Tutorials
100 Days of ML
100 Days of DL
100 Days of NLP
MCP Trilogy
FastAPI for ML
PyTorch Deep Learning
LangChain & GenAI
LangGraph Agents
Code Lab (LeetCode AI)
Syllabus Path
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Complete LangGraph and Agentic AI curriculum — master stateful graphs, persistence, human-in-the-loop, ReAct agent patterns, advanced RAG architectures, and production scaling.
A comprehensive, step-by-step tutorial series on LangGraph — from the fundamentals of cycles and state management to advanced persistence, checkpointers, human-in-the-loop, and production scaling.
Build resilient, stateful, and production-ready multi-agent workflows. Click any module card below to access the detailed notes, code blocks, and architectures.
Start with the core theory: what Agentic AI is, differences from standard Generative AI, and top framework architectures.
Learn core LangGraph primitives: graphs, states, nodes, edges, conditional routing, and iterative workflows.
Implement production persistence and memory patterns: memory checkpointers, breakpoints, and state restoration.
Integrate logging, tracing, and analytics with LangSmith to track latency, token usage, and graph flow.
Fuse agents with documents and external systems using Model Context Protocol (MCP) and Agentic RAG patterns.
Design complex hierarchical multi-agent architectures using subgraphs and shared state configurations.
Start with Module 1 — learn the foundational mechanics of Agentic AI before building your first compiled graph.
LangGraph is a framework built on top of LangChain that enables stateful, graph-based agentic AI workflows. While LangChain handles linear chains and simple agents, LangGraph supports complex multi-agent systems with cycles, branching, persistence, and human-in-the-loop control. Our free LangGraph tutorial covers everything from foundations to production deployment.
To build multi-agent systems with LangGraph, you define a StateGraph, add agent nodes (each with specific roles and tools), connect them with edges and conditional routing, compile the graph, and run it with an initial state. Our free LangGraph tutorial covers multi-agent design patterns, supervisor agents, and parallel execution in Hindi and English.
A stateful AI agent maintains memory of past interactions and decisions across multiple steps in a workflow. Unlike simple LLM calls, stateful agents can pause, resume, and branch based on intermediate results. LangGraph is the leading framework for building stateful AI agents, and our free LangGraph course teaches it from scratch.
Yes. GenAIWallah's LangGraph course is completely free — no signup or paywall. It covers LangGraph fundamentals, stateful graphs, multi-agent systems, RAG architectures, human-in-the-loop patterns, and production deployment in Hindi and English.