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openai/openai-python

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TLDR

Official Python library for calling OpenAI's API (GPT, vision, etc.). Handles networking, type hints, async support, and streaming so you can focus on building.

Mindmap

mindmap
  root((repo))
    What it does
      Call GPT models
      Handle images
      Stream responses
    How it works
      Client object
      Type hints
      Async support
    Features
      Vision input
      Workload identity
      Server-Sent Events
    Tech stack
      Python 3.9+
      httpx
      aiohttp optional
    Use cases
      Chatbots
      Text generation
      Image analysis

Things people build with this

USE CASE 1

Build a chatbot that answers questions using GPT by calling the library's chat.completions.create method.

USE CASE 2

Add image understanding to your app by passing a photo URL to the vision endpoint and asking questions about it.

USE CASE 3

Stream text responses word-by-word in a web app using Server-Sent Events for a more interactive feel.

USE CASE 4

Handle thousands of concurrent API requests in a cloud environment using async/await syntax.

Tech stack

Pythonhttpxaiohttpasync/await

Getting it running

Difficulty · easy Time to first run · 5min

Requires a valid OpenAI API key to make actual API calls.

MIT license, use freely for any purpose, including commercial, as long as you keep the copyright notice.

In plain English

This is the official Python library that lets your code talk to OpenAI's models. It wraps OpenAI's REST API so that instead of building HTTP requests by hand, you import a client, pass your API key, and call methods like generating text from a model or asking it questions about an image. The README says the library works from any Python 3.9+ application and ships with type definitions for every request parameter and response field, which is what gives your editor autocompletion and type checks. Both synchronous and asynchronous clients are included; the async version is powered by httpx by default, with aiohttp available as an alternative HTTP backend for higher concurrency. The library is generated from OpenAI's published OpenAPI specification using Stainless. The primary path described in the README is the Responses API for text generation; the older Chat Completions API is also supported indefinitely. The README walks through installing with pip install openai, sending text prompts, sending image prompts as a URL or as base64, and streaming responses back using Server-Side Events. It also documents workload-identity authentication for Kubernetes, Azure managed identity, and Google Cloud Platform, so you can use short-lived cloud-issued tokens instead of long-lived API keys. Someone would reach for this any time they want a Python program, a script, web backend, or notebook, to call OpenAI's models. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Show me how to create a simple chatbot using openai-python that sends a message and prints the response.
Prompt 2
How do I use the openai-python library to analyze an image from a URL and describe what's in it?
Prompt 3
Write a Python script using openai-python that streams a response word-by-word instead of waiting for the full answer.
Prompt 4
How do I set up workload identity authentication with openai-python in a Kubernetes cluster instead of using API keys?
Prompt 5
Show me an async example using openai-python to send 100 requests concurrently and collect all responses.
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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.