MassGen: Scaling AI Through Multi-Agent Collaboration

Diagram showing collaborative AI agents working together in parallel threads

Scaling AI Through Multi-Agent Collaboration

🔥 PyTorch OpenAgents Conference

Building the Future of Multi-Agent AI

🌐 pytorch.org

🌐 massgen.ai | GitHub

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How Do We Scale Up AI?

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Traditional Scaling Laws Hit Limits

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Power Crisis

4-16 GW by 2030
Enough to power entire cities

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Data Depletion

Depleted by 2026-2028
Quality text data exhausted

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Performance Plateau

GPT-5 delayed >1 year
Early training runs failed

⚠️ Inference-time scaling

No universal way to leverage improvements & address limitations

The New Paradigm:

Model → Agent → Multi-Agent Systems

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The Promise of Multi-Agent Collaboration

Study Group Dynamics: Like humans collaborating on complex problems
Cross-Ecosystem Integration: Bridge Claude, Gemini, GPT, Grok, and specialized coding agents
Emergent Intelligence: Collective problem-solving beyond individual capabilities
Real-time Intelligence Sharing: Agents learn and adapt from each other

Visual representation of collaborative AI reasoning and cognitive processes

The Promise of Collaborative Reasoning

📖 root.massgen.ai - "Myth of Reasoning"

🏗️ Built on AG2's foundational multi-agent research and community

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Proven Performance Gains

Grok-4 Standard

1

Single Agent Processing

38.6%

Last Human Exam Score

$30/month

Grok-4 Heavy

A1

A2

A3

Multi-Agent Collaboration

44.4%

Last Human Exam Score

$300/month

Gemini 2.5 DeepThink

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Competition Gold Medals

IMO + ICPC

5/6 IMO Problems (2024)

10/12 ICPC Problems (2025)

First AI Gold Medals

🚀 Multi-Agent Revolution
"Individual AI excellence + Multi-agent coordination = Next frontier of AI capabilities"

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MassGen Orchestrator

Task Distribution & Coordination

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Agent 1

Anthropic/Claude

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Agent 2

Claude Code

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Agent 3

Google/Gemini

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Agent 4

OpenAI/GPT

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Agent 5

xAI/Grok

↕ Real-time Collaboration ↕

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Shared Collaboration Hub

Real-time Notification & Consensus

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Case Study: Success Through Peer Correction

Graduate-level physics question from GPQA-Diamond benchmark

🌌 The Problem

A quasar shows a peak at 790 nm wavelength. Given Lambda-CDM cosmological parameters (H₀ = 70 km/s/Mpc, Ωₘ = 0.3, ΩΛ = 0.7), what is the comoving distance?

Options: A) 8 Gpc B) 7 Gpc C) 6 Gpc D) 9 Gpc

🎯 Final Result

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Correct Answer: A (8 Gpc)

Orchestration succeeded where individual agents initially failed

🤖 Initial Answers

Agent 1: "I calculate ~6 Gpc → Answer C"
Agent 2: "I get ~8.95 Gpc → Answer D"
Agent 3: "~6.1 Gpc → Answer C"

🔄 Self-Correction Process

Agent 1 observes: "There is significant discrepancy in calculations: Agent1 gets ~6.1 Gpc, Agent2 gets ~8.95 Gpc. Let me re-examine..."

✨ Breakthrough Moment

Agent 1 revises: "Standard cosmological calculators yield 8000-8500 Mpc for z=5.5. This equals 8.0-8.5 Gpc, closest to option A."

Result: 3/4 agents converge on correct answer

💡 Success Mechanism:
Peer observation → Discrepancy detection → Self-correction → Consensus

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Live Demonstrations

🛠️ Custom Tools & Self-Evolution (v0.1.1): Agents use custom Python tools to analyze GitHub issues and market trends, driving autonomous feature prioritization

Result: AI systems that improve themselves through user feedback analysis and market research

🛡️ MCP Planning Mode (v0.0.29): Agents plan Discord messaging without execution during coordination, preventing duplicate notifications

Result: Safe MCP tool coordination - only winner executes planned actions

📁 Unified Filesystem (v0.0.16): Cross-backend collaboration between Gemini and Claude Code agents creating educational content with shared workspace management

Result: First-time cross-backend coordination producing comprehensive 25-slide presentations

🌐 Claude Code Workspace (v0.0.14): Multiple Claude Code agents develop websites in parallel with enhanced logging and workspace isolation

Result: Conflict-free development with per-agent versioning and final workspace snapshots

📚 case.massgen.ai - Complete Case Studies

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Get Started in 60 Seconds

                # 1. Install via PyPI

                pip install massgen

                # 2. Interactive setup (first time only)

                massgen --setup  # Guided API key configuration

                # 3. Run single agent (quick test)

                massgen --model gemini-2.5-flash "When is your knowledge up to"

                # 4. Run multi-agent collaboration

                massgen --config @examples/basic/multi/three_agents_default "Summarize latest news of github.com/Leezekun/MassGen"

✅ Supported Models & Providers

🏢 Major Providers:

Anthropic Claude • Google Gemini • OpenAI GPT • xAI Grok • ZAI GLM

🏠 Local & Extended:

Cerebras • Fireworks • Groq • LM Studio • OpenRouter • SGLang • Together • vLLM...

🤖 Agents & Frameworks

AG2 • Claude Code CLI

🛠️ Advanced Tools

Browser Automation • Code Execution • File Operations • MCP • Multimodal • Web Search

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Join the Multi-Agent Revolution

Build Scalable, Collaborative AI Systems

⭐ github.massgen.ai 💬 discord.massgen.ai 🐦 x.massgen.ai 📖 docs.massgen.ai