**LangChain with LangGraph** is a practical guide to building modern Generative AI applications, LLM-powered solutions, RAG systems, and intelligent AI agents using Python.

This book/course takes you from the fundamentals of LangChain to advanced LangGraph concepts, helping you understand how to design, develop, and manage AI applications with large language models (LLMs).

You will learn how to work with LLMs, prompts, chains, tools, agents, embeddings, vector databases, retrieval-augmented generation (RAG), memory, workflows, and stateful AI applications. You will also explore LangGraph for creating multi-step, stateful, and agentic workflows where AI agents can reason, use tools, maintain state, and interact with external systems.

### Key Topics Covered

• LangChain fundamentals and architecture
• Working with Large Language Models (LLMs)
• Prompt templates and prompt engineering
• Chains and LCEL
• Embeddings and vector databases
• Retrieval-Augmented Generation (RAG)
• Document loading, splitting and retrieval
• Tools and tool calling
• AI agents and agent workflows
• LangGraph fundamentals and architecture
• State, nodes, edges and graph-based workflows
• Stateful and multi-step AI applications
• Human-in-the-loop workflows
• Memory and persistence
• Multi-agent systems
• Generative AI application development
• Agentic AI concepts and implementations
• Building practical LLM-powered projects with Python

Designed for students, developers, software engineers, AI/ML professionals, and anyone who wants to build practical Generative AI and Agentic AI applications, this book focuses on hands-on development and real-world implementation rather than only theoretical concepts.

By the end, you will have a strong foundation in LangChain and LangGraph and the practical skills required to build intelligent, scalable, and workflow-driven AI applications.