Feb 24
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LangGraph for Beginners | GenAI, Agents & Workflows (Tamil) Unlock the full potential of Generative AI, AI Agents, and LangGraph with this beginner-friendly yet in-depth Tamil video. Whether you're a student, developer, data scientist, or AI enthusiast, this series takes you from the basics of LangChain & LangGraph all the way to building real-world, production-ready agent systems. Through step-by-step explanations, practical demos, and clear visuals, this videp helps you understand how modern AI systems work — including Agent workflows, Graph-based orchestration, Message Passing, Tool Calling, Memory, Multi-Agent Systems, RAG Agents, and more. 🎯 What You’ll Learn The fundamentals of LangGraph and how graphs help orchestrate complex AI workflows. How to build Agents using LangChain + LangGraph from scratch. Real-time examples of tool calling, state management, callbacks, memory and conditional nodes. How Search, Reasoning, RAG, Chat History, and Tool Use work behind the scenes. How to connect multiple agents using React Agent, RAG Agent, Supervisor/Master Agent, and more. Beginner-friendly implementations of Agent Studio, Prompting, and Pre-requisite concepts for building GenAI applications. This video is structured to take you from zero to expert: Introduction to LangGraph & Setup Start with the basics — what LangGraph is, why it's used in modern GenAI systems, and essential prerequisites every beginner needs. Core Concepts Learn Nodes, Edges, State, Memory, Tool Calling, and Message Passing with hands-on examples. Building Tools & Agents Understand how LLMs trigger tools, call functions, and interact with external data and APIs. RAG + Agents Build Retrieval-Augmented Agents, connect vector stores, and design smart retrieval workflows. Multi-Agent Patterns Learn Supervisor (Manager) Agents, Conversation History Management, and Agent collaboration. Advanced Graph Features Conditional branching, tool selection, agent loops, and creating production-like agent pipelines. End-to-End Real-World Examples Full workflows such as the Tavily Search Agent, React Agent, and a fully integrated Graph-Based AI System in Tamil. 💡 Who Is This video For? ✔ Beginners exploring GenAI ✔ Developers building Agent workflows ✔ Data Scientists learning LangGraph ✔ Students preparing for AI projects ✔ Anyone curious about how modern AI systems like ChatGPT Agents work under the hood 🔥 Why This video Stands Out All explanations in simple Tamil Real-world examples using top industry tools Covers basic → intermediate → advanced Includes practical agent building & problem solving Ideal for both learners and professionals 🚀 Start Learning AI Agents Today This video will help you master LangGraph and build scalable, production-ready AI workflows. Begin your journey and take your GenAI skills to the next level! RAG Video - https://www.youtube.com/watch?v=4Qp5D5hcE4A CrewAI Agents - https://www.youtube.com/watch?v=PgPo9WHQczw Colab Notebooks: https://github.com/akash-balakrishnan-22/langgraph-beginners/tree/main/colab-notebooks Code used: https://github.com/akash-balakrishnan-22/langgraph-beginners/tree/main/src ----- 00:00 - LangGraph Intro 05:25 - Project Intro 11:42 - Simple Graph 26:50 - Core Components 37:10 - Tools 45:29 - Simple Agent 52:28 - Router Agent 58:17 - LangGraph Studio 01:10:30 - ReAct and CoT 01:21:14 - Conversation Memory 01:27:27 - Conversation Memory using External DB 01:25:32 - Tavily Web search Tool 01:46:30 - Tavily in LangGraph 01:53:34 - Full Integration