ChatOpenAI - Docs by LangChain

You can find information about OpenAI’s latest models, their costs, context windows, and supported input types in the OpenAI Platform docs.

API ReferenceFor detailed documentation of all features and configuration options, head to the ChatOpenAI API reference.

Chat Completions API compatibilityChatOpenAI is fully compatible with OpenAI’s Chat Completions API. If you are looking to connect to other model providers that support the Chat Completions API, you can do so – see instructions.

Overview

Integration details

Class	Package		Serializable	JS/TS Support	Downloads	Latest Version
`ChatOpenAI`	`langchain-openai`	❌	beta	✅ (npm)

Model features

Tool calling	Structured output	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	✅	❌	✅	✅	✅	✅

Setup

To access OpenAI models you’ll need to install the langchain-openai integration package and acquire an OpenAI Platform API key.

Installation

pip install -U langchain-openai

uv add langchain-openai

Credentials

Head to the OpenAI Platform to sign up and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable in your environment:

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

If you want to get automated tracing of your model calls you can also set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Instantiation

Now we can instantiate our model object and generate responses:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5-nano",
    # stream_usage=True,
    # temperature=None,
    # max_tokens=None,
    # timeout=None,
    # reasoning_effort="low",
    # max_retries=2,
    # api_key="...",  # If you prefer to pass api key in directly
    # base_url="...",
    # organization="...",
    # other params...
)

See the ChatOpenAI API Reference for the full set of available model parameters.

Token parameter deprecationOpenAI deprecated max_tokens in favor of max_completion_tokens in September 2024. While max_tokens is still supported for backwards compatibility, it’s automatically converted to max_completion_tokens internally.

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-63219b22-03e3-4561-8cc4-78b7c7c3a3ca-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})

print(ai_msg.text)

J'adore la programmation.

Streaming usage metadata

OpenAI’s Chat Completions API does not stream token usage statistics by default (see API reference here). To recover token counts when streaming with ChatOpenAI or AzureChatOpenAI, set stream_usage=True as an initialization parameter or on invocation:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1-mini", stream_usage=True)  

Using with Azure OpenAI

Azure OpenAI v1 API supportAs of langchain-openai>=1.0.1, ChatOpenAI can be used directly with Azure OpenAI endpoints using the new v1 API. This provides a unified way to use OpenAI models whether hosted on OpenAI or Azure.For the traditional Azure-specific implementation, continue to use AzureChatOpenAI.

Using Azure OpenAI v1 API with API Key

To use ChatOpenAI with Azure OpenAI, set the base_url to your Azure endpoint with /openai/v1/ appended:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5-mini",  # Your Azure deployment name
    base_url="https://{your-resource-name}.openai.azure.com/openai/v1/",
    api_key="your-azure-api-key"
)

response = llm.invoke("Hello, how are you?")
print(response.content)

Using Azure OpenAI with Microsoft Entra ID

The v1 API adds native support for Microsoft Entra ID (formerly Azure AD) authentication with automatic token refresh. Pass a token provider callable to the api_key parameter:

from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from langchain_openai import ChatOpenAI

# Create a token provider that handles automatic refresh
token_provider = get_bearer_token_provider(
    DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default"
)

llm = ChatOpenAI(
    model="gpt-5-mini",  # Your Azure deployment name
    base_url="https://{your-resource-name}.openai.azure.com/openai/v1/",
    api_key=token_provider  # Callable that handles token refresh
)

# Use the model as normal
messages = [
    ("system", "You are a helpful assistant."),
    ("human", "Translate 'I love programming' to French.")
]
response = llm.invoke(messages)
print(response.content)

The token provider is a callable that automatically retrieves and refreshes authentication tokens, eliminating the need to manually manage token expiration.

Installation requirementsTo use Microsoft Entra ID authentication, install the Azure Identity library:

pip install azure-identity

You can also pass a token provider callable to the api_key parameter when using asynchronous functions. You must import DefaultAzureCredential from azure.identity.aio:

from azure.identity.aio import DefaultAzureCredential
from langchain_openai import ChatOpenAI

credential = DefaultAzureCredential()

llm_async = ChatOpenAI(
    model="gpt-5-nano",
    api_key=credential
)

# Use async methods when using async callable
response = await llm_async.ainvoke("Hello!")

When using an async callable for the API key, you must use async methods (ainvoke, astream, etc.). Sync methods will raise an error.

Tool calling

OpenAI has a tool calling (we use “tool calling” and “function calling” interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.

Bind tools

With ChatOpenAI.bind_tools, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:

{
    "name": "...",
    "description": "...",
    "parameters": {...}  # JSONSchema
}

…and are passed in every model invocation.

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

llm_with_tools = llm.bind_tools([GetWeather])

ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

AIMessage(content='', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-1617c9b2-dda5-4120-996b-0333ed5992e2-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_o9udf3EVOWiV4Iupktpbpofk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})

Strict mode

Requires langchain-openai>=0.1.21

As of Aug 6, 2024, OpenAI supports a strict argument when calling tools that will enforce that the tool argument schema is respected by the model. See more.

If strict=True the tool definition will also be validated, and a subset of JSON schema are accepted. Crucially, schema cannot have optional args (those with default values).Read the full docs on what types of schema are supported.

llm_with_tools = llm.bind_tools([GetWeather], strict=True)
ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'function': {'arguments': '{"location":"San Francisco, CA"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5e3356a9-132d-4623-8e73-dd5a898cf4a6-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})

Tool calls

Notice that the AIMessage has a tool_calls attribute. This contains in a standardized ToolCall format that is model-provider agnostic.

ai_msg.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'San Francisco, CA'},
  'id': 'call_jUqhd8wzAIzInTJl72Rla8ht',
  'type': 'tool_call'}]

For more on binding tools and tool call outputs, head to the tool calling docs.

Structured output and tool calls

OpenAI’s structured output feature can be used simultaneously with tool-calling. The model will either generate tool calls or a response adhering to a desired schema. See example below:

from langchain_openai import ChatOpenAI
from pydantic import BaseModel


def get_weather(location: str) -> None:
    """Get weather at a location."""
    return "It's sunny."


class OutputSchema(BaseModel):
    """Schema for response."""

    answer: str
    justification: str


llm = ChatOpenAI(model="gpt-4.1")

structured_llm = llm.bind_tools(
    [get_weather],
    response_format=OutputSchema,
    strict=True,
)

# Response contains tool calls:
tool_call_response = structured_llm.invoke("What is the weather in SF?")

# structured_response.additional_kwargs["parsed"] contains parsed output
structured_response = structured_llm.invoke(
    "What weighs more, a pound of feathers or a pound of gold?"
)

Custom tools

Requires langchain-openai>=0.3.29

Custom tools support tools with arbitrary string inputs. They can be particularly useful when you expect your string arguments to be long or complex.

from langchain_openai import ChatOpenAI, custom_tool
from langchain.agents import create_agent


@custom_tool
def execute_code(code: str) -> str:
    """Execute python code."""
    return "27"


llm = ChatOpenAI(model="gpt-5", use_responses_api=True)

agent = create_agent(llm, [execute_code])

input_message = {"role": "user", "content": "Use the tool to calculate 3^3."}
for step in agent.stream(
    {"messages": [input_message]},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

================================ Human Message =================================

Use the tool to calculate 3^3.
================================== Ai Message ==================================

[{'id': 'rs_68b7336cb72081a080da70bf5e980e4e0d6082d28f91357a', 'summary': [], 'type': 'reasoning'}, {'call_id': 'call_qyKsJ4XlGRudbIJDrXVA2nQa', 'input': 'print(3**3)', 'name': 'execute_code', 'type': 'custom_tool_call', 'id': 'ctc_68b7336f718481a0b39584cd35fbaa5d0d6082d28f91357a', 'status': 'completed'}]
Tool Calls:
  execute_code (call_qyKsJ4XlGRudbIJDrXVA2nQa)
 Call ID: call_qyKsJ4XlGRudbIJDrXVA2nQa
  Args:
    __arg1: print(3**3)
================================= Tool Message =================================
Name: execute_code

[{'type': 'custom_tool_call_output', 'output': '27'}]
================================== Ai Message ==================================

[{'type': 'text', 'text': '27', 'annotations': [], 'id': 'msg_68b73371e9e081a0927f54f88f2cd7a20d6082d28f91357a'}]

Context-free grammars

OpenAI supports the specification of a context-free grammar for custom tool inputs in lark or regex format. See OpenAI docs for details. The format parameter can be passed into @custom_tool as shown below:

from langchain_openai import ChatOpenAI, custom_tool
from langchain.agents import create_agent


grammar = """
start: expr
expr: term (SP ADD SP term)* -> add
| term
term: factor (SP MUL SP factor)* -> mul
| factor
factor: INT
SP: " "
ADD: "+"
MUL: "*"
%import common.INT
"""

format_ = {"type": "grammar", "syntax": "lark", "definition": grammar}


@custom_tool(format=format_)  
def do_math(input_string: str) -> str:
    """Do a mathematical operation."""
    return "27"


llm = ChatOpenAI(model="gpt-5", use_responses_api=True)

agent = create_agent(llm, [do_math])

input_message = {"role": "user", "content": "Use the tool to calculate 3^3."}
for step in agent.stream(
    {"messages": [input_message]},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

================================ Human Message =================================

Use the tool to calculate 3^3.
================================== Ai Message ==================================

[{'id': 'rs_68b733f066a48194a41001c0cc1081760811f11b6f4bae47', 'summary': [], 'type': 'reasoning'}, {'call_id': 'call_7hTYtlTj9NgWyw8AQGqETtV9', 'input': '3 * 3 * 3', 'name': 'do_math', 'type': 'custom_tool_call', 'id': 'ctc_68b733f3a0a08194968b8338d33ad89f0811f11b6f4bae47', 'status': 'completed'}]
Tool Calls:
  do_math (call_7hTYtlTj9NgWyw8AQGqETtV9)
 Call ID: call_7hTYtlTj9NgWyw8AQGqETtV9
  Args:
    __arg1: 3 * 3 * 3
================================= Tool Message =================================
Name: do_math

[{'type': 'custom_tool_call_output', 'output': '27'}]
================================== Ai Message ==================================

[{'type': 'text', 'text': '27', 'annotations': [], 'id': 'msg_68b733f4bb008194937130796372bd0f0811f11b6f4bae47'}]

Responses API

Requires langchain-openai>=0.3.9

OpenAI supports a Responses API that is oriented toward building agentic applications. It includes a suite of built-in tools, including web and file search. It also supports management of conversation state, allowing you to continue a conversational thread without explicitly passing in previous messages, as well as the output from reasoning processes. ChatOpenAI will route to the Responses API if one of these features is used. You can also specify use_responses_api=True when instantiating ChatOpenAI.

Web search

To trigger a web search, pass {"type": "web_search_preview"} to the model as you would another tool.

You can also pass built-in tools as invocation params:

llm.invoke("...", tools=[{"type": "web_search_preview"}])

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1-mini")

tool = {"type": "web_search_preview"}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke("What was a positive news story from today?")

Note that the response includes structured content blocks that include both the text of the response and OpenAI annotations citing its sources. The output message will also contain information from any tool invocations:

response.content_blocks

[{'type': 'server_tool_call',
  'name': 'web_search',
  'args': {'query': 'positive news stories today', 'type': 'search'},
  'id': 'ws_68cd6f8d72e4819591dab080f4b0c340080067ad5ea8144a'},
 {'type': 'server_tool_result',
  'tool_call_id': 'ws_68cd6f8d72e4819591dab080f4b0c340080067ad5ea8144a',
  'status': 'success'},
 {'type': 'text',
  'text': 'Here are some positive news stories from today...',
  'annotations': [{'end_index': 410,
    'start_index': 337,
    'title': 'Positive News | Real Stories. Real Positive Impact',
    'type': 'citation',
    'url': 'https://www.positivenews.press/?utm_source=openai'},
   {'end_index': 969,
    'start_index': 798,
    'title': "From Green Innovation to Community Triumphs: Uplifting US Stories Lighting Up September 2025 | That's Great News",
    'type': 'citation',
    'url': 'https://info.thatsgreatnews.com/from-green-innovation-to-community-triumphs-uplifting-us-stories-lighting-up-september-2025/?utm_source=openai'},
  'id': 'msg_68cd6f8e8d448195a807b89f483a1277080067ad5ea8144a'}]

You can recover just the text content of the response as a string by using response.text. For example, to stream response text:

for token in llm_with_tools.stream("..."):
    print(token.text, end="|")

See the streaming guide for more detail.

Image generation

Requires langchain-openai>=0.3.19

To trigger an image generation, pass {"type": "image_generation"} to the model as you would another tool.

You can also pass built-in tools as invocation params:

llm.invoke("...", tools=[{"type": "image_generation"}])

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1-mini")

tool = {"type": "image_generation", "quality": "low"}

llm_with_tools = llm.bind_tools([tool])

ai_message = llm_with_tools.invoke(
    "Draw a picture of a cute fuzzy cat with an umbrella"
)

import base64

from IPython.display import Image

image = next(
    item for item in ai_message.content_blocks if item["type"] == "image"
)
Image(base64.b64decode(image["base64"]), width=200)

File search

To trigger a file search, pass a file search tool to the model as you would another tool. You will need to populate an OpenAI-managed vector store and include the vector store ID in the tool definition. See OpenAI documentation for more detail.

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4.1-mini",
    include=["file_search_call.results"],  # optionally include search results
)

openai_vector_store_ids = [
    "vs_...",  # your IDs here
]

tool = {
    "type": "file_search",
    "vector_store_ids": openai_vector_store_ids,
}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke("What is deep research by OpenAI?")
print(response.text)

Deep Research by OpenAI is...

As with web search, the response will include content blocks with citations:

[block["type"] for block in response.content_blocks]

['server_tool_call', 'server_tool_result', 'text']

text_block = next(block for block in response.content_blocks if block["type"] == "text")

text_block["annotations"][:2]

[{'type': 'citation',
  'title': 'deep_research_blog.pdf',
  'extras': {'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k', 'index': 2712}},
 {'type': 'citation',
  'title': 'deep_research_blog.pdf',
  'extras': {'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k', 'index': 2712}}]

It will also include information from the built-in tool invocations:

response.content_blocks[0]

{'type': 'server_tool_call',
 'name': 'file_search',
 'id': 'fs_68cd704c191c81959281b3b2ec6b139908f8f7fb31b1123c',
 'args': {'queries': ['deep research by OpenAI']}}

Computer use

ChatOpenAI supports the "computer-use-preview" model, which is a specialized model for the built-in computer use tool. To enable, pass a computer use tool as you would pass another tool. Currently, tool outputs for computer use are present in the message content field. To reply to the computer use tool call, construct a ToolMessage with {"type": "computer_call_output"} in its additional_kwargs. The content of the message will be a screenshot. Below, we demonstrate a simple example. First, load two screenshots:

import base64


def load_png_as_base64(file_path):
    with open(file_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
        return encoded_string.decode("utf-8")


screenshot_1_base64 = load_png_as_base64(
    "/path/to/screenshot_1.png"
)  # perhaps a screenshot of an application
screenshot_2_base64 = load_png_as_base64(
    "/path/to/screenshot_2.png"
)  # perhaps a screenshot of the Desktop

from langchain_openai import ChatOpenAI

# Initialize model
llm = ChatOpenAI(model="computer-use-preview", truncation="auto")

# Bind computer-use tool
tool = {
    "type": "computer_use_preview",
    "display_width": 1024,
    "display_height": 768,
    "environment": "browser",
}
llm_with_tools = llm.bind_tools([tool])

# Construct input message
input_message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": (
                "Click the red X to close and reveal my Desktop. "
                "Proceed, no confirmation needed."
            ),
        },
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_1_base64}",
        },
    ],
}

# Invoke model
response = llm_with_tools.invoke(
    [input_message],
    reasoning={
        "generate_summary": "concise",
    },
)

The response will include a call to the computer-use tool in its content:

response.content

[{'id': 'rs_685da051742c81a1bb35ce46a9f3f53406b50b8696b0f590',
  'summary': [{'text': "Clicking red 'X' to show desktop",
    'type': 'summary_text'}],
  'type': 'reasoning'},
 {'id': 'cu_685da054302481a1b2cc43b56e0b381706b50b8696b0f590',
  'action': {'button': 'left', 'type': 'click', 'x': 14, 'y': 38},
  'call_id': 'call_zmQerFBh4PbBE8mQoQHkfkwy',
  'pending_safety_checks': [],
  'status': 'completed',
  'type': 'computer_call'}]

We next construct a ToolMessage with these properties:

It has a tool_call_id matching the call_id from the computer-call.
It has {"type": "computer_call_output"} in its additional_kwargs.
Its content is either an image_url or an input_image output block (see OpenAI docs for formatting).

from langchain.messages import ToolMessage

tool_call_id = next(
    item["call_id"] for item in response.content if item["type"] == "computer_call"
)

tool_message = ToolMessage(
    content=[
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_2_base64}",
        }
    ],
    # content=f"data:image/png;base64,{screenshot_2_base64}",  # <-- also acceptable
    tool_call_id=tool_call_id,
    additional_kwargs={"type": "computer_call_output"},
)

We can now invoke the model again using the message history:

messages = [
    input_message,
    response,
    tool_message,
]

response_2 = llm_with_tools.invoke(
    messages,
    reasoning={
        "generate_summary": "concise",
    },
)

response_2.text

'VS Code has been closed, and the desktop is now visible.'

Instead of passing back the entire sequence, we can also use the previous_response_id:

previous_response_id = response.response_metadata["id"]

response_2 = llm_with_tools.invoke(
    [tool_message],
    previous_response_id=previous_response_id,
    reasoning={
        "generate_summary": "concise",
    },
)

response_2.text

'The VS Code window is closed, and the desktop is now visible. Let me know if you need any further assistance.'

Code interpreter

OpenAI implements a code interpreter tool to support the sandboxed generation and execution of code.

Example use

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4.1-mini",
    include=["code_interpreter_call.outputs"],  # optionally include outputs
)

llm_with_tools = llm.bind_tools(
    [
        {
            "type": "code_interpreter",
            # Create a new container
            "container": {"type": "auto"},
        }
    ]
)
response = llm_with_tools.invoke(
    "Write and run code to answer the question: what is 3^3?"
)

Note that the above command created a new container. We can also specify an existing container ID:

code_interpreter_calls = [
    item for item in response.content if item["type"] == "code_interpreter_call"
]
assert len(code_interpreter_calls) == 1
container_id = code_interpreter_calls[0]["extras"]["container_id"]  

llm_with_tools = llm.bind_tools(
    [
        {
            "type": "code_interpreter",
            # Use an existing container
            "container": container_id,  
        }
    ]
)

Remote MCP

OpenAI implements a remote MCP tool that allows for model-generated calls to MCP servers.

Example use

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1-mini")

llm_with_tools = llm.bind_tools(
    [
        {
            "type": "mcp",
            "server_label": "deepwiki",
            "server_url": "https://mcp.deepwiki.com/mcp",
            "require_approval": "never",
        }
    ]
)
response = llm_with_tools.invoke(
    "What transport protocols does the 2025-03-26 version of the MCP "
    "spec (modelcontextprotocol/modelcontextprotocol) support?"
)

MCP Approvals

OpenAI will at times request approval before sharing data with a remote MCP server.In the above command, we instructed the model to never require approval. We can also configure the model to always request approval, or to always request approval for specific tools:

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"]
                }
            }
        }
    ]
)
response = llm_with_tools.invoke(
    "What transport protocols does the 2025-03-26 version of the MCP "
    "spec (modelcontextprotocol/modelcontextprotocol) support?"
)

Responses may then include blocks with type "mcp_approval_request".To submit approvals for an approval request, structure it into a content block in an input message:

approval_message = {
    "role": "user",
    "content": [
        {
            "type": "mcp_approval_response",
            "approve": True,
            "approval_request_id": block["id"],
        }
        for block in response.content
        if block["type"] == "mcp_approval_request"
    ]
}

next_response = llm_with_tools.invoke(
    [approval_message],
    # continue existing thread
    previous_response_id=response.response_metadata["id"]
)

Managing conversation state

The Responses API supports management of conversation state.

Manually manage state

You can manage the state manually or using LangGraph, as with other chat models:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1-mini", use_responses_api=True)

first_query = "Hi, I'm Bob."
messages = [{"role": "user", "content": first_query}]

response = llm.invoke(messages)
print(response.text)

Hi Bob! Nice to meet you. How can I assist you today?

second_query = "What is my name?"

messages.extend(
    [
        response,
        {"role": "user", "content": second_query},
    ]
)
second_response = llm.invoke(messages)
print(second_response.text)

You mentioned that your name is Bob. How can I assist you further, Bob?

You can use LangGraph to manage conversational threads for you in a variety of backends, including in-memory and Postgres. See this tutorial to get started.

Passing `previous_response_id`

When using the Responses API, LangChain messages will include an "id" field in its metadata. Passing this ID to subsequent invocations will continue the conversation. Note that this is equivalent to manually passing in messages from a billing perspective.

second_response = llm.invoke(
    "What is my name?",
    previous_response_id=response.id,  
)
print(second_response.text)

Your name is Bob. How can I help you today, Bob?

ChatOpenAI can also automatically specify previous_response_id using the last response in a message sequence:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4.1-mini",
    use_previous_response_id=True,  
)

If we set use_previous_response_id=True, input messages up to the most recent response will be dropped from request payloads, and previous_response_id will be set using the ID of the most recent response. That is,

llm.invoke(
    [
        HumanMessage("Hello"),
        AIMessage("Hi there!", id="resp_123"),
        HumanMessage("How are you?"),
    ]
)

…is equivalent to:

llm.invoke([HumanMessage("How are you?")], previous_response_id="resp_123")

Reasoning output

Some OpenAI models will generate separate text content illustrating their reasoning process. See OpenAI’s reasoning documentation for details. OpenAI can return a summary of the model’s reasoning (although it doesn’t expose the raw reasoning tokens). To configure ChatOpenAI to return this summary, specify the reasoning parameter. ChatOpenAI will automatically route to the Responses API if this parameter is set.

from langchain_openai import ChatOpenAI

reasoning = {
    "effort": "medium",  # 'low', 'medium', or 'high'
    "summary": "auto",  # 'detailed', 'auto', or None
}

llm = ChatOpenAI(model="gpt-5-nano", reasoning=reasoning)
response = llm.invoke("What is 3^3?")

# Output
response.text

'3³ = 3 × 3 × 3 = 27.'

# Reasoning
for block in response.content_blocks:
    if block["type"] == "reasoning":
        print(block["reasoning"])

**Calculating the power of three**

The user is asking about 3 raised to the power of 3. That's a pretty simple calculation! I know that 3^3 equals 27, so I can say, "3 to the power of 3 equals 27." I might also include a quick explanation that it's 3 multiplied by itself three times: 3 × 3 × 3 = 27. So, the answer is definitely 27.

Troubleshooting: Empty responses from reasoning modelsIf you’re getting empty responses from reasoning models like gpt-5-nano, this is likely due to restrictive token limits. The model uses tokens for internal reasoning and may not have any left for the final output.Ensure max_tokens is set to None or increase the token limit to allow sufficient tokens for both reasoning and output generation.

Fine-tuning

You can call fine-tuned OpenAI models by passing in your corresponding modelName parameter. This generally takes the form of ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}. For example:

fine_tuned_model = ChatOpenAI(
    temperature=0, model_name="ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR"
)

fine_tuned_model.invoke(messages)

AIMessage(content="J'adore la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0f39b30e-c56e-4f3b-af99-5c948c984146-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})

Multimodal Inputs (images, PDFs, audio)

OpenAI has models that support multimodal inputs. You can pass in images, PDFs, or audio to these models. For more information on how to do this in LangChain, head to the multimodal inputs docs. You can see the list of models that support different modalities in OpenAI’s documentation. For all modalities, LangChain supports both its cross-provider standard as well as OpenAI’s native content-block format. To pass multimodal data into ChatOpenAI, create a content block containing the data and incorporate it into a message, e.g., as below:

message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            # Update prompt as desired
            "text": "Describe the (image / PDF / audio...)",
        },
        content_block,  
    ],
}

See below for examples of content blocks.

Images

Refer to examples in the how-to guide here.

URLs

# LangChain format
content_block = {
    "type": "image",
    "url": url_string,
}

# OpenAI Chat Completions format
content_block = {
    "type": "image_url",
    "image_url": {"url": url_string},
}

In-line base64 data

# LangChain format
content_block = {
    "type": "image",
    "base64": base64_string,
    "mime_type": "image/jpeg",
}

# OpenAI Chat Completions format
content_block = {
    "type": "image_url",
    "image_url": {
        "url": f"data:image/jpeg;base64,{base64_string}",
    },
}

PDFs

Note: OpenAI requires file-names be specified for PDF inputs. When using LangChain’s format, include the filename key.Read more here.Refer to examples in the how-to guide here.

In-line base64 data

# LangChain format
content_block = {
    "type": "file",
    "base64": base64_string,
    "mime_type": "application/pdf",
    "filename": "my-file.pdf",  
}

# OpenAI Chat Completions format
content_block = {
    "type": "file",
    "file": {
        "filename": "my-file.pdf",
        "file_data": f"data:application/pdf;base64,{base64_string}",
    }
}

Audio

See supported models, e.g., "gpt-4o-audio-preview".Refer to examples in the how-to guide here.

In-line base64 data

# LangChain format
content_block = {
    "type": "audio",
    "mime_type": "audio/wav",  # or appropriate mime-type
    "base64": base64_string,
}

# OpenAI Chat Completions format
content_block = {
    "type": "input_audio",
    "input_audio": {"data": base64_string, "format": "wav"},
}

Predicted output

Requires langchain-openai>=0.2.6

Some OpenAI models (such as their gpt-4o and gpt-4o-mini series) support Predicted Outputs, which allow you to pass in a known portion of the LLM’s expected output ahead of time to reduce latency. This is useful for cases such as editing text or code, where only a small part of the model’s output will change. Here’s an example:

code = """
/// <summary>
/// Represents a user with a first name, last name, and username.
/// </summary>
public class User
{
    /// <summary>
    /// Gets or sets the user's first name.
    /// </summary>
    public string FirstName { get; set; }

    /// <summary>
    /// Gets or sets the user's last name.
    /// </summary>
    public string LastName { get; set; }

    /// <summary>
    /// Gets or sets the user's username.
    /// </summary>
    public string Username { get; set; }
}
"""

llm = ChatOpenAI(model="gpt-4o")
query = (
    "Replace the Username property with an Email property. "
    "Respond only with code, and with no markdown formatting."
)
response = llm.invoke(
    [{"role": "user", "content": query}, {"role": "user", "content": code}],
    prediction={"type": "content", "content": code},
)
print(response.content)
print(response.response_metadata)

/// <summary>
/// Represents a user with a first name, last name, and email.
/// </summary>
public class User
{
    /// <summary>
    /// Gets or sets the user's first name.
    /// </summary>
    public string FirstName { get; set; }

    /// <summary>
    /// Gets or sets the user's last name.
    /// </summary>
    public string LastName { get; set; }

    /// <summary>
    /// Gets or sets the user's email.
    /// </summary>
    public string Email { get; set; }
}
{'token_usage': {'completion_tokens': 226, 'prompt_tokens': 166, 'total_tokens': 392, 'completion_tokens_details': {'accepted_prediction_tokens': 49, 'audio_tokens': None, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 107}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_45cf54deae', 'finish_reason': 'stop', 'logprobs': None}

Note that currently predictions are billed as additional tokens and may increase your usage and costs in exchange for this reduced latency.

Audio Generation (Preview)

Requires langchain-openai>=0.2.3

OpenAI has a new audio generation feature that allows you to use audio inputs and outputs with the gpt-4o-audio-preview model.

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-audio-preview",
    temperature=0,
    model_kwargs={
        "modalities": ["text", "audio"],
        "audio": {"voice": "alloy", "format": "wav"},
    },
)

output_message = llm.invoke(
    [
        ("human", "Are you made by OpenAI? Just answer yes or no"),
    ]
)

output_message.additional_kwargs['audio'] will contain a dictionary like

{
    'data': '<audio data b64-encoded',
    'expires_at': 1729268602,
    'id': 'audio_67127d6a44348190af62c1530ef0955a',
    'transcript': 'Yes.'
}

…and the format will be what was passed in model_kwargs['audio']['format']. We can also pass this message with audio data back to the model as part of a message history before openai expires_at is reached.

**Output audio is stored under the audio key in AIMessage.additional_kwargs, but input content blocks are typed with an input_audio type and key in HumanMessage.content lists. **For more information, see OpenAI’s audio docs.

history = [
    ("human", "Are you made by OpenAI? Just answer yes or no"),
    output_message,
    ("human", "And what is your name? Just give your name."),
]
second_output_message = llm.invoke(history)

Prompt caching

OpenAI’s prompt caching feature automatically caches prompts longer than 1024 tokens to reduce costs and improve response times. This feature is enabled for all recent models (gpt-4o and newer).

Manual caching

You can use the prompt_cache_key parameter to influence OpenAI’s caching and optimize cache hit rates:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")

# Use a cache key for repeated prompts
messages = [
    {"role": "system", "content": "You are a helpful assistant that translates English to French."},
    {"role": "user", "content": "I love programming."},
]

response = llm.invoke(
    messages,
    prompt_cache_key="translation-assistant-v1"
)

# Check cache usage
cache_read_tokens = response.usage_metadata.input_token_details.cache_read
print(f"Cached tokens used: {cache_read_tokens}")

Cache hits require the prompt prefix to match exactly

Cache key strategies

You can use different cache key strategies based on your application’s needs:

# Static cache keys for consistent prompt templates
customer_response = llm.invoke(
    messages,
    prompt_cache_key="customer-support-v1"
)

support_response = llm.invoke(
    messages,
    prompt_cache_key="internal-support-v1"
)

# Dynamic cache keys based on context
user_type = "premium"
cache_key = f"assistant-{user_type}-v1"
response = llm.invoke(messages, prompt_cache_key=cache_key)

Model-level caching

You can also set a default cache key at the model level using model_kwargs:

llm = ChatOpenAI(
    model="gpt-4o-mini",
    model_kwargs={"prompt_cache_key": "default-cache-v1"}
)

# Uses default cache key
response1 = llm.invoke(messages)

# Override with specific cache key
response2 = llm.invoke(messages, prompt_cache_key="override-cache-v1")

Flex processing

OpenAI offers a variety of service tiers. The “flex” tier offers cheaper pricing for requests, with the trade-off that responses may take longer and resources might not always be available. This approach is best suited for non-critical tasks, including model testing, data enhancement, or jobs that can be run asynchronously. To use it, initialize the model with service_tier="flex":

llm = ChatOpenAI(model="o4-mini", service_tier="flex")

Note that this is a beta feature that is only available for a subset of models. See OpenAI docs for more detail.

API reference

For detailed documentation of all features and configuration options, head to the ChatOpenAI API reference.

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​Overview

​Integration details

​Model features

​Setup

​Installation

​Credentials

​Instantiation

​Invocation

​Streaming usage metadata

​Using with Azure OpenAI

​Tool calling

​Bind tools

​Strict mode

​Tool calls

​Structured output and tool calls

​Custom tools

​Responses API

​Web search

​Image generation

​File search

​Computer use

​Code interpreter

​Remote MCP

​Managing conversation state

​Manually manage state

​Passing previous_response_id

​Reasoning output

​Fine-tuning

​Multimodal Inputs (images, PDFs, audio)

​Predicted output

​Note that currently predictions are billed as additional tokens and may increase your usage and costs in exchange for this reduced latency.

​Audio Generation (Preview)

​Prompt caching

​Manual caching

​Cache key strategies

​Model-level caching

​Flex processing

​API reference

Overview

Integration details

Model features

Setup

Installation

Credentials

Instantiation

Invocation

Streaming usage metadata

Using with Azure OpenAI

Tool calling

Bind tools

Strict mode

Tool calls

Structured output and tool calls

Custom tools

Responses API

Web search

Image generation

File search

Computer use

Code interpreter

Remote MCP

Managing conversation state

Manually manage state

Passing `previous_response_id`

Reasoning output

Fine-tuning

Multimodal Inputs (images, PDFs, audio)

Predicted output

Note that currently predictions are billed as additional tokens and may increase your usage and costs in exchange for this reduced latency.

Audio Generation (Preview)

Prompt caching

Manual caching

Cache key strategies

Model-level caching

Flex processing

API reference