OpenAI Cookbook
Example code and guides for common tasks with the OpenAI API.
- Stars
- 76.9k
- Forks
- 13k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 5 days ago
- Type
- Learning resource
What it is
The OpenAI Cookbook is a collection of example code and written guides showing how to accomplish common tasks with the OpenAI API. Most examples are Jupyter notebooks written in Python, though the README notes the ideas can be applied in other languages. It is mainly useful as a set of working references you can read, run, and adapt.
- Follow a guide to make your first call to the OpenAI API
- Run a notebook example to see a common task implemented in Python
- Adapt example code as a starting point for your own application
- Browse the related resources page for courses and tools from around the web
Great for
- Developers who are new to the OpenAI API and want working examples
- Python programmers who want to see API usage patterns in notebook form
- Students or self-learners exploring how to build with language models
- Teams looking for reference code to adapt for their own projects
Not ideal for
- People without an OpenAI account or API key, since the examples require one (the README says a free account can be created)
- Non-programmers looking for a ready-made app, because this is a set of code examples rather than a finished product
- Developers who need a non-OpenAI model provider, since the material is specific to the OpenAI API
How to get it
- 1
Clone and build from source
You'll need: OpenAI account and API key · Jupyter or an IDE that runs notebooks (the README mentions Visual Studio Code)
Clone the repository
git clone https://github.com/openai/openai-cookbook.git - 2
Run it
- Create an OpenAI account and generate an API key
- Set an environment variable named OPENAI_API_KEY, or add it to a .env file at the repo root
- Open one of the Jupyter notebooks in the repository
- Run the notebook cells in order to see the example output
- 3
Get the most out of it
- Read the guides on cookbook.openai.com alongside the notebooks, since the site is the README's main navigation point
- Use the .env approach so the notebooks pick up your key automatically in editors such as Visual Studio Code
- Check the related resources page for additional courses and tools
Downloads
Strengths
- Official examples maintained under the OpenAI organization
- Organized as runnable notebooks that show code and explanation together
- Released under the permissive MIT license
Trade-offs
- Examples depend on an OpenAI account and API key, which may incur usage costs
- Most code is in Python, so other-language users must translate the concepts
- The README is short and does not describe a version schedule or release history
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| openai-cookbook (this) | 76.9k | Learning resource | beginner | 5 days ago | Developers who are new to the OpenAI API and want working examples |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| llama_index | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| semantic-kernel | 28.7k | Library | intermediate | 4 days ago | Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents |
| one-api | 37.3k | App | intermediate | 10 months ago | Developers who want one OpenAI-compatible endpoint for several model providers |
| botpress | 15k | Dev tool | intermediate | 6 days ago | Developers who want to write integrations and bots as TypeScript code using the Botpress CLI and SDK |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.