Jan 20
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Building an AI agent that can remember conversations, reason across tools, and retrieve knowledge from your own documents** sounds complex — but with LangChain, it becomes practical and production-ready. In this video, I break down how modern AI agents actually work and why using a raw LLM alone is not enough for real-world applications. You’ll learn how LangChain helps you orchestrate models, tools, memory, and guardrails to build reliable AI systems used in real products. 🎯 What You’ll Learn • The essential building blocks of a production AI agent (LLMs, multi-agent systems, middleware, guardrails) • Why traditional LLM-only approaches fail at scale • How LangChain simplifies complex agent workflows • Building chat pipelines using LangChain Expression Language (LCEL) • Implementing RAG to retrieve answers from company documents • Taking an AI chatbot from local development to production deployment 🚀 Hands-On, Code-First Walkthrough This is a step-by-step, practical tutorial. You’ll build a complete AI chatbot while learning prompt chaining, runnable pipelines, memory handling, multi-agent coordination, and RAG — all with real code you can run immediately. 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 Source code for this Video Lesson Code : https://github.com/Bienfait-ijambo/langchain-full-course TimesCode 0:00 - Introduction 03:13 - Chapter 1-ChatModels 12:02 - Chapter 2-Formatting LLM Response 15:21 - Chapter 3-Runnables in Langchain 21:23 - Chapter 4-PromptTemplate 29:51 - Chapter 5-Multi Agent System 57:15 - Chapter 6-MiddleWare in LangChain 1:18:16 - Chapter 7-GuardRails in LangChain