Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph

by Ben Auffarth, Leonid Kuligin

Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications

Key FeaturesBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis second edition tackles the biggest challenge facing companies in AI moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.

You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.

Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.

What you will learnDesign and implement multi-agent systems using LangGraphImplement testing strategies that identify issues before deploymentDeploy observability and monitoring solutions for production environmentsBuild agentic RAG systems with re-ranking capabilitiesArchitect scalable, production-ready AI agents using LangGraph and MCPWork with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-miniDesign secure, compliant AI systems aligned with modern ethical practicesWho this book is forThis book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.

Table of ContentsThe Rise of Generative From Language Models to AgentsFirst Steps with LangChainBuilding Workflows with LangGraphBuilding Intelligent RAG Systems with LangChainBuilding Intelligent AgentsAdvanced Applications and Multi-Agent SystemsSoftware Development and Data Analysis AgentsEvaluation

Genres: Nonfiction

713 pages, Kindle Edition

Published May 23, 2025

Reviews

[Johnny · Rating 2 out of 5]

The book has a lot to offer, but it is too wordy and lacks flow. Many valuable ideas feel buried under far too much text, making it harder to stay engaged.

At times, the writing even feels AI-generated, as if the author did not know when to stop. With tighter editing and a clearer structure, the book could have been much stronger.

[Yoly · Rating 5 out of 5]

Excellent book on Langchain/Langraph. With up to date information, practical examples and detailed explanations you'll have more than enough information to get started with agents.