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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
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README preview
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.
Harness strengths from diverse frontier model-powered agents
⚡ Parallel Processing
Multiple agents tackle problems simultaneously
👥 Intelligence Sharing
Agents share and learn from each other's work
🔄 Consensus Building
Natural convergence through collaborative refinement
🖥️ Live Visualization
Interactive 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. (Honest scope: the prompt is best-effort alignment; the OS sandbox is the enforcement.)
MassGen operates through an architecture designed for seamless multi-agent collaboration:
graph TB
O[🚀 MassGen Orchestrator<br/>📋 Task Distribution & Coordination]
subgraph Collaborative Agents
A1[Agent 1<br/>🏗️ Anthropic/Claude + Tools]
A2[Agent 2<br/>🌟 Google/Gemini + Tools]
A3[Agent 3<br/>🤖 OpenAI/GPT + Tools]
A4[Agent 4<br/>⚡ xAI/Grok + Tools]
end
H[🔄 Shared Collaboration Hub<br/>📡 Real-time Notification & Consensus]
O --> A1 & A2 & A3 & A4
A1 & A2 & A3 & A4 <--> H
classDef orchestrator fill:#e1f5fe,stroke:#0288d1,stroke-width:3px
classDef agent fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
classDef hub fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
class O orchestrator
class A1,A2,A3,A4 agent
class H hub
The system's workflow is defined by the following key principles:
Parallel Processing - Multiple agents tackle the same task simultaneously, each leveraging their unique capabilities (different models, tools, and specialized approaches).
Real-time Collaboration - Agents continuously share their working summaries and insights through a notification system, allowing them to learn from each other's approaches and build upon collective knowledge.
Convergence Detection - The system intelligently monitors when agents have reached stability in their solutions and achieved consensus through natural collaboration rather than forced agreement.
Adaptive Coordination - Agents can restart and refine their work when they receive new insights from others, creating a dynamic and responsive problem-solving environment.
This collaborative approach ensures that the final output leverages collective intelligence from multiple AI systems, leading to more robust and well-rounded results than any single agent could achieve alone.
📖 Complete Documentation: For comprehensive guides, API reference, and detailed examples, visit MassGen Official Documentation
# Install MassGen via pip
pip install massgen
# Or with uv (faster)
pip install uv
uv venv && source .venv/bin/activate
uv pip install massgen
# If you install massgen in uv, make sure you either activate your venv using source .venv/bin/activate
# Or include "uv run" before all commands
Quickstart Setup (Fastest way to get running):
# Step 1: Set up API keys, Docker, and skills
uv run massgen --setup
# Step 2: Create a simple config and start
uv run massgen --quickstart
The --setup command will:
Configure your API keys (OpenAI, Anthropic, Google, xAI)
