MassGen: Multi-Agent Scaling System for GenAI#
What is MassGen?#
MassGen is a cutting-edge multi-agent system that leverages the power of collaborative AI to solve complex tasks. It assigns a task to multiple AI agents who work in parallel, observe each other’s progress, and refine their approaches to converge on the best solution to deliver a comprehensive and high-quality result.
How It Works:
Work in Parallel - Multiple agents tackle the problem simultaneously, each bringing unique capabilities
See Recent Answers - At each step, agents view the most recent answers from other agents
Decide Next Action - Each agent chooses to provide a new answer or vote for an existing answer
Share Workspaces - When agents provide answers, their workspace is captured so others can review their work
Natural Consensus - Coordination continues until all agents vote, then the agent with most votes presents the final answer
Think of it as a “parallel study group” for AI - inspired by advanced systems like xAI’s Grok Heavy and Google DeepMind’s Gemini Deep Think. Agents learn from each other to produce better results than any single agent could achieve alone.
How Does MassGen Compare?#
MassGen vs LLM Council: While LLM Council follows a fixed 3-stage pipeline, MassGen agents autonomously decide to contribute new answers or vote for others, reaching consensus organically. Plus, MassGen agents can use tools, execute code, and read/write files in your codebase — backed by active development with regular releases. See full comparison →
Quick Start#
pip install uv # if needed
uv venv && source .venv/bin/activate
uv pip install massgen
uv run massgen # Setup wizard, then ask your first question
Rich terminal UI with real-time streaming, multi-turn conversations, and YAML configuration.
pip install uv # if needed
uv venv && source .venv/bin/activate
uv pip install massgen
uv run massgen --web # Open http://localhost:8000
Browser-based UI with real-time agent streaming, vote visualization, and workspace browsing.
from dotenv import load_dotenv
load_dotenv() # Load OPENROUTER_API_KEY from .env
import litellm
from massgen import register_with_litellm
register_with_litellm()
response = litellm.completion(
model="massgen/build",
messages=[{"role": "user", "content": "Your question"}],
optional_params={"models": ["openrouter/openai/gpt-5", "openrouter/anthropic/claude-sonnet-4.5"]}
)
print(response.choices[0].message.content)
Standard OpenAI-compatible interface for seamless integration with existing applications.
Key Features#
Use Claude, Gemini, GPT, Grok together - each agent can use a different model.
Multiple agents work simultaneously with voting and consensus detection.
Model Context Protocol for web search, code execution, file operations, and custom tools.
Full async Python API and LiteLLM integration for seamless application embedding.
Real-time terminal display showing agents’ working processes and coordination.
Interactive conversations with context preservation across turns.
Integrate external frameworks (AG2, LangGraph, AgentScope, OpenAI, SmolAgent) as tools.
Work directly with your codebase using context paths with granular read/write permissions.
Recent Releases#
v0.1.20 (December 3, 2025) - Web UI & Auto Docker Setup
Browser-based real-time visualization with React frontend, WebSocket streaming, timeline views, and workspace browsing. Automatic Docker container setup for computer use agents with Ubuntu 22.04, Xfce desktop, and pre-configured tools.
v0.1.19 (December 2, 2025) - LiteLLM Provider & Claude Strict Tool Use
LiteLLM custom provider integration with programmatic API (run(), build_config()). Claude strict tool use with structured outputs support via enable_strict_tool_use and output_schema. Gemini exponential backoff for rate limit resilience.
v0.1.18 (November 28, 2025) - Agent Communication & Claude Advanced Tooling
Agent-to-agent and human broadcast communication via ask_others() tool with three modes (disabled, agents-only, human-only). Claude programmatic tool calling from code execution via enable_programmatic_flow flag. Claude tool search for deferred tool discovery via enable_tool_search.
Supported Models#
Claude (Anthropic) · Gemini (Google) · GPT (OpenAI) · Grok (xAI) · Azure OpenAI · Groq · Together · LM Studio · and more…