OpenAI Python API library

The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.10+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by HTTPX2.

It is generated from our OpenAPI specification.

Documentation

The REST API documentation can be found on platform.openai.com. The full API of this library can be found in api.md.

Installation

# install from PyPI
pip install openai

Usage

The full API of this library can be found in api.md.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {
            "role": "user",
            "content": "How do I check if a Python object is an instance of a class?",
        },
    ],
)

print(completion.choices[0].message.content)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add OPENAI_API_KEY="My API Key" to your .env file so that your API key is not stored in source control. Get an API key here.

Workload Identity Authentication

For secure, automated environments like cloud-managed Kubernetes, Azure, and Google Cloud Platform, you can use workload identity authentication with short-lived tokens from cloud identity providers instead of long-lived API keys.

Kubernetes (service account tokens)

from openai import OpenAI
from openai.auth import k8s_service_account_token_provider

client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": k8s_service_account_token_provider("/var/run/secrets/kubernetes.io/serviceaccount/token"),
    },
)

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "Hello!"}],
)

Azure (managed identity)

from openai import OpenAI
from openai.auth import azure_managed_identity_token_provider

client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": azure_managed_identity_token_provider(
            resource="https://management.azure.com/",
        ),
    },
)

Google Cloud Platform (compute engine metadata)

from openai import OpenAI
from openai.auth import gcp_id_token_provider

client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": gcp_id_token_provider(audience="https://api.openai.com/v1"),
    },
)

Custom subject token provider

from openai import OpenAI


def get_custom_token() -> str:
    return "your-jwt-token"


client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": {
            "token_type": "jwt",
            "get_token": get_custom_token,
        },
    }
)

You can also customize the token refresh buffer (default is 1200 seconds (20 minutes) before expiration):

from openai import OpenAI
from openai.auth import k8s_service_account_token_provider

client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": k8s_service_account_token_provider("/var/token"),
        "refresh_buffer_seconds": 120.0,
    }
)

X.509 workload identity (mutual TLS)

For X.509 workload identity federation, configure the client certificate and server trust on an HTTPX2 client, then pass only the identity-provider and service-account IDs to the SDK:

import os
import ssl

from openai import OpenAI, DefaultHttpx2Client
from openai.auth import x509_workload_identity

tls_context = ssl.create_default_context(
    cafile=os.getenv("OPENAI_MTLS_CA_BUNDLE"),
)
tls_context.load_cert_chain(
    certfile=os.environ["OPENAI_MTLS_CERTIFICATE_CHAIN"],
    keyfile=os.environ["OPENAI_MTLS_PRIVATE_KEY"],
    password=os.getenv("OPENAI_MTLS_PRIVATE_KEY_PASSWORD"),
)

client = OpenAI(
    workload_identity=x509_workload_identity(
        identity_provider_id=os.environ["OPENAI_IDENTITY_PROVIDER_ID"],
        service_account_id=os.environ["OPENAI_SERVICE_ACCOUNT_ID"],
        # refresh_buffer_seconds=120.0,
    ),
    http_client=DefaultHttpx2Client(
        verify=tls_context,
        follow_redirects=False,
    ),
)

X.509 mode defaults to https://mtls.api.openai.com/v1 when neither base_url nor OPENAI_BASE_URL is set. The same configured HTTP client presents its certificate to the fixed mTLS token-exchange endpoint and to the API. Tokens are exchanged lazily, cached, and refreshed automatically. Certificate files, private keys, passwords, server trust, proxies, and rotation remain application and transport concerns.

X.509 API requests require HTTPS and must stay on the configured API origin. The effective HTTP Host authority must match that origin. Provider API-key and proxy-only headers cannot be sent to the API alongside X.509 authentication. Token exchanges do not inherit API request hooks, authentication, or cookies. Identity settings are captured when the client is constructed; create a new client to change the identity. Azure clients do not support X.509 workload identity.

For asynchronous requests, use AsyncOpenAI with DefaultAsyncHttpx2Client. See the complete sync rollout-toggle example and async rollout-toggle example, which select API-key or X.509 authentication with the application-owned OPENAI_AUTH_MODE environment variable. X.509 workload identity currently supports HTTP APIs; Realtime and WebSockets are not included.

Vision

With an image URL:

prompt = "What is in this image?"
img_url = "https://api.nga.gov/iiif/a2e6da57-3cd1-4235-b20e-95dcaefed6c8/full/!800,800/0/default.jpg"

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"{img_url}"},
            ],
        }
    ],
)

With the image as a base64 encoded string:

import base64
from openai import OpenAI

client = OpenAI()

prompt = "What is in this image?"
with open("path/to/image.png", "rb") as image_file:
    b64_image = base64.b64encode(image_file.read()).decode("utf-8")

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"data:image/png;base64,{b64_image}"},
            ],
        }
    ],
)

Async usage

Simply import AsyncOpenAI instead of OpenAI and use await with each API call:

import os
import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)


async def main() -> None:
    response = await client.responses.create(
        model="gpt-5.5", input="Explain disestablishmentarianism to a smart five year old."
    )
    print(response.output_text)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses HTTPX2. For improved concurrency performance, you may also use aiohttp as the HTTPX2 transport.

You can enable this by installing aiohttp:

# install from PyPI
pip install openai[aiohttp]

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import os
import asyncio
from openai import DefaultAioHttpClient
from openai import AsyncOpenAI


async def main() -> None:
    async with AsyncOpenAI(
        api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
        http_client=DefaultAioHttpClient(),
    ) as client:
        chat_completion = await client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": "Say this is a test",
                }
            ],
            model="gpt-5.5",
        )


asyncio.run(main())

HTTPX2 migration

HTTPX2 is the default HTTP client. If you configure a custom HTTP client, transport, timeout, authentication handler, event hook, or request mock, see the HTTPX2 migration guide.

Streaming responses

We provide support for streaming responses using Server-Sent Events (SSE).

from openai import OpenAI

client = OpenAI()

stream = client.responses.create(
    model="gpt-5.5",
    input="Write a one-sentence bedtime story about a unicorn.",
    stream=True,
)

for event in stream:
    print(event)

The async client uses the exact same interface.

```python import asyncio from openai import AsyncOpenAI

client = AsyncOpenAI()

async def main(): stream = await client.responses.create( model="gpt-5.5", input="Write a one-sentence bedtime story about a unicorn.", stream=True, )

async for event in stream: