Skip to content

Claude Cookbooks

What it is

Claude Cookbooks is Anthropic's public repository of example code, workflows, and reference material for building with Claude.

What problem it solves

It gives teams a practical set of implementation examples so they do not have to infer every integration pattern from API reference docs alone.

Where it fits in the stack

Development & Ops / Reference Implementations. It is a learning and acceleration resource for Claude builders.

Typical use cases

  • Learning Claude API usage patterns
  • Bootstrapping demos and internal prototypes
  • Reviewing implementation examples before building custom flows

Example company use cases

  • Prototype phase: use cookbook examples to shorten the first version of an internal assistant or workflow tool.
  • Platform team: use cookbook patterns as review references when standardizing how Claude is used across products.
  • Training and onboarding: point new engineers to concrete examples before they design their own integrations.

Strengths

  • First-party reference material
  • More implementation-oriented than high-level product pages

Limitations

  • Examples are starting points, not complete production designs
  • Cookbook coverage may not match your exact stack

When to use it

  • When you want example-driven guidance for Claude integrations

When not to use it

  • When you need a production-ready architecture without further engineering

Common Patterns

The cookbooks are organized around several core patterns: - RAG (Retrieval-Augmented Generation): Using Claude with vector databases. - Tool Use (Function Calling): Defining and executing tools to interact with the real world. - Prompt Engineering: System prompt design and multi-shot examples. - Output Structuring: Forcing Claude to return valid JSON or specific schemas.

Contribution Guide

Developers can contribute to the community-driven sections by: 1. Forking the claude-cookbooks repository. 2. Adding a new notebook or Python script in the relevant category. 3. Submitting a Pull Request with a clear explanation of the new pattern.

Technical Examples

Python: Using a Cookbook Pattern for JSON Extraction

import anthropic

client = anthropic.Anthropic()

# Based on 'Structured Output' cookbook pattern
response = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    system="You are a data extractor. Always return JSON.",
    messages=[{"role": "user", "content": "Extract name and age: John Doe is 30."}]
)
print(response.content)

TypeScript: Async Streaming Implementation

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

// Based on 'Streaming' cookbook pattern
async function main() {
  const stream = await anthropic.messages.create({
    max_tokens: 1024,
    messages: [{ role: 'user', content: 'Explain quantum physics.' }],
    model: 'claude-3-5-sonnet-20240620',
    stream: true,
  });
  for await (const messageStreamEvent of stream) {
    console.log(messageStreamEvent.type);
  }
}

main();

Selection comments

  • Claude Cookbooks is first-party implementation guidance, not an architecture substitute.
  • Use it early in the build process, then move to your own patterns once the team knows what works.
  • Pair it with Context7 for current third-party docs and Superpowers for execution discipline.

Sources / References

Contribution Metadata

  • Last reviewed: 2026-05-18
  • Confidence: high