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MassGen

massgen · massgen/MassGen

MassGen is an open-source multi-agent scaling system that allows AI agents to collaborate, reason, and iteratively refine complex tasks, installable as a skill for various coding agents.

🚀 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

1.1k stars178License: No licenseUpdated 4 months ago0 opens

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What you can do with it

  • Deploy multiple AI agents to collaboratively solve a complex problem with iterative refinement.
  • Use MassGen to scale up AI-driven content generation or research tasks by coordinating agent efforts.
  • Integrate its collaborative reasoning into your existing AI coding agent workflow as a skill.

Safety check

Caution

Usable, but read the notes first.

  • ●No clear license: check with the author before using it in a commercial product.

Automated scan of the repo files. Always review code you run.

From the README

🚀 MassGen: Multi-Agent Scaling System for GenAI

MassGen is a cutting-edge multi-agent system that leverages the power of collaborative AI to solve complex tasks.

Scaling AI with collaborative, continuously improving agents (4x speed)

MassGen is a cutting-edge multi-agent framework that coordinates AI agents to solve complex tasks through redundancy and iterative refinement. Every agent tackles the full problem, observing, critiquing, and building on each other's work across cycles of refinement and restarts. When agents believe there is a strong enough answer, they vote, and the best collectively validated answer wins. This approach to parallel refinement and collective validation lays the groundwork for principled multi-agent scaling, where the system continuously improves its outputs by leveraging diverse agent perspectives and enforcing quality through consensus.

This project started with the "threads of thought" and "iterative refinement" ideas presented in The Myth of Reasoning, and extends the classic "multi-agent conversation" idea in AG2. Here is a video recording of the background context introduction presented at the Berkeley Agentic AI Summit 2025.

🧩 Use MassGen as a Skill: npx skills add massgen/skills --all — then type invoke the skill in Claude Code, Cursor, Copilot, or 40+ other agents. Learn more →

📚 For Contributors: See MassGen Contributor Handbook - Centralized policies and resources for development and research teams

📋 Table of Contents

✨ Key Features

🆕 Latest Features

🏗️ System Design

🚀 Quick Start

🤖 Automation & LLM Integration

💡 Case Studies & Examples

🗺️ Roadmap

📚 Additional Resources

✨ Key Features

FeatureDescription
🤝 Cross-Model/Agent SynergyHarness strengths from diverse frontier model-powered agents
⚡ Parallel ProcessingMultiple agents tackle problems simultaneously
👥 Intelligence SharingAgents share and learn from each other's work
🔄 Consensus BuildingNatural convergence through collaborative refinement
🖥️ Live VisualizationInteractive Textual TUI with timeline, agent cards, and vote tracking (default). Also available: Web UI, Rich display.

🆕 Latest Features (v0.1.97)

🎉 Released: June 12, 2026

What's New in v0.1.97 (Application-Layer Permission Engine):

  • 🛡️ Layered Permission Engine - Opt-in permissions: block routes every tool call through a non-overridable hardline floor (rm -rf /, fork bombs), declarative allow/ask/deny rules over a small action(target) algebra (deny-wins), and a blast-radius risk classifier — auto-allowing reads/in-workspace edits and asking only for the dangerous tail (egress, force-push, publish, privilege). The app-layer companion to v0.1.96's OS sandbox.
  • ✋ Approval That Fits the Run - An ask resolves via an automation policy (risk-based / deny-all / allow-all) or a file request/response handshake for headless/remote approval (Slack bot, /approve <id>, …) — fail-closed by design.
  • 🧑‍🤝‍🧑 Roles, Audit & Guards - Per-agent role presets (e.g. read-only, which also empties the agent's OS-sandbox writable set), an append-only JSONL audit ledger of every decision, a runaway-loop budget, always-grant persistence, and a channel-based guardrail prompt that nudges the model to surface blocks rather than circumvent them while keeping ask sanctioned
View source on GitHub