Agentic AI with LangGraph

Agentic AI with LangGraph is a practical and comprehensive guide to building intelligent AI agents and autonomous workflows using LangGraph, LangChain, Generative AI, and Large Language Models (LLMs).

The book explains how AI agents can reason about tasks, make decisions, plan actions, use external tools, maintain state and memory, interact with APIs and databases, and complete multi-step tasks. You will learn how LangGraph provides a powerful framework for designing stateful, controllable, and flexible agentic workflows.

Starting with the fundamentals of Generative AI and LLMs, the book gradually progresses to prompt engineering, RAG, embeddings, vector databases, LangChain, LangGraph, tool calling, human-in-the-loop systems, and multi-agent architectures.

Through practical examples and real-world projects, you will learn how to design and develop AI agents for research, data analysis, document processing, customer support, coding, automation, and other real-world applications.

What You Will Learn
Fundamentals of Artificial Intelligence and Generative AI
Generative AI vs. Agentic AI
Large Language Models (LLMs)
Transformer architecture
Tokens, context windows, and model parameters
Prompt Engineering
Zero-shot, one-shot, and few-shot prompting
Structured output and prompt templates
AI agent fundamentals and architecture
Components of an AI agent
LLM as a reasoning engine
Planning and decision making
Task decomposition
Agent memory and state
Tool usage and tool calling
Function calling
Building custom AI tools
Integrating APIs, databases, and web search
Retrieval-Augmented Generation (RAG)
Document processing and text splitting
Embeddings and vector databases
Semantic search and retrievers
LangChain fundamentals
LangChain models and chat models
Prompt templates and chains
Runnables and workflows
LangChain agents and tools
Introduction to LangGraph
LangGraph architecture
Graphs, nodes, and edges
State management
Conditional routing
Loops and iterative workflows
ToolNode and tool execution
Stateful AI agents
Human-in-the-loop workflows
Agent supervision and control
Error handling and recovery
Agent evaluation and monitoring
LangSmith integration
Multi-agent systems
Supervisor and worker agent architectures
Agent communication and collaboration
Autonomous AI workflows
External tool and API integration
Hugging Face and LLM API integration
Hallucination reduction
Prompt injection and AI security
Tool permissions and access control
Cost and performance optimization
Deployment of AI agent applications
Real-World Projects
AI Chatbot Agent
LangGraph Research Assistant
RAG-Based AI Agent
PDF Document Analysis Agent
Web Search Research Agent
AI Data Analysis Agent
Database Query Agent
Customer Support Agent
Coding Assistant Agent
Human-in-the-Loop AI Agent
Multi-Agent Research System
Supervisor-Worker Multi-Agent System
Intelligent Document Processing Agent
Autonomous Task Automation Agent
AI Agent with External Tools and APIs