add helpers

libs/core/langchain_core/language_models/chat_models.py

@@ -311,6 +311,20 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
     does not properly support streaming.
     """
 
+    output_version: Literal["v0", "v1"] = "v0"
+    """Version of AIMessage output format to use.
+
+    This field is used to roll-out new output formats for chat model AIMessages
+    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:: 0.3.68
+    """
+
     @model_validator(mode="before")
     @classmethod
     def raise_deprecation(cls, values: dict) -> Any:

libs/core/langchain_core/messages/__init__.py

@@ -33,6 +33,15 @@ if TYPE_CHECKING:
     )
     from langchain_core.messages.chat import ChatMessage, ChatMessageChunk
     from langchain_core.messages.content_blocks import (
+        Base64ContentBlock,
+        ContentBlock,
+        DocumentCitation,
+        NonStandardAnnotation,
+        NonStandardContentBlock,
+        ReasoningContentBlock,
+        TextContentBlock,
+        ToolCallContentBlock,
+        UrlCitation,
         convert_to_openai_data_block,
         convert_to_openai_image_block,
         is_data_content_block,
@@ -66,23 +75,32 @@ __all__ = (
     "AIMessage",
     "AIMessageChunk",
     "AnyMessage",
+    "Base64ContentBlock",
     "BaseMessage",
     "BaseMessageChunk",
     "ChatMessage",
     "ChatMessageChunk",
+    "ContentBlock",
+    "DocumentCitation",
     "FunctionMessage",
     "FunctionMessageChunk",
     "HumanMessage",
     "HumanMessageChunk",
     "InvalidToolCall",
     "MessageLikeRepresentation",
+    "NonStandardAnnotation",
+    "NonStandardContentBlock",
+    "ReasoningContentBlock",
     "RemoveMessage",
     "SystemMessage",
     "SystemMessageChunk",
+    "TextContentBlock",
     "ToolCall",
     "ToolCallChunk",
+    "ToolCallContentBlock",
     "ToolMessage",
     "ToolMessageChunk",
+    "UrlCitation",
     "_message_from_dict",
     "convert_to_messages",
     "convert_to_openai_data_block",
@@ -103,25 +121,34 @@ __all__ = (
 _dynamic_imports = {
     "AIMessage": "ai",
     "AIMessageChunk": "ai",
+    "Base64ContentBlock": "content_blocks",
     "BaseMessage": "base",
     "BaseMessageChunk": "base",
     "merge_content": "base",
     "message_to_dict": "base",
     "messages_to_dict": "base",
+    "ContentBlock": "content_blocks",
     "ChatMessage": "chat",
     "ChatMessageChunk": "chat",
+    "DocumentCitation": "content_blocks",
     "FunctionMessage": "function",
     "FunctionMessageChunk": "function",
     "HumanMessage": "human",
     "HumanMessageChunk": "human",
+    "NonStandardAnnotation": "content_blocks",
+    "NonStandardContentBlock": "content_blocks",
+    "ReasoningContentBlock": "content_blocks",
     "RemoveMessage": "modifier",
     "SystemMessage": "system",
     "SystemMessageChunk": "system",
     "InvalidToolCall": "tool",
+    "TextContentBlock": "content_blocks",
     "ToolCall": "tool",
     "ToolCallChunk": "tool",
+    "ToolCallContentBlock": "content_blocks",
     "ToolMessage": "tool",
     "ToolMessageChunk": "tool",
+    "UrlCitation": "content_blocks",
     "AnyMessage": "utils",
     "MessageLikeRepresentation": "utils",
     "_message_from_dict": "utils",

libs/core/langchain_core/messages/ai.py

@@ -8,6 +8,7 @@ from typing import Any, Literal, Optional, Union, cast
 from pydantic import model_validator
 from typing_extensions import NotRequired, Self, TypedDict, override
 
+from langchain_core.messages import content_blocks as types
 from langchain_core.messages.base import (
     BaseMessage,
     BaseMessageChunk,
@@ -196,6 +197,60 @@ class AIMessage(BaseMessage):
             "invalid_tool_calls": self.invalid_tool_calls,
         }
 
+    @property
+    def beta_content(self) -> list[types.ContentBlock]:
+        """Return the content as a list of standard ContentBlocks.
+
+        To use this property, the corresponding chat model must support
+        ``output_version="v1"`` or higher:
+
+        .. code-block:: python
+
+            from langchain.chat_models import init_chat_model
+
+            llm = init_chat_model("...", output_version="v1")
+
+        otherwise, does best-effort parsing to standard 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
+
+        # 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.ToolCallContentBlock = {
+                    "type": "tool_call",
+                    "id": id_,
+                }
+                blocks.append(tool_call_block)
+
+        return blocks
+
     # TODO: remove this logic if possible, reducing breaking nature of changes
     @model_validator(mode="before")
     @classmethod

libs/core/langchain_core/messages/content_blocks.py

@@ -1,13 +1,152 @@
 """Types for content blocks."""
 
 import warnings
-from typing import Any, Literal, Union
+from typing import Any, Literal, Optional, Union
 
 from pydantic import TypeAdapter, ValidationError
-from typing_extensions import NotRequired, TypedDict
+from typing_extensions import NotRequired, TypedDict, get_args, get_origin
 
 
-class BaseDataContentBlock(TypedDict, total=False):
+# Text and annotations
+class UrlCitation(TypedDict):
+    """Citation from a URL."""
+
+    type: Literal["url_citation"]
+
+    url: str
+    """Source URL."""
+
+    title: NotRequired[str]
+    """Source title."""
+
+    cited_text: NotRequired[str]
+    """Text from the source that is being cited."""
+
+    start_index: NotRequired[int]
+    """Start index of the response text for which the annotation applies."""
+
+    end_index: NotRequired[int]
+    """End index of the response text for which the annotation applies."""
+
+
+class DocumentCitation(TypedDict):
+    """Annotation for data from a document."""
+
+    type: Literal["document_citation"]
+
+    title: NotRequired[str]
+    """Source title."""
+
+    cited_text: NotRequired[str]
+    """Text from the source that is being cited."""
+
+    start_index: NotRequired[int]
+    """Start index of the response text for which the annotation applies."""
+
+    end_index: NotRequired[int]
+    """End index of the response text for which the annotation applies."""
+
+
+class NonStandardAnnotation(TypedDict):
+    """Provider-specific annotation format."""
+
+    type: Literal["non_standard_annotation"]
+    """Type of the content block."""
+    value: dict[str, Any]
+    """Provider-specific annotation data."""
+
+
+class TextContentBlock(TypedDict):
+    """Content block for text output."""
+
+    type: Literal["text"]
+    """Type of the content block."""
+    text: str
+    """Block text."""
+    annotations: NotRequired[
+        list[Union[UrlCitation, DocumentCitation, NonStandardAnnotation]]
+    ]
+    """Citations and other annotations."""
+
+
+def make_text_block(
+    text: str, annotations: Optional[list[dict[str, Any]]] = None
+) -> dict[str, Any]:
+    """Return a dict matching TextContentBlock.
+
+    {
+        "type": "text",
+        "text": <text>,
+        "annotations": [ ... ]  # optional
+    }
+    """
+    block: dict[str, Any] = {
+        "type": "text",
+        "text": text,
+    }
+    if annotations is not None:
+        block["annotations"] = annotations
+    return block
+
+
+# Tool calls
+class ToolCallContentBlock(TypedDict):
+    """Content block for tool calls.
+
+    These are references to a :class:`~langchain_core.messages.tool.ToolCall` in the
+    message's ``tool_calls`` attribute.
+    """
+
+    type: Literal["tool_call"]
+    """Type of the content block."""
+    id: str
+    """Tool call ID."""
+
+
+def make_tool_call_block(
+    tool_call_id: str,
+) -> dict[str, Any]:
+    """Return a dict matching ToolCallContentBlock.
+
+    {
+        "type": "tool_call",
+        "id": <tool_call_id>
+    }
+    """
+    return {
+        "type": "tool_call",
+        "id": tool_call_id,
+    }
+
+
+# Reasoning
+class ReasoningContentBlock(TypedDict):
+    """Content block for reasoning output."""
+
+    type: Literal["reasoning"]
+    """Type of the content block."""
+    reasoning: NotRequired[str]
+    """Reasoning text."""
+
+
+def make_reasoning_block(
+    reasoning: Optional[str] = None,
+) -> dict[str, Any]:
+    """Return a dict matching ReasoningContentBlock.
+
+    {
+        "type": "reasoning",
+        "reasoning": <reasoning>  # optional
+    }
+    """
+    block: dict[str, Any] = {"type": "reasoning"}
+    if reasoning is not None:
+        block["reasoning"] = reasoning
+    return block
+
+
+# Multi-modal
+class BaseDataContentBlock(TypedDict):
     """Base class for data content blocks."""
 
     mime_type: NotRequired[str]
@@ -25,6 +164,30 @@ class URLContentBlock(BaseDataContentBlock):
     """URL for data."""
 
 
+def make_url_content_block(
+    url: str,
+    mime_type: Optional[str] = None,
+    type_: Literal["image", "audio", "file"] = "file",
+) -> dict[str, Any]:
+    """Return a dict matching URLContentBlock.
+
+    {
+        "type": <type>,
+        "source_type": "url",
+        "url": <url>,
+        "mime_type": <mime_type>  # optional
+    }
+    """
+    block: dict[str, Any] = {
+        "type": type_,
+        "source_type": "url",
+        "url": url,
+    }
+    if mime_type is not None:
+        block["mime_type"] = mime_type
+    return block
+
+
 class Base64ContentBlock(BaseDataContentBlock):
     """Content block for inline data from a base64 string."""
 
@@ -36,6 +199,30 @@ class Base64ContentBlock(BaseDataContentBlock):
     """Data as a base64 string."""
 
 
+def make_base64_content_block(
+    data: str,
+    mime_type: Optional[str] = None,
+    type_: Literal["image", "audio", "file"] = "file",
+) -> dict[str, Any]:
+    """Return a dict matching Base64ContentBlock.
+
+    {
+        "type": <type>,
+        "source_type": "base64",
+        "data": <base64_data>,
+        "mime_type": <mime_type>  # optional
+    }
+    """
+    block: dict[str, Any] = {
+        "type": type_,
+        "source_type": "base64",
+        "data": data,
+    }
+    if mime_type is not None:
+        block["mime_type"] = mime_type
+    return block
+
+
 class PlainTextContentBlock(BaseDataContentBlock):
     """Content block for plain text data (e.g., from a document)."""
 
@@ -47,7 +234,30 @@ class PlainTextContentBlock(BaseDataContentBlock):
     """Text data."""
 
 
-class IDContentBlock(TypedDict):
+def make_plain_text_content_block(
+    text: str,
+    mime_type: Optional[str] = None,
+) -> dict[str, Any]:
+    """Return a dict matching PlainTextContentBlock.
+
+    {
+        "type": "file",
+        "source_type": "text",
+        "text": <text>,
+        "mime_type": <mime_type>  # optional
+    }
+    """
+    block: dict[str, Any] = {
+        "type": "file",
+        "source_type": "text",
+        "text": text,
+    }
+    if mime_type is not None:
+        block["mime_type"] = mime_type
+    return block
+
+
+class IDContentBlock(BaseDataContentBlock):
     """Content block for data specified by an identifier."""
 
     type: Literal["image", "audio", "file"]
@@ -58,6 +268,30 @@ class IDContentBlock(TypedDict):
     """Identifier for data source."""
 
 
+def make_id_content_block(
+    id_: str,
+    mime_type: Optional[str] = None,
+    type_: Literal["image", "audio", "file"] = "file",
+) -> dict[str, Any]:
+    """Return a dict matching IDContentBlock.
+
+    {
+        "type": <type>,
+        "source_type": "id",
+        "id": <id>,
+        "mime_type": <mime_type>  # optional
+    }
+    """
+    block: dict[str, Any] = {
+        "type": type_,
+        "source_type": "id",
+        "id": id_,
+    }
+    if mime_type is not None:
+        block["mime_type"] = mime_type
+    return block
+
+
 DataContentBlock = Union[
     URLContentBlock,
     Base64ContentBlock,
@@ -68,6 +302,59 @@ DataContentBlock = Union[
 _DataContentBlockAdapter: TypeAdapter[DataContentBlock] = TypeAdapter(DataContentBlock)
 
 
+# Non-standard
+class NonStandardContentBlock(TypedDict):
+    """Content block provider-specific data.
+
+    This block contains data for which there is not yet a standard type.
+    """
+
+    type: Literal["non_standard"]
+    """Type of the content block."""
+    value: dict[str, Any]
+    """Provider-specific data."""
+
+
+def make_non_standard_content_block(value: dict[str, Any]) -> dict[str, Any]:
+    """Return a dict matching NonStandardContentBlock.
+
+    {
+        "type": "non_standard",
+        "value": <value>
+    }
+    """
+    return {
+        "type": "non_standard",
+        "value": value,
+    }
+
+
+ContentBlock = Union[
+    TextContentBlock,
+    ToolCallContentBlock,
+    ReasoningContentBlock,
+    DataContentBlock,
+    NonStandardContentBlock,
+]
+
+
+def _extract_typedict_type_values(union_type: Any) -> set[str]:
+    """Extract the values of the 'type' field from a TypedDict union type."""
+    result: set[str] = set()
+    for value in get_args(union_type):
+        annotation = value.__annotations__["type"]
+        if get_origin(annotation) is Literal:
+            result.update(get_args(annotation))
+        else:
+            msg = f"{value} 'type' is not a Literal"
+            raise ValueError(msg)
+    return result
+
+
+# {"text", "tool_call", "reasoning", "non_standard", "image", "audio", "file"}
+KNOWN_BLOCK_TYPES = _extract_typedict_type_values(ContentBlock)
+
+
 def is_data_content_block(
     content_block: dict,
 ) -> bool:

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

@@ -300,8 +300,9 @@ 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/messages/test_ai.py

@@ -1,5 +1,8 @@
+from typing import Union, cast
+
 from langchain_core.load import dumpd, load
 from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.messages import content_blocks as types
 from langchain_core.messages.ai import (
     InputTokenDetails,
     OutputTokenDetails,
@@ -196,3 +199,71 @@ def test_add_ai_message_chunks_usage() -> None:
             output_token_details=OutputTokenDetails(audio=1, reasoning=2),
         ),
     )
+
+
+class ReasoningContentBlockWithID(types.ReasoningContentBlock):
+    id: str
+
+
+def test_beta_content() -> None:
+    # Simple case
+    message = AIMessage("Hello")
+    assert len(message.beta_content) == 1
+    assert message.beta_content[0]["type"] == "text"
+    for block in message.beta_content:
+        if block["type"] == "text":
+            text_block: types.TextContentBlock = block
+            assert text_block == {"type": "text", "text": "Hello"}
+
+    # With tool calls
+    message = AIMessage(
+        "",
+        tool_calls=[
+            {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"}
+        ],
+    )
+    assert len(message.beta_content) == 1
+    assert message.beta_content[0]["type"] == "tool_call"
+    for block in message.beta_content:
+        if block["type"] == "tool_call":
+            tool_call_block: types.ToolCallContentBlock = block
+            assert tool_call_block == {"type": "tool_call", "id": "abc_123"}
+
+    # With standard blocks
+    reasoning_with_id: ReasoningContentBlockWithID = {
+        "type": "reasoning",
+        "reasoning": "foo",
+        "id": "rs_abc123",
+    }
+    standard_content: list[types.ContentBlock] = [
+        {"type": "reasoning", "reasoning": "foo"},
+        reasoning_with_id,
+        {"type": "text", "text": "bar"},
+        {
+            "type": "text",
+            "text": "baz",
+            "annotations": [{"type": "url_citation", "url": "http://example.com"}],
+        },
+        {
+            "type": "image",
+            "source_type": "url",
+            "url": "http://example.com/image.png",
+        },
+        {
+            "type": "non_standard",
+            "value": {"custom_key": "custom_value", "another_key": 123},
+        },
+        {
+            "type": "tool_call",
+            "id": "abc_123",
+        },
+    ]
+    message = AIMessage(
+        cast("list[Union[str, dict]]", standard_content),
+        tool_calls=[
+            {"type": "tool_call", "name": "foo", "args": {"a": "b"}, "id": "abc_123"},
+            {"type": "tool_call", "name": "bar", "args": {"c": "d"}, "id": "abc_234"},
+        ],
+    )
+    missing_tool_call = {"type": "tool_call", "id": "abc_234"}
+    assert message.beta_content == [*standard_content, missing_tool_call]

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

@@ -6,21 +6,30 @@ EXPECTED_ALL = [
     "AIMessage",
     "AIMessageChunk",
     "AnyMessage",
+    "Base64ContentBlock",
     "BaseMessage",
     "BaseMessageChunk",
+    "ContentBlock",
     "ChatMessage",
     "ChatMessageChunk",
+    "DocumentCitation",
     "FunctionMessage",
     "FunctionMessageChunk",
     "HumanMessage",
     "HumanMessageChunk",
     "InvalidToolCall",
+    "NonStandardAnnotation",
+    "NonStandardContentBlock",
     "SystemMessage",
     "SystemMessageChunk",
+    "TextContentBlock",
     "ToolCall",
     "ToolCallChunk",
+    "ToolCallContentBlock",
     "ToolMessage",
     "ToolMessageChunk",
+    "UrlCitation",
+    "ReasoningContentBlock",
     "RemoveMessage",
     "convert_to_messages",
     "get_buffer_string",

libs/core/tests/unit_tests/test_messages.py

@@ -30,6 +30,7 @@ from langchain_core.messages import (
     messages_from_dict,
     messages_to_dict,
 )
+from langchain_core.messages.content_blocks import KNOWN_BLOCK_TYPES
 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
@@ -1197,3 +1198,15 @@ def test_convert_to_openai_image_block() -> None:
     }
     result = convert_to_openai_image_block(input_block)
     assert result == expected
+
+
+def test_known_block_types() -> None:
+    assert {
+        "text",
+        "tool_call",
+        "reasoning",
+        "non_standard",
+        "image",
+        "audio",
+        "file",
+    } == KNOWN_BLOCK_TYPES

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"``,
+``"responses/v1"``, and ``"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
 
@@ -56,18 +60,22 @@ reasoning items), ChatOpenAI now stores these items in the content sequence:
 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.
 
+``"v1"`` represents LangChain's cross-provider standard format.
+
 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
+from typing import Any, Union, cast
 
-from langchain_core.messages import AIMessage
+from langchain_core.messages import AIMessage, AIMessageChunk, is_data_content_block
 
 _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:
@@ -252,3 +260,290 @@ def _convert_from_v03_ai_message(message: AIMessage) -> AIMessage:
         },
         deep=False,
     )
+
+
+# v1 / Chat Completions
+def _convert_to_v1_from_chat_completions(message: AIMessage) -> AIMessage:
+    """Mutate a Chat Completions message to v1 format."""
+    if isinstance(message.content, str):
+        if message.content:
+            message.content = [{"type": "text", "text": message.content}]
+        else:
+            message.content = []
+
+    for tool_call in message.tool_calls:
+        if id_ := tool_call.get("id"):
+            message.content.append({"type": "tool_call", "id": id_})
+
+    if "tool_calls" in message.additional_kwargs:
+        _ = message.additional_kwargs.pop("tool_calls")
+
+    if "token_usage" in message.response_metadata:
+        _ = message.response_metadata.pop("token_usage")
+
+    return message
+
+
+def _convert_to_v1_from_chat_completions_chunk(chunk: AIMessageChunk) -> AIMessageChunk:
+    result = _convert_to_v1_from_chat_completions(cast(AIMessage, chunk))
+    return cast(AIMessageChunk, result)
+
+
+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]) -> dict[str, Any]:
+    annotation_type = annotation.get("type")
+
+    if annotation_type == "url_citation":
+        url_citation = {}
+        for field in ("end_index", "start_index", "title"):
+            if field in annotation:
+                url_citation[field] = annotation[field]
+        url_citation["type"] = "url_citation"
+        url_citation["url"] = annotation["url"]
+        return url_citation
+
+    elif annotation_type == "file_citation":
+        document_citation = {"type": "document_citation"}
+        if "filename" in annotation:
+            document_citation["title"] = annotation["filename"]
+        for field in ("file_id", "index"):  # OpenAI-specific
+            if field in annotation:
+                document_citation[field] = annotation[field]
+        return document_citation
+
+    # TODO: standardise container_file_citation?
+    else:
+        non_standard_annotation = {
+            "type": "non_standard_annotation",
+            "value": annotation,
+        }
+        return non_standard_annotation
+
+
+def _explode_reasoning(block: dict[str, Any]) -> Iterable[dict[str, Any]]:
+    if block.get("type") != "reasoning" or "summary" not in block:
+        yield block
+        return
+
+    if not block["summary"]:
+        _ = block.pop("summary", None)
+        yield block
+        return
+
+    # Common part for every exploded line, except 'summary'
+    common = {k: v for k, v in block.items() if k != "summary"}
+
+    # Optional keys that must appear only in the first exploded item
+    first_only = {
+        k: common.pop(k) for k in ("encrypted_content", "status") if k in common
+    }
+
+    for idx, part in enumerate(block["summary"]):
+        new_block = dict(common)
+        new_block["reasoning"] = part.get("text", "")
+        if idx == 0:
+            new_block.update(first_only)
+        yield new_block
+
+
+def _convert_to_v1_from_responses(message: AIMessage) -> AIMessage:
+    """Mutate a Responses message to v1 format."""
+    if not isinstance(message.content, list):
+        return message
+
+    def _iter_blocks() -> Iterable[dict[str, Any]]:
+        for block in message.content:
+            if not isinstance(block, dict):
+                continue
+            block_type = block.get("type")
+
+            if block_type == "text":
+                if "annotations" in block:
+                    block["annotations"] = [
+                        _convert_annotation_to_v1(a) for a in block["annotations"]
+                    ]
+                yield 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", "source_type": "base64", "data": result}
+                if output_format := block.get("output_format"):
+                    new_block["mime_type"] = f"image/{output_format}"
+                for extra_key in (
+                    "id",
+                    "index",
+                    "status",
+                    "background",
+                    "output_format",
+                    "quality",
+                    "revised_prompt",
+                    "size",
+                ):
+                    if extra_key in block:
+                        new_block[extra_key] = block[extra_key]
+                yield new_block
+
+            elif block_type == "function_call":
+                new_block = {"type": "tool_call", "id": block.get("call_id", "")}
+                if "id" in block:
+                    new_block["item_id"] = block["id"]
+                for extra_key in ("arguments", "name", "index"):
+                    if extra_key in block:
+                        new_block[extra_key] = block[extra_key]
+                yield new_block
+
+            else:
+                new_block = {"type": "non_standard", "value": block}
+                if "index" in new_block["value"]:
+                    new_block["index"] = new_block["value"].pop("index")
+                yield new_block
+
+    # Replace the list with the fully converted one
+    message.content = list(_iter_blocks())
+
+    return message
+
+
+def _convert_annotation_from_v1(annotation: dict[str, Any]) -> dict[str, Any]:
+    annotation_type = annotation.get("type")
+
+    if annotation_type == "document_citation":
+        new_ann: dict[str, Any] = {"type": "file_citation"}
+
+        if "title" in annotation:
+            new_ann["filename"] = annotation["title"]
+
+        for fld in ("file_id", "index"):
+            if fld in annotation:
+                new_ann[fld] = annotation[fld]
+
+        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": []}
+            oai_format["type"] = oai_format.pop("type", "reasoning")
+            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"}
+
+        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 _convert_from_v1_to_responses(message: AIMessage) -> AIMessage:
+    if not isinstance(message.content, list):
+        return message
+
+    new_content: list = []
+    for block in message.content:
+        if isinstance(block, dict):
+            block_type = block.get("type")
+            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 "item_id" in block:
+                    new_block["id"] = block["item_id"]
+                if "name" in block and "arguments" in block:
+                    new_block["name"] = block["name"]
+                    new_block["arguments"] = block["arguments"]
+                else:
+                    tool_call = next(
+                        call for call in message.tool_calls if call["id"] == block["id"]
+                    )
+                    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(block)
+                and block["type"] == "image"
+                and block["source_type"] == "base64"
+            ):
+                new_block = {"type": "image_generation_call", "result": block["data"]}
+                for extra_key in ("id", "status"):
+                    if extra_key in block:
+                        new_block[extra_key] = block[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)
+        else:
+            new_content.append(block)
+
+    new_content = list(_implode_reasoning_blocks(new_content))
+
+    return message.model_copy(update={"content": new_content})

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

@@ -108,7 +108,12 @@ 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,
+    _convert_to_v1_from_chat_completions,
+    _convert_to_v1_from_chat_completions_chunk,
+    _convert_to_v1_from_responses,
 )
 
 if TYPE_CHECKING:
@@ -650,7 +655,7 @@ 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
@@ -661,9 +666,9 @@ 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
-    recommended.
+    ``output_version="v1"`` is recommended.
 
     .. versionadded:: 0.3.25
 
@@ -850,6 +855,10 @@ 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 = _convert_to_v1_from_chat_completions_chunk(
+                    cast(AIMessageChunk, generation_chunk.message)
+                )
             return generation_chunk
 
         choice = choices[0]
@@ -877,6 +886,20 @@ class BaseChatOpenAI(BaseChatModel):
         if usage_metadata and isinstance(message_chunk, AIMessageChunk):
             message_chunk.usage_metadata = usage_metadata
 
+        if self.output_version == "v1":
+            message_chunk = cast(AIMessageChunk, message_chunk)
+            # Convert to v1 format
+            if isinstance(message_chunk.content, str):
+                message_chunk = _convert_to_v1_from_chat_completions_chunk(
+                    message_chunk
+                )
+                if message_chunk.content:
+                    message_chunk.content[0]["index"] = 0  # type: ignore[index]
+            else:
+                message_chunk = _convert_to_v1_from_chat_completions_chunk(
+                    message_chunk
+                )
+
         generation_chunk = ChatGenerationChunk(
             message=message_chunk, generation_info=generation_info or None
         )
@@ -1169,7 +1192,12 @@ 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(
@@ -1235,6 +1263,11 @@ class BaseChatOpenAI(BaseChatModel):
             if hasattr(message, "refusal"):
                 generations[0].message.additional_kwargs["refusal"] = message.refusal
 
+        if self.output_version == "v1":
+            _ = llm_output.pop("token_usage", None)
+            generations[0].message = _convert_to_v1_from_chat_completions(
+                cast(AIMessage, generations[0].message)
+            )
         return ChatResult(generations=generations, llm_output=llm_output)
 
     async def _astream(
@@ -3475,6 +3508,7 @@ 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)
+            lc_msg = _convert_from_v1_to_responses(lc_msg)
         msg = _convert_message_to_dict(lc_msg)
         # "name" parameter unsupported
         if "name" in msg:
@@ -3618,7 +3652,7 @@ 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:
@@ -3757,6 +3791,26 @@ def _construct_lc_result_from_responses_api(
     )
     if output_version == "v0":
         message = _convert_to_v03_ai_message(message)
+    elif output_version == "v1":
+        message = _convert_to_v1_from_responses(message)
+        if response.tools and any(
+            tool.type == "image_generation" for tool in response.tools
+        ):
+            # Get mime_time from tool definition and add to image generations
+            # if missing (primarily for tracing purposes).
+            image_generation_call = next(
+                tool for tool in response.tools if tool.type == "image_generation"
+            )
+            if image_generation_call.output_format:
+                mime_type = f"image/{image_generation_call.output_format}"
+                for block in message.beta_content:  # type: ignore[assignment]
+                    # OK to mutate output message
+                    if (
+                        block.get("type") == "image"
+                        and block["source_type"] == "base64"
+                        and "mime_type" not in block
+                    ):
+                        block["mime_type"] = mime_type
     else:
         pass
     return ChatResult(generations=[ChatGeneration(message=message)])
@@ -3770,7 +3824,7 @@ 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.
@@ -3835,9 +3889,29 @@ 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})
+        if output_version == "v1":
+            content.append(
+                {
+                    "type": "text",
+                    "text": "",
+                    "annotations": [annotation],
+                    "index": current_index,
+                }
+            )
+        else:
+            content.append({"annotations": [annotation], "index": current_index})
     elif chunk.type == "response.output_text.done":
-        content.append({"id": chunk.item_id, "index": current_index})
+        if output_version == "v1":
+            content.append(
+                {
+                    "type": "text",
+                    "text": "",
+                    "id": chunk.item_id,
+                    "index": current_index,
+                }
+            )
+        else:
+            content.append({"id": chunk.item_id, "index": current_index})
     elif chunk.type == "response.created":
         id = chunk.response.id
         response_metadata["id"] = chunk.response.id  # Backwards compatibility
@@ -3913,21 +3987,34 @@ 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)
     elif chunk.type == "response.reasoning_summary_part.added":
-        _advance(chunk.output_index)
-        content.append(
-            {
-                # langchain-core uses the `index` key to aggregate text blocks.
-                "summary": [
-                    {"index": chunk.summary_index, "type": "summary_text", "text": ""}
-                ],
-                "index": current_index,
-                "type": "reasoning",
-            }
-        )
+        if output_version in ("v0", "responses/v1"):
+            _advance(chunk.output_index)
+            content.append(
+                {
+                    # langchain-core uses the `index` key to aggregate text blocks.
+                    "summary": [
+                        {
+                            "index": chunk.summary_index,
+                            "type": "summary_text",
+                            "text": "",
+                        }
+                    ],
+                    "index": current_index,
+                    "type": "reasoning",
+                }
+            )
+        else:
+            block: dict = {"type": "reasoning", "reasoning": ""}
+            if chunk.summary_index > 0:
+                _advance(chunk.output_index, chunk.summary_index)
+                block["id"] = chunk.item_id
+            block["index"] = current_index
+            content.append(block)
     elif chunk.type == "response.image_generation_call.partial_image":
         # Partial images are not supported yet.
         pass
@@ -3962,6 +4049,8 @@ def _convert_responses_chunk_to_generation_chunk(
             AIMessageChunk,
             _convert_to_v03_ai_message(message, has_reasoning=has_reasoning),
         )
+    elif output_version == "v1":
+        message = cast(AIMessageChunk, _convert_to_v1_from_responses(message))
     else:
         pass
     return (

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

@@ -52,9 +52,11 @@ def _check_response(response: Optional[BaseMessage]) -> None:
     assert response.response_metadata["service_tier"]
 
 
+@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"}],
@@ -110,7 +112,10 @@ def test_web_search() -> None:
     for msg in [first_response, full, response]:
         assert isinstance(msg, AIMessage)
         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 == ["non_standard", "text"]
 
 
 @pytest.mark.flaky(retries=3, delay=1)
@@ -141,13 +146,15 @@ 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 +181,15 @@ 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 +311,8 @@ 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 +372,32 @@ 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",
@@ -398,20 +417,39 @@ 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
             if block["type"] == "reasoning"  # type: ignore[index]
         )
         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:
+            if block["type"] == "reasoning":
+                total_reasoning_blocks += 1
+                assert isinstance(block["id"], str) and block["id"].startswith("rs_")
+                assert isinstance(block["reasoning"], str)
+                assert isinstance(block["index"], int)
+        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."}
@@ -419,9 +457,13 @@ 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"}}]
     )
@@ -430,15 +472,32 @@ 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"
+        ]
+    elif output_version == "responses/v1":
+        tool_outputs = [
+            item
+            for item in response.content
+            if isinstance(item, dict) and item["type"] == "code_interpreter_call"
+        ]
+    else:
+        # v1
+        tool_outputs = [
+            item["value"]
+            for item in response.beta_content
+            if item["type"] == "non_standard"
+        ]
+        assert tool_outputs[0]["type"] == "code_interpreter_call"
+    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"]
     llm_with_tools = llm.bind_tools(
         [{"type": "code_interpreter", "container": container_id}]
@@ -449,9 +508,26 @@ 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"]
+    if output_version == "v0":
+        tool_outputs = [
+            item
+            for item in response.additional_kwargs["tool_outputs"]
+            if item["type"] == "code_interpreter_call"
+        ]
+    elif output_version == "responses/v1":
+        tool_outputs = [
+            item
+            for item in response.content
+            if isinstance(item, dict) and item["type"] == "code_interpreter_call"
+        ]
+    else:
+        tool_outputs = [
+            item["value"]
+            for item in response.beta_content
+            if item["type"] == "non_standard"
+        ]
+        assert tool_outputs[0]["type"] == "code_interpreter_call"
     assert tool_outputs
-    assert any(output["type"] == "code_interpreter_call" for output in tool_outputs)
 
     # Test we can pass back in
     next_message = {"role": "user", "content": "Please add more comments to the code."}
@@ -546,10 +622,14 @@ 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_image_generation_streaming.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_image_generation_streaming(output_version: str) -> 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.
@@ -596,15 +676,37 @@ 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)
+    elif output_version == "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)
+    else:
+        # v1
+        standard_keys = {"type", "source_type", "data", "id", "status", "index"}
+        tool_output = next(
+            block
+            for block in complete_ai_message.beta_content
+            if block["type"] == "image"
+        )
+        assert set(standard_keys).issubset(tool_output.keys())
 
 
-@pytest.mark.vcr()
-def test_image_generation_multi_turn() -> None:
+@pytest.mark.default_cassette("test_image_generation_multi_turn.yaml.gz")
+@pytest.mark.vcr
+@pytest.mark.parametrize("output_version", ["v0", "responses/v1", "v1"])
+def test_image_generation_multi_turn(output_version: str) -> 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",
@@ -620,10 +722,39 @@ 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", "source_type", "data", "id", "status"}
+        tool_output = next(
+            block for block in ai_message.beta_content if block["type"] == "image"
+        )
+        assert set(standard_keys).issubset(tool_output.keys())
+
+    # Example tool output for an image (v0)
     # {
     #     "background": "opaque",
     #     "id": "ig_683716a8ddf0819888572b20621c7ae4029ec8c11f8dacf8",
@@ -639,20 +770,6 @@ 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
@@ -669,6 +786,22 @@ 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)
+    elif output_version == "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)
+    else:
+        standard_keys = {"type", "source_type", "data", "id", "status"}
+        tool_output = next(
+            block for block in ai_message2.beta_content if block["type"] == "image"
+        )
+        assert set(standard_keys).issubset(tool_output.keys())

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

@@ -51,7 +51,11 @@ 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,
+    _convert_to_v1_from_chat_completions,
+    _convert_to_v1_from_responses,
 )
 from langchain_openai.chat_models.base import (
     _construct_lc_result_from_responses_api,
@@ -2296,7 +2300,7 @@ def test_mcp_tracing() -> None:
     assert payload["tools"][0]["headers"]["Authorization"] == "Bearer PLACEHOLDER"
 
 
-def test_compat() -> None:
+def test_compat_responses_v1() -> None:
     # Check compatibility with v0.3 message format
     message_v03 = AIMessage(
         content=[
@@ -2357,6 +2361,421 @@ 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"},
+                    {
+                        "type": "text",
+                        "text": "Hello, world!",
+                        "annotations": [
+                            {"type": "url_citation", "url": "https://example.com"}
+                        ],
+                    },
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+            AIMessage(
+                [{"type": "text", "text": "Hello, world!"}],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+        )
+    ],
+)
+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
+
+    # Check no mutation
+    assert message_v1 != result
+
+
+@pytest.mark.parametrize(
+    "message_chat_completions, expected",
+    [
+        (
+            AIMessage(
+                "Hello, world!", id="chatcmpl-123", response_metadata={"foo": "bar"}
+            ),
+            AIMessage(
+                [{"type": "text", "text": "Hello, world!"}],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+        ),
+        (
+            AIMessage(
+                [{"type": "text", "text": "Hello, world!"}],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+            AIMessage(
+                [
+                    {"type": "text", "text": "Hello, world!"},
+                    {"type": "tool_call", "id": "call_123"},
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+        ),
+        (
+            AIMessage(
+                "",
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+                additional_kwargs={"tool_calls": [{"foo": "bar"}]},
+            ),
+            AIMessage(
+                [{"type": "tool_call", "id": "call_123"}],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    }
+                ],
+                id="chatcmpl-123",
+                response_metadata={"foo": "bar"},
+            ),
+        ),
+    ],
+)
+def test_convert_to_v1_from_chat_completions(
+    message_chat_completions: AIMessage, expected: AIMessage
+) -> None:
+    result = _convert_to_v1_from_chat_completions(message_chat_completions)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "message_v1, expected",
+    [
+        (
+            AIMessage(
+                [
+                    {"type": "reasoning", "id": "abc123"},
+                    {"type": "reasoning", "id": "abc234", "reasoning": "foo "},
+                    {"type": "reasoning", "id": "abc234", "reasoning": "bar"},
+                    {"type": "tool_call", "id": "call_123"},
+                    {
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_2",
+                        "arguments": '{"location": "New York"}',
+                        "item_id": "fc_123",
+                    },
+                    {"type": "text", "text": "Hello "},
+                    {
+                        "type": "text",
+                        "text": "world",
+                        "annotations": [
+                            {"type": "url_citation", "url": "https://example.com"},
+                            {
+                                "type": "document_citation",
+                                "title": "my doc",
+                                "index": 1,
+                                "file_id": "file_123",
+                            },
+                            {
+                                "type": "non_standard_annotation",
+                                "value": {"bar": "baz"},
+                            },
+                        ],
+                    },
+                    {
+                        "type": "image",
+                        "source_type": "base64",
+                        "data": "...",
+                        "id": "img_123",
+                    },
+                    {
+                        "type": "non_standard",
+                        "value": {"type": "something_else", "foo": "bar"},
+                    },
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        # Make values different to check we pull from content when
+                        # available
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_3",
+                        "args": {"location": "Boston"},
+                    },
+                ],
+                id="resp123",
+                response_metadata={"foo": "bar"},
+            ),
+            AIMessage(
+                [
+                    {"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": "img_123", "result": "..."},
+                    {"type": "something_else", "foo": "bar"},
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        # Make values different to check we pull from content when
+                        # available
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_3",
+                        "args": {"location": "Boston"},
+                    },
+                ],
+                id="resp123",
+                response_metadata={"foo": "bar"},
+            ),
+        )
+    ],
+)
+def test_convert_from_v1_to_responses(
+    message_v1: AIMessage, expected: AIMessage
+) -> None:
+    result = _convert_from_v1_to_responses(message_v1)
+    assert result == expected
+
+    # Check no mutation
+    assert message_v1 != result
+
+
+@pytest.mark.parametrize(
+    "message_responses, expected",
+    [
+        (
+            AIMessage(
+                [
+                    {"type": "reasoning", "id": "abc123"},
+                    {
+                        "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": "img_123", "result": "..."},
+                    {"type": "something_else", "foo": "bar"},
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        # Make values different to check we pull from content when
+                        # available
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_3",
+                        "args": {"location": "Boston"},
+                    },
+                ],
+                id="resp123",
+                response_metadata={"foo": "bar"},
+            ),
+            AIMessage(
+                [
+                    {"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",
+                        "arguments": '{"location": "San Francisco"}',
+                    },
+                    {
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_2",
+                        "arguments": '{"location": "New York"}',
+                        "item_id": "fc_123",
+                    },
+                    {"type": "text", "text": "Hello "},
+                    {
+                        "type": "text",
+                        "text": "world",
+                        "annotations": [
+                            {"type": "url_citation", "url": "https://example.com"},
+                            {
+                                "type": "document_citation",
+                                "title": "my doc",
+                                "index": 1,
+                                "file_id": "file_123",
+                            },
+                            {
+                                "type": "non_standard_annotation",
+                                "value": {"bar": "baz"},
+                            },
+                        ],
+                    },
+                    {
+                        "type": "image",
+                        "source_type": "base64",
+                        "data": "...",
+                        "id": "img_123",
+                    },
+                    {
+                        "type": "non_standard",
+                        "value": {"type": "something_else", "foo": "bar"},
+                    },
+                ],
+                tool_calls=[
+                    {
+                        "type": "tool_call",
+                        "id": "call_123",
+                        "name": "get_weather",
+                        "args": {"location": "San Francisco"},
+                    },
+                    {
+                        # Make values different to check we pull from content when
+                        # available
+                        "type": "tool_call",
+                        "id": "call_234",
+                        "name": "get_weather_3",
+                        "args": {"location": "Boston"},
+                    },
+                ],
+                id="resp123",
+                response_metadata={"foo": "bar"},
+            ),
+        )
+    ],
+)
+def test_convert_to_v1_from_responses(
+    message_responses: AIMessage, expected: AIMessage
+) -> None:
+    result = _convert_to_v1_from_responses(message_responses)
+    assert result == expected
+
+
 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,
@@ -610,8 +611,97 @@ 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",
+                        },
+                    ],
+                    "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": 0,
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "another reasoning block",
+                    "id": "rs_123",
+                    "index": 1,
+                },
+                {"type": "text", "text": "text block one", "index": 2, "id": "msg_123"},
+                {
+                    "type": "text",
+                    "text": "another text block",
+                    "index": 3,
+                    "id": "msg_123",
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "more reasoning",
+                    "id": "rs_234",
+                    "index": 4,
+                },
+                {
+                    "type": "reasoning",
+                    "reasoning": "still more reasoning",
+                    "id": "rs_234",
+                    "index": 5,
+                },
+                {"type": "text", "text": "more", "index": 6, "id": "msg_234"},
+                {"type": "text", "text": "text", "index": 7, "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:
@@ -620,36 +710,14 @@ 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
-    assert isinstance(full, AIMessageChunk)
+            chunks.append(chunk)
 
-    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 isinstance(full, AIMessageChunk)
     assert full.content == expected_content
     assert full.additional_kwargs == {}
     assert full.id == "resp_123"