AI and LangGraph with Agentic AI (A Complete Guide to Generative AI, LLMs, LangGraph, AI Agents, RAG, Tool Calling, Workflows, and Real-World Agentic AI Applications) by Aravind choudhary

Generative AI and LangGraph with Agentic AI is a practical and comprehensive guide designed for students, Python developers, AI enthusiasts, and software professionals who want to learn how to build intelligent applications using Generative AI, Large Language Models (LLMs), LangGraph, and Agentic AI.

The book begins with the fundamentals of Generative AI and LLMs and gradually progresses to advanced concepts such as Prompt Engineering, RAG, embeddings, vector databases, AI agents, tool calling, state management, workflows, memory, and multi-agent systems.

Readers will learn how LangGraph can be used to design stateful, controllable, and multi-step AI workflows. The book also explains how to build Agentic AI systems capable of reasoning, planning, using tools, interacting with external data, and completing complex tasks.

With a hands-on and project-oriented approach, this book provides practical knowledge for developing modern AI-powered and autonomous applications using Python and popular Generative AI frameworks.

What You Will Learn
Introduction to Artificial Intelligence
Generative AI Fundamentals
Large Language Models (LLMs)
LLM Architecture and Working
Tokens, Context Windows, and Embeddings
Prompt Engineering
Advanced Prompting Techniques
AI Chatbots and LLM Applications
Retrieval-Augmented Generation (RAG)
Document Processing and Text Splitting
Embeddings and Vector Databases
Semantic Search and Retrieval
Introduction to LangChain
LangGraph Fundamentals
LangGraph Architecture
Graph-Based AI Workflows
State Management
Nodes and Edges
Conditional Routing
ToolNode and Tool Calling
Function Calling
AI Agents
Agentic AI Fundamentals
Reasoning and Planning
Agent Memory
Human-in-the-Loop Systems
Middleware and Agent Control
Multi-Agent Systems
Agent Collaboration
Autonomous AI Workflows
External API and Tool Integration
Hugging Face and LLM Integration
AI Agent Evaluation
Error Handling and Reliability
AI Agent Security
Building AI Applications with Python
Real-World Generative AI Projects
Real-World LangGraph and Agentic AI Projects