Claude Cookbooks vs LangChain
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 set of Jupyter notebook recipes showing how to build with the Claude API.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Our verdict
Claude Cookbooks is a collection of copy-able Jupyter notebook examples showing how to call the Claude API for specific tasks like tool use, vision, and prompt caching. LangChain is a Python framework, a library you build applications on top of, with a standard interface for swapping model providers and connecting LLMs to data and tools. They serve different purposes: choose Cookbooks to learn Claude-specific patterns by example, and LangChain to structure a larger LLM application that may use several providers.
Pick Claude Cookbooks if you are a Python developer who wants working, task-specific code examples for using Claude directly.
Pick LangChain if you are a Python developer building an LLM application that needs a reusable framework and integrations across model providers.
| Key difference | Claude Cookbooks | LangChain |
|---|---|---|
| Type | Learning resource of Jupyter notebook recipes and guides | Python framework (library) for building LLM apps and agents |
| Model scope | Focused on the Claude API from Anthropic | Standard interface designed to switch between model providers |
| How you use it | Copy and adapt code snippets from individual notebooks | Install as a package and build your application on it |
| Setup | Clone and build from source; intermediate difficulty | Install with a package manager; intermediate difficulty |
| Best at | Guided examples for tool use, vision, RAG, and evaluations | Connecting LLMs to your own data, tools, and swappable models |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose Claude Cookbooks if you need…
- Python developers who want working examples of Claude API calls
- Developers exploring tool use or retrieval augmented generation with Claude
- Teams evaluating how to add vision or PDF handling to an application
- People new to the Claude API who want to follow guided examples
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
Side-by-side
| Claude Cookbooks | LangChain | |
|---|---|---|
| Type | Learning resource | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 54.1k | 149k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 7 days ago | 4 days ago |
What each one is
Claude Cookbooks
This repository is a collection of Jupyter notebooks and guides with copy-able code examples for using Claude, Anthropic's AI model, through its API. Each recipe covers a specific task, such as classification, retrieval augmented generation, tool use, vision, summarization, prompt caching, or automated evaluations. Developers use it as a starting point and adapt the snippets into their own projects.
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.
Best for
Claude Cookbooks
- Python developers who want working examples of Claude API calls
- Developers exploring tool use or retrieval augmented generation with Claude
- Teams evaluating how to add vision or PDF handling to an application
- People new to the Claude API who want to follow guided examples
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
Strengths
Claude Cookbooks
- Covers many common tasks, from classification to vision to agents, in one place
- Code is meant to be copied and integrated into real projects
- Released under the MIT license, which permits reuse
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
Not ideal for
Claude Cookbooks
- Non-programmers, since the recipes are code notebooks that assume coding knowledge
- People looking for a finished end-user app, because this is example code rather than a product
- Anyone needing non-Python examples, since the README says the code is primarily Python and other languages must be adapted
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
Trade-offs
Claude Cookbooks
- The README is short and does not list versions or detailed setup steps, so you must read each notebook for specifics
- Some recipes link to external repositories or third-party services that need their own accounts or setup
- Examples may reflect the Claude API at the time they were written, so check current docs before relying on them
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
Getting started
Claude Cookbooks
Clone the repository
git clone https://github.com/anthropics/claude-cookbooksFrequently asked questions
Should I use Claude Cookbooks or LangChain?
Claude Cookbooks is a collection of copy-able Jupyter notebook examples showing how to call the Claude API for specific tasks like tool use, vision, and prompt caching. LangChain is a Python framework, a library you build applications on top of, with a standard interface for swapping model providers and connecting LLMs to data and tools. They serve different purposes: choose Cookbooks to learn Claude-specific patterns by example, and LangChain to structure a larger LLM application that may use several providers. Pick Claude Cookbooks if you are a Python developer who wants working, task-specific code examples for using Claude directly. Pick LangChain if you are a Python developer building an LLM application that needs a reusable framework and integrations across model providers.
What is the difference between Claude Cookbooks and LangChain?
Claude Cookbooks: A set of Jupyter notebook recipes showing how to build with the Claude API. LangChain: A Python framework for building AI agents and apps powered by language models. Claude Cookbooks suits python developers who want working examples of claude api calls, while LangChain suits python developers building llm-powered applications.
Are Claude Cookbooks and LangChain free?
Claude Cookbooks: Free and open source. LangChain: Free and open source.
Is Claude Cookbooks or LangChain more popular?
LangChain has more GitHub stars (149k) than Claude Cookbooks (54.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Claude Cookbooks and LangChain?
Other options people compare them with include LlamaIndex, OpenAI Cookbook, Semantic Kernel, CrewAI, OpenUI.
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.