LangChain vs OpenAI Cookbook
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
A Python framework for building AI agents and apps powered by language models.
Open source · MIT
Example code and guides for common tasks with the OpenAI API.
Our verdict
LangChain is a Python framework you code against to build LLM-powered apps that can switch between model providers and connect to your own data and tools. OpenAI Cookbook is not a framework at all: it is a set of official, runnable OpenAI API examples to read and adapt. They serve different purposes, so pick based on whether you are building an application or learning how the OpenAI API works.
Pick LangChain if you are a Python developer building an LLM application and want to swap model providers or connect a model to your own data and tools.
Pick OpenAI Cookbook if you are new to the OpenAI API and want working code examples to learn from and adapt.
| Key difference | LangChain | OpenAI Cookbook |
|---|---|---|
| Type | Framework (library you code against) for building LLM apps and agents | Learning resource of example code and guides for the OpenAI API |
| Model providers | Designed to work across providers with a standard interface | Specific to the OpenAI API; needs an OpenAI key |
| Setup | Intermediate; install with a package manager | Beginner; clone and build from source, mostly Jupyter notebooks |
| Main language | Python; JavaScript/TypeScript users are pointed to LangChain.js | Jupyter Notebook (Python), though ideas apply to other languages |
| Cost to use | Free and open source (MIT); you still need access to the model provider's API | Free and open source (MIT); examples may incur OpenAI API usage costs |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
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
Side-by-side
| LangChain | OpenAI Cookbook | |
|---|---|---|
| Type | Framework | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 149k | 76.9k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Jupyter Notebook |
| Last code update | 4 days ago | 5 days ago |
What each one is
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
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.
Best for
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
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
Strengths
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
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
Not ideal for
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
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
Trade-offs
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
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
Getting started
OpenAI Cookbook
Clone the repository
git clone https://github.com/openai/openai-cookbook.gitFrequently asked questions
Should I use LangChain or OpenAI Cookbook?
LangChain is a Python framework you code against to build LLM-powered apps that can switch between model providers and connect to your own data and tools. OpenAI Cookbook is not a framework at all: it is a set of official, runnable OpenAI API examples to read and adapt. They serve different purposes, so pick based on whether you are building an application or learning how the OpenAI API works. Pick LangChain if you are a Python developer building an LLM application and want to swap model providers or connect a model to your own data and tools. Pick OpenAI Cookbook if you are new to the OpenAI API and want working code examples to learn from and adapt.
What is the difference between LangChain and OpenAI Cookbook?
LangChain: A Python framework for building AI agents and apps powered by language models. OpenAI Cookbook: Example code and guides for common tasks with the OpenAI API. LangChain suits python developers building llm-powered applications, while OpenAI Cookbook suits developers who are new to the openai api and want working examples.
Are LangChain and OpenAI Cookbook free?
LangChain: Free and open source. OpenAI Cookbook: Free and open source.
Which is easier to set up, LangChain or OpenAI Cookbook?
OpenAI Cookbook is easier to get started with (rated beginner); LangChain is rated intermediate and usually needs more setup.
Is LangChain or OpenAI Cookbook more popular?
LangChain has more GitHub stars (149k) than OpenAI Cookbook (76.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangChain and OpenAI Cookbook?
Other options people compare them with include LlamaIndex, Semantic Kernel, CrewAI, OpenUI, VoltAgent.
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.