LangChain & LangGraph Masterclass: Build AI Agents & RAG Pipelines

Description

🚀 Welcome to the Ultimate LangChain + LangGraph Masterclass! This in-depth 6-hour tutorial takes you from LLM basics to advanced AI Agentic Systems — covering LangChain, LangGraph, RAG pipelines, structured outputs, chains, and tool integration — everything you need to build real-world AI agents like ChatGPT-style apps, document assistants, and automation tools. Whether you’re a beginner exploring LangChain or an AI engineer diving into LangGraph’s agentic workflows, this course will guide you step-by-step through practical projects and hands-on examples using Python and Google Gemini. 📘 What You’ll Learn: ✅ LangChain Fundamentals – ChatPromptTemplate, Structured Output, Chains, Tools ✅ RAG (Retrieval-Augmented Generation) Pipeline – Build a YouTube Q&A RAG System ✅ LangGraph Core Concepts – Nodes, States, Conditional Flows, and Loops ✅ AI Document Editing & PDF RAG Agent with Gemini ✅ Building Intelligent, Tool-Driven, Context-Aware AI Agents 🎯 Perfect For: AI & ML Enthusiasts Developers building LLM applications Researchers exploring Agentic AI frameworks Anyone who wants to master LangChain and LangGraph in one go 🔗 GitHub Repository: (Add your repo link here) 🎓 More AI Tutorials: Subscribe for upcoming videos on advanced Agentic AI, LangChain updates, and custom RAG frameworks. 📅 Timestamps (Chapters): 00:00:00 Introduction 00:01:36 First Interaction with Langchain 00:06:49 Chat history & message types for context awareness 00:15:48 ChatPromptTemplate 00:20:48 MessagePlaceHolder 00:28:04 StructuredOutput 00:36:33 Advanced Structured Outputs 00:44:59 StructuredOutputParser with Pydantic 00:53:47 StrOutputParser and Chains 01:08:19 StructuredOutputParser and OutputFixingParser 01:19:14 PydanticOutputParser 01:28:42 Sequential Chains 01:35:58 Conditional Chains 01:48:53 Parallel Chains 02:01:48 LLMChain 02:07:28 Understanding Embeddings 02:20:07 Building a RAG pipeline 02:34:14 RunnableSequence 02:41:27 RunnableParallel 02:48:19 DocumentLoaders 03:06:15 VectorStore 03:18:38 Retrievers 03:24:10 Chroma VectorStore Retriever 03:31:27 Project: YouTube Q&A RAG 03:47:41 Tools 03:53:13 StructuredTool 03:57:55 Toolkits 04:03:30 Tool Binding 04:08:57 EnforcedToolExecution 04:15:56 InjectedTool Explained 04:15:56 Introduction (LangGraph) 04:18:03 First LangGraph example - Turning logic into flow 04:27:16 Structured states & multi-step reasoning 04:38:51 Multi-branch decision flow 04:49:27 Building loops and iterative logic 05:01:13 State-based conversational AI 05:13:45 Conditional Routing 05:21:50 Langgraph Tool Nodes 05:39:05 AI Document Editing Agent 06:02:48 PDF RAG Agent with Gemini