Mastering LangChain and LangGraph: Build RAG and Agentic AI Applications with LLMs, MCP, and LangChain Agents

Mastering LangChain and LangGraph is a comprehensive, hands‑on guide for developers, data scientists, and AI practitioners looking to build robust, production‑ready applications using large language models, retrieval‑augmented generation (RAG), and agentic systems. The book begins by establishing a clear foundation, introducing the LLM ecosystem and core concepts such as RAG and AI agents, before guiding readers into the LangChain framework and its practical abstractions. Readers will explore essential building blocks including chat models, prompt templates, and structured output generation, followed by in‑depth coverage of document loaders, text splitters, embeddings, vector stores, and retrievers—key components for creating scalable, knowledge‑grounded AI systems. As the book progresses, it introduces LangGraph, enabling readers to design stateful, multi‑step, and resilient agent workflows with fine‑grained control over execution. Advanced chapters dive into tools and the Model Context Protocol (MCP), checkpointing, memory management, and middleware design, providing the infrastructure needed to manage complexity in real‑world applications. Topics such as human‑in‑the‑loop workflows, time travel, and streaming demonstrate how to build systems that are transparent, debuggable, and interactive. The book concludes with a focused exploration of LangChain agents, tying together tools, memory, and control flow into cohesive agentic architectures. Blending conceptual clarity with practical implementation guidance, this book equips readers with the skills to design, build, and scale modern AI applications that go beyond simple prompts—delivering intelligent, reliable, and extensible systems ready for production use. What you will learn:  Understand the LLM ecosystem, including Retrieval‑Augmented Generation (RAG) and agent‑based AI systems. Build scalable, knowledge‑grounded applications using LangChain components like prompts, embeddings, vector stores, and retrievers. Design structured, stateful, and multi‑step workflows with LangGraph for reliable agent execution. Implement tools, memory, checkpointing, and middleware to manage complexity in real‑world AI applications. Create production‑ready agentic systems with human‑in‑the‑loop, streaming, and debugging capabilities. Who this book is for: This book is for software developers, data scientists, and AI practitioners who want to move beyond basic prompt engineering and build production‑ready AI applications using large language models. It is ideal for engineers working with LangChain who want a deeper, structured understanding of its components and how they fit together in real‑world systems.