LangGraph for beginners : Agentic Workflows in simple steps

About this course

Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?

In this beginner-friendly course, you’ll master LangGraph, an open-source library built on top of LangChain, designed for orchestrating multi-agent applications using a graph-based architecture. Whether you’re building intelligent agents, dynamic RAG pipelines, or real-world enterprise solutions, this course gives you the solid foundation you need.

What You’ll Learn

• What LangGraph is and how it fits into the GenAI ecosystem
• Build your first LangGraph workflow using a state machine
• Validate and structure your state using Pydantic models
• Use async and streaming to build responsive applications
• Implement conditional routing based on LLM output
• Understand reducers and how they manage state transitions
• Master tool calling with LangGraph’s built-in ToolNode
• Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage
• Build Agentic RAG workflows using tools and retrievers
• Implement Human-in-the-Loop workflows using Interrupt and resume
• Modularize complex graphs using subgraphs
• Apply everything in a real-time Hospital Insurance Claim Management use case
• Add tracing and observability using LangSmith
• Explore essential agentic design patterns to scale your applications

Who This Course Is For

• AI developers looking to build production-grade agentic apps
• LangChain users who want to level up to graph-based orchestration
• Backend engineers interested in tool use, memory, and state control
• Anyone working on LLM workflows in real-world use cases

Prerequisites

• Basic Python knowledge
• Experience with LangChain

By the end of this course, you’ll be able to:

• Confidently bu

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