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The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.9+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

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What is the OpenAI Python SDK?

The OpenAI Python SDK is the official Python library for interacting with OpenAI’s API. It simplifies the process of building AI-powered applications by providing:
  • Type-safe interfaces - Complete type definitions using TypedDicts and Pydantic models
  • Sync and async support - Choose between synchronous OpenAI and asynchronous AsyncOpenAI clients
  • Automatic retries - Built-in retry logic with exponential backoff for failed requests
  • Streaming responses - Server-sent events (SSE) support for real-time streaming
  • Pagination helpers - Auto-paginating iterators for list endpoints
  • Error handling - Comprehensive exception types for different error scenarios

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Key Features

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Responses API

The primary API for interacting with OpenAI models. Generate text, handle conversations, and process multi-modal inputs: 
from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.2",
    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)

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Chat Completions API

The previous standard for generating text, supported indefinitely: 
from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.2",
    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)

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Vision Support

Process images alongside text for multi-modal interactions: 
response = client.responses.create(
    model="gpt-5.2",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": "What is in this image?"},
                {"type": "input_image", "image_url": "https://example.com/image.jpg"},
            ],
        }
    ],
)

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Async Support

Use AsyncOpenAI for asynchronous operations: 
import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()

async def main():
    response = await client.responses.create(
        model="gpt-5.2",
        input="Explain quantum computing to a five year old."
    )
    print(response.output_text)

asyncio.run(main())

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Streaming Responses

Stream responses in real-time using Server-Sent Events: 
stream = client.responses.create(
    model="gpt-5.2",
    input="Write a short story about a robot.",
    stream=True,
)

for event in stream:
    print(event)

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Realtime API

Build low-latency, multi-modal conversational experiences with WebSocket support: 
import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI()

    async with client.realtime.connect(model="gpt-realtime") as connection:
        await connection.session.update(
            session={"type": "realtime", "output_modalities": ["text"]}
        )

        await connection.conversation.item.create(
            item={
                "type": "message",
                "role": "user",
                "content": [{"type": "input_text", "text": "Say hello!"}],
            }
        )
        await connection.response.create()

        async for event in connection:
            if event.type == "response.output_text.delta":
                print(event.delta, flush=True, end="")
            elif event.type == "response.done":
                break

asyncio.run(main())

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API Resources

The SDK provides access to the following OpenAI API resources:

Responses

Primary API for generating text and handling conversations

Chat Completions

Generate chat-based completions with message history

Embeddings

Create vector embeddings for text

Images

Generate and edit images with DALL·E

Audio

Speech-to-text and text-to-speech

Files

Upload and manage files

Fine-tuning

Create custom models with your data

Batches

Process async batch requests

Realtime

Low-latency multi-modal conversations

Moderations

Classify content for safety

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Requirements

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Next Steps

Installation

Install the SDK and set up your environment

Quickstart

Make your first API call in minutes

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