{"repo":"seehiong/multi-agent-system-using-langgraph","free":true,"listed":false,"github":"https://github.com/seehiong/multi-agent-system-using-langgraph","clone":"git clone https://github.com/seehiong/multi-agent-system-using-langgraph.git","description":"A complete LangGraph multi-agent system demo using SQL tools, Tavily search, MCP Toolbox, and OpenRouter models — with reproducible notebooks and a full supervisor-led agent workflow.","language":"Jupyter Notebook","stars":38,"topics":["agentic-ai","data-engineering","hdb","langchain","langgraph","llm","mcp-toolbox","multi-agent","openrouter","postgres"],"license":"MIT","category":"ai-agents","readme_excerpt":"Multi-Agent System Using LangGraph This repository contains the full source code and Jupyter notebooks for my blog post “Building a LangGraph Multi-Agent System” . It demonstrates how to build: - A simple deterministic chain (TOTO generator) - A ReAct-style agent with Postgres (via MCP Toolbox), Tavily Search, and custom Python tools - A modular multi-agent system (MAS) with a Supervisor, SQL Agent, Amenities Agent, and Web Research Agent All examples use LangGraph , LangChain , OpenRouter models , Tavily , and MCP Toolbox . --- 📁 Project Structure --- 🚀 Getting Started 1. Clone the repo 2. Create and activate environment (using uv) 3. Install dependencies 4. Set up MCP Toolbox Download the Toolbox binary: Start Toolbox: The tools defined in tools.yaml will automatically load, including: - Postgres SQL tools - HDB resale queries - Amenities and percentile price computations 🧪 Running the Examples Open the notebooks inside notebook/ : 1. toto generator.ipynb - Simple LangGraph chain generating TOTO numbers. 2. langgraph react agent.ipynb - Full ReAct agent with Tavily + SQL tools. 3. langgraph mas.ipynb - Supervisor-led Multi-Agent System with three specialist agents. You may run them using Jupyter or VS Code. ▶️ Running the MAS with LangGraph CLI (Optional) Then visit: This opens LangSmith Studio , where you can explore, debug, and interact with your MAS graph. 📝 Requirements - Python 3.10+ - uv (recommended) - Postgres (if running HDB examples) - Tavily API key (optional","default_branch":null,"files":null,"tree":[],"storefront":"/r/seehiong","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/seehiong/multi-agent-system-using-langgraph/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}