Agent Mux
Agent Mux is a tmux - based agent pool management system that provides project management and multi - agent interaction functions through the MCP protocol, supporting the creation, switching, and closing of projects, as well as the generation, control, and communication of agents.
rating : 2.5 points
downloads : 4.7K
What is Agent Mux?
Agent Mux is an innovative AI agent management tool that combines the traditional terminal session manager tmux with the modern Model Context Protocol (MCP). You can think of it as an 'AI agent command center' - it allows you to create different projects (each project corresponds to a tmux session) and run multiple AI agents in each project (each agent corresponds to a tmux window). These agents can be different AI models (such as Claude, Codex, Gemini, etc.), and they can run simultaneously and collaborate with each other.How to use Agent Mux?
Using Agent Mux is very simple: First, connect to the Agent Mux server through the MCP client, then create projects and start AI agents. You can interact with the agents by sending text and read their outputs, just like having a conversation with a real terminal program. All interactions will be automatically recorded in log files for subsequent review and analysis.Applicable scenarios
Agent Mux is particularly suitable for the following scenarios: 1. Collaborative development of AI agents: Multiple AI agents work together in the same project. 2. Long - term task execution: Management of AI tasks that need to run persistently. 3. Experimentation and testing: Quickly create and destroy different AI agent configurations. 4. Teaching demonstrations: Show the running status of multi - agent systems. 5. Automated workflows: Integrate AI agents into automated processes.Main features
tmux - based session management
Use the mature tmux terminal multiplexer as the underlying technology to ensure the stability and persistence of sessions. Each project and agent has an independent tmux session and window, supporting manual inspection and intervention.
MCP server integration
Provide a standardized interface through the Model Context Protocol, which can be seamlessly integrated with various MCP clients (such as Claude Desktop) to provide a unified tool - calling experience.
Multi - agent pool management
Support running multiple different types of AI agents (Claude, Codex, Gemini, etc.) in the same project, with each agent running independently and without interference.
Persistent registry
The configuration information of all projects and agents is saved in JSON files, and the previous state can be restored even after the system restarts.
Automatic log recording
The complete output of each agent will be automatically recorded in an independent log file for easy debugging and auditing.
Project isolation
Different projects are completely isolated, with independent working directories, agent configurations, and log files to avoid cross - interference.
Advantages
Visual monitoring: You can directly view the real - time running status of agents through tmux.
High reliability: Based on the mature technology of tmux, sessions are persistent and stable.
Flexible expansion: Support multiple types of AI agents, and it is easy to add new agent types.
Simple operation: Provide a unified command - line interface through MCP, with a low learning cost.
Resource - friendly: Share the tmux infrastructure, with relatively low resource consumption.
Limitations
Dependent on tmux: The system needs to have tmux installed, and additional configuration may be required on Windows.
Learning curve: Basic terminal operation knowledge is required.
Real - time limitation: There is a certain delay in output reading, which is not suitable for scenarios requiring millisecond - level response.
Limited agent types: Currently, it mainly supports a few specific AI agents.
Complex configuration: Advanced configuration requires an understanding of tmux and JSON configuration formats.
How to use
Environment preparation
Ensure that Node.js 18+ and tmux are installed on the system. If you need to use the default AI agents, you also need to install the corresponding command - line tools (claude, codex, gemini).
Install Agent Mux
Clone the code from the GitHub repository and install the dependencies.
Configure the MCP client
Add the Agent Mux server configuration to the configuration file of your MCP client (such as Claude Desktop).
Start and use
Connect to Agent Mux through the MCP client, and then use the provided tools to create projects, start agents, and interact with them.
Usage cases
Code review collaboration
Use two different AI agents to review the same code snippet simultaneously and compare their feedback and suggestions.
Long - term research task
Run an AI research task that requires a long - time execution, such as literature analysis or data organization.
Multi - agent dialogue simulation
Simulate a dialogue or debate scenario between multiple AI agents.
Frequently Asked Questions
What is the difference between Agent Mux and directly running AI agent command - lines?
Can I use Agent Mux on Windows?
How to add a custom AI agent type?
What should I do if there is a delay in the agent's output?
How to view the complete running history of an agent?
Can multiple users use the same Agent Mux instance simultaneously?
Related resources
GitHub repository
The code repository for the latest version Hydra of Agent Mux
Model Context Protocol documentation
The official specification and documentation of the MCP protocol
tmux usage guide
The usage tutorial and best practices of the tmux terminal multiplexer
Node.js official website
Download and documentation for the Node.js runtime environment

Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
21.6K
4.5 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
73.1K
4.3 points

Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
35.8K
5 points

Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
26.0K
4.3 points

Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
64.1K
4.5 points

Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
32.6K
5 points

Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
22.1K
4.5 points

Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
48.7K
4.8 points




