**Agentic AI with LangGraph** is a practical guide to building intelligent, autonomous AI applications using LangGraph, LangChain, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), tools, and agentic workflows.

As Generative AI evolves from simple chatbots to intelligent systems capable of reasoning, planning, using tools, maintaining state, and completing complex tasks, understanding Agentic AI has become an essential skill for AI developers and technology professionals.

This book provides a hands-on approach to developing AI agents and agentic workflows with LangGraph. You will learn how to design stateful workflows, connect LLMs with external tools, implement memory and persistence, build RAG-based agents, handle human-in-the-loop interactions, and create multi-agent systems.

### What You Will Learn

* Fundamentals of Agentic AI and AI Agents
* Introduction to LangGraph and its architecture
* LangChain and LLM integration
* Building stateful AI workflows
* Nodes, edges, states, and graph-based execution
* Tool calling and function calling
* Retrieval-Augmented Generation (RAG)
* Vector databases and embeddings
* Memory and persistent agent state
* Human-in-the-loop AI systems
* Conditional routing and workflow control
* Planning, reasoning, and autonomous task execution
* Multi-agent systems and agent collaboration
* Building practical AI agent applications
* Error handling and workflow management
* Designing scalable and production-oriented agentic applications

Whether you are a student, Python developer, software engineer, AI/ML professional, or someone looking to move from traditional Generative AI applications toward autonomous AI systems, this book provides practical concepts and examples to help you build real-world Agentic AI applications.

By the end of this book, you will have a strong foundation in LangGraph and the skills required to design and develop intelligent AI agents and complex agentic workflows.