Complete LangGraph Course for Beginners | Build AI Agents with Python

Description

Welcome to the Complete LangGraph Course for Beginners! 🌙 In this comprehensive course, you’ll learn how to build real-world AI applications using LangGraph, LangChain, and Python — starting from the fundamentals and gradually moving toward advanced, production-ready AI systems. 🚀 Whether you’re a Python developer, AI enthusiast, or someone looking to build LLM-powered applications, this course will give you a strong foundation in modern AI orchestration, agentic workflows, graph-based systems, and intelligent applications using LangGraph. 📂 GitHub Repository All Code Examples 🔗 https://github.com/yashjaincodex/langgraph-youtube 🎯 What You’ll Learn in This Course 🔰 LangGraph Fundamentals ✅ Basic Graph — Understand nodes, edges, and state in LangGraph ✅ Sequential Graph — Build graphs where nodes execute one after another ✅ Conditional Graph — Route execution dynamically based on state ✅ Looping Graph — Create graph cycles and iterative workflows ✅ Parallel Graph — Run multiple nodes in parallel using fan-out and fan-in patterns ✅ Reducers in Graph — Manage and merge complex state updates 🤖 Working with LLMs in LangGraph ✅ LLM in Graph — Use a language model as a graph node ✅ LLM with Conditional Graph — Route graph execution based on LLM output ✅ Multi-Turn Chatbot — Build conversational apps with state management ✅ Stream Response in Graph — Stream real-time LLM responses inside LangGraph 🧰 Tools, Memory & Human Interaction ✅ Tool Node in Graph — Connect LangGraph with external tools and APIs ✅ Tool Node with LLM — Combine LLM reasoning with tool execution ✅ Persistence & Checkpoint Memory — Save and restore graph state ✅ Human-in-the-Loop — Pause execution for human review, approval, or correction 🧠 Advanced LangGraph Concepts ✅ Subgraph in LangGraph — Build reusable and modular graph components ✅ Send API in Graph — Dynamically dispatch messages to nodes ✅ Multi-Agent in Graph — Orchestrate multiple AI agents together ✅ Agentic RAG in Graph — Combine vector search, LLM reasoning, and graph workflows With hands-on coding, real-world projects, and production-focused workflows, this course will help you confidently build: ✅ AI Agents ✅ Multi-Agent Systems ✅ Chatbots ✅ RAG Applications ✅ AI Workflows ✅ Human-in-the-Loop Systems ✅ Tool-Calling Agents ✅ Streaming AI Apps ✅ Production-Ready LangGraph Pipelines using LangGraph, LangChain, Python, and LLMs. 🚀 LangChain Course: https://youtu.be/kAf0en1XT6U 💬 Don’t forget to Like, Comment, and Subscribe if you found this course helpful — your support keeps these AI coding lessons coming! ✨ Before you sleep, make sure you’ve learned something new. ✨ #LangGraph #LangGraphCourse #LangChain #AIAgents #MultiAgentSystems #RAG #PythonAI #LLM #AIDevelopment #LearnAI #GenerativeAI #AgenticAI #PythonCourse #AIForBeginners #CodeBeforeYouSleep #YashJain