ChatOpenAI

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

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)

Popular Providers

Integrations by component

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

Popular Providers

Integrations by component

​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

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