Anthropic Cookbook
The Claude Cookbooks is a collection of code examples and guides for building applications with Anthropic's Claude API. It provides copy-able Python snippets covering capabilities like classification, RAG, summarization, tool use, multimodal processing, and advanced techniques. It is intended for developers who want to integrate Claude into their projects.
✨ Key features
- Copy-able code snippets for easy integration
- Covers classification, RAG, summarization, and more
- Tool use examples including customer service and SQL
- Multimodal examples for vision and image generation
- Advanced techniques like sub-agents and prompt caching
- Cost optimization and evaluation strategies
🎯 Use cases
- Build a customer service agent with tool use
- Enhance Claude with external knowledge via RAG
- Implement text classification and summarization
- Extract content from images and PDFs
- Create a content moderation filter
📦 Installation
🧰 Requirements: Requires a Claude API key (free sign-up at anthropic.com). Examples are in Python but adaptable to other languages.
Clone the repository:
git clone https://github.com/anthropics/claude-cookbooks.git
Then install dependencies as needed for each notebook.
🚀 Usage
Open any notebook (e.g., capabilities/classification) in Jupyter and run cells after setting your API key.
import anthropic
client = anthropic.Anthropic(api_key="your-api-key")
# Example: classification
response = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=100,
messages=[{"role":"user","content":"Classify this text: ..."}]
)
print(response.content)
⚠️ Good to know
The cookbook is community-driven and may have incomplete or unmaintained examples; some third-party samples may require modification.
❓ FAQ
Do I need a paid API key to use the cookbook?
You need a Claude API key, which you can sign up for free at anthropic.com.
Can I use the examples in languages other than Python?
Yes, the concepts can be adapted to any language that supports the Claude API.
Are there examples for integrating with vector databases?
Yes, there is a Pinecone example for RAG in the third-party integrations section.
How can I contribute to the cookbook?
You can submit ideas or fixes via the issues page or pull requests, after reviewing existing ones.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.