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TL;DR: Claude Cookbooks is Anthropic's official repository of code examples and guides for building with Claude AI. With 44k+ GitHub stars and 77+ contributors, it's the definitive resource for developers learning to harness Claude's capabilities—from basic text classification to advanced agentic workflows.
Claude Cookbooks is Anthropic's open-source collection of:
Repository: github.com/anthropics/claude-cookbooks (44.1k stars, 5.1k forks)
License: MIT (completely free to use commercially)
Languages: Primarily Python (95.1%), with TypeScript examples (0.4%)
Last Updated: Active development (updated yesterday)
Unlike third-party tutorials that may be outdated or inaccurate, Claude Cookbooks are maintained by Anthropic's team, ensuring:
From basics to bleeding-edge features:
Not toy examples—real implementations you can adapt:
With 77+ contributors and 5.1k forks:
1. Claude API Key
2. Python Environment (recommended)
# Python 3.8+ required
python --version
# Install the Anthropic SDK
pip install anthropic
3. Basic Python Knowledge
git clone https://github.com/anthropics/claude-cookbooks.git
cd claude-cookbooks
# Using pip
pip install -r requirements-dev.txt
# Using uv (faster, recommended)
pip install uv
uv sync
# Option 1: Environment variable
export ANTHROPIC_API_KEY='your-api-key-here'
# Option 2: .env file
echo "ANTHROPIC_API_KEY=your-api-key-here" > .env
Location: capabilities/classification/
What you'll learn:
Example use cases:
Key techniques:
from anthropic import Anthropic
client = Anthropic()
def classify_text(text, categories):
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"""Classify this text into one of these categories: {categories}
Text: {text}
Respond with only the category name."""
}]
)
return response.content[0].text
Location: capabilities/summarization/
What you'll learn:
Example use cases:
Advanced patterns:
Location: capabilities/retrieval_augmented_generation/
What you'll learn:
Integrations covered:
RAG Architecture:
1. Query → 2. Retrieve relevant docs → 3. Pass to Claude → 4. Generate response
Example implementation:
# Simplified RAG pattern
def rag_query(query, vector_db):
# 1. Generate query embedding
query_embedding = get_embedding(query)
# 2. Retrieve relevant documents
relevant_docs = vector_db.search(query_embedding, top_k=5)
# 3. Construct prompt with context
context = "\n\n".join([doc.content for doc in relevant_docs])
# 4. Query Claude with context
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=2048,
messages=[{
"role": "user",
"content": f"""Context:
{context}
Question: {query}
Answer based on the provided context."""
}]
)
return response.content[0].text
Location: tool_use/
What you'll learn:
Example tools covered:
Tool Definition Example:
tools = [
{
"name": "calculator",
"description": "Perform mathematical calculations",
"input_schema": {
"type": "object",
"properties": {
"operation": {
"type": "string",
"enum": ["add", "subtract", "multiply", "divide"],
"description": "The mathematical operation"
},
"a": {"type": "number", "description": "First number"},
"b": {"type": "number", "description": "Second number"}
},
"required": ["operation", "a", "b"]
}
}
]
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
tools=tools,
messages=[{"role": "user", "content": "What is 127 * 89?"}]
)
Location: tool_use/customer_service_agent/
Real-world implementation of a customer service bot with:
Architecture:
Location: tool_use/sql_queries/
What you'll learn:
Safety patterns:
Location: multimodal/vision/
Guides include:
Supported image formats:
Example: Analyzing a chart:
import base64
def analyze_chart(image_path):
with open(image_path, "rb") as f:
image_data = base64.b64encode(f.read()).decode()
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=2048,
messages=[{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": image_data
}
},
{
"type": "text",
"text": "Extract all data points from this chart and provide them in a table format."
}
]
}]
)
return response.content[0].text
Use cases:
Location: multimodal/generate_images/
Integration with Stable Diffusion to:
Pattern:
User request → Claude generates SD prompt → SD creates image → Claude refines → Final image
Location: misc/prompt_caching/
What you'll learn:
When to use caching:
Cost savings:
Example:
# Automatic caching (recommended)
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
system=[
{
"type": "text",
"text": "Very long system prompt...", # Auto-cached if >1024 tokens
}
],
messages=[{"role": "user", "content": "Question"}]
)
# Check cache performance
print(f"Cache read tokens: {response.usage.cache_read_input_tokens}")
print(f"Cache creation tokens: {response.usage.cache_creation_input_tokens}")
Location: misc/enable_json_mode/
Techniques for consistent JSON output:
Pattern:
def get_structured_output(prompt, schema):
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=2048,
messages=[{
"role": "user",
"content": f"""{prompt}
Respond with valid JSON matching this schema:
{json.dumps(schema, indent=2)}"""
}]
)
# Parse and validate
return json.loads(response.content[0].text)
Location: misc/automated_evaluations/
Use Claude to evaluate Claude:
Evaluation framework:
def evaluate_response(prompt, response, criteria):
eval_result = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"""Evaluate this AI response:
Prompt: {prompt}
Response: {response}
Criteria:
{criteria}
Provide scores (1-10) for each criterion and explain reasoning."""
}]
)
return parse_evaluation(eval_result.content[0].text)
Location: misc/content_moderation/
Build moderation systems for:
Moderation categories:
Location: misc/upload_pdfs/
PDF processing techniques:
Libraries used:
Location: patterns/agents/sub_agents/
Orchestration strategies:
Pattern:
Main agent (Opus) → Delegates to sub-agents (Haiku) → Aggregates results
Use cases:
Location: claude_agent_sdk/
Build production agents with:
Featured cookbooks:
Location: extended_thinking/
What you'll learn:
Best for:
Location: tool_evaluation/
Evaluate tool use performance:
Location: observability/
Monitor Claude applications:
Integrations:
Location: coding/
Claude for software development:
Location: finetuning/
Custom model training:
Goal: Automate 70% of tier-1 support tickets
Implementation:
Results (from community reports):
Goal: Extract structured data from invoices
Implementation:
Results:
Goal: Automated code review suggestions
Implementation:
Results:
From the cookbooks:
Example:
# Good: Structured, specific, with examples
system_prompt = """You are a customer service assistant.
Rules:
- Always be polite and professional
- Verify customer identity before sharing account info
- Escalate to human if customer is upset
- Use the knowledge base tool before making claims
Examples:
<example>
Customer: Where is my order?
Assistant: I'd be happy to help you track your order. Could you please provide your order number?
</example>"""
Strategies from the cookbooks:
Patterns from the cookbooks:
from anthropic import APIError, APITimeoutError, RateLimitError
def robust_api_call(messages, max_retries=3):
for attempt in range(max_retries):
try:
return client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=messages
)
except RateLimitError:
wait_time = 2 ** attempt # Exponential backoff
time.sleep(wait_time)
except APITimeoutError:
if attempt == max_retries - 1:
raise
continue
except APIError as e:
logging.error(f"API error: {e}")
raise
raise Exception("Max retries exceeded")
From security-focused cookbooks:
Cost optimization techniques:
| Model | Cost | Speed | Best For |
|---|---|---|---|
| Claude Haiku 4.5 | $ | ⚡⚡⚡ | Classification, moderation, simple queries |
| Claude Sonnet 4.5 | $$ | ⚡⚡ | Most tasks, balanced performance/cost |
| Claude Opus 4.5 | $$$ | ⚡ | Complex reasoning, coding, analysis |
From the cookbooks: "Use Haiku by default, Sonnet when quality matters, Opus when you need the best."
With 77+ contributors, notable cookbook authors include:
# 1. Fork the repository
# 2. Create a branch
git checkout -b feature/my-new-cookbook
# 3. Add your cookbook
# Follow the structure: category/subcategory/cookbook.ipynb
# 4. Test your notebook
make test-notebooks
# 5. Format code
make format
# 6. Submit PR
git push origin feature/my-new-cookbook
# Then create PR on GitHub
From CONTRIBUTING.md:
docs.anthropic.com - Official API documentation
Covers:
Free course for beginners covering:
Join 50k+ developers discussing:
AWS-specific examples for:
"Using the customer service agent cookbook, we automated 80% of our support tickets. Our team of 5 now handles what used to require 20 people." - SaaS founder
"The document processing cookbook helped us build a compliance review system. We reduced review time from 2 weeks to 2 days." - Fortune 500 legal team
"I built a content moderation API using the cookbooks and now make $5k/month licensing it to small platforms." - Solo developer
Problem: High costs for repeated context
Solution: Enable automatic caching for prompts >1024 tokens
# Anthropic automatically caches long system prompts
# Just structure your prompts to reuse context
Problem: Using Opus for everything = 60x cost vs. Haiku
Solution: Match model to task complexity (see cookbook examples)
Problem: Hitting context limits mid-conversation
Solution: Implement context windowing (see RAG cookbooks)
Problem: API failures breaking production apps
Solution: Use retry logic from the cookbooks
Problem: Claude occasionally produces incorrect formats
Solution: Implement validation (see JSON mode cookbook)
As of May 2026:
Upcoming cookbooks (based on community requests):
Claude Cookbooks is more than a tutorial collection—it's a masterclass in building AI applications, refined by Anthropic's engineers and thousands of community developers.
Start here:
The best part: Every cookbook is copy-paste ready. Pick one, run it, modify it for your use case, and ship it.
With 44,000+ stars and growing, Claude Cookbooks represents the collective wisdom of the Claude developer community.
Your next breakthrough application might be just one cookbook away.
Get started now:
Pro tip: Star the repo and enable GitHub notifications to stay updated on new cookbooks and techniques.
Ready to build? The cookbooks are waiting. Your AI application journey starts now.