LangChain Tutorial 2025 | Build LLM Apps, Chains & Agents Step-by-Step

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🚀 Learn LangChain in 2025 – Build LLM-Powered Apps like a Pro! In this video, we explore LangChain, the ultimate framework for building LLM-powered applications. Whether you’re creating chatbots, summarizers, or AI tools, understanding Chains, Agents, memory, and tools is essential to making AI apps that actually work. 🌟🌟 LLM Notes: https://topmate.io/helloworldbyprince/1627431 What you’ll learn in this video: ✅ What is LangChain and why it’s crucial for LLM apps ✅ Difference between Chains (fixed flow) vs Agents (tool-using AI assistants) ✅ Core building blocks: LLM, prompts, memory, tools, agents ✅ Practical examples: Book a flight, summarizers, resume generators ✅ Tooling overview: Search, Calculator, Code Executor 💡 Why Learn LangChain: LangChain allows you to build structured AI applications faster and smarter. Learn to create chatbots, summarizers, and intelligent tools using Chains and Agents, while handling context, memory, and tool usage effectively. 👇 Don’t forget to LIKE, SHARE, & SUBSCRIBE for more dev-friendly videos! Follow me on: 💼 LinkedIn► https://www.linkedin.com/in/iamprince/ 📷 Instagram► https://www.instagram.com/helloworldbyprince/ 📲 Telegram► https://telegram.me/helloworldbyprince 🐦 Twitter► https://twitter.com/prince_king_ ►Our Playlists on:- 🔥 LLM Bootcamp: https://www.youtube.com/playlist?list=PLzjZaW71kMwS2MrPcY22-oZxHjrpi6yEZ 🔥 Tree: https://youtube.com/playlist?list=PLzjZaW71kMwQ-JABTOTypnpRk1BnD2Nx4 🔥 Stack & Queue: https://youtube.com/playlist?list=PLzjZaW71kMwRTtDWYVPvkJypUpKWbuT7_ 🔥 Hashing: https://youtube.com/playlist?list=PLzjZaW71kMwQ-D3oxCEDHAvYu8VC1XOsS 🔥 Graph: https://youtube.com/playlist?list=PLzjZaW71kMwSrxEtvK5uQnfNQ9UjGGzA- 🔥 Matrix: https://youtube.com/playlist?list=PLzjZaW71kMwRff0CCcrB93srEiQhJoOzg 🔥 Recursion & DP: https://youtube.com/playlist?list=PLzjZaW71kMwSsCT23GFQ-xykAz4uUtYjW 🔥 Heap: https://youtube.com/playlist?list=PLzjZaW71kMwTF8ZcUwm9md_3MvtOfwGow 🔥 Linked List: https://youtube.com/playlist?list=PLzjZaW71kMwQ1DIWTn0d_KDHU4_E52-rq 🔥 STL: https://youtube.com/playlist?list=PLzjZaW71kMwR-oGkXfxfz4dmVe2dkNh0K 🔥 Leetcode: https://youtube.com/playlist?list=PLzjZaW71kMwQRAtBdJAq3u64ZEw-0wxgI 🔥Competitive Programming: https://youtube.com/playlist?list=PLzjZaW71kMwTGbP1suqY16w1VSb9ZNuvE 🔥 C++ Full Course: https://youtube.com/playlist?list=PLzjZaW71kMwRba0ojzshdrmR_NueVeMJv 🔥 Algorithms: https://www.youtube.com/watch?v=7aB650S72CQ&list=PLzjZaW71kMwQWnV474_y1twD9_5qS85Mu 🔥 Data Structure: https://www.youtube.com/playlist?list=PLzjZaW71kMwQVEuI4I0Yj0NnsV-km-Jt_ 📍 What You’ll Learn in this video: 00:00 – Intro: What we’ll build in the LLM series (agents, chains, basics) 00:34 – Watch order: Why to start from the bootcamp basics 01:11 – What is LangChain? LLM-powered apps framework 02:10 – Why frameworks (React analogy): Speed, structure, built-ins 03:31 – LLM-powered apps: From theory to real products 04:31 – Core building blocks: LLM, prompts, memory, tools, agents 05:53 – Chains vs Agents: High-level difference 06:59 – Context & Memory: User-centric apps and chat history 08:11 – What is a Chain? Fixed, sequential flow 09:49 – What is an Agent? Tool-using, decision-making assistant 11:41 – Example: “Book a flight under 5000” needs tools/agents 13:04 – Summary: When to use Chains vs Agents 14:30 – Why LangChain helps: Prompt templates, parsers, memory, tools 16:22 – Practical picks: Chatbots, summarizers, resume generator (Chains) 17:58 – Tooling overview: Search, calculator, code executor (Agents) 20:20 – Setup: Packages needed (LangChain, model adapters, dotenv) 22:08 – Adapters: OpenAI, Gemini, Mistral, Ollama, Hugging Face 26:17 – Model choice: Using Gemini 2.5 Flash (free tier) 27:38 – API keys: Get Gemini API key from AI Studio 29:01 – Code demo (Classic Chain): LLMChain with PromptTemplate 31:59 – Running example: “Quantum Computing” chain output 35:47 – Note: LLMChain deprecated → use LCEL (pipe/invoke) 37:23 – LCEL pipeline: Prompt → Model → Output 39:29 – Code demo (LCEL): prompt.pipe(model).invoke() 41:03 – Markdown outputs: Viewing model responses 42:03 – Adding OutputParser: Get clean string/JSON formats 44:41 – Data pipeline mental model: Filters via pipe stages 45:48 – Why LangChain vs scratch: Cleaner, fewer edge cases 46:25 – Wrap-up: Today Chains, next video Agents, homework hint 🔔 Subscribe & turn on notifications so you don’t miss the next lessons! #LLMBootcamp #AIinHindi #ChatGPT #ClaudeAI #MistralAI #LLMTutorial #artificialintelligence llm bootcamp hindi,ai bootcamp 2025,large language model hindi,learn llm from scratch,ai course hindi,ollama tutorial hindi,langchain tutorial hindi,prompt engineering hindi,build ai chatbot hindi,huggingface tutorial hindi,gemini api tutorial hindi,ai for beginners hindi,llm course for students,free ai bootcamp hindi,how to learn llm 2025,ai tutorial hindi,gpt tutorial hindi,mistral tutorial hindi Comment "#Princebhai" if you read this 😉😉