🤖 What is LangChain? (Beginner-Friendly)

👉 Teach LangChain JS from absolute zero to advanced with clean, runnable examples.
👉 Each topic = a self-contained lesson (folder) with its own README + code.
👉 Explained in ELI5 style: super simple, super practical.

This repository is structured like a hands-on course, not just random scripts.

🧱 How to Learn This Repo (Start → End)

Many people get confused where to start, so here is the official recommended order:

✔ Start with `index.js`

This gives you:

How model initialization works
How a basic .invoke() works
Simple structure before learning chains/tools

✔ Then follow numeric order:

01 → 02 → 03 → 04 → 05 → 06 → 07 → 08 → 09 → 10 → 11 → 12 → 13

Each file builds on top of the previous one.

Flow of Learning:

Basic LLM usage — index.js
Prompt Template + Chains — 01, 02
Output parsing — 03
Custom steps / preprocessing — 04
Embeddings + vector search — 05
Basic RAG — 06
Tools (RunnableLambda) — 07
Scraper tool — 08
LLM as a tool — 09
Agent with scraper — 10
Basic LangGraph agent — 11
Multi-agent system — 12
Advanced multi-agent with real search & scraping — 13

After these 13, you understand: ✔ LLM basics
✔ Chains
✔ Tools
✔ RAG
✔ Embeddings
✔ Agents
✔ Multi-agents
✔ LangGraph
✔ Routing
✔ Real scraping
✔ Real internet search

This is full AI backend mastery.

🎯 Purpose

Building real AI applications requires more than calling an LLM.
You need:

Chains
Tools
Agents
RAG
Embeddings
Web scraping
Automation
Multi-agent systems
LangGraph workflows

However, official docs are scattered and lack practical examples.

This repo fixes that by providing:

✔ Simple explanations
✔ Real code you can directly run
✔ Modern LangChain v1 structure
✔ Multi-agent + tool + automation examples
✔ Practical steps for real-world AI development

🧠 Why This Repo Will Help You

✔ Makes LangChain JS extremely beginner-friendly
✔ Every lesson works directly via node <filename>
✔ Covers everything needed for modern AI backend development
✔ Perfect for MERN developers transitioning into AI
✔ Each concept explained with clarity + comments
✔ Updated continuously with new lessons & examples
✔ Helps you build production-grade AI systems, not toy demos
✔ Great for your resume + GitHub profile

🚧 Status: Ongoing (New Chapters Coming)

This project is actively being improved.
The author will continuously push updates, new folders, and new explanations.

📁 Current Chapters (Already Included)

Right now, topics are single JS files.
Soon each will have its own dedicated folder + README + code.

01 - Prompt Chain
02 - Pipe (Basic Chaining)
03 - Output Parser
04 - Custom Step Logic
05 - Embeddings + Vector Search
06 - Basic RAG
07 - Tool Basics
08 - Web Scraper Tool (Puppeteer + Cheerio)
09 - LLM as a Tool
10 - Agent Demo with Scraper
11 - LangGraph Agent Basics
12 - Multi-Agent System (Basic)
13 - Multi-Agent System (Extended)
index.js – Starter example

🛠️ Tech & Packages Used

LangChain JS
LangGraph
Google Gemini 2.0 Flash
Puppeteer
Cheerio
Zod
dotenv
Node.js
Embeddings
Vector Search
RAG
Tools
Agents
Multi-agents

This repo will eventually become a full Agentic AI backend learning resource.

🚀 Roadmap (Future Topics To Be Added)

These are topics planned for future lessons. Exact filenames/folders will be decided later.

Advanced RAG techniques
Structured outputs
Using multiple tools together
Agent memory systems
LangGraph advanced flows
Multi-agent workflows with supervisor
Multi-agent research systems
Combining LangChain + Tavily search
Browser automation agents
Error handling + retries + guardrails
Practical real-world agent workflows
AI-powered automation pipelines (end to end)

More topics will get added as the repo grows.

🧩 How to Run Any Example

Clone the repo:

git clone https://github.com/iparesh18/langchain-js-zero-to-advanced

Install dependencies:

npm install

This will install the following dependencies used across all chapters:

@langchain/core — core LangChain components
@langchain/classic — legacy abstractions
@langchain/google-genai — Gemini 2.0 models
@langchain/openai — OpenAI models (optional)
@langchain/langgraph — multi-agent + workflow engine
dotenv — load .env API keys
openai — direct OpenAI SDK
serpapi — Google Search API
zod — schema validation

Create .env:

cp .env.example .env

Run any lesson:

node 01-prompt-chain.js

Each example is fully self-contained.

🤝 Contributing

This is an open-source learning project — contributions are welcome.

You can contribute by:

Improving explanations
Adding new lessons
Fixing bugs
Adding better examples
Enhancing tool/agent demos

Submit a PR anytime.

🏆 License

Licensed under the MIT License.
Free for everyone to learn, modify, and build on.

⭐ Support the Project

If this repo helped you, consider giving it a ⭐ on GitHub —
It motivates the author to push more chapters and make this project world-class.