LangChain

LLMsChainsRAGAgents

Build powerful AI applications with LangChain.js. Learn to work with LLMs, create chains, implement RAG, and build production chatbots with React and Next.js.

Free Tutorial

Learn LangChain - Build AI Apps with React & Next.js

Master LangChain.js, the framework for building LLM-powered applications. Learn to create intelligent chatbots, implement RAG for knowledge retrieval, build autonomous agents, and integrate AI features into your React and Next.js applications.

Prerequisites

Before learning LangChain, you should be comfortable with JavaScript/TypeScript fundamentals and have basic knowledge of React. Familiarity with async/await and APIs is helpful.

JavaScript Tutorial React Tutorial Next.js Tutorial

What You'll Learn

✓ Set up LangChain.js projects
✓ Work with LLMs and prompts
✓ Build chains and add memory
✓ Implement RAG systems
✓ Create autonomous agents
✓ Integrate with React apps
✓ Build Next.js AI features
✓ Deploy production chatbots

Course Topics

Introduction to LangChain

Learn what LangChain is, its core concepts, and how to set up your first project

LangChain Models & Prompts

Work with LLMs, chat models, and create effective prompt templates

Chains & Memory

Build sequential chains and add conversation memory to your applications

RAG (Retrieval Augmented Generation)

Build knowledge-based AI apps with document loading, embeddings, and vector stores

LangChain Agents

Create autonomous agents that can use tools and make decisions

LangChain with React

Integrate LangChain into React applications with streaming and state management

LangChain with Next.js

Build full-stack AI applications using LangChain with Next.js App Router

Building AI Chatbots

Create production-ready AI chatbots with context, memory, and streaming responses

Vector Databases & Embeddings

Learn how to use vector databases and embeddings to store, search, and retrieve semantic data for RAG applications

Document Loaders & Text Splitters

Load data from PDFs, web pages, CSVs, and more, then split them into optimal chunks for RAG

Structured Output & JSON Parsing

Get reliable structured JSON responses from LLMs using output parsers, Zod schemas, and function calling

LangChain Streaming

Stream LLM responses in real-time for better UX. Learn token streaming, chain streaming, and server-sent events

Tool Calling & Function Calling

Give LLMs the ability to call functions, access APIs, and interact with external systems using tool calling

LangGraph & AI Workflows

Build complex multi-step AI workflows with LangGraph. Learn graphs, nodes, edges, conditional logic, and state management

Multi-Modal AI (Images + Text)

Build applications that process images, describe visuals, and combine vision with text using LangChain's multi-modal support

Evaluating & Testing LLM Apps

Test and evaluate your LLM applications for quality, accuracy, and reliability using LangSmith and custom evaluators

Production Deployment & LangSmith

Deploy LangChain apps to production with best practices for caching, error handling, cost control, and monitoring with LangSmith

Building a Full RAG Application

Build a complete Retrieval-Augmented Generation application from scratch with document ingestion, vector search, and conversational AI

Ready to Build AI Apps?

Begin your LangChain journey with the introduction. You'll learn what LangChain is, its core concepts, and how to set up your first project.

Start Learning LangChain →