OpenAI Cookbook: OpenAI API Examples and Guides
/openai-cookbook
OpenAI Cookbook is a GitHub repository of Python-first sample code and walkthroughs for the OpenAI API. It's a reference, not a course: you copy a working notebook, swap in your own API key, and adapt the pattern. Reach for it when you need a concrete starting point for one API task; skip it if you want a structured, from-zero course on large language models, since it assumes you already know what you're building.
What is the OpenAI Cookbook?
The OpenAI Cookbook is the GitHub repository openai/openai-cookbook, holding runnable code and how-to write-ups for everyday OpenAI API jobs. Most of it is Python. The README says the underlying ideas still carry over to other languages. Each notebook needs an OpenAI account and API key, wired in via an environment variable or a local .env file, and the whole repo is readable on GitHub or via cookbook.openai.com under the MIT license.
What the Cookbook Offers
- โRunnable code samples and companion write-ups for everyday OpenAI API tasks, rather than prose-only explanations.
- โA companion site at cookbook.openai.com that presents the same material as a browsable, categorized front end instead of raw GitHub folders.
- โNotebooks that lean heavily on Python, though the README notes the underlying ideas aren't tied to any one language.
- โGitHub topic tags on the repo itself โ chatgpt, gpt-4, openai, openai-api โ give a rough sense of what the content clusters around.
- โA related_resources page pointing to outside guides, tools, and courses about the OpenAI API that fall outside the cookbook's own scope.
- โAPI-key setup is part of the workflow by design: examples expect an OPENAI_API_KEY environment variable, or a local .env file that IDEs like Visual Studio Code pick up automatically.
Common OpenAI API Tasks Covered
- โขStarting a new integration with the OpenAI API by copying a working notebook instead of building the request/response cycle from scratch.
- โขLooking up a working pattern for a specific task before writing code, by browsing the categorized front end at cookbook.openai.com rather than scanning raw repo folders.
- โขLearning how the OpenAI API behaves specifically in Python, since the README says most examples are written in Python even though the underlying concepts aren't Python-only.
- โขFinding a jumping-off point for a task the cookbook doesn't cover, via the related_resources page's links to outside guides, tools, and courses.
Strengths
- โEvery example is runnable code, not just prose, so you can copy a notebook and get to a working call quickly.
- โA companion site sits on top of the same content, so you're not limited to browsing raw GitHub folders.
- โMIT license, so example code can be dropped straight into a commercial project without a separate license negotiation.
- โMaintained by OpenAI directly, per the repo namespace and description, rather than a third-party fan collection.
- โGitHub stars and forks, per the repo's current metadata โ a plain usage count, not a claim about being the best resource available.
Considerations for Use
- โณNo specific count of notebooks or list of topic categories appears in the facts provided, so there's no way to judge topic breadth without browsing the repo or site directly.
- โณEvery example needs a working OpenAI account and API key; the cookbook doesn't include a way to run its code against OpenAI's models for free.
- โณIdeas apply broadly, but the README says most of the code itself is Python, so anyone working in another language has to translate the pattern by hand.
- โณNo version numbers or update cadence for individual notebooks appear in the facts provided, so there's no documented way to tell how current a given example is against OpenAI's current API.
Other Learning Resources
Frequently Asked Questions
Most OpenAI Cookbook examples are written in Python, according to the README, though the ideas behind them work in other programming languages too.
OpenAI Cookbook examples require an OpenAI account and API key, set as the OPENAI_API_KEY environment variable or picked up from a local .env file in IDEs like Visual Studio Code.
OpenAI Cookbook is licensed under MIT, a permissive license that allows reuse of its example code in commercial projects.
OpenAI Cookbook links out to a related_resources page at cookbook.openai.com/related_resources, pointing to other tools, guides, and courses about the OpenAI API from around the web.
The provided facts don't list which specific OpenAI models each example targets; GitHub's own topic tags for the repo include gpt-4 and chatgpt, but a full model-by-model breakdown isn't documented here.
Who should try it โ and who should skip
Try the OpenAI Cookbook if you already have an OpenAI account and API key and just want a working Python notebook to adapt for a specific task, rather than a conceptual explanation first. It also suits developers comparing how a task looks across different provider APIs, since it's concrete code rather than an abstraction layer. Skip it if you're after a structured course that starts from zero on large language models or prompt-engineering fundamentals; the cookbook assumes you can already navigate the OpenAI API and just want a shortcut past the boilerplate.
Related repositories
Still deciding about openai-cookbook?
One click hands the question to an AI along with this page โ see what it says about openai-cookbook.