Generative AI with LangChain

About this book

This second edition tackles the challenge facing companies in AI today: moving from prototypes to production. Fully updated for the latest LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments, with a focus on multi-agent architectures, LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.

You'll explore design patterns for agentic systems, with practical multi-agent setups for complex tasks. It covers reasoning techniques such as Tree-of-Thoughts, structured generation, and agent handoffs, with error-handling examples throughout. Expanded chapters on testing, evaluation, and deployment show how to build secure, compliant AI systems with safeguards built in. RAG coverage grows with hybrid search, re-ranking, and fact-checking pipelines for more accurate output.

Whether you're extending existing workflows or architecting multi-agent systems from scratch, the book gives you the technical depth and practical instruction to ship LLM applications in production.

Highlights

• Bridge the gap from prototype to production with LangGraph agent architectures
• Apply production practices for testing, observability, and monitoring
• Build specialized agents for software development and data analysis
• Free PDF eBook included with every print or Kindle purchase

What you'll learn

• Design and implement multi-agent systems using LangGraph
• Build testing strategies that catch issues before deployment
• Deploy observability and monitoring for production environments
• Build agentic RAG systems with re-ranking
• Architect scalable AI agents using LangGraph and MCP
• Work with the latest models from Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI
• Design secure, compliant AI systems aligned with responsible-AI practices

Who this book is for

For developers, researchers, and teams learning LangChain and LangGraph, with a focus on enterprise deployment patterns. The second edition expands beyond individual developers to support engineering teams and decision-makers working on LLM strategy at scale. A basic understanding of Python is required; familiarity with machine learning helps.