Mar 18
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In this complete LangGraph tutorial, you will learn how to build advanced AI agents, multi-agent systems, and Retrieval-Augmented Generation (RAG) applications from scratch. LangGraph is one of the most powerful frameworks for building stateful AI workflows and autonomous agents. In this full course, we go from beginner concepts to advanced real-world AI architectures used in modern AI applications. By the end of this tutorial, you will understand how to build production-ready AI agent systems using LangGraph and integrate them with RAG pipelines, tools, memory, and multi-agent orchestration. This course is perfect for developers interested in AI agents, LLM applications, RAG systems, and advanced LangChain architectures. ## Who This Course Is For • Developers interested in AI agents and automation • Engineers learning LangGraph and LangChain • AI builders working on RAG systems • Anyone building LLM-powered applications ## Technologies Used LangGraph LangChain Javacript TypeScript Vector Databases Retrieval-Augmented Generation (RAG) Become a Wizard Member https://www.patreon.com/14026907/join JOIN THE AI HERO COURSE ⭐🌟✨ Join here : https://forms.gle/1B1tKJ4CzgjnBXFY6 Check out NotebookLM : https://youtu.be/qci2YEqDbFk Check out N8N Clone : https://youtu.be/jNtq3oJf6qM Check out RAG Full-Course : https://youtu.be/x6ozBq4Tqao Source code for this Video Lesson Code : https://github.com/Bienfait-ijambo/langgaph-course TimesCode 0:00 - Introduction 01:16 - What is LangGraph ? 09:12 - Building Your First Graph in LangGraph 15:56 - Build a QA over Document Workflow using Naive RAG 53:14 - Build a Corrective RAG Pipeline 1:22:59 - Build an Adaptive RAG Pipeline 1:32:20 - Reflection & Reflexion 1:53:55 - Build a Multi-Agent system using Handoff Pattern #LangChain #AI #MLOps #Python #GenerativeAI #AIAgents #RAG #MachineLearning #ArtificialIntelligence