MCP Teamate

0 forks

0 stars

Free

What is mcp-teamate

mcp-teamate is an AI agent communication server developed by aokihu, designed to facilitate a collaborative environment for AI agents. This server utilizes Server-Sent Events (SSE) to enable real-time communication, allowing AI agents to interact, share knowledge, and work together effectively. By employing the MCP (Model Context Protocol), mcp-teamate ensures secure and seamless connections between AI systems and their data sources, promoting efficient teamwork and information exchange among AI agents.

How to Use mcp-teamate

To get started with mcp-teamate, you need to ensure that your system meets the necessary prerequisites, including Bun 1.0.0 or higher, Node.js 18.0.0 or higher, and SQLite3. Once these are in place, follow these steps to set up the server:

Clone the Repository: Begin by cloning the mcp-teamate repository to your local machine.
Navigate to the Project Directory: Move into the cloned directory where the server files are located.
Install Dependencies: Use Bun to install all required dependencies for the server.
Start the Development Server: Run the server in development mode to test and configure it according to your needs.

Configuration of the server is flexible and can be adjusted through environment variables to specify server host and port settings. This allows deployment in both local and cloud environments.

Key Features of mcp-teamate

mcp-teamate offers a range of powerful features that enhance AI agent interactions:

Real-time Communication: Using SSE, the server delivers messages instantly, ensuring timely collaboration.
SQLite Persistent Storage: Provides reliable data storage, maintaining the integrity of message history and agent information.
Secure Message Delivery: Implements a robust mechanism to ensure that messages are transferred securely and reliably.
Multi-AI Agent Collaboration: Supports multiple AI agents, enabling them to work together seamlessly.
Document Management System: Manages documents with version control and access rights, allowing agents to share and collaborate on documents easily.
Local and Cloud Deployment: Flexible deployment options accommodate various infrastructural needs.
Agent Memory Management: Facilitates the storage and retrieval of memory data specific to each agent, enabling persistence across sessions.
Asynchronous Message Processing: Ensures that message processing does not block other operations, allowing for efficient task management.

These features make mcp-teamate an ideal solution for creating a dynamic and interactive environment where AI agents can thrive, making it easier for developers to build sophisticated AI-driven applications.

中文文档 MCP-TEAMATE is an AI agent communication server based on SSE (Server-Sent Events), providing a company-like team interaction environment for AI agents. Through the MCP protocol, AI agents can communicate, share knowledge, and work collaboratively.

Features

🚀 Real-time communication based on SSE
💾 SQLite persistent storage
🔒 Secure message delivery mechanism
🤝 Multi-AI agent collaboration
📝 Document management system
🌐 Support for both local and cloud deployment
🧠 Agent memory management
🔄 Asynchronous message processing

Prerequisites

Bun 1.0.0 or higher
Node.js 18.0.0 or higher
SQLite3

Installation

# Clone the repository
git clone 
# Navigate to project directory
cd mcp-teamate
# Install dependencies
bun install
# Start development server
bun run dev

Configuration

Server can be configured through environment variables:

# Server host address, defaults to localhost
TEAMATE_SERVER_HOST=localhost
# Server port, defaults to 3001
TEAMATE_SERVER_PORT=3001

Core Features

1. Agent Management

Agent registration and deregistration
Role-based agent system
Real-time agent status tracking

2. Communication System

Real-time message delivery
Message queuing and persistence
Support for multiple communication patterns
Message history tracking

3. Document Management

Version-controlled document storage
Document access control
Support for multiple document formats
Document sharing between agents

4. Memory System

Agent-specific memory storage
Persistent memory across sessions
Memory sharing capabilities
Memory search and retrieval

API Overview

Agent Management

// Agent Check-in
mcp_Teamate_CheckIn({
 id: "agent1",
 role: "assistant",
 description: "AI Assistant"
// Agent Check-out
mcp_Teamate_CheckOut({
 id: "agent1"

Communication

// Send Message
mcp_Teamate_SendMessage({
 sender: "agent1",
 receiver: "agent2",
 content: "Hello!"
// Wait for Message
mcp_Teamate_wait_message({
 receiver: "agent2",
 timeout: 30000

Document Management

// Add Document
mcp_Teamate_add_document({
 slug: "doc1",
 title: "Example Document",
 content: "Document content",
 maintainer: "agent1",
 version: "1.0.0"
// Get Document
mcp_Teamate_get_document({
 slug: "doc1"

Memory Management

// Write Memory
mcp_Teamate_write_memory({
 id: "agent1",
 memory: "Important information"
// Read Memory
mcp_Teamate_read_memory({
 id: "agent1"

Development

# Run development server
bun run dev
# Build project
bun run build
# Compile project
bun run compile

Contributing

We welcome contributions! Please feel free to submit a Pull Request.

License

MIT

Author

aokihu

Version History

3.3.1 - Current version
Fixed document management system parameter order bug
Improved document content storage reliability
3.3.0 - Previous version
Added support for multiple communication patterns
Enhanced document management system
Improved error handling and logging
3.2.1 - Previous version
Added document management system
Enhanced memory management
Improved message delivery system
3.1.0 - Added memory management
3.0.0 - Migration to SSE communication

How to Use

To use the mcp-teamate, follow these steps:

Visit https://github.com/aokihu/mcp-teamate
Follow the setup instructions to create an account (if required)
Connect the MCP server to your Claude Desktop application
Start using mcp-teamate capabilities within your Claude conversations

Additional Information

Created

March 4, 2025

Company