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MCP server · Data

Multi Agent System Using Langgraph

by · github.com/seehiong/multi-agent-system-using-langgraph

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.

Unclaimed MIT last commit 10 months ago datadata-engineeringlangchainmcp-toolboxmulti-agentllmhdbagentic-ailanggraph
InstallView source
61Good

Scored 5 months ago · breakdown

About Multi Agent System Using Langgraph

Multi Agent System Using Langgraph is an MCP server published by seehiong in the Data category: 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.

The repository has 42 stars and 3 forks, with the last commit 10 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/seehiong/multi-agent-system-using-langgraph

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

Static checks derived from the repository 6 hours ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
  • ✓Dependencies pinnedA lockfile pins dependency versions.
  • ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

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

├── notebook/
│ ├── toto_generator.ipynb
│ ├── langgraph_react_agent.ipynb
│ └── langgraph_mas.ipynb
│
├── graph.py # Standalone MAS graph (used by LangGraph app/CLI)
├── tools.yaml # MCP Toolbox config (Postgres + custom SQL tools)
├── pyproject.toml # uv project configuration
├── uv.lock
├── .python-version
├── .gitignore
└── README.md

> **Note:**  
> - `app/` (generated by `langgraph new app`) is excluded from the repo.  
> - `toolbox.exe` is excluded — users should download their own binary.  
> - `.venv/` and cache files are excluded.

🚀 Getting Started

1. Clone the repo

git clone https://github.com/seehiong/multi-agent-system-using-langgraph.git
cd multi-agent-system-using-langgraph

2. Create and activate environment (using uv)

uv venv
source .venv/bin/activate      # macOS/Linux
.venv\Scripts\activate         # Windows

3. Install dependencies

uv sync

4. Set up MCP Toolbox

Download the Toolbox binary:

$VERSION="0.18.0"
Invoke-WebRequest -Uri "https://storage.googleapis.com/genai-toolbox/v$VERSION/windows/amd64/toolbox.exe" -OutFile "toolbox.exe"

Start Toolbox:

./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)

langgraph dev

Then visit:

http://127.0.0.1:2024

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)
  • OpenRouter API key

🔗 Related Blog Post

This repository accompanies my full walkthrough article:

👉 Building a LangGraph Multi-Agent System
https://seehiong.github.io/posts/2025/11/building-a-langgraph-multi-agent-system/

The blog post explains the design decisions, architecture, and step-by-step thought process behind the examples in this repo.

📄 License

MIT License. You are free to fork, modify, and build on this example.

⭐ Acknowledgements

  • LangChain / LangGraph
  • OpenRouter
  • Tavily
  • MCP Toolbox
  • Singapore HDB datasets (data.gov.sg)

If you found this useful, please ⭐ star the repo!

README fetched from the repository 6 hours ago. The original is authoritative.

Questions

About Multi Agent System Using Langgraph

How do I install Multi Agent System Using Langgraph?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Multi Agent System Using Langgraph safe to use with an AI agent?

Its trust score is 61 out of 100 (good). It passes 4 of the 6 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Multi Agent System Using Langgraph still maintained?

The last commit was 10 months ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.