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mcp-supervisor

MCP.Pizza Chef: lpetralli

mcp-supervisor is a server that enables building supervisor-based multi-agent architectures integrated with MCP. It leverages LangChain projects to provide a nearly no-code approach for creating AI assistants, allowing easy connection to external MCP services like Zapier. This server simplifies multi-agent orchestration and tool integration with minimal coding, ideal for developers building complex AI workflows.

Use This MCP server To

Build supervisor-based multi-agent AI systems with minimal code Integrate external MCP servers like Zapier for tool access Orchestrate multiple AI agents in LangGraph environments Rapidly prototype AI assistants using LangChain templates Manage multi-agent workflows with centralized supervision

README

mcp-supervisor

This repository combines two powerful LangChain projects to create a supervisor-based multi-agent architecture with MCP integration:

LangGraph Supervisor Template

A ready-to-use template for quickly building supervisor-based multi-agent architectures in LangGraph.

LangChain MCP Adapters

A repository that demonstrates how to integrate MCP servers within LangGraph applications.

Overview

This project showcases a nearly no-code approach to building AI assistants.

  • Create a supervisor-based multi-agent system with just a few lines of code
  • Connect to external services like Zapier MCP (no-code MCP Server management) - https://zapier.com/mcp

MCP Server Management Simply include your Zapier MCP server URL in your environment variables, and all these tools become instantly available to your agents

The repository is designed for immediate deployment to LangGraph Cloud using the LangGraph Cloud Quick Start guide. When deployed to LangGraph Cloud, you automatically get LangSmith tracing for comprehensive monitoring and debugging. During development, LangGraph Studio provides a no-code environment for testing and debugging your application.

For a quick and easy user interface, simply connect your application to Agent Chat UI - a pre-built, customizable UI designed specifically for LangGraph applications. This approach eliminates the need to build a frontend from scratch, allowing you to focus on your assistant's capabilities rather than implementation details.

This project leverages the complete LangChain stack for an end-to-end AI application:

LangChain Stack

Getting Started

  1. Clone this repository

    git clone https://github.com/yourusername/mcp-supervisor.git
cd mcp-supervisor

  2. Choose your LLM, agent architecture, and prompts

    • Modify graph.py to select your preferred LLM
    • Customize agent prompts and roles based on your use case
    • Define your tools:
      • Use existing MCP servers (like Zapier)
      • Create custom tools with LangChain

  3. Deploy your application

    • Deploy to LangGraph Cloud (recommended) by setting your environment variables as shown in .env.example

  4. Use your agent through Agent Chat UI

    • Connect your deployed application to Agent Chat UI for a ready-to-use interface
    • Interact with your multi-agent system through a user-friendly chat interface
    • Test and refine your agent's capabilities

mcp-supervisor FAQ

How do I connect mcp-supervisor to external MCP servers like Zapier?
Simply include your Zapier MCP server URL in your environment variables to enable tool access for agents.
What programming languages or frameworks does mcp-supervisor support?
It primarily integrates with Python-based LangChain and LangGraph frameworks for multi-agent orchestration.
Is coding required to build multi-agent systems with mcp-supervisor?
Minimal coding is needed; it offers a nearly no-code approach using ready templates.
Can mcp-supervisor manage multiple agents simultaneously?
Yes, it is designed to supervise and coordinate multiple AI agents in a unified architecture.
Does mcp-supervisor support integration with no-code platforms?
Yes, it supports integration with no-code MCP server management platforms like Zapier.
How does mcp-supervisor simplify AI assistant development?
By combining LangChain templates and MCP integration, it reduces complexity and accelerates development.
What is the role of LangGraph in mcp-supervisor?
LangGraph provides the supervisor template and environment for multi-agent orchestration within mcp-supervisor.
Can I extend mcp-supervisor with custom MCP servers?
Yes, you can connect additional MCP servers by configuring their URLs in the environment variables.