LangChain and LangGraph with Agentic AI

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

The book begins with the fundamentals of Generative AI and LLMs and gradually progresses to advanced concepts such as prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG), LangChain workflows, LangGraph state management, tool calling, AI agents, memory, and multi-agent systems.

You will learn how LangChain can be used to connect LLMs with prompts, documents, tools, APIs, databases, and external services, while LangGraph provides a powerful framework for designing stateful, multi-step, controllable, and agentic workflows.

With practical examples and real-world projects, this book helps you understand how to design AI agents that can reason, plan tasks, make decisions, use tools, maintain state, interact with external systems, and collaborate with other agents.

What You Will Learn
Fundamentals of Artificial Intelligence and Generative AI
Generative AI and 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
Introduction to LangChain
LangChain architecture and components
LLMs and Chat Models
Prompt Templates
Output Parsers
Chains and workflows
Runnables and LCEL
Document Loaders
Text Splitting and Chunking
Embeddings and semantic similarity
Vector databases and vector stores
Retrievers and semantic search
Retrieval-Augmented Generation (RAG)
Building document question-answering systems
LangChain Agents
Tools and Custom Tools
Function Calling and Tool Calling
API and database integration
Web search integration
Conversational memory
Introduction to LangGraph
LangGraph architecture
Graphs, Nodes, and Edges
State management
Conditional routing
Loops and iterative workflows
ToolNode and tool execution
Stateful AI applications
Building AI Agents with LangGraph
Agent architecture and workflows
Reasoning and decision making
Planning and task decomposition
Agent memory and state
Autonomous AI workflows
Human-in-the-loop systems
Error handling and recovery
Agent evaluation and monitoring
LangSmith integration
Multi-Agent Systems
Supervisor and Worker agent architecture
Agent communication and collaboration
External APIs, databases, and web tools
Hugging Face and LLM API integration
Hallucination reduction
Prompt injection and AI security
Tool permissions and access control
Cost and performance optimization
Deployment of Agentic AI application