MassGen
🚀 MassGen is an open-source multi-agent scaling system that runs in your terminal, autonomously orchestrating frontier models and agents to collaborate, reason, and produce high-quality results. | Join us on Discord: discord.massgen.ai
What is MassGen?
MassGen is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 🚀 massgen is an open-source multi-agent scaling system that runs in your terminal, autonomously orchestrating frontier models and agents to collaborate, reason, and produce high-quality results. | jo...
🚀 MassGen is an open-source multi-agent scaling system that runs in your terminal, autonomously orchestrating frontier models and agents to collaborate, reason, and produce high-quality results. | Join us on Discord: discord.massgen.ai
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- 🚀 MassGen is an open-source multi-agent scaling system that
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx massgenConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MassGen
MassGen is an open-source multi-agent scaling system that runs in the terminal and autonomously orchestrates multiple frontier LLM agents — from OpenAI, Anthropic, Google Gemini, xAI Grok, and local models — to collaborate, reason in parallel, and converge on high-quality answers through a voting mechanism. It supports MCP server integration, tool use (web search, code execution, file operations, computer use), YAML-based agent configuration, and an OpenAI-compatible HTTP API. Engineers and researchers use it to tackle complex tasks that benefit from diverse model perspectives and parallel reasoning.
Prerequisites
- Python 3.11 or later
- pip or uv package manager (`pip install uv` recommended)
- At least one LLM API key: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`, or `XAI_API_KEY`
- Optional: Docker for sandboxed code execution
- An MCP-compatible client if using MassGen as an MCP consumer (not required to run MassGen itself)
Install MassGen
Install the `massgen` package from PyPI. Using `uv` is faster and handles environment isolation automatically.
pip install massgen
# Or with uv (recommended)
uv venv && source .venv/bin/activate
uv pip install massgenCreate a .env file with your API keys
Create a `.env` file in your working directory and add the API keys for the LLM providers you want to use.
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=...
XAI_API_KEY=...Run automated setup
Run `massgen --setup` to walk through an interactive configuration wizard that validates API keys, configures Docker for sandboxing, and installs any optional skills.
massgen --setupTest with a single-agent query
Run a quick single-agent task to verify the installation and API key connectivity.
massgen --model claude-sonnet-4-5-20250929 "Explain the difference between RAG and fine-tuning in 3 bullet points."Run a multi-agent collaboration
Use a built-in multi-agent configuration to send a task to multiple models simultaneously. They collaborate and vote on the best answer.
massgen --config @examples/basic/multi/three_agents_default \
"Analyze the trade-offs between microservices and monolithic architecture."Use interactive mode
Start an interactive multi-agent session in the Textual TUI for ongoing collaboration. Type `/help` inside the session for available commands.
massgen --config @examples/basic/multi/three_agents_defaultMassGen Examples
Client configuration
Example YAML agent config for a three-agent MassGen session using Claude, GPT, and Gemini with web search enabled via MCP.
{
"mcpServers": {
"massgen-serve": {
"command": "massgen",
"args": ["serve", "--host", "127.0.0.1", "--port", "4000"],
"env": {
"OPENAI_API_KEY": "sk-...",
"ANTHROPIC_API_KEY": "sk-ant-...",
"GOOGLE_API_KEY": "..."
}
}
}
}Prompts to try
Sample tasks suited for MassGen's multi-agent collaboration capabilities.
- "Analyze the pros and cons of Rust vs Go for building a high-throughput API server."
- "Write a Python web scraper for HackerNews that stores results in SQLite, then review it for bugs."
- "Research the current state of quantum computing and produce a 500-word summary with citations."
- "Generate three different architectural designs for a real-time chat system and vote on the best one."
- "Review this code diff and identify security vulnerabilities: [paste diff]"Troubleshooting MassGen
API key not recognized — model returns authentication error
Ensure the `.env` file is in the directory where you run `massgen`, or export the variables in your shell (`export ANTHROPIC_API_KEY=sk-ant-...`). Verify with `massgen --setup` which checks key validity during configuration.
Multi-agent mode hangs without producing a final answer
One agent may have failed silently. Run with `--debug` to see per-agent output and identify which model timed out or errored. Try reducing the number of agents in your YAML config or switching to a model with a higher rate limit.
Code execution tool is blocked or sandboxing fails
MassGen's sandboxed execution requires Docker to be running. Start Docker Desktop (or the Docker daemon) and re-run. If you prefer to skip sandboxing, check the permission engine documentation at docs.massgen.ai for how to configure `--cwd-context ro` (read-only) mode instead.
Frequently Asked Questions about MassGen
What is MassGen?
MassGen is a Model Context Protocol (MCP) server that 🚀 massgen is an open-source multi-agent scaling system that runs in your terminal, autonomously orchestrating frontier models and agents to collaborate, reason, and produce high-quality results. | join us on discord: discord.massgen.ai It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MassGen?
Follow the installation instructions on the MassGen GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MassGen?
MassGen works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MassGen free to use?
Yes, MassGen is open source and available under the NOASSERTION license. You can use it freely in both personal and commercial projects.
MassGen Alternatives — Similar Coding Agents Servers
Looking for alternatives to MassGen? Here are other popular coding agents servers you can use with Claude, Cursor, and VS Code.
Dify
★ 142.2kProduction-ready platform for agentic workflow development.
Ruflo
★ 54.0k🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integrat
Goose
★ 45.7kan open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
Antigravity Awesome Skills
★ 38.3kInstallable GitHub library of 1,400+ agentic skills for Claude Code, Cursor, Codex CLI, Gemini CLI, Antigravity, and more. Includes installer CLI, bundles, workflows, and official/community skill collections.
AgentScope
★ 25.5kBuild and run agents you can see, understand and trust.
Serena
★ 24.5kA coding agent toolkit that provides IDE-like semantic code retrieval and editing tools, enabling LLMs to efficiently navigate and modify codebases using symbol-level operations instead of basic file reading and string replacements.
Browse More Coding Agents MCP Servers
Explore all coding agents servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up MassGen in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
Ready to use MassGen?
Browse our complete directory of 33,000+ MCP servers, read setup guides for your editor, and start building with the Model Context Protocol.