microsoft/langchainjs-for-beginners

microsoft/langchainjs-for-beginners

Description: LangChain.js for Beginners course

Language: TypeScript

License: MIT

Stars: 155

Forks: 33

Open issues: 1

Created: 2025-11-11T18:36:36Z

Pushed: 2026-06-13T01:36:35Z

Default branch: main

Fork: no

Archived: no

README:

LangChain.js for Beginners

🚀 Welcome!

Welcome to LangChain.js for Beginners, your hands-on guide to building AI-powered applications with LangChain.js and TypeScript.

Building an AI application is a lot like cooking a gourmet meal. You can gather every ingredient yourself, make each sauce from scratch, and craft your own tools. Or, you can work in a well-equipped kitchen stocked with quality ingredients and reliable recipes. LangChain.js is that well-equipped kitchen for AI development.

What You'll Learn and Build

This course takes you from zero to building robust AI applications:

• 🤖 Conversational AI - Build context-aware chatbots with streaming responses and customizable behavior
• 🔍 Semantic Search - Create search systems that understand meaning, not just keywords
• 🛠️ Function Calling & Tools - Give AI the ability to use tools and extract structured data
• 🚀 Autonomous Agents - Build agents that reason, make decisions, and choose tools autonomously
• 🌐 MCP Integration - Connect AI to external services using the Model Context Protocol standard
• 🎯 Agentic RAG - Build intelligent Q&A systems where agents decide when to search your knowledge base

By the end, you'll have a solid understanding of LangChain.js and understand how to build real AI applications you can deploy!

> 🎯 Teaching Approach: This course uses an agent-first progression. You'll learn about tools, then agents, then combine them with document retrieval to build agentic RAG systems. This mirrors how modern production AI systems are built.

> [!TIP] > New to AI development? Check out our companion course **Generative AI with JavaScript** to learn AI fundamentals first!

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📚 Table of Contents

This course contains 9 chapters (setup + 8 chapters), each building on the previous to teach you LangChain.js from the ground up. Each chapter includes conceptual explanations, working code examples, and hands-on challenges.

| # | Chapter | Description | Key Concepts | |---|---------|-------------|--------------| | 0 | [Course Setup](./00-course-setup/README.md) | Set up your development environment (local or cloud-based) | Node.js, GitHub Models/Azure AI, Codespaces, environment variables | | 1 | [Introduction to LangChain.js](./01-introduction/README.md) | Understanding the framework and core concepts | LangChain fundamentals, first LLM call | | 2 | [Chat Models & Basic Interactions](./02-chat-models/README.md) | Chat models, messages, and conversations | Message types, streaming, error handling, temperature | | 3 | [Prompts, Messages, and Structured Outputs](./03-prompts-messages-outputs/README.md) | Working with prompts, message arrays, and type-safe outputs | Messages, templates, structured outputs, Zod schemas | | 4 | [Function Calling & Tools](./04-function-calling-tools/README.md) | Extending AI capabilities with function calling and tools | Zod schemas, tool binding, type safety | | 5 | [Getting Started with Agents](./05-agents/README.md) | Building autonomous agents that reason and choose tools | ReAct pattern, agent loops, createAgent(), middleware | | 6 | [Model Context Protocol (MCP)](./06-mcp/README.md) | Connect AI to external services using the MCP standard | MCP servers, HTTP/stdio transports, tool integration, multi-server patterns | | 7 | [Documents, Embeddings & Semantic Search](./07-documents-embeddings-semantic-search/README.md) | Loading documents, creating embeddings, and building semantic search | Document loading, chunking, vector embeddings, similarity search | | 8 | [Building Agentic RAG Systems](./08-agentic-rag-systems/README.md) | Building RAG systems where agents intelligently decide when to search documents | Agentic RAG (agents decide when to search), retrieval tools, intelligent Q&A |

Each chapter includes:

• 📖 Conceptual explanations with real-world analogies
• 💻 Code examples you can run immediately
• 🎯 Hands-on challenges to test your understanding
• 🔑 Key takeaways to reinforce learning

We're planning to expand this course over time with additional topics as well. Stay tuned for updates!

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📋 Prerequisites

Before starting this course, you should be comfortable with:

• JavaScript/TypeScript fundamentals - Variables, functions, objects, async/await
• Node.js (LTS) and npm - Package management and CLI tools
• Basic Generative AI concepts - Basic understanding of LLMs, prompts, tokens which are covered in our GenAI with JavaScript course

Required Tools

• Node.js (LTS)
• Code editor (VS Code recommended)
• Terminal/Command line
• Git

AI Provider Account

You'll need access to an AI provider. We recommend:

• ✅ GitHub Models - Free for learning and experimentation
• ✅ Microsoft Foundry - For production deployments

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📖 Course Resources and Additional Samples

• [Glossary](./GLOSSARY.md) - Comprehensive definitions of all terms used throughout the course
• [LangChain.js Documentation](https://docs.langchain.com/oss/javascript/langchain/overview) - Official LangChain.js docs for deeper dives
• [AI Agent with MCP tools Sample](https://github.com/microsoft/ai-agents-for-beginners-sample) - Learn how to build a complete burger ordering system with a serverless API, web interfaces, and an MCP server that enables AI agents to browse menus, place orders, and track order status.
• [Serverless AI Chat with RAG using LangChain.js](https://github.com/Azure-Samples/serverless-chat-langchainjs) - Build a serverless AI chat experience with Retrieval-Augmented Generation using LangChain.js and Azure.
• [AI Agent with MCP tools using LangChain.js](https://github.com/Azure-Samples/mcp-agent-langchainjs) - Build AI agents that interact with real-world APIs using the Model Context Protocol (MCP) that runs on Azure Functions.
• [AI Travel Agents Sample](https://github.com/Azure-Samples/azure-ai-travel-agents) - Learn how to orchestrate multiple AI agents to create a...