",
+            "id": "abc234",
+            "reasoning": "baz",
+            "index": "lc_rs_315f31",
+        },
+    ]
+    assert full.content_blocks == expected_content_blocks

libs/core/tests/unit_tests/messages/test_ai.py

@@ -1,5 +1,6 @@
 from langchain_core.load import dumpd, load
 from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content as types
 from langchain_core.messages.ai import (
     InputTokenDetails,
     OutputTokenDetails,
@@ -196,3 +197,116 @@ def test_add_ai_message_chunks_usage() -> None:
             output_token_details=OutputTokenDetails(audio=1, reasoning=2),
         ),
     )
+
+
+def test_content_blocks() -> None:
+    message = AIMessage(
+        "",
+        tool_calls=[
+            {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"}
+        ],
+    )
+    assert len(message.content_blocks) == 1
+    assert message.content_blocks[0]["type"] == "tool_call"
+    assert message.content_blocks == [
+        {"type": "tool_call", "id": "abc_123", "name": "foo", "args": {"a": "b"}}
+    ]
+    assert message.content == ""
+
+    message = AIMessage(
+        "foo",
+        tool_calls=[
+            {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"}
+        ],
+    )
+    assert len(message.content_blocks) == 2
+    assert message.content_blocks[0]["type"] == "text"
+    assert message.content_blocks[1]["type"] == "tool_call"
+    assert message.content_blocks == [
+        {"type": "text", "text": "foo"},
+        {"type": "tool_call", "id": "abc_123", "name": "foo", "args": {"a": "b"}},
+    ]
+    assert message.content == "foo"
+
+    # With standard blocks
+    standard_content: list[types.ContentBlock] = [
+        {"type": "reasoning", "reasoning": "foo"},
+        {"type": "text", "text": "bar"},
+        {
+            "type": "text",
+            "text": "baz",
+            "annotations": [{"type": "citation", "url": "http://example.com"}],
+        },
+        {
+            "type": "image",
+            "url": "http://example.com/image.png",
+            "extras": {"foo": "bar"},
+        },
+        {
+            "type": "non_standard",
+            "value": {"custom_key": "custom_value", "another_key": 123},
+        },
+        {
+            "type": "tool_call",
+            "name": "foo",
+            "args": {"a": "b"},
+            "id": "abc_123",
+        },
+    ]
+    missing_tool_call: types.ToolCall = {
+        "type": "tool_call",
+        "name": "bar",
+        "args": {"c": "d"},
+        "id": "abc_234",
+    }
+    message = AIMessage(
+        content_blocks=standard_content,
+        tool_calls=[
+            {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"},
+            missing_tool_call,
+        ],
+    )
+    assert message.content_blocks == [*standard_content, missing_tool_call]
+
+    # Check we auto-populate tool_calls
+    standard_content = [
+        {"type": "text", "text": "foo"},
+        {
+            "type": "tool_call",
+            "name": "foo",
+            "args": {"a": "b"},
+            "id": "abc_123",
+        },
+        missing_tool_call,
+    ]
+    message = AIMessage(content_blocks=standard_content)
+    assert message.tool_calls == [
+        {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"},
+        missing_tool_call,
+    ]
+
+    # Chunks
+    message = AIMessageChunk(
+        content="",
+        tool_call_chunks=[
+            {
+                "type": "tool_call_chunk",
+                "name": "foo",
+                "args": "",
+                "id": "abc_123",
+                "index": 0,
+            }
+        ],
+    )
+    assert len(message.content_blocks) == 1
+    assert message.content_blocks[0]["type"] == "tool_call_chunk"
+    assert message.content_blocks == [
+        {
+            "type": "tool_call_chunk",
+            "name": "foo",
+            "args": "",
+            "id": "abc_123",
+            "index": 0,
+        }
+    ]
+    assert message.content == ""

libs/core/tests/unit_tests/messages/test_imports.py

@@ -5,26 +5,51 @@ EXPECTED_ALL = [
     "_message_from_dict",
     "AIMessage",
     "AIMessageChunk",
+    "Annotation",
     "AnyMessage",
+    "AudioContentBlock",
     "BaseMessage",
     "BaseMessageChunk",
+    "ContentBlock",
     "ChatMessage",
     "ChatMessageChunk",
+    "Citation",
+    "CodeInterpreterCall",
+    "CodeInterpreterOutput",
+    "CodeInterpreterResult",
+    "DataContentBlock",
+    "FileContentBlock",
     "FunctionMessage",
     "FunctionMessageChunk",
     "HumanMessage",
     "HumanMessageChunk",
+    "ImageContentBlock",
     "InvalidToolCall",
+    "LC_AUTO_PREFIX",
+    "LC_ID_PREFIX",
+    "NonStandardAnnotation",
+    "NonStandardContentBlock",
+    "PlainTextContentBlock",
     "SystemMessage",
     "SystemMessageChunk",
+    "TextContentBlock",
     "ToolCall",
     "ToolCallChunk",
     "ToolMessage",
     "ToolMessageChunk",
+    "VideoContentBlock",
+    "WebSearchCall",
+    "WebSearchResult",
+    "ReasoningContentBlock",
     "RemoveMessage",
     "convert_to_messages",
+    "ensure_id",
     "get_buffer_string",
     "is_data_content_block",
+    "is_reasoning_block",
+    "is_text_block",
+    "is_tool_call_block",
+    "is_tool_call_chunk",
     "merge_content",
     "message_chunk_to_message",
     "message_to_dict",

libs/core/tests/unit_tests/messages/test_utils.py

@@ -1215,13 +1215,14 @@ def test_convert_to_openai_messages_developer() -> None:
 
 
 def test_convert_to_openai_messages_multimodal() -> None:
+    """v0 and v1 content to OpenAI messages conversion."""
     messages = [
         HumanMessage(
             content=[
+                # Prior v0 blocks
                 {"type": "text", "text": "Text message"},
                 {
                     "type": "image",
-                    "source_type": "url",
                     "url": "https://example.com/test.png",
                 },
                 {
@@ -1238,6 +1239,7 @@ def test_convert_to_openai_messages_multimodal() -> None:
                     "filename": "test.pdf",
                 },
                 {
+                    # OpenAI Chat Completions file format
                     "type": "file",
                     "file": {
                         "filename": "draconomicon.pdf",
@@ -1262,22 +1264,47 @@ def test_convert_to_openai_messages_multimodal() -> None:
                         "format": "wav",
                     },
                 },
+                # v1 Additions
+                {
+                    "type": "image",
+                    "source_type": "url",  # backward compatibility v0 block field
+                    "url": "https://example.com/test.png",
+                },
+                {
+                    "type": "image",
+                    "base64": "<base64 string>",
+                    "mime_type": "image/png",
+                },
+                {
+                    "type": "file",
+                    "base64": "<base64 string>",
+                    "mime_type": "application/pdf",
+                    "filename": "test.pdf",  # backward compatibility v0 block field
+                },
+                {
+                    "type": "file",
+                    "file_id": "file-abc123",
+                },
+                {
+                    "type": "audio",
+                    "base64": "<base64 string>",
+                    "mime_type": "audio/wav",
+                },
             ]
         )
     ]
     result = convert_to_openai_messages(messages, text_format="block")
     assert len(result) == 1
     message = result[0]
-    assert len(message["content"]) == 8
+    assert len(message["content"]) == 13
 
-    # Test adding filename
+    # Test auto-adding filename
     messages = [
         HumanMessage(
             content=[
                 {
                     "type": "file",
-                    "source_type": "base64",
-                    "data": "<base64 string>",
+                    "base64": "<base64 string>",
                     "mime_type": "application/pdf",
                 },
             ]
@@ -1290,6 +1317,7 @@ def test_convert_to_openai_messages_multimodal() -> None:
     assert len(message["content"]) == 1
     block = message["content"][0]
     assert block == {
+        # OpenAI Chat Completions file format
         "type": "file",
         "file": {
             "file_data": "data:application/pdf;base64,<base64 string>",

libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr

@@ -726,7 +726,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -752,6 +752,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -763,6 +767,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -781,9 +796,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -998,12 +1014,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -1015,6 +1042,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -1026,9 +1064,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -2158,7 +2197,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -2184,6 +2223,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -2195,6 +2238,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -2213,9 +2267,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -2430,12 +2485,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -2447,6 +2513,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -2458,9 +2535,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',

libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr

@@ -1129,7 +1129,7 @@
               'description': '''
                 Allowance for errors made by LLM.
                 
-                Here we add an `error` key to surface errors made during generation
+                Here we add an ``error`` key to surface errors made during generation
                 (e.g., invalid JSON arguments.)
               ''',
               'properties': dict({
@@ -1155,6 +1155,10 @@
                   ]),
                   'title': 'Error',
                 }),
+                'extras': dict({
+                  'title': 'Extras',
+                  'type': 'object',
+                }),
                 'id': dict({
                   'anyOf': list([
                     dict({
@@ -1166,6 +1170,17 @@
                   ]),
                   'title': 'Id',
                 }),
+                'index': dict({
+                  'anyOf': list([
+                    dict({
+                      'type': 'integer',
+                    }),
+                    dict({
+                      'type': 'string',
+                    }),
+                  ]),
+                  'title': 'Index',
+                }),
                 'name': dict({
                   'anyOf': list([
                     dict({
@@ -1184,9 +1199,10 @@
                 }),
               }),
               'required': list([
+                'type',
+                'id',
                 'name',
                 'args',
-                'id',
                 'error',
               ]),
               'title': 'InvalidToolCall',
@@ -1401,12 +1417,23 @@
                 
                     This represents a request to call the tool named "foo" with arguments {"a": 1}
                     and an identifier of "123".
+                
+                .. note::
+                    ``create_tool_call`` may also be used as a factory to create a
+                    ``ToolCall``. Benefits include:
+                
+                    * Automatic ID generation (when not provided)
+                    * Required arguments strictly validated at creation time
               ''',
               'properties': dict({
                 'args': dict({
                   'title': 'Args',
                   'type': 'object',
                 }),
+                'extras': dict({
+                  'title': 'Extras',
+                  'type': 'object',
+                }),
                 'id': dict({
                   'anyOf': list([
                     dict({
@@ -1418,6 +1445,17 @@
                   ]),
                   'title': 'Id',
                 }),
+                'index': dict({
+                  'anyOf': list([
+                    dict({
+                      'type': 'integer',
+                    }),
+                    dict({
+                      'type': 'string',
+                    }),
+                  ]),
+                  'title': 'Index',
+                }),
                 'name': dict({
                   'title': 'Name',
                   'type': 'string',
@@ -1429,9 +1467,10 @@
                 }),
               }),
               'required': list([
+                'type',
+                'id',
                 'name',
                 'args',
-                'id',
               ]),
               'title': 'ToolCall',
               'type': 'object',

libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr

@@ -2674,7 +2674,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -2700,6 +2700,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -2711,6 +2715,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -2728,9 +2743,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -2943,12 +2959,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -2960,6 +2987,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -2970,9 +3008,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -4150,7 +4189,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -4176,6 +4215,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -4187,6 +4230,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -4204,9 +4258,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -4438,12 +4493,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -4455,6 +4521,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -4465,9 +4542,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -5657,7 +5735,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -5683,6 +5761,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -5694,6 +5776,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -5711,9 +5804,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -5945,12 +6039,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -5962,6 +6067,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -5972,9 +6088,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -7039,7 +7156,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -7065,6 +7182,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -7076,6 +7197,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -7093,9 +7225,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -7308,12 +7441,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -7325,6 +7469,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -7335,9 +7490,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -8557,7 +8713,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -8583,6 +8739,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -8594,6 +8754,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -8611,9 +8782,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -8845,12 +9017,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -8862,6 +9045,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -8872,9 +9066,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -9984,7 +10179,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -10010,6 +10205,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -10021,6 +10220,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -10038,9 +10248,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -10253,12 +10464,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -10270,6 +10492,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -10280,9 +10513,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -11410,7 +11644,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -11436,6 +11670,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -11447,6 +11685,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -11464,9 +11713,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -11709,12 +11959,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -11726,6 +11987,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -11736,9 +12008,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',
@@ -12878,7 +13151,7 @@
         'description': '''
           Allowance for errors made by LLM.
           
-          Here we add an `error` key to surface errors made during generation
+          Here we add an ``error`` key to surface errors made during generation
           (e.g., invalid JSON arguments.)
         ''',
         'properties': dict({
@@ -12904,6 +13177,10 @@
             ]),
             'title': 'Error',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -12915,6 +13192,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'anyOf': list([
               dict({
@@ -12932,9 +13220,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
           'error',
         ]),
         'title': 'InvalidToolCall',
@@ -13166,12 +13455,23 @@
           
               This represents a request to call the tool named "foo" with arguments {"a": 1}
               and an identifier of "123".
+          
+          .. note::
+              ``create_tool_call`` may also be used as a factory to create a
+              ``ToolCall``. Benefits include:
+          
+              * Automatic ID generation (when not provided)
+              * Required arguments strictly validated at creation time
         ''',
         'properties': dict({
           'args': dict({
             'title': 'Args',
             'type': 'object',
           }),
+          'extras': dict({
+            'title': 'Extras',
+            'type': 'object',
+          }),
           'id': dict({
             'anyOf': list([
               dict({
@@ -13183,6 +13483,17 @@
             ]),
             'title': 'Id',
           }),
+          'index': dict({
+            'anyOf': list([
+              dict({
+                'type': 'integer',
+              }),
+              dict({
+                'type': 'string',
+              }),
+            ]),
+            'title': 'Index',
+          }),
           'name': dict({
             'title': 'Name',
             'type': 'string',
@@ -13193,9 +13504,10 @@
           }),
         }),
         'required': list([
+          'type',
+          'id',
           'name',
           'args',
-          'id',
         ]),
         'title': 'ToolCall',
         'type': 'object',

libs/core/tests/unit_tests/stubs.py

@@ -29,6 +29,11 @@ def _any_id_document(**kwargs: Any) -> Document:
 
 def _any_id_ai_message(**kwargs: Any) -> AIMessage:
     """Create ai message with an any id field."""
+    # Add default output_version if not specified and no additional_kwargs provided
+    if "additional_kwargs" not in kwargs:
+        kwargs["additional_kwargs"] = {"output_version": "v0"}
+    elif "output_version" not in kwargs.get("additional_kwargs", {}):
+        kwargs["additional_kwargs"]["output_version"] = "v0"
     message = AIMessage(**kwargs)
     message.id = AnyStr()
     return message
@@ -36,6 +41,11 @@ def _any_id_ai_message(**kwargs: Any) -> AIMessage:
 
 def _any_id_ai_message_chunk(**kwargs: Any) -> AIMessageChunk:
     """Create ai message with an any id field."""
+    # Add default output_version if not specified and no additional_kwargs provided
+    if "additional_kwargs" not in kwargs:
+        kwargs["additional_kwargs"] = {"output_version": "v0"}
+    elif "output_version" not in kwargs.get("additional_kwargs", {}):
+        kwargs["additional_kwargs"]["output_version"] = "v0"
     message = AIMessageChunk(**kwargs)
     message.id = AnyStr()
     return message

libs/core/tests/unit_tests/test_messages.py

@@ -3,6 +3,7 @@ import uuid
 from typing import Optional, Union
 
 import pytest
+from typing_extensions import get_args
 
 from langchain_core.documents import Document
 from langchain_core.load import dumpd, load
@@ -30,6 +31,7 @@ from langchain_core.messages import (
     messages_from_dict,
     messages_to_dict,
 )
+from langchain_core.messages.content import KNOWN_BLOCK_TYPES, ContentBlock
 from langchain_core.messages.tool import invalid_tool_call as create_invalid_tool_call
 from langchain_core.messages.tool import tool_call as create_tool_call
 from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
@@ -178,21 +180,23 @@ def test_message_chunks() -> None:
     assert AIMessageChunk(content="") + left == left
     assert right + AIMessageChunk(content="") == right
 
+    default_id = "lc_run--abc123"
+    meaningful_id = "msg_def456"
+
     # Test ID order of precedence
-    null_id = AIMessageChunk(content="", id=None)
-    default_id = AIMessageChunk(
-        content="", id="run-abc123"
+    null_id_chunk = AIMessageChunk(content="", id=None)
+    default_id_chunk = AIMessageChunk(
+        content="", id=default_id
     )  # LangChain-assigned run ID
-    meaningful_id = AIMessageChunk(content="", id="msg_def456")  # provider-assigned ID
+    provider_chunk = AIMessageChunk(
+        content="", id=meaningful_id
+    )  # provided ID (either by user or provider)
 
-    assert (null_id + default_id).id == "run-abc123"
-    assert (default_id + null_id).id == "run-abc123"
+    assert (null_id_chunk + default_id_chunk).id == default_id
+    assert (null_id_chunk + provider_chunk).id == meaningful_id
 
-    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"
+    # Provider assigned IDs have highest precedence
+    assert (default_id_chunk + provider_chunk).id == meaningful_id
 
 
 def test_chat_message_chunks() -> None:
@@ -207,7 +211,7 @@ def test_chat_message_chunks() -> None:
     ):
         ChatMessageChunk(role="User", content="I am") + ChatMessageChunk(
             role="Assistant", content=" indeed."
-        )
+        )  # type: ignore[reportUnusedExpression, unused-ignore]
 
     assert ChatMessageChunk(role="User", content="I am") + AIMessageChunk(
         content=" indeed."
@@ -316,7 +320,7 @@ def test_function_message_chunks() -> None:
     ):
         FunctionMessageChunk(name="hello", content="I am") + FunctionMessageChunk(
             name="bye", content=" indeed."
-        )
+        )  # type: ignore[reportUnusedExpression, unused-ignore]
 
 
 def test_ai_message_chunks() -> None:
@@ -332,7 +336,7 @@ def test_ai_message_chunks() -> None:
     ):
         AIMessageChunk(example=True, content="I am") + AIMessageChunk(
             example=False, content=" indeed."
-        )
+        )  # type: ignore[reportUnusedExpression, unused-ignore]
 
 
 class TestGetBufferString(unittest.TestCase):
@@ -1038,12 +1042,13 @@ def test_tool_message_content() -> None:
     ToolMessage(["foo"], tool_call_id="1")
     ToolMessage([{"foo": "bar"}], tool_call_id="1")
 
-    assert ToolMessage(("a", "b", "c"), tool_call_id="1").content == ["a", "b", "c"]  # type: ignore[arg-type]
-    assert ToolMessage(5, tool_call_id="1").content == "5"  # type: ignore[arg-type]
-    assert ToolMessage(5.1, tool_call_id="1").content == "5.1"  # type: ignore[arg-type]
-    assert ToolMessage({"foo": "bar"}, tool_call_id="1").content == "{'foo': 'bar'}"  # type: ignore[arg-type]
+    # Ignoring since we're testing that tuples get converted to lists in `coerce_args`
+    assert ToolMessage(("a", "b", "c"), tool_call_id="1").content == ["a", "b", "c"]  # type: ignore[call-overload]
+    assert ToolMessage(5, tool_call_id="1").content == "5"  # type: ignore[call-overload]
+    assert ToolMessage(5.1, tool_call_id="1").content == "5.1"  # type: ignore[call-overload]
+    assert ToolMessage({"foo": "bar"}, tool_call_id="1").content == "{'foo': 'bar'}"  # type: ignore[call-overload]
     assert (
-        ToolMessage(Document("foo"), tool_call_id="1").content == "page_content='foo'"  # type: ignore[arg-type]
+        ToolMessage(Document("foo"), tool_call_id="1").content == "page_content='foo'"  # type: ignore[call-overload]
     )
 
 
@@ -1113,26 +1118,45 @@ def test_message_text() -> None:
 
 
 def test_is_data_content_block() -> None:
+    # Test all DataContentBlock types with various data fields
+
+    # Image blocks
+    assert is_data_content_block({"type": "image", "url": "https://..."})
     assert is_data_content_block(
-        {
-            "type": "image",
-            "source_type": "url",
-            "url": "https://...",
-        }
+        {"type": "image", "base64": "<base64 data>", "mime_type": "image/jpeg"}
     )
+
+    # Video blocks
+    assert is_data_content_block({"type": "video", "url": "https://video.mp4"})
     assert is_data_content_block(
-        {
-            "type": "image",
-            "source_type": "base64",
-            "data": "<base64 data>",
-            "mime_type": "image/jpeg",
-        }
+        {"type": "video", "base64": "<base64 video>", "mime_type": "video/mp4"}
     )
+    assert is_data_content_block({"type": "video", "file_id": "vid_123"})
+
+    # Audio blocks
+    assert is_data_content_block({"type": "audio", "url": "https://audio.mp3"})
+    assert is_data_content_block(
+        {"type": "audio", "base64": "<base64 audio>", "mime_type": "audio/mp3"}
+    )
+    assert is_data_content_block({"type": "audio", "file_id": "aud_123"})
+
+    # Plain text blocks
+    assert is_data_content_block({"type": "text-plain", "text": "document content"})
+    assert is_data_content_block({"type": "text-plain", "url": "https://doc.txt"})
+    assert is_data_content_block({"type": "text-plain", "file_id": "txt_123"})
+
+    # File blocks
+    assert is_data_content_block({"type": "file", "url": "https://file.pdf"})
+    assert is_data_content_block(
+        {"type": "file", "base64": "<base64 file>", "mime_type": "application/pdf"}
+    )
+    assert is_data_content_block({"type": "file", "file_id": "file_123"})
+
+    # Blocks with additional metadata (should still be valid)
     assert is_data_content_block(
         {
             "type": "image",
-            "source_type": "base64",
-            "data": "<base64 data>",
+            "base64": "<base64 data>",
             "mime_type": "image/jpeg",
             "cache_control": {"type": "ephemeral"},
         }
@@ -1140,65 +1164,145 @@ def test_is_data_content_block() -> None:
     assert is_data_content_block(
         {
             "type": "image",
-            "source_type": "base64",
-            "data": "<base64 data>",
+            "base64": "<base64 data>",
             "mime_type": "image/jpeg",
             "metadata": {"cache_control": {"type": "ephemeral"}},
         }
     )
-
-    assert not is_data_content_block(
+    assert is_data_content_block(
         {
-            "type": "text",
-            "text": "foo",
+            "type": "image",
+            "base64": "<base64 data>",
+            "mime_type": "image/jpeg",
+            "extras": "hi",
         }
     )
+
+    # Invalid cases - wrong type
+    assert not is_data_content_block({"type": "text", "text": "foo"})
     assert not is_data_content_block(
         {
             "type": "image_url",
             "image_url": {"url": "https://..."},
-        }
-    )
-    assert not is_data_content_block(
-        {
-            "type": "image",
-            "source_type": "base64",
-        }
-    )
-    assert not is_data_content_block(
-        {
-            "type": "image",
-            "source": "<base64 data>",
-        }
+        }  # This is OpenAI Chat Completions
     )
+    assert not is_data_content_block({"type": "tool_call", "name": "func", "args": {}})
+    assert not is_data_content_block({"type": "invalid", "url": "something"})
+
+    # Invalid cases - valid type but no data or `source_type` fields
+    assert not is_data_content_block({"type": "image"})
+    assert not is_data_content_block({"type": "video", "mime_type": "video/mp4"})
+    assert not is_data_content_block({"type": "audio", "extras": {"key": "value"}})
+
+    # Invalid cases - valid type but wrong data field name
+    assert not is_data_content_block({"type": "image", "source": "<base64 data>"})
+    assert not is_data_content_block({"type": "video", "data": "video_data"})
+
+    # Edge cases - empty or missing values
+    assert not is_data_content_block({})
+    assert not is_data_content_block({"url": "https://..."})  # missing type
 
 
 def test_convert_to_openai_image_block() -> None:
-    input_block = {
-        "type": "image",
-        "source_type": "url",
-        "url": "https://...",
-        "cache_control": {"type": "ephemeral"},
-    }
-    expected = {
-        "type": "image_url",
-        "image_url": {"url": "https://..."},
-    }
-    result = convert_to_openai_image_block(input_block)
-    assert result == expected
-
-    input_block = {
-        "type": "image",
-        "source_type": "base64",
-        "data": "<base64 data>",
-        "mime_type": "image/jpeg",
-        "cache_control": {"type": "ephemeral"},
-    }
-    expected = {
-        "type": "image_url",
-        "image_url": {
-            "url": "data:image/jpeg;base64,<base64 data>",
+    for input_block in [
+        {
+            "type": "image",
+            "url": "https://...",
+            "cache_control": {"type": "ephemeral"},
         },
+        {
+            "type": "image",
+            "source_type": "url",
+            "url": "https://...",
+            "cache_control": {"type": "ephemeral"},
+        },
+    ]:
+        expected = {
+            "type": "image_url",
+            "image_url": {"url": "https://..."},
+        }
+        result = convert_to_openai_image_block(input_block)
+        assert result == expected
+
+    for input_block in [
+        {
+            "type": "image",
+            "base64": "<base64 data>",
+            "mime_type": "image/jpeg",
+            "cache_control": {"type": "ephemeral"},
+        },
+        {
+            "type": "image",
+            "source_type": "base64",
+            "data": "<base64 data>",
+            "mime_type": "image/jpeg",
+            "cache_control": {"type": "ephemeral"},
+        },
+    ]:
+        expected = {
+            "type": "image_url",
+            "image_url": {
+                "url": "data:image/jpeg;base64,<base64 data>",
+            },
+        }
+        result = convert_to_openai_image_block(input_block)
+        assert result == expected
+
+
+def test_known_block_types() -> None:
+    expected = {
+        bt
+        for bt in get_args(ContentBlock)
+        for bt in get_args(bt.__annotations__["type"])
     }
-    result = convert_to_openai_image_block(input_block)
-    assert result == expected
+    # Normalize any Literal[...] types in block types to their string values.
+    # This ensures all entries are plain strings, not Literal objects.
+    expected = {
+        t
+        if isinstance(t, str)
+        else t.__args__[0]
+        if hasattr(t, "__args__") and len(t.__args__) == 1
+        else t
+        for t in expected
+    }
+    assert expected == KNOWN_BLOCK_TYPES
+
+
+def test_typed_init() -> None:
+    ai_message = AIMessage(content_blocks=[{"type": "text", "text": "Hello"}])
+    assert ai_message.content == [{"type": "text", "text": "Hello"}]
+    assert ai_message.content_blocks == ai_message.content
+
+    human_message = HumanMessage(content_blocks=[{"type": "text", "text": "Hello"}])
+    assert human_message.content == [{"type": "text", "text": "Hello"}]
+    assert human_message.content_blocks == human_message.content
+
+    system_message = SystemMessage(content_blocks=[{"type": "text", "text": "Hello"}])
+    assert system_message.content == [{"type": "text", "text": "Hello"}]
+    assert system_message.content_blocks == system_message.content
+
+    tool_message = ToolMessage(
+        content_blocks=[{"type": "text", "text": "Hello"}],
+        tool_call_id="abc123",
+    )
+    assert tool_message.content == [{"type": "text", "text": "Hello"}]
+    assert tool_message.content_blocks == tool_message.content
+
+    for message_class in [AIMessage, HumanMessage, SystemMessage]:
+        message = message_class("Hello")
+        assert message.content == "Hello"
+        assert message.content_blocks == [{"type": "text", "text": "Hello"}]
+
+        message = message_class(content="Hello")
+        assert message.content == "Hello"
+        assert message.content_blocks == [{"type": "text", "text": "Hello"}]
+
+    # Test we get type errors for malformed blocks (type checker will complain if
+    # below type-ignores are unused).
+    _ = AIMessage(content_blocks=[{"type": "text", "bad": "Hello"}])  # type: ignore[list-item]
+    _ = HumanMessage(content_blocks=[{"type": "text", "bad": "Hello"}])  # type: ignore[list-item]
+    _ = SystemMessage(content_blocks=[{"type": "text", "bad": "Hello"}])  # type: ignore[list-item]
+    _ = ToolMessage(
+        content_blocks=[{"type": "text", "bad": "Hello"}],  # type: ignore[list-item]
+        tool_call_id="abc123",
+    )

libs/core/tests/unit_tests/test_tools.py

@@ -2281,7 +2281,7 @@ def test_tool_injected_tool_call_id() -> None:
     @tool
     def foo(x: int, tool_call_id: Annotated[str, InjectedToolCallId]) -> ToolMessage:
         """Foo."""
-        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[arg-type]
+        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[call-overload]
 
     assert foo.invoke(
         {
@@ -2290,7 +2290,7 @@ def test_tool_injected_tool_call_id() -> None:
             "name": "foo",
             "id": "bar",
         }
-    ) == ToolMessage(0, tool_call_id="bar")  # type: ignore[arg-type]
+    ) == ToolMessage(0, tool_call_id="bar")  # type: ignore[call-overload]
 
     with pytest.raises(
         ValueError,
@@ -2302,7 +2302,7 @@ def test_tool_injected_tool_call_id() -> None:
     @tool
     def foo2(x: int, tool_call_id: Annotated[str, InjectedToolCallId()]) -> ToolMessage:
         """Foo."""
-        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[arg-type]
+        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[call-overload]
 
     assert foo2.invoke(
         {
@@ -2311,14 +2311,14 @@ def test_tool_injected_tool_call_id() -> None:
             "name": "foo",
             "id": "bar",
         }
-    ) == ToolMessage(0, tool_call_id="bar")  # type: ignore[arg-type]
+    ) == ToolMessage(0, tool_call_id="bar")  # type: ignore[call-overload]
 
 
 def test_tool_uninjected_tool_call_id() -> None:
     @tool
     def foo(x: int, tool_call_id: str) -> ToolMessage:
         """Foo."""
-        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[arg-type]
+        return ToolMessage(x, tool_call_id=tool_call_id)  # type: ignore[call-overload]
 
     with pytest.raises(ValueError, match="1 validation error for foo"):
         foo.invoke({"type": "tool_call", "args": {"x": 0}, "name": "foo", "id": "bar"})
@@ -2330,7 +2330,7 @@ def test_tool_uninjected_tool_call_id() -> None:
             "name": "foo",
             "id": "bar",
         }
-    ) == ToolMessage(0, tool_call_id="zap")  # type: ignore[arg-type]
+    ) == ToolMessage(0, tool_call_id="zap")  # type: ignore[call-overload]
 
 
 def test_tool_return_output_mixin() -> None:

libs/langchain/langchain/agents/output_parsers/tools.py

@@ -47,7 +47,12 @@ def parse_ai_message_to_tool_action(
             try:
                 args = json.loads(function["arguments"] or "{}")
                 tool_calls.append(
-                    ToolCall(name=function_name, args=args, id=tool_call["id"]),
+                    ToolCall(
+                        type="tool_call",
+                        name=function_name,
+                        args=args,
+                        id=tool_call["id"],
+                    ),
                 )
             except JSONDecodeError as e:
                 msg = (

libs/langchain/tests/unit_tests/chat_models/test_base.py

@@ -258,10 +258,11 @@ def test_configurable_with_default() -> None:
         "name": None,
         "bound": {
             "name": None,
+            "output_version": "v0",
             "disable_streaming": False,
             "model": "claude-3-7-sonnet-20250219",
             "mcp_servers": None,
-            "max_tokens": 1024,
+            "max_tokens": 64000,
             "temperature": None,
             "thinking": None,
             "top_k": None,
@@ -277,6 +278,7 @@ def test_configurable_with_default() -> None:
             "model_kwargs": {},
             "streaming": False,
             "stream_usage": True,
+            "output_version": "v0",
         },
         "kwargs": {
             "tools": [{"name": "foo", "description": "foo", "input_schema": {}}],

libs/langchain_v1/tests/unit_tests/chat_models/test_chat_models.py

@@ -258,10 +258,11 @@ def test_configurable_with_default() -> None:
         "name": None,
         "bound": {
             "name": None,
+            "output_version": "v0",
             "disable_streaming": False,
             "model": "claude-3-7-sonnet-20250219",
             "mcp_servers": None,
-            "max_tokens": 1024,
+            "max_tokens": 64000,
             "temperature": None,
             "thinking": None,
             "top_k": None,
@@ -277,6 +278,7 @@ def test_configurable_with_default() -> None:
             "model_kwargs": {},
             "streaming": False,
             "stream_usage": True,
+            "output_version": "v0",
         },
         "kwargs": {
             "tools": [{"name": "foo", "description": "foo", "input_schema": {}}],

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -7,7 +7,7 @@ import warnings
 from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from functools import cached_property
 from operator import itemgetter
-from typing import Any, Callable, Literal, Optional, Union, cast
+from typing import Any, Callable, Final, Literal, Optional, Union, cast
 
 import anthropic
 from langchain_core._api import beta, deprecated
@@ -61,6 +61,32 @@ _message_type_lookups = {
 }
 
 
+_MODEL_DEFAULT_MAX_OUTPUT_TOKENS: Final[dict[str, int]] = {
+    "claude-opus-4-1": 32000,
+    "claude-opus-4": 32000,
+    "claude-sonnet-4": 64000,
+    "claude-3-7-sonnet": 64000,
+    "claude-3-5-sonnet": 8192,
+    "claude-3-5-haiku": 8192,
+    "claude-3-haiku": 4096,
+}
+_FALLBACK_MAX_OUTPUT_TOKENS: Final[int] = 4096
+
+
+def _default_max_tokens_for(model: str | None) -> int:
+    """Return the default max output tokens for an Anthropic model (with fallback).
+
+    Can find the Max Tokens limits here: https://docs.anthropic.com/en/docs/about-claude/models/overview#model-comparison-table
+    """
+    if not model:
+        return _FALLBACK_MAX_OUTPUT_TOKENS
+
+    parts = model.split("-")
+    family = "-".join(parts[:-1]) if len(parts) > 1 else model
+
+    return _MODEL_DEFAULT_MAX_OUTPUT_TOKENS.get(family, _FALLBACK_MAX_OUTPUT_TOKENS)
+
+
 class AnthropicTool(TypedDict):
     """Anthropic tool definition."""
 
@@ -1229,7 +1255,7 @@ class ChatAnthropic(BaseChatModel):
     model: str = Field(alias="model_name")
     """Model name to use."""
 
-    max_tokens: int = Field(default=1024, alias="max_tokens_to_sample")
+    max_tokens: Optional[int] = Field(default=None, alias="max_tokens_to_sample")
     """Denotes the number of tokens to predict per generation."""
 
     temperature: Optional[float] = None
@@ -1367,6 +1393,15 @@ class ChatAnthropic(BaseChatModel):
             ls_params["ls_stop"] = ls_stop
         return ls_params
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_max_tokens(cls, values: dict[str, Any]) -> Any:
+        """Set default max_tokens."""
+        if values.get("max_tokens") is None:
+            model = values.get("model") or values.get("model_name")
+            values["max_tokens"] = _default_max_tokens_for(model)
+        return values
+
     @model_validator(mode="before")
     @classmethod
     def build_extra(cls, values: dict) -> Any:

libs/partners/anthropic/tests/integration_tests/test_chat_models.py

@@ -901,7 +901,10 @@ def test_image_tool_calling() -> None:
 
 @pytest.mark.vcr
 def test_web_search() -> None:
-    llm = ChatAnthropic(model="claude-3-5-sonnet-latest")  # type: ignore[call-arg]
+    llm = ChatAnthropic(
+        model="claude-3-5-sonnet-latest",  # type: ignore[call-arg]
+        max_tokens=1024,
+    )
 
     tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 1}
     llm_with_tools = llm.bind_tools([tool])

libs/partners/anthropic/tests/unit_tests/test_chat_models.py

@@ -111,6 +111,45 @@ def test_anthropic_proxy_from_environment() -> None:
         assert llm.anthropic_proxy == explicit_proxy
 
 
+def test_set_default_max_tokens() -> None:
+    """Test the set_default_max_tokens function."""
+    # Test claude-opus-4 models
+    llm = ChatAnthropic(model="claude-opus-4-20250514", anthropic_api_key="test")
+    assert llm.max_tokens == 32000
+
+    # Test claude-sonnet-4 models
+    llm = ChatAnthropic(model="claude-sonnet-4-latest", anthropic_api_key="test")
+    assert llm.max_tokens == 64000
+
+    # Test claude-3-7-sonnet models
+    llm = ChatAnthropic(model="claude-3-7-sonnet-latest", anthropic_api_key="test")
+    assert llm.max_tokens == 64000
+
+    # Test claude-3-5-sonnet models
+    llm = ChatAnthropic(model="claude-3-5-sonnet-latest", anthropic_api_key="test")
+    assert llm.max_tokens == 8192
+
+    # Test claude-3-5-haiku models
+    llm = ChatAnthropic(model="claude-3-5-haiku-latest", anthropic_api_key="test")
+    assert llm.max_tokens == 8192
+
+    # Test claude-3-haiku models (should default to 4096)
+    llm = ChatAnthropic(model="claude-3-haiku-latest", anthropic_api_key="test")
+    assert llm.max_tokens == 4096
+
+    # Test that existing max_tokens values are preserved
+    llm = ChatAnthropic(
+        model="claude-3-5-sonnet-latest", max_tokens=2048, anthropic_api_key="test"
+    )
+    assert llm.max_tokens == 2048
+
+    # Test that explicitly set max_tokens values are preserved
+    llm = ChatAnthropic(
+        model="claude-3-5-sonnet-latest", max_tokens=4096, anthropic_api_key="test"
+    )
+    assert llm.max_tokens == 4096
+
+
 @pytest.mark.requires("anthropic")
 def test_anthropic_model_name_param() -> None:
     llm = ChatAnthropic(model_name="foo")  # type: ignore[call-arg, call-arg]

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

@@ -1,7 +1,10 @@
 """
-This module converts between AIMessage output formats for the Responses API.
+This module converts between AIMessage output formats, which are governed by the
+``output_version`` attribute on ChatOpenAI. Supported values are ``"v0"`` and
+``"responses/v1"``.
 
-ChatOpenAI v0.3 stores reasoning and tool outputs in AIMessage.additional_kwargs:
+``"v0"`` corresponds to the format as of ChatOpenAI v0.3. For the Responses API, it
+stores reasoning and tool outputs in AIMessage.additional_kwargs:
 
 .. code-block:: python
 
@@ -28,8 +31,9 @@ ChatOpenAI v0.3 stores reasoning and tool outputs in AIMessage.additional_kwargs
         id="msg_123",
     )
 
-To retain information about response item sequencing (and to accommodate multiple
-reasoning items), ChatOpenAI now stores these items in the content sequence:
+``"responses/v1"`` is only applicable to the Responses API. It retains information
+about response item sequencing and accommodates multiple reasoning items by
+representing these items in the content sequence:
 
 .. code-block:: python
 
@@ -57,18 +61,20 @@ There are other, small improvements as well-- e.g., we store message IDs on text
 content blocks, rather than on the AIMessage.id, which now stores the response ID.
 
 For backwards compatibility, this module provides functions to convert between the
-old and new formats. The functions are used internally by ChatOpenAI.
-
+formats. The functions are used internally by ChatOpenAI.
 """  # noqa: E501
 
 import json
-from typing import Union
+from collections.abc import Iterable, Iterator
+from typing import Any, Literal, Union, cast
 
-from langchain_core.messages import AIMessage
+from langchain_core.messages import AIMessage, is_data_content_block
+from langchain_core.messages import content as types
 
 _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__"
 
 
+# v0.3 / Responses
 def _convert_to_v03_ai_message(
     message: AIMessage, has_reasoning: bool = False
 ) -> AIMessage:
@@ -253,3 +259,241 @@ def _convert_from_v03_ai_message(message: AIMessage) -> AIMessage:
         },
         deep=False,
     )
+
+
+# v1 / Chat Completions
+def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
+    """Convert a v1 message to the Chat Completions format."""
+    if isinstance(message.content, list):
+        new_content: list = []
+        for block in message.content:
+            if isinstance(block, dict):
+                block_type = block.get("type")
+                if block_type == "text":
+                    # Strip annotations
+                    new_content.append({"type": "text", "text": block["text"]})
+                elif block_type in ("reasoning", "tool_call"):
+                    pass
+                else:
+                    new_content.append(block)
+            else:
+                new_content.append(block)
+        return message.model_copy(update={"content": new_content})
+
+    return message
+
+
+# v1 / Responses
+def _convert_annotation_from_v1(annotation: types.Annotation) -> dict[str, Any]:
+    if annotation["type"] == "citation":
+        new_ann: dict[str, Any] = {}
+        for field in ("end_index", "start_index"):
+            if field in annotation:
+                new_ann[field] = annotation[field]
+
+        if "url" in annotation:
+            # URL citation
+            if "title" in annotation:
+                new_ann["title"] = annotation["title"]
+            new_ann["type"] = "url_citation"
+            new_ann["url"] = annotation["url"]
+        else:
+            # Document citation
+            new_ann["type"] = "file_citation"
+            if "title" in annotation:
+                new_ann["filename"] = annotation["title"]
+
+        if extra_fields := annotation.get("extras"):
+            for field, value in extra_fields.items():
+                new_ann[field] = value
+
+        return new_ann
+
+    elif annotation["type"] == "non_standard_annotation":
+        return annotation["value"]
+
+    else:
+        return dict(annotation)
+
+
+def _implode_reasoning_blocks(blocks: list[dict[str, Any]]) -> Iterable[dict[str, Any]]:
+    i = 0
+    n = len(blocks)
+
+    while i < n:
+        block = blocks[i]
+
+        # Skip non-reasoning blocks or blocks already in Responses format
+        if block.get("type") != "reasoning" or "summary" in block:
+            yield dict(block)
+            i += 1
+            continue
+        elif "reasoning" not in block and "summary" not in block:
+            # {"type": "reasoning", "id": "rs_..."}
+            oai_format = {**block, "summary": []}
+            if "extras" in oai_format:
+                oai_format.update(oai_format.pop("extras"))
+            oai_format["type"] = oai_format.pop("type", "reasoning")
+            if "encrypted_content" in oai_format:
+                oai_format["encrypted_content"] = oai_format.pop("encrypted_content")
+            yield oai_format
+            i += 1
+            continue
+        else:
+            pass
+
+        summary: list[dict[str, str]] = [
+            {"type": "summary_text", "text": block.get("reasoning", "")}
+        ]
+        # 'common' is every field except the exploded 'reasoning'
+        common = {k: v for k, v in block.items() if k != "reasoning"}
+        if "extras" in common:
+            common.update(common.pop("extras"))
+
+        i += 1
+        while i < n:
+            next_ = blocks[i]
+            if next_.get("type") == "reasoning" and "reasoning" in next_:
+                summary.append(
+                    {"type": "summary_text", "text": next_.get("reasoning", "")}
+                )
+                i += 1
+            else:
+                break
+
+        merged = dict(common)
+        merged["summary"] = summary
+        merged["type"] = merged.pop("type", "reasoning")
+        yield merged
+
+
+def _consolidate_calls(
+    items: Iterable[dict[str, Any]],
+    call_name: Literal["web_search_call", "code_interpreter_call"],
+    result_name: Literal["web_search_result", "code_interpreter_result"],
+) -> Iterator[dict[str, Any]]:
+    """
+    Generator that walks through *items* and, whenever it meets the pair
+
+        {"type": "web_search_call",    "id": X, ...}
+        {"type": "web_search_result",  "id": X}
+
+    merges them into
+
+        {"id": X,
+         "action": …,
+         "status": …,
+         "type": "web_search_call"}
+
+    keeping every other element untouched.
+    """
+    items = iter(items)  # make sure we have a true iterator
+    for current in items:
+        # Only a call can start a pair worth collapsing
+        if current.get("type") != call_name:
+            yield current
+            continue
+
+        try:
+            nxt = next(items)  # look-ahead one element
+        except StopIteration:  # no “result” – just yield the call back
+            yield current
+            break
+
+        # If this really is the matching “result” – collapse
+        if nxt.get("type") == result_name and nxt.get("id") == current.get("id"):
+            if call_name == "web_search_call":
+                collapsed = {"id": current["id"]}
+                if "action" in current:
+                    collapsed["action"] = current["action"]
+                collapsed["status"] = current["status"]
+                collapsed["type"] = "web_search_call"
+
+            if call_name == "code_interpreter_call":
+                collapsed = {"id": current["id"]}
+                for key in ("code", "container_id"):
+                    if key in current:
+                        collapsed[key] = current[key]
+                    elif key in current.get("extras", {}):
+                        collapsed[key] = current["extras"][key]
+                    else:
+                        pass
+
+                for key in ("outputs", "status"):
+                    if key in nxt:
+                        collapsed[key] = nxt[key]
+                    elif key in nxt.get("extras", {}):
+                        collapsed[key] = nxt["extras"][key]
+                    else:
+                        pass
+                collapsed["type"] = "code_interpreter_call"
+
+            yield collapsed
+
+        else:
+            # Not a matching pair – emit both, in original order
+            yield current
+            yield nxt
+
+
+def _convert_from_v1_to_responses(
+    content: list[types.ContentBlock], tool_calls: list[types.ToolCall]
+) -> list[dict[str, Any]]:
+    new_content: list = []
+    for block in content:
+        if block["type"] == "text" and "annotations" in block:
+            # Need a copy because we’re changing the annotations list
+            new_block = dict(block)
+            new_block["annotations"] = [
+                _convert_annotation_from_v1(a) for a in block["annotations"]
+            ]
+            new_content.append(new_block)
+        elif block["type"] == "tool_call":
+            new_block = {"type": "function_call", "call_id": block["id"]}
+            if "extras" in block and "item_id" in block["extras"]:
+                new_block["id"] = block["extras"]["item_id"]
+            if "name" in block:
+                new_block["name"] = block["name"]
+            if "extras" in block and "arguments" in block["extras"]:
+                new_block["arguments"] = block["extras"]["arguments"]
+            if any(key not in block for key in ("name", "arguments")):
+                matching_tool_calls = [
+                    call for call in tool_calls if call["id"] == block["id"]
+                ]
+                if matching_tool_calls:
+                    tool_call = matching_tool_calls[0]
+                    if "name" not in block:
+                        new_block["name"] = tool_call["name"]
+                    if "arguments" not in block:
+                        new_block["arguments"] = json.dumps(tool_call["args"])
+            new_content.append(new_block)
+        elif (
+            is_data_content_block(cast(dict, block))
+            and block["type"] == "image"
+            and "base64" in block
+            and isinstance(block.get("id"), str)
+            and block["id"].startswith("ig_")
+        ):
+            new_block = {"type": "image_generation_call", "result": block["base64"]}
+            for extra_key in ("id", "status"):
+                if extra_key in block:
+                    new_block[extra_key] = block[extra_key]  # type: ignore[typeddict-item]
+                elif extra_key in block.get("extras", {}):
+                    new_block[extra_key] = block["extras"][extra_key]
+            new_content.append(new_block)
+        elif block["type"] == "non_standard" and "value" in block:
+            new_content.append(block["value"])
+        else:
+            new_content.append(block)
+
+    new_content = list(_implode_reasoning_blocks(new_content))
+    new_content = list(
+        _consolidate_calls(new_content, "web_search_call", "web_search_result")
+    )
+    new_content = list(
+        _consolidate_calls(
+            new_content, "code_interpreter_call", "code_interpreter_result"
+        )
+    )
+
+    return new_content

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

@@ -69,6 +69,10 @@ from langchain_core.messages.ai import (
     OutputTokenDetails,
     UsageMetadata,
 )
+from langchain_core.messages.block_translators.openai import (
+    translate_content,
+    translate_content_chunk,
+)
 from langchain_core.messages.tool import tool_call_chunk
 from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
 from langchain_core.output_parsers.openai_tools import (
@@ -108,6 +112,8 @@ from langchain_openai.chat_models._client_utils import (
 )
 from langchain_openai.chat_models._compat import (
     _convert_from_v03_ai_message,
+    _convert_from_v1_to_chat_completions,
+    _convert_from_v1_to_responses,
     _convert_to_v03_ai_message,
 )
 
@@ -202,7 +208,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
         return ChatMessage(content=_dict.get("content", ""), role=role, id=id_)  # type: ignore[arg-type]
 
 
-def _format_message_content(content: Any) -> Any:
+def _format_message_content(content: Any, responses_ai_msg: bool = False) -> Any:
     """Format message content."""
     if content and isinstance(content, list):
         formatted_content = []
@@ -214,7 +220,13 @@ def _format_message_content(content: Any) -> Any:
                 and block["type"] in ("tool_use", "thinking", "reasoning_content")
             ):
                 continue
-            elif isinstance(block, dict) and is_data_content_block(block):
+            elif (
+                isinstance(block, dict)
+                and is_data_content_block(block)
+                # Responses API messages handled separately in _compat (parsed into
+                # image generation calls)
+                and not responses_ai_msg
+            ):
                 formatted_content.append(convert_to_openai_data_block(block))
             # Anthropic image blocks
             elif (
@@ -247,7 +259,9 @@ def _format_message_content(content: Any) -> Any:
     return formatted_content
 
 
-def _convert_message_to_dict(message: BaseMessage) -> dict:
+def _convert_message_to_dict(
+    message: BaseMessage, responses_ai_msg: bool = False
+) -> dict:
     """Convert a LangChain message to a dictionary.
 
     Args:
@@ -256,7 +270,11 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     Returns:
         The dictionary.
     """
-    message_dict: dict[str, Any] = {"content": _format_message_content(message.content)}
+    message_dict: dict[str, Any] = {
+        "content": _format_message_content(
+            message.content, responses_ai_msg=responses_ai_msg
+        )
+    }
     if (name := message.name or message.additional_kwargs.get("name")) is not None:
         message_dict["name"] = name
 
@@ -291,15 +309,25 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         if "function_call" in message_dict or "tool_calls" in message_dict:
             message_dict["content"] = message_dict["content"] or None
 
-        if "audio" in message.additional_kwargs:
-            # openai doesn't support passing the data back - only the id
-            # https://platform.openai.com/docs/guides/audio/multi-turn-conversations
+        audio: Optional[dict[str, Any]] = None
+        for block in message.content:
+            if (
+                isinstance(block, dict)
+                and block.get("type") == "audio"
+                and (id_ := block.get("id"))
+                and not responses_ai_msg
+            ):
+                # openai doesn't support passing the data back - only the id
+                # https://platform.openai.com/docs/guides/audio/multi-turn-conversations
+                audio = {"id": id_}
+        if not audio and "audio" in message.additional_kwargs:
             raw_audio = message.additional_kwargs["audio"]
             audio = (
                 {"id": message.additional_kwargs["audio"]["id"]}
                 if "id" in raw_audio
                 else raw_audio
             )
+        if audio:
             message_dict["audio"] = audio
     elif isinstance(message, SystemMessage):
         message_dict["role"] = message.additional_kwargs.get(
@@ -681,7 +709,7 @@ class BaseChatOpenAI(BaseChatModel):
     .. versionadded:: 0.3.9
     """
 
-    output_version: Literal["v0", "responses/v1"] = "v0"
+    output_version: str = "v0"
     """Version of AIMessage output format to use.
 
     This field is used to roll-out new output formats for chat model AIMessages
@@ -692,8 +720,9 @@ class BaseChatOpenAI(BaseChatModel):
     - ``'v0'``: AIMessage format as of langchain-openai 0.3.x.
     - ``'responses/v1'``: Formats Responses API output
       items into AIMessage content blocks.
+    - ``"v1"``: v1 of LangChain cross-provider standard.
 
-    Currently only impacts the Responses API. ``output_version='responses/v1'`` is
+    Currently only impacts the Responses API. ``output_version='v1'`` is
     recommended.
 
     .. versionadded:: 0.3.25
@@ -896,6 +925,10 @@ class BaseChatOpenAI(BaseChatModel):
                 message=default_chunk_class(content="", usage_metadata=usage_metadata),
                 generation_info=base_generation_info,
             )
+            if self.output_version == "v1":
+                generation_chunk.message.content = []
+                generation_chunk.message.response_metadata["output_version"] = "v1"
+
             return generation_chunk
 
         choice = choices[0]
@@ -908,6 +941,7 @@ class BaseChatOpenAI(BaseChatModel):
         generation_info = {**base_generation_info} if base_generation_info else {}
 
         if finish_reason := choice.get("finish_reason"):
+            generation_info["model_provider"] = "openai"
             generation_info["finish_reason"] = finish_reason
             if model_name := chunk.get("model"):
                 generation_info["model_name"] = model_name
@@ -923,6 +957,13 @@ class BaseChatOpenAI(BaseChatModel):
         if usage_metadata and isinstance(message_chunk, AIMessageChunk):
             message_chunk.usage_metadata = usage_metadata
 
+        if self.output_version == "v1":
+            message_chunk.content = cast(
+                "Union[str, list[Union[str, dict]]]",
+                translate_content_chunk(cast(AIMessageChunk, message_chunk)),
+            )
+            message_chunk.response_metadata["output_version"] = "v1"
+
         generation_chunk = ChatGenerationChunk(
             message=message_chunk, generation_info=generation_info or None
         )
@@ -1216,7 +1257,12 @@ class BaseChatOpenAI(BaseChatModel):
             else:
                 payload = _construct_responses_api_payload(messages, payload)
         else:
-            payload["messages"] = [_convert_message_to_dict(m) for m in messages]
+            payload["messages"] = [
+                _convert_message_to_dict(_convert_from_v1_to_chat_completions(m))
+                if isinstance(m, AIMessage)
+                else _convert_message_to_dict(m)
+                for m in messages
+            ]
         return payload
 
     def _create_chat_result(
@@ -1265,6 +1311,7 @@ class BaseChatOpenAI(BaseChatModel):
             generations.append(gen)
         llm_output = {
             "token_usage": token_usage,
+            "model_provider": "openai",
             "model_name": response_dict.get("model", self.model_name),
             "system_fingerprint": response_dict.get("system_fingerprint", ""),
         }
@@ -1282,6 +1329,13 @@ class BaseChatOpenAI(BaseChatModel):
             if hasattr(message, "refusal"):
                 generations[0].message.additional_kwargs["refusal"] = message.refusal
 
+        if self.output_version == "v1":
+            generations[0].message.content = cast(
+                Union[str, list[Union[str, dict]]],
+                translate_content(cast(AIMessage, generations[0].message)),
+            )
+            generations[0].message.response_metadata["output_version"] = "v1"
+
         return ChatResult(generations=generations, llm_output=llm_output)
 
     async def _astream(
@@ -1496,7 +1550,7 @@ class BaseChatOpenAI(BaseChatModel):
 
     def get_num_tokens_from_messages(
         self,
-        messages: list[BaseMessage],
+        messages: Sequence[BaseMessage],
         tools: Optional[
             Sequence[Union[dict[str, Any], type, Callable, BaseTool]]
         ] = None,
@@ -3384,6 +3438,20 @@ def _oai_structured_outputs_parser(
             return parsed
     elif ai_msg.additional_kwargs.get("refusal"):
         raise OpenAIRefusalError(ai_msg.additional_kwargs["refusal"])
+    elif any(
+        isinstance(block, dict)
+        and block.get("type") == "non_standard"
+        and "refusal" in block["value"]
+        for block in ai_msg.content
+    ):
+        refusal = next(
+            block["value"]["refusal"]
+            for block in ai_msg.content
+            if isinstance(block, dict)
+            and block["type"] == "non_standard"
+            and "refusal" in block["value"]
+        )
+        raise OpenAIRefusalError(refusal)
     elif ai_msg.tool_calls:
         return None
     else:
@@ -3500,7 +3568,7 @@ def _get_last_messages(
         msg = messages[i]
         if isinstance(msg, AIMessage):
             response_id = msg.response_metadata.get("id")
-            if response_id:
+            if response_id and response_id.startswith("resp_"):
                 return messages[i + 1 :], response_id
             else:
                 return messages, None
@@ -3609,23 +3677,45 @@ def _construct_responses_api_payload(
     return payload
 
 
-def _make_computer_call_output_from_message(message: ToolMessage) -> dict:
-    computer_call_output: dict = {
-        "call_id": message.tool_call_id,
-        "type": "computer_call_output",
-    }
+def _make_computer_call_output_from_message(
+    message: ToolMessage,
+) -> Optional[dict[str, Any]]:
+    computer_call_output: Optional[dict[str, Any]] = None
     if isinstance(message.content, list):
-        # Use first input_image block
-        output = next(
-            block
-            for block in message.content
-            if cast(dict, block)["type"] == "input_image"
-        )
+        for block in message.content:
+            if (
+                message.additional_kwargs.get("type") == "computer_call_output"
+                and isinstance(block, dict)
+                and block.get("type") == "input_image"
+            ):
+                # Use first input_image block
+                computer_call_output = {
+                    "call_id": message.tool_call_id,
+                    "type": "computer_call_output",
+                    "output": block,
+                }
+                break
+            elif (
+                isinstance(block, dict)
+                and block.get("type") == "non_standard"
+                and block.get("value", {}).get("type") == "computer_call_output"
+            ):
+                computer_call_output = block["value"]
+                break
+            else:
+                pass
     else:
-        # string, assume image_url
-        output = {"type": "input_image", "image_url": message.content}
-    computer_call_output["output"] = output
-    if "acknowledged_safety_checks" in message.additional_kwargs:
+        if message.additional_kwargs.get("type") == "computer_call_output":
+            # string, assume image_url
+            computer_call_output = {
+                "call_id": message.tool_call_id,
+                "type": "computer_call_output",
+                "output": {"type": "input_image", "image_url": message.content},
+            }
+    if (
+        computer_call_output is not None
+        and "acknowledged_safety_checks" in message.additional_kwargs
+    ):
         computer_call_output["acknowledged_safety_checks"] = message.additional_kwargs[
             "acknowledged_safety_checks"
         ]
@@ -3642,6 +3732,15 @@ def _make_custom_tool_output_from_message(message: ToolMessage) -> Optional[dict
                 "output": block.get("output") or "",
             }
             break
+        elif (
+            isinstance(block, dict)
+            and block.get("type") == "non_standard"
+            and block.get("value", {}).get("type") == "custom_tool_call_output"
+        ):
+            custom_tool_output = block["value"]
+            break
+        else:
+            pass
 
     return custom_tool_output
 
@@ -3666,20 +3765,33 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
     for lc_msg in messages:
         if isinstance(lc_msg, AIMessage):
             lc_msg = _convert_from_v03_ai_message(lc_msg)
-        msg = _convert_message_to_dict(lc_msg)
+            msg = _convert_message_to_dict(lc_msg, responses_ai_msg=True)
+            if isinstance(msg.get("content"), list) and all(
+                isinstance(block, dict) for block in msg["content"]
+            ):
+                msg["content"] = _convert_from_v1_to_responses(
+                    msg["content"], lc_msg.tool_calls
+                )
+        else:
+            msg = _convert_message_to_dict(lc_msg)
+            # Get content from non-standard content blocks
+            if isinstance(msg["content"], list):
+                for i, block in enumerate(msg["content"]):
+                    if isinstance(block, dict) and block.get("type") == "non_standard":
+                        msg["content"][i] = block["value"]
         # "name" parameter unsupported
         if "name" in msg:
             msg.pop("name")
         if msg["role"] == "tool":
             tool_output = msg["content"]
+            computer_call_output = _make_computer_call_output_from_message(
+                cast(ToolMessage, lc_msg)
+            )
             custom_tool_output = _make_custom_tool_output_from_message(lc_msg)  # type: ignore[arg-type]
-            if custom_tool_output:
-                input_.append(custom_tool_output)
-            elif lc_msg.additional_kwargs.get("type") == "computer_call_output":
-                computer_call_output = _make_computer_call_output_from_message(
-                    cast(ToolMessage, lc_msg)
-                )
+            if computer_call_output:
                 input_.append(computer_call_output)
+            elif custom_tool_output:
+                input_.append(custom_tool_output)
             else:
                 if not isinstance(tool_output, str):
                     tool_output = _stringify(tool_output)
@@ -3828,7 +3940,7 @@ def _construct_lc_result_from_responses_api(
     response: Response,
     schema: Optional[type[_BM]] = None,
     metadata: Optional[dict] = None,
-    output_version: Literal["v0", "responses/v1"] = "v0",
+    output_version: str = "v0",
 ) -> ChatResult:
     """Construct ChatResponse from OpenAI Response API response."""
     if response.error:
@@ -3855,6 +3967,7 @@ def _construct_lc_result_from_responses_api(
     if metadata:
         response_metadata.update(metadata)
     # for compatibility with chat completion calls.
+    response_metadata["model_provider"] = "openai"
     response_metadata["model_name"] = response_metadata.get("model")
     if response.usage:
         usage_metadata = _create_usage_metadata_responses(response.usage.model_dump())
@@ -3966,6 +4079,7 @@ def _construct_lc_result_from_responses_api(
             additional_kwargs["parsed"] = parsed
         except json.JSONDecodeError:
             pass
+
     message = AIMessage(
         content=content_blocks,
         id=response.id,
@@ -3977,6 +4091,11 @@ def _construct_lc_result_from_responses_api(
     )
     if output_version == "v0":
         message = _convert_to_v03_ai_message(message)
+    elif output_version == "v1":
+        message.content = cast(
+            Union[str, list[Union[str, dict]]], translate_content(message)
+        )
+        message.response_metadata["output_version"] = "v1"
     else:
         pass
     return ChatResult(generations=[ChatGeneration(message=message)])
@@ -3990,7 +4109,7 @@ def _convert_responses_chunk_to_generation_chunk(
     schema: Optional[type[_BM]] = None,
     metadata: Optional[dict] = None,
     has_reasoning: bool = False,
-    output_version: Literal["v0", "responses/v1"] = "v0",
+    output_version: str = "v0",
 ) -> tuple[int, int, int, Optional[ChatGenerationChunk]]:
     def _advance(output_idx: int, sub_idx: Optional[int] = None) -> None:
         """Advance indexes tracked during streaming.
@@ -4056,9 +4175,12 @@ def _convert_responses_chunk_to_generation_chunk(
             annotation = chunk.annotation
         else:
             annotation = chunk.annotation.model_dump(exclude_none=True, mode="json")
-        content.append({"annotations": [annotation], "index": current_index})
+
+        content.append(
+            {"type": "text", "annotations": [annotation], "index": current_index}
+        )
     elif chunk.type == "response.output_text.done":
-        content.append({"id": chunk.item_id, "index": current_index})
+        content.append({"type": "text", "id": chunk.item_id, "index": current_index})
     elif chunk.type == "response.created":
         id = chunk.response.id
         response_metadata["id"] = chunk.response.id  # Backwards compatibility
@@ -4151,6 +4273,7 @@ def _convert_responses_chunk_to_generation_chunk(
         content.append({"type": "refusal", "refusal": chunk.refusal})
     elif chunk.type == "response.output_item.added" and chunk.item.type == "reasoning":
         _advance(chunk.output_index)
+        current_sub_index = 0
         reasoning = chunk.item.model_dump(exclude_none=True, mode="json")
         reasoning["index"] = current_index
         content.append(reasoning)
@@ -4164,6 +4287,7 @@ def _convert_responses_chunk_to_generation_chunk(
                 ],
                 "index": current_index,
                 "type": "reasoning",
+                "id": chunk.item_id,
             }
         )
     elif chunk.type == "response.image_generation_call.partial_image":
@@ -4200,6 +4324,11 @@ def _convert_responses_chunk_to_generation_chunk(
             AIMessageChunk,
             _convert_to_v03_ai_message(message, has_reasoning=has_reasoning),
         )
+    elif output_version == "v1":
+        message.content = cast(
+            Union[str, list[Union[str, dict]]], translate_content_chunk(message)
+        )
+        message.response_metadata["output_version"] = "v1"
     else:
         pass
     return (

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

@@ -28,8 +28,9 @@ def _check_response(response: Optional[BaseMessage]) -> None:
     for block in response.content:
         assert isinstance(block, dict)
         if block["type"] == "text":
-            assert isinstance(block["text"], str)
-            for annotation in block["annotations"]:
+            assert isinstance(block.get("text"), str)
+            annotations = block.get("annotations", [])
+            for annotation in annotations:
                 if annotation["type"] == "file_citation":
                     assert all(
                         key in annotation
@@ -40,8 +41,12 @@ def _check_response(response: Optional[BaseMessage]) -> None:
                         key in annotation
                         for key in ["end_index", "start_index", "title", "type", "url"]
                     )
-
-    text_content = response.text()
+                elif annotation["type"] == "citation":
+                    assert all(key in annotation for key in ["title", "type"])
+                    if "url" in annotation:
+                        assert "start_index" in annotation
+                        assert "end_index" in annotation
+    text_content = response.text()  # type: ignore[operator,misc]
     assert isinstance(text_content, str)
     assert text_content
     assert response.usage_metadata
@@ -49,12 +54,14 @@ def _check_response(response: Optional[BaseMessage]) -> None:
     assert response.usage_metadata["output_tokens"] > 0
     assert response.usage_metadata["total_tokens"] > 0
     assert response.response_metadata["model_name"]
-    assert response.response_metadata["service_tier"]
+    assert response.response_metadata["service_tier"]  # type: ignore[typeddict-item]
 
 
+@pytest.mark.default_cassette("test_web_search.yaml.gz")
 @pytest.mark.vcr
-def test_web_search() -> None:
-    llm = ChatOpenAI(model=MODEL_NAME, output_version="responses/v1")
+@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
+def test_web_search(output_version: Literal["responses/v1", "v1"]) -> None:
+    llm = ChatOpenAI(model=MODEL_NAME, output_version=output_version)
     first_response = llm.invoke(
         "What was a positive news story from today?",
         tools=[{"type": "web_search_preview"}],
@@ -82,20 +89,9 @@ def test_web_search() -> None:
     # Manually pass in chat history
     response = llm.invoke(
         [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": "What was a positive news story from today?",
-                    }
-                ],
-            },
+            {"role": "user", "content": "What was a positive news story from today?"},
             first_response,
-            {
-                "role": "user",
-                "content": [{"type": "text", "text": "what about a negative one"}],
-            },
+            {"role": "user", "content": "what about a negative one"},
         ],
         tools=[{"type": "web_search_preview"}],
     )
@@ -108,9 +104,12 @@ def test_web_search() -> None:
     _check_response(response)
 
     for msg in [first_response, full, response]:
-        assert isinstance(msg, AIMessage)
+        assert msg is not None
         block_types = [block["type"] for block in msg.content]  # type: ignore[index]
-        assert block_types == ["web_search_call", "text"]
+        if output_version == "responses/v1":
+            assert block_types == ["web_search_call", "text"]
+        else:
+            assert block_types == ["web_search_call", "web_search_result", "text"]
 
 
 @pytest.mark.flaky(retries=3, delay=1)
@@ -141,13 +140,15 @@ async def test_web_search_async() -> None:
         assert tool_output["type"] == "web_search_call"
 
 
-@pytest.mark.flaky(retries=3, delay=1)
-def test_function_calling() -> None:
+@pytest.mark.default_cassette("test_function_calling.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_function_calling(output_version: Literal["v0", "responses/v1", "v1"]) -> None:
     def multiply(x: int, y: int) -> int:
         """return x * y"""
         return x * y
 
-    llm = ChatOpenAI(model=MODEL_NAME)
+    llm = ChatOpenAI(model=MODEL_NAME, output_version=output_version)
     bound_llm = llm.bind_tools([multiply, {"type": "web_search_preview"}])
     ai_msg = cast(AIMessage, bound_llm.invoke("whats 5 * 4"))
     assert len(ai_msg.tool_calls) == 1
@@ -174,8 +175,15 @@ class FooDict(TypedDict):
     response: str
 
 
-def test_parsed_pydantic_schema() -> None:
-    llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
+@pytest.mark.default_cassette("test_parsed_pydantic_schema.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_parsed_pydantic_schema(
+    output_version: Literal["v0", "responses/v1", "v1"],
+) -> None:
+    llm = ChatOpenAI(
+        model=MODEL_NAME, use_responses_api=True, output_version=output_version
+    )
     response = llm.invoke("how are ya", response_format=Foo)
     parsed = Foo(**json.loads(response.text()))
     assert parsed == response.additional_kwargs["parsed"]
@@ -297,8 +305,8 @@ def test_function_calling_and_structured_output() -> None:
 
 @pytest.mark.default_cassette("test_reasoning.yaml.gz")
 @pytest.mark.vcr
-@pytest.mark.parametrize("output_version", ["v0", "responses/v1"])
-def test_reasoning(output_version: Literal["v0", "responses/v1"]) -> None:
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_reasoning(output_version: Literal["v0", "responses/v1", "v1"]) -> None:
     llm = ChatOpenAI(
         model="o4-mini", use_responses_api=True, output_version=output_version
     )
@@ -358,27 +366,32 @@ def test_computer_calls() -> None:
 
 def test_file_search() -> None:
     pytest.skip()  # TODO: set up infra
-    llm = ChatOpenAI(model=MODEL_NAME)
+    llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
     tool = {
         "type": "file_search",
         "vector_store_ids": [os.environ["OPENAI_VECTOR_STORE_ID"]],
     }
-    response = llm.invoke("What is deep research by OpenAI?", tools=[tool])
+
+    input_message = {"role": "user", "content": "What is deep research by OpenAI?"}
+    response = llm.invoke([input_message], tools=[tool])
     _check_response(response)
 
     full: Optional[BaseMessageChunk] = None
-    for chunk in llm.stream("What is deep research by OpenAI?", tools=[tool]):
+    for chunk in llm.stream([input_message], tools=[tool]):
         assert isinstance(chunk, AIMessageChunk)
         full = chunk if full is None else full + chunk
     assert isinstance(full, AIMessageChunk)
     _check_response(full)
 
+    next_message = {"role": "user", "content": "Thank you."}
+    _ = llm.invoke([input_message, full, next_message])
+
 
 @pytest.mark.default_cassette("test_stream_reasoning_summary.yaml.gz")
 @pytest.mark.vcr
-@pytest.mark.parametrize("output_version", ["v0", "responses/v1"])
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
 def test_stream_reasoning_summary(
-    output_version: Literal["v0", "responses/v1"],
+    output_version: Literal["v0", "responses/v1", "v1"],
 ) -> None:
     llm = ChatOpenAI(
         model="o4-mini",
@@ -398,7 +411,14 @@ def test_stream_reasoning_summary(
     if output_version == "v0":
         reasoning = response_1.additional_kwargs["reasoning"]
         assert set(reasoning.keys()) == {"id", "type", "summary"}
-    else:
+        summary = reasoning["summary"]
+        assert isinstance(summary, list)
+        for block in summary:
+            assert isinstance(block, dict)
+            assert isinstance(block["type"], str)
+            assert isinstance(block["text"], str)
+            assert block["text"]
+    elif output_version == "responses/v1":
         reasoning = next(
             block
             for block in response_1.content
@@ -407,13 +427,27 @@ def test_stream_reasoning_summary(
         if isinstance(reasoning, str):
             reasoning = json.loads(reasoning)
         assert set(reasoning.keys()) == {"id", "type", "summary", "index"}
-    summary = reasoning["summary"]
-    assert isinstance(summary, list)
-    for block in summary:
-        assert isinstance(block, dict)
-        assert isinstance(block["type"], str)
-        assert isinstance(block["text"], str)
-        assert block["text"]
+        summary = reasoning["summary"]
+        assert isinstance(summary, list)
+        for block in summary:
+            assert isinstance(block, dict)
+            assert isinstance(block["type"], str)
+            assert isinstance(block["text"], str)
+            assert block["text"]
+    else:
+        # v1
+        total_reasoning_blocks = 0
+        for block in response_1.content_blocks:
+            if block["type"] == "reasoning":
+                total_reasoning_blocks += 1
+                assert isinstance(block.get("id"), str) and block.get(
+                    "id", ""
+                ).startswith("rs_")
+                assert isinstance(block.get("reasoning"), str)
+                assert isinstance(block.get("index"), str)
+        assert (
+            total_reasoning_blocks > 1
+        )  # This query typically generates multiple reasoning blocks
 
     # Check we can pass back summaries
     message_2 = {"role": "user", "content": "Thank you."}
@@ -421,9 +455,13 @@ def test_stream_reasoning_summary(
     assert isinstance(response_2, AIMessage)
 
 
+@pytest.mark.default_cassette("test_code_interpreter.yaml.gz")
 @pytest.mark.vcr
-def test_code_interpreter() -> None:
-    llm = ChatOpenAI(model="o4-mini", use_responses_api=True)
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_code_interpreter(output_version: Literal["v0", "responses/v1", "v1"]) -> None:
+    llm = ChatOpenAI(
+        model="o4-mini", use_responses_api=True, output_version=output_version
+    )
     llm_with_tools = llm.bind_tools(
         [{"type": "code_interpreter", "container": {"type": "auto"}}]
     )
@@ -432,16 +470,43 @@ def test_code_interpreter() -> None:
         "content": "Write and run code to answer the question: what is 3^3?",
     }
     response = llm_with_tools.invoke([input_message])
+    assert isinstance(response, AIMessage)
     _check_response(response)
-    tool_outputs = response.additional_kwargs["tool_outputs"]
-    assert tool_outputs
-    assert any(output["type"] == "code_interpreter_call" for output in tool_outputs)
+    if output_version == "v0":
+        tool_outputs = [
+            item
+            for item in response.additional_kwargs["tool_outputs"]
+            if item["type"] == "code_interpreter_call"
+        ]
+        assert len(tool_outputs) == 1
+    elif output_version == "responses/v1":
+        tool_outputs = [
+            item
+            for item in response.content
+            if isinstance(item, dict) and item["type"] == "code_interpreter_call"
+        ]
+        assert len(tool_outputs) == 1
+    else:
+        # v1
+        tool_outputs = [
+            item
+            for item in response.content_blocks
+            if item["type"] == "code_interpreter_call"
+        ]
+        code_interpreter_result = next(
+            item
+            for item in response.content_blocks
+            if item["type"] == "code_interpreter_result"
+        )
+        assert tool_outputs
+        assert code_interpreter_result
+    assert len(tool_outputs) == 1
 
     # Test streaming
     # Use same container
-    tool_outputs = response.additional_kwargs["tool_outputs"]
-    assert len(tool_outputs) == 1
-    container_id = tool_outputs[0]["container_id"]
+    container_id = tool_outputs[0].get("container_id") or tool_outputs[0].get(
+        "extras", {}
+    ).get("container_id")
     llm_with_tools = llm.bind_tools(
         [{"type": "code_interpreter", "container": container_id}]
     )
@@ -451,9 +516,34 @@ def test_code_interpreter() -> None:
         assert isinstance(chunk, AIMessageChunk)
         full = chunk if full is None else full + chunk
     assert isinstance(full, AIMessageChunk)
-    tool_outputs = full.additional_kwargs["tool_outputs"]
-    assert tool_outputs
-    assert any(output["type"] == "code_interpreter_call" for output in tool_outputs)
+    if output_version == "v0":
+        tool_outputs = [
+            item
+            for item in response.additional_kwargs["tool_outputs"]
+            if item["type"] == "code_interpreter_call"
+        ]
+        assert tool_outputs
+    elif output_version == "responses/v1":
+        tool_outputs = [
+            item
+            for item in response.content
+            if isinstance(item, dict) and item["type"] == "code_interpreter_call"
+        ]
+        assert tool_outputs
+    else:
+        # v1
+        code_interpreter_call = next(
+            item
+            for item in full.content_blocks
+            if item["type"] == "code_interpreter_call"
+        )
+        code_interpreter_result = next(
+            item
+            for item in full.content_blocks
+            if item["type"] == "code_interpreter_result"
+        )
+        assert code_interpreter_call
+        assert code_interpreter_result
 
     # Test we can pass back in
     next_message = {"role": "user", "content": "Please add more comments to the code."}
@@ -548,10 +638,69 @@ def test_mcp_builtin_zdr() -> None:
     _ = llm_with_tools.invoke([input_message, full, approval_message])
 
 
-@pytest.mark.vcr()
-def test_image_generation_streaming() -> None:
+@pytest.mark.default_cassette("test_mcp_builtin_zdr.yaml.gz")
+@pytest.mark.vcr
+def test_mcp_builtin_zdr_v1() -> None:
+    llm = ChatOpenAI(
+        model="o4-mini",
+        output_version="v1",
+        store=False,
+        include=["reasoning.encrypted_content"],
+    )
+
+    llm_with_tools = llm.bind_tools(
+        [
+            {
+                "type": "mcp",
+                "server_label": "deepwiki",
+                "server_url": "https://mcp.deepwiki.com/mcp",
+                "require_approval": {"always": {"tool_names": ["read_wiki_structure"]}},
+            }
+        ]
+    )
+    input_message = {
+        "role": "user",
+        "content": (
+            "What transport protocols does the 2025-03-26 version of the MCP spec "
+            "support?"
+        ),
+    }
+    full: Optional[BaseMessageChunk] = None
+    for chunk in llm_with_tools.stream([input_message]):
+        assert isinstance(chunk, AIMessageChunk)
+        full = chunk if full is None else full + chunk
+
+    assert isinstance(full, AIMessageChunk)
+    assert all(isinstance(block, dict) for block in full.content)
+
+    approval_message = HumanMessage(
+        [
+            {
+                "type": "non_standard",
+                "value": {
+                    "type": "mcp_approval_response",
+                    "approve": True,
+                    "approval_request_id": block["value"]["id"],  # type: ignore[index]
+                },
+            }
+            for block in full.content_blocks
+            if block["type"] == "non_standard"
+            and block["value"]["type"] == "mcp_approval_request"  # type: ignore[index]
+        ]
+    )
+    _ = llm_with_tools.invoke([input_message, full, approval_message])
+
+
+@pytest.mark.default_cassette("test_image_generation_streaming.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1"])
+def test_image_generation_streaming(
+    output_version: Literal["v0", "responses/v1"],
+) -> None:
     """Test image generation streaming."""
-    llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True)
+    llm = ChatOpenAI(
+        model="gpt-4.1", use_responses_api=True, output_version=output_version
+    )
     tool = {
         "type": "image_generation",
         # For testing purposes let's keep the quality low, so the test runs faster.
@@ -598,15 +747,69 @@ def test_image_generation_streaming() -> None:
     # At the moment, the streaming API does not pick up annotations fully.
     # So the following check is commented out.
     # _check_response(complete_ai_message)
-    tool_output = complete_ai_message.additional_kwargs["tool_outputs"][0]
-    assert set(tool_output.keys()).issubset(expected_keys)
+    if output_version == "v0":
+        assert complete_ai_message.additional_kwargs["tool_outputs"]
+        tool_output = complete_ai_message.additional_kwargs["tool_outputs"][0]
+        assert set(tool_output.keys()).issubset(expected_keys)
+    else:
+        # "responses/v1"
+        tool_output = next(
+            block
+            for block in complete_ai_message.content
+            if isinstance(block, dict) and block["type"] == "image_generation_call"
+        )
+        assert set(tool_output.keys()).issubset(expected_keys)
 
 
-@pytest.mark.vcr()
-def test_image_generation_multi_turn() -> None:
+@pytest.mark.default_cassette("test_image_generation_streaming.yaml.gz")
+@pytest.mark.vcr
+def test_image_generation_streaming_v1() -> None:
+    """Test image generation streaming."""
+    llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True, output_version="v1")
+    tool = {
+        "type": "image_generation",
+        "quality": "low",
+        "output_format": "jpeg",
+        "output_compression": 100,
+        "size": "1024x1024",
+    }
+
+    standard_keys = {"type", "base64", "mime_type", "id", "index"}
+    extra_keys = {
+        "background",
+        "output_format",
+        "quality",
+        "revised_prompt",
+        "size",
+        "status",
+    }
+
+    full: Optional[BaseMessageChunk] = None
+    for chunk in llm.stream("Draw a random short word in green font.", tools=[tool]):
+        assert isinstance(chunk, AIMessageChunk)
+        full = chunk if full is None else full + chunk
+    complete_ai_message = cast(AIMessageChunk, full)
+
+    tool_output = next(
+        block
+        for block in complete_ai_message.content
+        if isinstance(block, dict) and block["type"] == "image"
+    )
+    assert set(standard_keys).issubset(tool_output.keys())
+    assert set(extra_keys).issubset(tool_output["extras"].keys())
+
+
+@pytest.mark.default_cassette("test_image_generation_multi_turn.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1"])
+def test_image_generation_multi_turn(
+    output_version: Literal["v0", "responses/v1"],
+) -> None:
     """Test multi-turn editing of image generation by passing in history."""
     # Test multi-turn
-    llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True)
+    llm = ChatOpenAI(
+        model="gpt-4.1", use_responses_api=True, output_version=output_version
+    )
     # Test invocation
     tool = {
         "type": "image_generation",
@@ -622,10 +825,41 @@ def test_image_generation_multi_turn() -> None:
         {"role": "user", "content": "Draw a random short word in green font."}
     ]
     ai_message = llm_with_tools.invoke(chat_history)
+    assert isinstance(ai_message, AIMessage)
     _check_response(ai_message)
-    tool_output = ai_message.additional_kwargs["tool_outputs"][0]
 
-    # Example tool output for an image
+    expected_keys = {
+        "id",
+        "background",
+        "output_format",
+        "quality",
+        "result",
+        "revised_prompt",
+        "size",
+        "status",
+        "type",
+    }
+
+    if output_version == "v0":
+        tool_output = ai_message.additional_kwargs["tool_outputs"][0]
+        assert set(tool_output.keys()).issubset(expected_keys)
+    elif output_version == "responses/v1":
+        tool_output = next(
+            block
+            for block in ai_message.content
+            if isinstance(block, dict) and block["type"] == "image_generation_call"
+        )
+        assert set(tool_output.keys()).issubset(expected_keys)
+    else:
+        standard_keys = {"type", "base64", "id", "status"}
+        tool_output = next(
+            block
+            for block in ai_message.content
+            if isinstance(block, dict) and block["type"] == "image"
+        )
+        assert set(standard_keys).issubset(tool_output.keys())
+
+    # Example tool output for an image (v0)
     # {
     #     "background": "opaque",
     #     "id": "ig_683716a8ddf0819888572b20621c7ae4029ec8c11f8dacf8",
@@ -641,20 +875,6 @@ def test_image_generation_multi_turn() -> None:
     #     "result": # base64 encode image data
     # }
 
-    expected_keys = {
-        "id",
-        "background",
-        "output_format",
-        "quality",
-        "result",
-        "revised_prompt",
-        "size",
-        "status",
-        "type",
-    }
-
-    assert set(tool_output.keys()).issubset(expected_keys)
-
     chat_history.extend(
         [
             # AI message with tool output
@@ -671,9 +891,89 @@ def test_image_generation_multi_turn() -> None:
     )
 
     ai_message2 = llm_with_tools.invoke(chat_history)
+    assert isinstance(ai_message2, AIMessage)
     _check_response(ai_message2)
-    tool_output2 = ai_message2.additional_kwargs["tool_outputs"][0]
-    assert set(tool_output2.keys()).issubset(expected_keys)
+
+    if output_version == "v0":
+        tool_output = ai_message2.additional_kwargs["tool_outputs"][0]
+        assert set(tool_output.keys()).issubset(expected_keys)
+    else:
+        # "responses/v1"
+        tool_output = next(
+            block
+            for block in ai_message2.content
+            if isinstance(block, dict) and block["type"] == "image_generation_call"
+        )
+        assert set(tool_output.keys()).issubset(expected_keys)
+
+
+@pytest.mark.default_cassette("test_image_generation_multi_turn.yaml.gz")
+@pytest.mark.vcr
+def test_image_generation_multi_turn_v1() -> None:
+    """Test multi-turn editing of image generation by passing in history."""
+    # Test multi-turn
+    llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True, output_version="v1")
+    # Test invocation
+    tool = {
+        "type": "image_generation",
+        "quality": "low",
+        "output_format": "jpeg",
+        "output_compression": 100,
+        "size": "1024x1024",
+    }
+    llm_with_tools = llm.bind_tools([tool])
+
+    chat_history: list[MessageLikeRepresentation] = [
+        {"role": "user", "content": "Draw a random short word in green font."}
+    ]
+    ai_message = llm_with_tools.invoke(chat_history)
+    assert isinstance(ai_message, AIMessage)
+    _check_response(ai_message)
+
+    standard_keys = {"type", "base64", "mime_type", "id"}
+    extra_keys = {
+        "background",
+        "output_format",
+        "quality",
+        "revised_prompt",
+        "size",
+        "status",
+    }
+
+    tool_output = next(
+        block
+        for block in ai_message.content
+        if isinstance(block, dict) and block["type"] == "image"
+    )
+    assert set(standard_keys).issubset(tool_output.keys())
+    assert set(extra_keys).issubset(tool_output["extras"].keys())
+
+    chat_history.extend(
+        [
+            # AI message with tool output
+            ai_message,
+            # New request
+            {
+                "role": "user",
+                "content": (
+                    "Now, change the font to blue. Keep the word and everything else "
+                    "the same."
+                ),
+            },
+        ]
+    )
+
+    ai_message2 = llm_with_tools.invoke(chat_history)
+    assert isinstance(ai_message2, AIMessage)
+    _check_response(ai_message2)
+
+    tool_output = next(
+        block
+        for block in ai_message2.content
+        if isinstance(block, dict) and block["type"] == "image"
+    )
+    assert set(standard_keys).issubset(tool_output.keys())
+    assert set(extra_keys).issubset(tool_output["extras"].keys())
 
 
 def test_verbosity_parameter() -> None:
@@ -689,14 +989,16 @@ def test_verbosity_parameter() -> None:
     assert response.content
 
 
-@pytest.mark.vcr()
-def test_custom_tool() -> None:
+@pytest.mark.default_cassette("test_custom_tool.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["responses/v1", "v1"])
+def test_custom_tool(output_version: Literal["responses/v1", "v1"]) -> None:
     @custom_tool
     def execute_code(code: str) -> str:
         """Execute python code."""
         return "27"
 
-    llm = ChatOpenAI(model="gpt-5", output_version="responses/v1").bind_tools(
+    llm = ChatOpenAI(model="gpt-5", output_version=output_version).bind_tools(
         [execute_code]
     )
 

libs/partners/openai/tests/unit_tests/chat_models/test_base.py

@@ -26,7 +26,7 @@ from langchain_core.runnables import RunnableLambda
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.schemas import Run
 from openai.types.responses import ResponseOutputMessage, ResponseReasoningItem
-from openai.types.responses.response import IncompleteDetails, Response, ResponseUsage
+from openai.types.responses.response import IncompleteDetails, Response
 from openai.types.responses.response_error import ResponseError
 from openai.types.responses.response_file_search_tool_call import (
     ResponseFileSearchToolCall,
@@ -43,6 +43,7 @@ from openai.types.responses.response_reasoning_item import Summary
 from openai.types.responses.response_usage import (
     InputTokensDetails,
     OutputTokensDetails,
+    ResponseUsage,
 )
 from pydantic import BaseModel, Field, SecretStr
 from typing_extensions import TypedDict
@@ -51,6 +52,8 @@ from langchain_openai import ChatOpenAI
 from langchain_openai.chat_models._compat import (
     _FUNCTION_CALL_IDS_MAP_KEY,
     _convert_from_v03_ai_message,
+    _convert_from_v1_to_chat_completions,
+    _convert_from_v1_to_responses,
     _convert_to_v03_ai_message,
 )
 from langchain_openai.chat_models.base import (
@@ -1231,7 +1234,7 @@ def test_structured_outputs_parser() -> None:
     serialized = dumps(llm_output)
     deserialized = loads(serialized)
     assert isinstance(deserialized, ChatGeneration)
-    result = output_parser.invoke(deserialized.message)
+    result = output_parser.invoke(cast(AIMessage, deserialized.message))
     assert result == parsed_response
 
 
@@ -2374,7 +2377,7 @@ def test_mcp_tracing() -> None:
     assert payload["tools"][0]["headers"]["Authorization"] == "Bearer PLACEHOLDER"
 
 
-def test_compat() -> None:
+def test_compat_responses_v03() -> None:
     # Check compatibility with v0.3 message format
     message_v03 = AIMessage(
         content=[
@@ -2435,6 +2438,152 @@ def test_compat() -> None:
     assert message_v03_output is not message_v03
 
 
+@pytest.mark.parametrize(
+    "message_v1, expected",
+    [
+        (
+            AIMessage(
+                [
+                    {"type": "reasoning", "reasoning": "Reasoning text"},
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        "type": "text",
+                        "text": "Hello, world!",
+                        "annotations": [
+                            {"type": "citation", "url": "https://example.com"}
+                        ],
+                    },
+                ],
+                id="chatcmpl-123",
+                response_metadata={"model_provider": "openai", "model_name": "gpt-4.1"},
+            ),
+            AIMessage(
+                [{"type": "text", "text": "Hello, world!"}],
+                id="chatcmpl-123",
+                response_metadata={"model_provider": "openai", "model_name": "gpt-4.1"},
+            ),
+        )
+    ],
+)
+def test_convert_from_v1_to_chat_completions(
+    message_v1: AIMessage, expected: AIMessage
+) -> None:
+    result = _convert_from_v1_to_chat_completions(message_v1)
+    assert result == expected
+    assert result.tool_calls == message_v1.tool_calls  # tool calls remain cached
+
+    # Check no mutation
+    assert message_v1 != result
+
+
+@pytest.mark.parametrize(
+    "message_v1, expected",
+    [
+        (
+            AIMessage(
+                content_blocks=[
+                    {"type": "reasoning", "id": "abc123"},
+                    {"type": "reasoning", "id": "abc234", "reasoning": "foo "},
+                    {"type": "reasoning", "id": "abc234", "reasoning": "bar"},
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_2",
+                        "args": {"location": "New York"},
+                        "extras": {"item_id": "fc_123"},
+                    },
+                    {"type": "text", "text": "Hello "},
+                    {
+                        "type": "text",
+                        "text": "world",
+                        "annotations": [
+                            {"type": "citation", "url": "https://example.com"},
+                            {
+                                "type": "citation",
+                                "title": "my doc",
+                                "extras": {"file_id": "file_123", "index": 1},
+                            },
+                            {
+                                "type": "non_standard_annotation",
+                                "value": {"bar": "baz"},
+                            },
+                        ],
+                    },
+                    {"type": "image", "base64": "...", "id": "ig_123"},
+                    {
+                        "type": "non_standard",
+                        "value": {"type": "something_else", "foo": "bar"},
+                    },
+                ],
+                id="resp123",
+            ),
+            [
+                {"type": "reasoning", "id": "abc123", "summary": []},
+                {
+                    "type": "reasoning",
+                    "id": "abc234",
+                    "summary": [
+                        {"type": "summary_text", "text": "foo "},
+                        {"type": "summary_text", "text": "bar"},
+                    ],
+                },
+                {
+                    "type": "function_call",
+                    "call_id": "call_123",
+                    "name": "get_weather",
+                    "arguments": '{"location": "San Francisco"}',
+                },
+                {
+                    "type": "function_call",
+                    "call_id": "call_234",
+                    "name": "get_weather_2",
+                    "arguments": '{"location": "New York"}',
+                    "id": "fc_123",
+                },
+                {"type": "text", "text": "Hello "},
+                {
+                    "type": "text",
+                    "text": "world",
+                    "annotations": [
+                        {"type": "url_citation", "url": "https://example.com"},
+                        {
+                            "type": "file_citation",
+                            "filename": "my doc",
+                            "index": 1,
+                            "file_id": "file_123",
+                        },
+                        {"bar": "baz"},
+                    ],
+                },
+                {"type": "image_generation_call", "id": "ig_123", "result": "..."},
+                {"type": "something_else", "foo": "bar"},
+            ],
+        )
+    ],
+)
+def test_convert_from_v1_to_responses(
+    message_v1: AIMessage, expected: list[dict[str, Any]]
+) -> None:
+    result = _convert_from_v1_to_responses(
+        message_v1.content_blocks, message_v1.tool_calls
+    )
+    assert result == expected
+
+    # Check no mutation
+    assert message_v1 != result
+
+
 def test_get_last_messages() -> None:
     messages: list[BaseMessage] = [HumanMessage("Hello")]
     last_messages, previous_response_id = _get_last_messages(messages)

libs/partners/openai/tests/unit_tests/chat_models/test_responses_stream.py

@@ -1,6 +1,7 @@
 from typing import Any, Optional
 from unittest.mock import MagicMock, patch
 
+import pytest
 from langchain_core.messages import AIMessageChunk, BaseMessageChunk
 from openai.types.responses import (
     ResponseCompletedEvent,
@@ -20,7 +21,7 @@ from openai.types.responses import (
     ResponseTextDeltaEvent,
     ResponseTextDoneEvent,
 )
-from openai.types.responses.response import Response, ResponseUsage
+from openai.types.responses.response import Response
 from openai.types.responses.response_output_text import ResponseOutputText
 from openai.types.responses.response_reasoning_item import Summary
 from openai.types.responses.response_reasoning_summary_part_added_event import (
@@ -32,6 +33,7 @@ from openai.types.responses.response_reasoning_summary_part_done_event import (
 from openai.types.responses.response_usage import (
     InputTokensDetails,
     OutputTokensDetails,
+    ResponseUsage,
 )
 from openai.types.shared.reasoning import Reasoning
 from openai.types.shared.response_format_text import ResponseFormatText
@@ -337,7 +339,7 @@ responses_stream = [
             id="rs_234",
             summary=[],
             type="reasoning",
-            encrypted_content=None,
+            encrypted_content="encrypted-content",
             status=None,
         ),
         output_index=2,
@@ -416,7 +418,7 @@ responses_stream = [
                 Summary(text="still more reasoning", type="summary_text"),
             ],
             type="reasoning",
-            encrypted_content=None,
+            encrypted_content="encrypted-content",
             status=None,
         ),
         output_index=2,
@@ -562,7 +564,7 @@ responses_stream = [
                         Summary(text="still more reasoning", type="summary_text"),
                     ],
                     type="reasoning",
-                    encrypted_content=None,
+                    encrypted_content="encrypted-content",
                     status=None,
                 ),
                 ResponseOutputMessage(
@@ -620,8 +622,104 @@ def _strip_none(obj: Any) -> Any:
         return obj
 
 
-def test_responses_stream() -> None:
-    llm = ChatOpenAI(model="o4-mini", output_version="responses/v1")
+@pytest.mark.parametrize(
+    "output_version, expected_content",
+    [
+        (
+            "responses/v1",
+            [
+                {
+                    "id": "rs_123",
+                    "summary": [
+                        {
+                            "index": 0,
+                            "type": "summary_text",
+                            "text": "reasoning block one",
+                        },
+                        {
+                            "index": 1,
+                            "type": "summary_text",
+                            "text": "another reasoning block",
+                        },
+                    ],
+                    "type": "reasoning",
+                    "index": 0,
+                },
+                {"type": "text", "text": "text block one", "index": 1, "id": "msg_123"},
+                {
+                    "type": "text",
+                    "text": "another text block",
+                    "index": 2,
+                    "id": "msg_123",
+                },
+                {
+                    "id": "rs_234",
+                    "summary": [
+                        {"index": 0, "type": "summary_text", "text": "more reasoning"},
+                        {
+                            "index": 1,
+                            "type": "summary_text",
+                            "text": "still more reasoning",
+                        },
+                    ],
+                    "encrypted_content": "encrypted-content",
+                    "type": "reasoning",
+                    "index": 3,
+                },
+                {"type": "text", "text": "more", "index": 4, "id": "msg_234"},
+                {"type": "text", "text": "text", "index": 5, "id": "msg_234"},
+            ],
+        ),
+        (
+            "v1",
+            [
+                {
+                    "type": "reasoning",
+                    "reasoning": "reasoning block one",
+                    "id": "rs_123",
+                    "index": "lc_rs_305f30",
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "another reasoning block",
+                    "id": "rs_123",
+                    "index": "lc_rs_305f31",
+                },
+                {
+                    "type": "text",
+                    "text": "text block one",
+                    "index": "lc_txt_1",
+                    "id": "msg_123",
+                },
+                {
+                    "type": "text",
+                    "text": "another text block",
+                    "index": "lc_txt_2",
+                    "id": "msg_123",
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "more reasoning",
+                    "id": "rs_234",
+                    "extras": {"encrypted_content": "encrypted-content"},
+                    "index": "lc_rs_335f30",
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "still more reasoning",
+                    "id": "rs_234",
+                    "index": "lc_rs_335f31",
+                },
+                {"type": "text", "text": "more", "index": "lc_txt_4", "id": "msg_234"},
+                {"type": "text", "text": "text", "index": "lc_txt_5", "id": "msg_234"},
+            ],
+        ),
+    ],
+)
+def test_responses_stream(output_version: str, expected_content: list[dict]) -> None:
+    llm = ChatOpenAI(
+        model="o4-mini", use_responses_api=True, output_version=output_version
+    )
     mock_client = MagicMock()
 
     def mock_create(*args: Any, **kwargs: Any) -> MockSyncContextManager:
@@ -630,36 +728,14 @@ def test_responses_stream() -> None:
     mock_client.responses.create = mock_create
 
     full: Optional[BaseMessageChunk] = None
+    chunks = []
     with patch.object(llm, "root_client", mock_client):
         for chunk in llm.stream("test"):
             assert isinstance(chunk, AIMessageChunk)
             full = chunk if full is None else full + chunk
+            chunks.append(chunk)
     assert isinstance(full, AIMessageChunk)
 
-    expected_content = [
-        {
-            "id": "rs_123",
-            "summary": [
-                {"index": 0, "type": "summary_text", "text": "reasoning block one"},
-                {"index": 1, "type": "summary_text", "text": "another reasoning block"},
-            ],
-            "type": "reasoning",
-            "index": 0,
-        },
-        {"type": "text", "text": "text block one", "index": 1, "id": "msg_123"},
-        {"type": "text", "text": "another text block", "index": 2, "id": "msg_123"},
-        {
-            "id": "rs_234",
-            "summary": [
-                {"index": 0, "type": "summary_text", "text": "more reasoning"},
-                {"index": 1, "type": "summary_text", "text": "still more reasoning"},
-            ],
-            "type": "reasoning",
-            "index": 3,
-        },
-        {"type": "text", "text": "more", "index": 4, "id": "msg_234"},
-        {"type": "text", "text": "text", "index": 5, "id": "msg_234"},
-    ]
     assert full.content == expected_content
     assert full.additional_kwargs == {}
     assert full.id == "resp_123"