LangGraph for beginners : Agentic Workflows in simple steps

<div>Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?</div><div><br></div><div>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.</div><div><br></div><div><span style="font-size: 1rem;">What You’ll Learn</span></div><div><br></div><div>• What LangGraph is and how it fits into the GenAI ecosystem</div><div><br></div><div>• Build your first LangGraph workflow using a state machine</div><div><br></div><div>• Validate and structure your state using Pydantic models</div><div><br></div><div>• Use async and streaming to build responsive applications</div><div><br></div><div>• Implement conditional routing based on LLM output</div><div><br></div><div>• Understand reducers and how they manage state transitions</div><div><br></div><div>• Master tool calling with LangGraph’s built-in ToolNode</div><div><br></div><div>• Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage</div><div><br></div><div>• Build Agentic RAG workflows using tools and retrievers</div><div><br></div><div>• Implement Human-in-the-Loop workflows using Interrupt and resume</div><div><br></div><div>• Modularize complex graphs using subgraphs</div><div><br></div><div>• Apply everything in a real-time Hospital Insurance Claim Management use case</div><div><br></div><div>• Add tracing and observability using LangSmith</div><div><br></div><div>• Explore essential agentic design patterns to scale your applications</div>

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
• All in simple steps