Go beyond simple prompts and build production-grade AI agents with LangChain and LangGraph, from ReAct reasoning loops to tool-calling, RAG, and context engineering.
Key Features
- Build and debug AI agents using LangChain, LangGraph, and LangSmith
- Implement ReAct reasoning loops, tool calling, and structured outputs
- Apply context engineering, MCP integration, and RAG to real-world agent workflows
Book Description
Agentic AI is transforming how developers build software with large language models. This practical guide shows you how modern AI agents work under the hood and how to build them yourself with Python, LangChain, and LangGraph. Rather than treating frameworks as black boxes, you'll explore the mechanics that power agent systems. Starting with prompts, chains, ReAct loops, and the agent loop, you'll progressively implement tool calling, structured outputs, memory, and context engineering while understanding the trade-offs behind each design choice. As your projects grow in sophistication, you'll work with LangSmith for tracing and debugging, integrate external tools and APIs, and build retrieval workflows using RAG and vector databases. Along the way, you'll learn how modern agent architectures coordinate reasoning, actions, and context to solve increasingly complex tasks. Every chapter is grounded in a practical project that evolves step by step, helping you connect concepts to implementation. By the end of the book, you'll have the skills to build, inspect, extend, and troubleshoot AI agents with confidence using LangChain and LangGraph.
What you will learn
- Build and run AI agents with LangChain and LangGraph
- Implement ReAct reasoning loops and agent workflows
- Apply tool calling and structured output techniques
- Engineer context for accuracy and long-horizon tasks
- Trace and debug agent applications with LangSmith
- Build RAG pipelines using vector databases
- Connect agents to external tools and live APIs
- Design scalable and maintainable agent architectures
Who this book is for
This book is for software developers and engineers, AI engineers, data scientists, researchers, and technical builders who want to understand how modern AI agents work and how to build them with LangChain and LangGraph. You should be comfortable with Python, Git, APIs, and basic debugging. No machine learning background is required, but the book assumes some programming experience and focuses on practical implementation rather than introductory coding.
