4-Day Series - Agentic AI with LangChain/LangGraph - Day 0

Welcome to the 4-day series on building Agentic AI applications with LangChain and LangGraph in Node.js.

Note: Though this series is mainly written in JS. However, there is a python version of the code available and working end-to-end in the repository

Why this Series?

Large Language Models (LLMs) are powerful, but building reliable applications requires more than just a prompt. This series focuses on Agentic AI—systems that can reason, use tools, and make decisions to solve complex problems.

By the end of this course, you will understand how to:

• Move beyond simple linear chains to cyclic graphs.
• Build agents that can browse the web and interact with external APIs.
• Orchestrate multiple agents working together.
• Implement "human-in-the-loop" workflows for safety and control.

This is essential for developers looking to build production-grade AI applications that are robust, stateful, and capable of autonomous action.

Prerequisites

1. Node.js: Ensure you have Node.js installed (v18+ recommended).
2. API Keys:
   • Create a .env file in the root directory (copy from .env.example).
   • Add your OPENAI_API_KEY.
   • (Optional) Add TAVILY_API_KEY if you want to use real search in Day 3 (code defaults to mock).

Installation

Nodejs

npm install

Python

pip install -r requirements.txt

Agenda & Curriculum

This series is designed to take you from basic LLM interactions to building a production-ready, human-in-the-loop agentic workflow.

Day 1: Foundations (RAG)

• Concepts: Embeddings, Vector Stores, Retrieval Augmented Generation.
• Goal: Build a "Smart Reader" that can answer questions about your private data.

• Run:
  

  node day1-foundations/1-simple-chat.js
  node day1-foundations/2-rag-chain.js
  # Python
  python day1-foundations/1-simple-chat.py
  python day1-foundations/2-rag-chain.py
  

Day 2: Intro to LangGraph (Agents)

• Concepts: StateGraphs, Nodes, Edges, Conditional Logic, Tools.
• Goal: Refactor the linear RAG chain into an autonomous Agent that decides when to search.

• Run:
  

  node day2-langgraph/agent.js
  # Python
  python day2-langgraph/agent.py
  

Day 3: Multi-Agent Systems

• Concepts: Supervisor Pattern, Specialized Agents, Shared State.
• Goal: Orchestrate a team of agents (Researcher + Writer) to collaborate on a task.

• Run:
  

  node day3-multi-agent/team.js
  # Python
  python day3-multi-agent/team.py
  

Day 4: Advanced Patterns (Persistence & Control)

• Concepts: Checkpointers (Memory), Interrupts, Human-in-the-loop.
• Goal: Add "Time Travel" and Human Approval steps to make the agent safe for production.

• Run:
  

  node day4-advanced/human-loop.js
  # Python
  python day4-advanced/human-loop.py
  

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