Langgraph

LangGraph is a practical and comprehensive guide to building reliable, stateful, and controllable AI applications using graph-based workflows. The book introduces the fundamentals of LangGraph and gradually progresses to advanced concepts for developing AI Agents and Agentic AI systems powered by Large Language Models (LLMs).

Readers will learn how to design workflows using nodes, edges, states, conditional routing, and loops. The book explains how LangGraph can be used to create AI agents capable of reasoning, planning, using external tools, maintaining state, interacting with users, and working with external knowledge through Retrieval-Augmented Generation (RAG).

The book also covers ToolNode, tool calling, memory, checkpoints, human-in-the-loop workflows, middleware, multi-agent systems, error handling, and real-world Agentic AI applications. Practical examples help readers understand how to move from simple LLM workflows to sophisticated autonomous and collaborative AI systems.

What You Will Learn
LangGraph Fundamentals
Introduction to LangGraph
LangGraph Architecture
LangGraph and Agentic AI
LangGraph vs Traditional LLM Chains
Setting Up the LangGraph Development Environment
Python for LangGraph
Graph-Based AI Workflows
State, Nodes, and Edges
START and END Nodes
State Management
Building Your First LangGraph Application
Workflows and Routing
Sequential Workflows
Conditional Edges
Dynamic Routing
Branching Workflows
Loops and Iterative Workflows
Multi-Step AI Workflows
State Updates and Reducers
Workflow Control and Execution
Human-Controlled Workflows
LLM and Agent Integration
Large Language Models and LangGraph
Chat Models
Prompt Engineering
Structured Outputs
LLM-Based Decision Making
AI Agent Architecture
Reasoning and Planning
Agent Loops
Tool Calling
Function Calling
ToolNode
Custom Tools
External APIs and Services
RAG and Knowledge-Based Agents
Retrieval-Augmented Generation (RAG)
Document Loading and Processing
Text Splitting and Chunking
Embeddings
Vector Databases
Semantic Search
Retrievers
Building RAG Workflows with LangGraph
Document Question-Answering Systems
Knowledge-Based AI Agents