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LangChain with TypeScript Build AI Apps, RAG & Agents
Published 9/2026
Created by Haider Malik
MP4 | Video: h264, 2560x1440 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 135 Lectures ( 6h 33m ) | Size: 4.8 GB
Learn LangChain.js, RAG, Tools, Agents, and LangGraph by building real-world AI applications with TypeScript
What you'll learn
Build AI applications with LangChain.js and TypeScript using modern LLM development patterns. Build Retrieval-Augmented Generation (RAG) applications using embeddings, vector stores, and retrievers. Build AI agents that use tools and understand how tools, agents, and workflows work together. Build advanced agentic workflows with LangGraph and understand how it fits into the LangChain ecosystem.Requirements
Basic JavaScript or TypeScript programming knowledge. Familiarity with Node.js and npm is recommended. You should have a computer with internet access. No prior LangChain, RAG, or AI agent experience is required.Description
Build real-world AI applications withLangChain.js, TypeScript, RAG, Tools, Agents, and LangGraph.
In this course, you'll learn how to use LangChain with TypeScript to build modern AI applications step by step. Instead of jumping directly into complex agents, you'll build a strong foundation and gradually move toward more advanced AI application patterns.
You'll start by understanding the core concepts of LangChain and then progress through prompts, output parsers, embeddings, memory, vector stores, retrievers, and RAG. From there, you'll learn how tools work, how agents use tools, and how LangGraph can be used to build more advanced agentic workflows.
What you'll learn
Build AI applications usingLangChain.js and TypeScript Understand LangChain's core building blocks and how they fit together Work with prompts and prompt templates Parse and structure model outputs Understand embeddings and how they are used in AI applications Work with vector stores and similarity search Build retrieval-augmented generation (RAG) applications Understand retrievers and how they work with RAG Give AI applications access to external tools and functions Understand the difference between tools, agents, and workflows Build AI agents using LangChain Understand how LangGraph fits into modern AI application development Build practical AI applications instead of only learning isolated conceptsA practical, step-by-step approach
Many AI tutorials jump straight into building an agent without explaining the concepts underneath it. This course takes a different approach.
You'll progressively build your knowledge
LangChain fundamentals → Prompts → Output Parsers → Embeddings → Vector Stores → Retrievers → RAG → Tools → Agents → LangGraph
This progression helps you understand not onlyhow to use LangChain, but alsowhy these different components exist and when you should use them.
Who is this course for?
This course is for developers who want to build AI-powered applications usingTypeScript and JavaScript.
It's a good fit if you're a
TypeScript or JavaScript developer Node.js developer Full-stack developer Developer interested in LLM applications Developer who wants to learn RAG and AI agents Developer who wants to use LangChain.js rather than learning only Python-based examplesYou don't need to become an AI researcher to follow this course. The focus is on understanding the concepts and applying them to real software development.
What you'll build
Throughout the course, you'll work with practical examples and progressively combine the concepts you've learned to build AI application functionality.
By the end of the course, you'll have a much clearer understanding of how the pieces of a modern LLM application fit together-from calling models and structuring outputs to retrieval, RAG, tools, agents, and graph-based workflows.
If you're a TypeScript developer who wants to move beyond basic LLM API calls and learn how to build more capable AI applications withLangChain.js, this course is for you.
Who this course is for
JavaScript and TypeScript developers who want to build AI-powered applications with LangChain.js. Ideal for Node.js and full-stack developers interested in RAG, AI agents, tools, and LangGraph.Homepage
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