**Master LangGraph and Build Powerful AI Agent Applications with Python**

Learn how to design, develop, and deploy intelligent AI agents and stateful LLM applications using **LangGraph, LangChain, Python, RAG, tools, and modern Agentic AI techniques**.

This practical guide takes you from the fundamentals of LangGraph to building real-world AI workflows. You will learn how to create stateful applications, manage agent workflows, connect LLMs with external tools, implement human-in-the-loop systems, and build multi-agent architectures.

### What You Will Learn

* Introduction to LangGraph and Agentic AI
* LangGraph architecture and core concepts
* Graphs, nodes, edges, and state management
* Building stateful LLM applications with Python
* LangChain and LangGraph integration
* Creating AI agents and agentic workflows
* Tool calling and external API integration
* RAG-based applications with LangGraph
* Memory and persistent conversations
* Human-in-the-loop AI systems
* Conditional routing and dynamic workflows
* Multi-agent systems and collaboration
* Checkpointing and workflow persistence
* Error handling and reliable agent execution
* Building practical AI projects with LangGraph
* Developing production-oriented LLM and Agentic AI applications

Whether you are a **Python developer, AI/ML learner, software developer, student, or professional**, this book provides a practical foundation for building modern AI agents and LLM-powered applications with LangGraph.

By the end of this book, you will have the knowledge and practical skills required to design **stateful AI agents, RAG systems, tool-using agents, and multi-agent workflows** using LangGraph and Python.