OpenAI Cookbook vs Semantic Kernel
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Example code and guides for common tasks with the OpenAI API.
Open source · MIT
Open-source SDK for building AI agents and multi-agent apps in Python, .NET and Java.
The short answer
Choose OpenAI Cookbook if you need…
- 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
Choose Semantic Kernel if you need…
- Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents
- Teams working in .NET, Python or Java who want one framework across languages
- Projects that need plugins built from native code, prompt templates, OpenAPI specs or MCP
- Existing Semantic Kernel users maintaining or migrating older code
Side-by-side
| OpenAI Cookbook | Semantic Kernel | |
|---|---|---|
| Type | Learning resource | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 76.9k | 28.7k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | C# |
| Last code update | 5 days ago | 4 days ago |
What each one is
OpenAI Cookbook
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.
Semantic Kernel
Semantic Kernel is a model-agnostic SDK from Microsoft that lets developers connect large language models (such as OpenAI or Azure OpenAI models) to their own apps, add tools and plugins, and orchestrate one or more AI agents. The README says it is now succeeded by Microsoft Agent Framework, so new projects should check that project first.
Best for
OpenAI Cookbook
- 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
Semantic Kernel
- Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents
- Teams working in .NET, Python or Java who want one framework across languages
- Projects that need plugins built from native code, prompt templates, OpenAPI specs or MCP
- Existing Semantic Kernel users maintaining or migrating older code
Strengths
OpenAI Cookbook
- Official examples maintained under the OpenAI organization
- Organized as runnable notebooks that show code and explanation together
- Released under the permissive MIT license
Semantic Kernel
- Supports several models and providers, including OpenAI, Azure OpenAI, Hugging Face and NVidia, per the README
- Offers examples in Python and C# with matching quickstarts
- Permissive MIT license and a large public project with about 28,700 stars
Not ideal for
OpenAI Cookbook
- 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
Semantic Kernel
- Non-programmers: it is a developer SDK and needs code to use, so no-code users should look elsewhere
- Teams starting fresh who want the newest path: the README directs users to Microsoft Agent Framework as the successor
- Simple scripts that only need one direct API call, where an SDK adds more structure than needed
Trade-offs
OpenAI Cookbook
- 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
Semantic Kernel
- The README states the project is succeeded by Microsoft Agent Framework, so long-term direction may point elsewhere
- Quickstarts assume Azure or OpenAI credentials and a configured environment, so setup needs some engineering work
- Java support is pointed to a separate build guide rather than documented here
Getting started
OpenAI Cookbook
Clone the repository
git clone https://github.com/openai/openai-cookbook.gitSemantic Kernel
Set Azure OpenAI API key
export AZURE_OPENAI_API_KEY=AAA....Set OpenAI API key (alternative)
export OPENAI_API_KEY=sk-...Frequently asked questions
What is the difference between OpenAI Cookbook and Semantic Kernel?
OpenAI Cookbook: Example code and guides for common tasks with the OpenAI API. Semantic Kernel: Open-source SDK for building AI agents and multi-agent apps in Python, .NET and Java. OpenAI Cookbook suits developers who are new to the openai api and want working examples, while Semantic Kernel suits developers already using azure openai or openai who want a structured sdk for agents.
Are OpenAI Cookbook and Semantic Kernel free?
OpenAI Cookbook: Free and open source. Semantic Kernel: Free and open source.
Which is easier to set up, OpenAI Cookbook or Semantic Kernel?
OpenAI Cookbook is easier to get started with (rated beginner); Semantic Kernel is rated intermediate and usually needs more setup.
Is OpenAI Cookbook or Semantic Kernel more popular?
OpenAI Cookbook has more GitHub stars (76.9k) than Semantic Kernel (28.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to OpenAI Cookbook and Semantic Kernel?
Other options people compare them with include LangChain, LlamaIndex, Microsoft Agent Framework, OpenAI Agents SDK, Semantica.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.