MassGen

MassGen

πŸš€ MassGen: An Open-source Multi-Agent Scaling System Inspired by Grok Heavy and Gemini Deep Think. Join the discord channel: https://discord.com/invite/VVrT2rQaz5

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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. The system operates through an architecture designed for seamless multi-agent collaboration, with key features including cross-model/agent synergy, parallel processing, intelligence sharing, consensus building, and live visualization. Users can install the system, configure API settings, and run MassGen for various tasks such as question answering, creative writing, research, development & coding tasks, and web automation & browser tasks. The roadmap includes plans for advanced agent collaboration, expanded model, tool & agent integration, improved performance & scalability, enhanced developer experience, and a web interface.

README:

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Python 3.11+ License Join our Discord

πŸš€ 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.

MassGen case study -- Berkeley Agentic AI Summit Question

Multi-agent scaling through intelligent collaboration in Grok Heavy style

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. The power of this "parallel study group" approach is exemplified by advanced systems like xAI's Grok Heavy and Google DeepMind's Gemini Deep Think.

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.


πŸ“‹ Table of Contents

✨ Key Features

πŸ†• Latest Features

πŸ—οΈ System Design

πŸš€ Quick Start

πŸ’‘ Case Studies & Examples

πŸ—ΊοΈ Roadmap

πŸ“š Additional Resources


✨ Key Features

Feature Description
🀝 Cross-Model/Agent Synergy Harness strengths from diverse frontier model-powered agents
⚑ Parallel Processing Multiple agents tackle problems simultaneously
πŸ‘₯ Intelligence Sharing Agents share and learn from each other's work
πŸ”„ Consensus Building Natural convergence through collaborative refinement
πŸ“Š Live Visualization See agents' working processes in real-time

πŸ†• Latest Features (v0.0.23)

What's New in v0.0.23:

  • Backend Architecture Refactoring - Major consolidation with new base_with_mcp.py class reducing ~1,932 lines across backends
  • Formatter Module - Extracted message and tool formatting logic into dedicated massgen/formatter/ module
  • Massive Code Deduplication - Streamlined chat_completions.py, claude.py, and response.py for better maintainability
  • Bug Fixes - Fixed coordination table escape handling on macOS and FastMCP integration

β†’ See all release examples


πŸ—οΈ System Design

MassGen operates through an architecture designed for seamless multi-agent collaboration:

graph TB
    O[πŸš€ MassGen Orchestrator<br/>πŸ“‹ Task Distribution & Coordination]

    subgraph Collaborative Agents
        A1[Agent 1<br/>πŸ—οΈ Anthropic/Claude + Tools]
        A2[Agent 2<br/>🌟 Google/Gemini + Tools]
        A3[Agent 3<br/>πŸ€– OpenAI/GPT + Tools]
        A4[Agent 4<br/>⚑ xAI/Grok + Tools]
    end

    H[πŸ”„ Shared Collaboration Hub<br/>πŸ“‘ 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.


πŸš€ Quick Start

1. πŸ“₯ Installation

Core Installation (requires Python 3.11+):

git clone https://github.com/Leezekun/MassGen.git
cd MassGen

pip install uv
uv venv

Optional CLI Tools (for enhanced capabilities):

# Claude Code CLI - Advanced coding assistant
npm install -g @anthropic-ai/claude-code

# LM Studio - Local model inference
# For MacOS/Linux
sudo ~/.lmstudio/bin/lms bootstrap
# For Windows
cmd /c %USERPROFILE%/.lmstudio/bin/lms.exe bootstrap

2. πŸ” API Configuration

Using the template file .env.example to create a .env file in the massgen directory with your API keys. Note that only the API keys of the models used by your MassGen agent team is needed.

# Copy example configuration
cp .env.example .env

Useful links to get API keys:

3. 🧩 Supported Models and Tools

Models

The system currently supports multiple model providers with advanced capabilities:

API-based Models:

  • Azure OpenAI (NEW in v0.0.10): GPT-4, GPT-4o, GPT-3.5-turbo, GPT-4.1, GPT-5-chat
  • Cerebras AI: GPT-OSS-120B...
  • Claude: Claude Haiku 3.5, Claude Sonnet 4, Claude Opus 4...
  • Claude Code: Native Claude Code SDK with comprehensive dev tools
  • Gemini: Gemini 2.5 Flash, Gemini 2.5 Pro...
  • Grok: Grok-4, Grok-3, Grok-3-mini...
  • OpenAI: GPT-5 series (GPT-5, GPT-5-mini, GPT-5-nano)...
  • Together AI, Fireworks AI, Groq, Kimi/Moonshot, Nebius AI Studio, OpenRouter: LLaMA, Mistral, Qwen...
  • Z AI: GLM-4.5

Local Model Support (NEW in v0.0.7):

  • LM Studio: Run open-weight models locally with automatic server management
    • Automatic LM Studio CLI installation
    • Auto-download and loading of models
    • Zero-cost usage reporting
    • Support for LLaMA, Mistral, Qwen and other open-weight models

More providers and local inference engines (vllm, sglang) are welcome to be added.

Tools

MassGen agents can leverage various tools to enhance their problem-solving capabilities. Both API-based and CLI-based backends support different tool capabilities.

Supported Built-in Tools by Backend:

Backend Live Search Code Execution File Operations MCP Support Advanced Features
Azure OpenAI (NEW in v0.0.10) ❌ ❌ ❌ ❌ Code interpreter, Azure deployment management
Claude API βœ… βœ… βœ… βœ… Web search, code interpreter, MCP integration
Claude Code βœ… βœ… βœ… βœ… Native Claude Code SDK, comprehensive dev tools, MCP integration
Gemini API βœ… βœ… βœ… βœ… Web search, code execution, MCP integration
Grok API βœ… ❌ βœ… βœ… Web search, MCP integration
OpenAI API βœ… βœ… βœ… βœ… Web search, code interpreter, MCP integration
ZAI API ❌ ❌ βœ… βœ… MCP integration

4. πŸƒ Run MassGen

πŸš€ Getting Started

CLI Configuration Parameters

Parameter Description
--config Path to YAML configuration file with agent definitions, model parameters, backend parameters and UI settings
--backend Backend type for quick setup without a config file (claude, claude_code, gemini, grok, openai, azure_openai, zai). Optional for models with default backends.
--model Model name for quick setup (e.g., gemini-2.5-flash, gpt-5-nano, ...). --config and --model are mutually exclusive - use one or the other.
--system-message System prompt for the agent in quick setup mode. If --config is provided, --system-message is omitted.
--no-display Disable real-time streaming UI coordination display (fallback to simple text output).
--no-logs Disable real-time logging.
--debug Enable debug mode with verbose logging (NEW in v0.0.13). Shows detailed orchestrator activities, agent messages, backend operations, and tool calls. Debug logs are saved to agent_outputs/log_{time}/massgen_debug.log.
"<your question>" Optional single-question input; if omitted, MassGen enters interactive chat mode.

1. Single Agent (Easiest Start)

Quick Start Commands:

# Quick test with any supported model - no configuration needed
uv run python -m massgen.cli --model claude-3-5-sonnet-latest "What is machine learning?"
uv run python -m massgen.cli --model gemini-2.5-flash "Explain quantum computing"
uv run python -m massgen.cli --model gpt-5-nano "Summarize the latest AI developments"

Configuration:

Use the agent field to define a single agent with its backend and settings:

agent:
  id: "<agent_name>"
  backend:
    type: "azure_openai" | "chatcompletion" | "claude" | "claude_code" | "gemini" | "grok" | "openai" | "zai" | "lmstudio" #Type of backend
    model: "<model_name>" # Model name
    api_key: "<optional_key>"  # API key for backend. Uses env vars by default.
  system_message: "..."    # System Message for Single Agent

β†’ See all single agent configs

2. Multi-Agent Collaboration (Recommended)

Configuration:

Use the agents field to define multiple agents, each with its own backend and config:

Quick Start Commands:

# Three powerful agents working together - Gemini, GPT-5, and Grok
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/three_agents_default.yaml \
  "Analyze the pros and cons of renewable energy"

This showcases MassGen's core strength:

  • Gemini 2.5 Flash - Fast research with web search
  • GPT-5 Nano - Advanced reasoning with code execution
  • Grok-3 Mini - Real-time information and alternative perspectives
agents:  # Multiple agents (alternative to 'agent')
  - id: "<agent1 name>"
    backend:
      type: "azure_openai" | "chatcompletion" | "claude" | "claude_code" | "gemini" | "grok" | "openai" |  "zai" | "lmstudio" #Type of backend
      model: "<model_name>" # Model name
      api_key: "<optional_key>"  # API key for backend. Uses env vars by default.
    system_message: "..."    # System Message for Single Agent
  - id: "..."
    backend:
      type: "..."
      model: "..."
      ...
    system_message: "..."

β†’ Explore more multi-agent setups

3. Model context protocol (MCP)

The Model context protocol (MCP) standardises how applications expose tools and context to language models. From the official documentation:

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

MCP Configuration Parameters:

Parameter Type Required Description
mcp_servers dict Yes (for MCP) Container for MCP server definitions
└─ type string Yes Transport: "stdio" or "streamable-http"
└─ command string stdio only Command to run the MCP server
└─ args list stdio only Arguments for the command
└─ url string http only Server endpoint URL
└─ env dict No Environment variables to pass
allowed_tools list No Whitelist specific tools (if omitted, all tools available)
exclude_tools list No Blacklist dangerous/unwanted tools

Quick Start Commands (Check backend MCP support here):

# Weather service with GPT-5
uv run python -m massgen.cli \
  --config massgen/configs/tools/mcp/gpt5_mini_mcp_example.yaml \
  "What's the weather forecast for San Francisco this week?"

# Multi-tool MCP with Gemini - Search + Weather + Filesystem
uv run python -m massgen.cli \
  --config massgen/configs/tools/mcp/multimcp_gemini.yaml \
  "Find the best restaurants in Paris and save the recommendations to a file"

Configuration:

agents:
  # Basic MCP Configuration:
  backend:
    type: "openai"              # Your backend choice
    model: "gpt-5-mini"         # Your model choice

    # Add MCP servers here
    mcp_servers:
      weather:                  # Server name (you choose this)
        type: "stdio"           # Communication type
        command: "npx"          # Command to run
        args: ["-y", "@modelcontextprotocol/server-weather"]  # MCP server package

  # That's it! The agent can now check weather.

  # Multiple MCP Tools Example:
  backend:
    type: "gemini"
    model: "gemini-2.5-flash"
    mcp_servers:
      # Web search
      search:
        type: "stdio"
        command: "npx"
        args: ["-y", "@modelcontextprotocol/server-brave-search"]
        env:
          BRAVE_API_KEY: "${BRAVE_API_KEY}"  # Set in .env file

      # HTTP-based MCP server (streamable-http transport)
      custodm_api:
        type: "streamable-http"   # For HTTP/SSE servers
        url: "http://localhost:8080/mcp/sse"  # Server endpoint


  # Tool configuration (MCP tools are auto-discovered)
  allowed_tools:                        # Optional: whitelist specific tools
    - "mcp__weather__get_current_weather"
    - "mcp__test_server__mcp_echo"
    - "mcp__test_server__add_numbers"

  exclude_tools:                        # Optional: blacklist specific tools
    - "mcp__test_server__current_time"

β†’ View more MCP examples

4. File System Operations & Workspace Management

MassGen provides comprehensive file system support through multiple backends, enabling agents to read, write, and manipulate files in organized workspaces.

Filesystem Configuration Parameters:

Parameter Type Required Description
cwd string Yes (for file ops) Working directory for file operations (agent-specific workspace)
snapshot_storage string Yes Directory for workspace snapshots
agent_temporary_workspace string Yes Parent directory for temporary workspaces

Quick Start Commands:

# File operations with Claude Code
uv run python -m massgen.cli \
  --config massgen/configs/tools/filesystem/claude_code_single.yaml \
  "Create a Python web scraper and save results to CSV"

# Multi-agent file collaboration
uv run python -m massgen.cli \
  --config massgen/configs/tools/filesystem/claude_code_context_sharing.yaml \
  "Generate a comprehensive project report with charts and analysis"

Configuration:

# Basic Workspace Setup:
agents:
  - id: "file-agent"
    backend:
      type: "claude_code"          # Backend with file support
      model: "claude-sonnet-4"     # Your model choice
      cwd: "workspace"             # Isolated workspace for file operations

# Multi-Agent Workspace Isolation:
agents:
  - id: "analyzer"
    backend:
      type: "claude_code"
      cwd: "workspace1"            # Agent-specific workspace

  - id: "reviewer"
    backend:
      type: "gemini"
      cwd: "workspace2"            # Separate workspace

orchestrator:
  snapshot_storage: "snapshots"              # Shared snapshots directory
  agent_temporary_workspace: "temp_workspaces" # Temporary workspace management

Available File Operations:

  • Claude Code: Built-in tools (Read, Write, Edit, MultiEdit, Bash, Grep, Glob, LS, TodoWrite)
  • Other Backends: Via MCP Filesystem Server

Workspace Management:

  • Isolated Workspaces: Each agent's cwd is fully isolated and writable
  • Snapshot Storage: Share workspace context between Claude Code agents
  • Temporary Workspaces: Agents can access previous coordination results

β†’ View more filesystem examples

5. Project Integration & User Context Paths (NEW in v0.0.21)

Work directly with your existing projects! User Context Paths allow you to share specific directories and files with all agents while maintaining granular permission control. This enables secure multi-agent collaboration on your real codebases, documentation, and data.

Project Integration Parameters:

Parameter Type Required Description
context_paths list Yes (for project integration) Shared directories/files for all agents
└─ path string Yes Absolute path to your project directory or file
└─ permission string Yes Access level: "read" or "write"

Quick Start Commands:

# Code analysis and security audit
uv run python -m massgen.cli \
  --config massgen/configs/tools/filesystem/fs_permissions_test.yaml \
  "Analyze all Python files in this project and create a comprehensive security audit report"

# Project modernization
uv run python -m massgen.cli \
  --config massgen/configs/tools/filesystem/claude_code_context_sharing.yaml \
  "Review this legacy codebase and create a modernization plan with updated dependencies"

Configuration:

# Basic Project Integration:
agents:
  - id: "code-reviewer"
    backend:
      type: "claude_code"
      cwd: "workspace"             # Agent's isolated work area

orchestrator:
  context_paths:
    - path: "/home/user/my-project/src"
      permission: "read"           # Agents can analyze your code
    - path: "/home/user/my-project/docs"
      permission: "write"          # Final agent can update docs

# Advanced: Multi-Agent Project Collaboration
agents:
  - id: "analyzer"
    backend:
      type: "gemini"
      cwd: "analysis_workspace"

  - id: "implementer"
    backend:
      type: "claude_code"
      cwd: "implementation_workspace"

orchestrator:
  context_paths:
    - path: "/home/user/legacy-app/src"
      permission: "read"           # Read existing codebase
    - path: "/home/user/legacy-app/tests"
      permission: "write"          # Write new tests
    - path: "/home/user/modernized-app"
      permission: "write"          # Create modernized version

This showcases project integration:

  • Real Project Access - Work with your actual codebases, not copies
  • Secure Permissions - Granular control over what agents can read/modify
  • Multi-Agent Collaboration - Multiple agents safely work on the same project
  • Context Agents (during coordination): Always READ-only access to protect your files
  • Final Agent (final execution): Gets the configured permission (READ or write)

Use Cases:

  • Code Review: Agents analyze your source code and suggest improvements
  • Documentation: Agents read project docs to understand context and generate updates
  • Data Processing: Agents access shared datasets and generate analysis reports
  • Project Migration: Agents examine existing projects and create modernized versions

β†’ Learn more about project integration

Security Considerations:

  • Agent ID Safety: Avoid using agent+incremental digits for IDs (e.g., agent1, agent2). This may cause ID exposure during voting
  • File Access Control: Restrict file access using MCP server configurations when needed
  • Path Validation: All paths are resolved to absolute paths to prevent directory traversal attacks

Additional Examples by Provider

Claude (Recursive MCP Execution - v0.0.20+)

# Claude with advanced tool chaining
uv run python -m massgen.cli \
  --config massgen/configs/tools/mcp/claude_mcp_example.yaml \
  "Research and compare weather in Beijing and Shanghai"

OpenAI (GPT-5 Series with MCP - v0.0.17+)

# GPT-5 with weather and external tools
uv run python -m massgen.cli \
  --config massgen/configs/tools/mcp/gpt5_mini_mcp_example.yaml \
  "What's the weather of Tokyo"

Gemini (Multi-Server MCP - v0.0.15+)

# Gemini with multiple MCP services
uv run python -m massgen.cli \
  --config massgen/configs/tools/mcp/multimcp_gemini.yaml \
  "Find accommodations in Paris with neighborhood analysis"    # (requires BRAVE_API_KEY in .env)

Claude Code (Development Tools)

# Professional development environment
uv run python -m massgen.cli \
  --backend claude_code \
  --model sonnet \
  "Create a Flask web app with authentication"

Local Models (LM Studio - v0.0.7+)

# Run open-source models locally
uv run python -m massgen.cli \
  --config massgen/configs/providers/local/lmstudio.yaml \
  "Explain machine learning concepts"

β†’ Browse by provider | Browse by tools | Browse teams

Additional Use Case Examples

Question Answering & Research:

# Complex research with multiple perspectives
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/gemini_4o_claude.yaml \
  "What's best to do in Stockholm in October 2025"

# Specific research requirements
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/gemini_4o_claude.yaml \
  "Give me all the talks on agent frameworks in Berkeley Agentic AI Summit 2025"

Creative Writing:

# Story generation with multiple creative agents
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/gemini_4o_claude.yaml \
  "Write a short story about a robot who discovers music"

Development & Coding:

# Full-stack development with file operations
uv run python -m massgen.cli \
  --config  massgen/configs/tools/filesystem/claude_code_single.yaml \
  "Create a Flask web app with authentication"

Web Automation: (still in test)

# Browser automation with screenshots and reporting
uv run python -m massgen.cli \
  --config massgen/configs/tools/code-execution/multi_agent_playwright_automation.yaml \
  "Browse https://github.com/Leezekun/MassGen and suggest improvements. Include screenshots in a PDF"

# Data extraction and analysis
uv run python -m massgen.cli \
  --config massgen/configs/tools/code-execution/multi_agent_playwright_automation.yaml \
  "Navigate to https://news.ycombinator.com, extract the top 10 stories, and create a summary report"

β†’ See detailed case studies with real session logs and outcomes

Interactive Mode & Advanced Usage

Multi-Turn Conversations:

# Start interactive chat (no initial question)
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/three_agents_default.yaml

# Debug mode for troubleshooting
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/three_agents_default.yaml \
  --debug "Your question"

Configuration Files

MassGen configurations are organized by features and use cases. See the Configuration Guide for detailed organization and examples.

Quick navigation:

See MCP server setup guides: Discord MCP | Twitter MCP

Backend Configuration Reference

For detailed configuration of all supported backends (OpenAI, Claude, Gemini, Grok, etc.), see:

β†’ Backend Configuration Guide

Interactive Multi-Turn Mode

MassGen supports an interactive mode where you can have ongoing conversations with the system:

# Start interactive mode with a single agent (no tool enabled by default)
uv run python -m massgen.cli --model gpt-5-mini

# Start interactive mode with configuration file
uv run python -m massgen.cli \
  --config massgen/configs/basic/multi/three_agents_default.yaml

Interactive Mode Features:

  • Multi-turn conversations: Multiple agents collaborate to chat with you in an ongoing conversation
  • Real-time coordination tracking: Live visualization of agent interactions, votes, and decision-making processes
  • Interactive coordination table: Press r to view complete history of agent coordination events and state transitions
  • Real-time feedback: Displays real-time agent and system status with enhanced coordination visualization
  • Clear conversation history: Type /clear to reset the conversation and start fresh
  • Easy exit: Type /quit, /exit, /q, or press Ctrl+C to stop

Watch the recorded demo:

MassGen Case Study

5. πŸ“Š View Results

The system provides multiple ways to view and analyze results:

Real-time Display

  • Live Collaboration View: See agents working in parallel through a multi-region terminal display
  • Status Updates: Real-time phase transitions, voting progress, and consensus building
  • Streaming Output: Watch agents' reasoning and responses as they develop

Watch an example here:

MassGen Case Study

Comprehensive Logging

All sessions are automatically logged with detailed information. The file can be viewed throught the interaction with UI.

Logging Storage Structure

Logging storage are organized in the following directory hierarchy:

massgen_logs/
└── log_{timestamp}/
    β”œβ”€β”€ agent_outputs/
    β”‚   β”œβ”€β”€ agent_id.txt
    β”‚   β”œβ”€β”€ final_presentation_agent_id.txt
    β”‚   └── system_status.txt
    β”œβ”€β”€ agent_id/
    β”‚   └── {answer_generation_timestamp}/
    β”‚       └── files_included_in_generated_answer
    β”œβ”€β”€ final_workspace/
    β”‚   └── agent_id/
    β”‚       └── {answer_generation_timestamp}/
    β”‚           └── files_included_in_generated_answer
    └── massgen.log / massgen_debug.log
Directory Structure Explanation
  • log_{timestamp}: Main log directory identified by session timestamp
  • agent_outputs/: Contains text outputs from each agent
    • agent_id.txt: Raw output from each agent
    • final_presentation_agent_id.txt: Final presentation for the selected agent
    • system_status.txt: System status information
  • agent_id/: Directory for each agent containing answer versions
    • {answer_generation_timestamp}/: Timestamp directory for each answer version
      • files_included_in_generated_answer: All relevant files in that version
  • final_workspace/: Final presentation for selected agents
    • agent_id/: Selected agent id
      • {answer_generation_timestamp}/: Timestamp directory for final presentation
        • files_included_in_generated_answer: All relevant files in final presentation
  • massgen.log or massgen_debug.log: Main log file, massgen.log for general logging, massgen_debug.log for verbose debugging information.
Important Note

The final presentation continues to be stored in each Claude Code Agent's workspace as before. After generating the final presentation, the relevant files will be copied to the final_workspace/ directory.

πŸ’‘ Case Studies

Case Studies

To see how MassGen works in practice, check out these detailed case studies based on real session logs:


πŸ—ΊοΈ Roadmap

MassGen is currently in its foundational stage, with a focus on parallel, asynchronous multi-agent collaboration and orchestration. Our roadmap is centered on transforming this foundation into a highly robust, intelligent, and user-friendly system, while enabling frontier research and exploration. An earlier version of MassGen can be found here.

⚠️ Early Stage Notice: As MassGen is in active development, please expect upcoming breaking architecture changes as we continue to refine and improve the system.

Recent Achievements (v0.0.23)

πŸŽ‰ Released: September 24, 2025

Version 0.0.23 introduces Backend Architecture Refactoring and Formatter Module, establishing cleaner, more maintainable codebase:

Backend Architecture Refactoring

  • Major Code Consolidation: New base_with_mcp.py base class consolidating common MCP functionality (488 lines)
  • Massive Line Reduction: Reduced ~1,932 lines across core backend files through deduplication
  • Standardized MCP Integration: Unified MCP client initialization and error handling across all backends
  • Improved Maintainability: Extracted shared MCP logic from individual backends into unified base class

Formatter Module

  • Dedicated Formatting Logic: New massgen/formatter/ module with specialized formatters
  • Message Formatting: message_formatter.py handles message formatting across backends
  • Tool Formatting: tool_formatter.py and mcp_tool_formatter.py manage tool call formatting
  • Better Code Organization: Separated formatting concerns from core backend logic

Bug Fixes and Improvements

  • Coordination Table: Fixed escape key handling on macOS with updated display components
  • FastMCP Integration: Added fastmcp to dependencies for workspace copy MCP support
  • Path Handling: Improved relative path handling for better portability

Recent Achievements (v0.0.23)

βœ… Backend Architecture Refactoring (v0.0.23): Major code consolidation with new base_with_mcp.py class reducing ~1,932 lines across backends, extracted formatter module for better code organization, and improved maintainability through unified MCP integration

βœ… Workspace Copy Tools via MCP (v0.0.22): Seamless file copying capabilities between workspaces, configuration organization with hierarchical structure, and enhanced file operations for large-scale collaboration

βœ… Grok MCP Integration (v0.0.21): Unified backend architecture with full MCP server support, filesystem capabilities through MCP servers, and enhanced configuration files

βœ… Claude Backend MCP Support (v0.0.20): Extended MCP integration to Claude backend, full MCP protocol and filesystem support, robust error handling, and comprehensive documentation

βœ… Comprehensive Coordination Tracking (v0.0.19): Complete coordination tracking and visualization system with event-based tracking, interactive coordination table display, and advanced debugging capabilities for multi-agent collaboration patterns

βœ… Comprehensive MCP Integration (v0.0.18): Extended MCP to all Chat Completions backends (Cerebras AI, Together AI, Fireworks AI, Groq, Nebius AI Studio, OpenRouter), cross-provider function calling compatibility, 9 new MCP configuration examples

βœ… OpenAI MCP Integration (v0.0.17): Extended MCP (Model Context Protocol) support to OpenAI backend with full tool discovery and execution capabilities for GPT models, unified MCP architecture across multiple backends, and enhanced debugging

βœ… Unified Filesystem Support with MCP Integration (v0.0.16): Complete FilesystemManager class providing unified filesystem access for Gemini and Claude Code backends, with MCP-based operations for file manipulation and cross-agent collaboration

βœ… MCP Integration Framework (v0.0.15): Complete MCP implementation for Gemini backend with multi-server support, circuit breaker patterns, and comprehensive security framework

βœ… Enhanced Logging (v0.0.14): Improved logging system for better agents' answer debugging, new final answer directory structure, and detailed architecture documentation

βœ… Unified Logging System (v0.0.13): Centralized logging infrastructure with debug mode and enhanced terminal display formatting

βœ… Windows Platform Support (v0.0.13): Windows platform compatibility with improved path handling and process management

βœ… Enhanced Claude Code Agent Context Sharing (v0.0.12): Claude Code agents now share workspace context by maintaining snapshots and temporary workspace in orchestrator's side

βœ… Documentation Improvement (v0.0.12): Updated README with current features and improved setup instructions

βœ… Custom System Messages (v0.0.11): Enhanced system message configuration and preservation with backend-specific system prompt customization

βœ… Claude Code Backend Enhancements (v0.0.11): Improved integration with better system message handling, JSON response parsing, and coordination action descriptions

βœ… Azure OpenAI Support (v0.0.10): Integration with Azure OpenAI services including GPT-4.1 and GPT-5-chat models with async streaming

βœ… MCP (Model Context Protocol) Support (v0.0.9): Integration with MCP for advanced tool capabilities in Claude Code Agent, including Discord and Twitter integration

βœ… Timeout Management System (v0.0.8): Orchestrator-level timeout with graceful fallback and enhanced error messages

βœ… Local Model Support (v0.0.7): Complete LM Studio integration for running open-weight models locally with automatic server management

βœ… GPT-5 Series Integration (v0.0.6): Support for OpenAI's GPT-5, GPT-5-mini, GPT-5-nano with advanced reasoning parameters

βœ… Claude Code Integration (v0.0.5): Native Claude Code backend with streaming capabilities and tool support

βœ… GLM-4.5 Model Support (v0.0.4): Integration with ZhipuAI's GLM-4.5 model family

βœ… Foundation Architecture (v0.0.3): Complete multi-agent orchestration system with async streaming, builtin tools, and multi-backend support

βœ… Extended Provider Ecosystem: Support for 15+ providers including Cerebras AI, Together AI, Fireworks AI, Groq, Nebius AI Studio, and OpenRouter

Key Future Enhancements

  • Advanced Agent Collaboration: Exploring improved communication patterns and consensus-building protocols to improve agent synergy
  • Expanded Model Integration: Adding support for more frontier models and local inference engines
  • Improved Performance & Scalability: Optimizing the streaming and logging mechanisms for better performance and resource management
  • Enhanced Developer Experience: Introducing a more modular agent design and a comprehensive benchmarking framework for easier extension and evaluation
  • Web Interface: Developing a web-based UI for better visualization and interaction with the agent ecosystem

We welcome community contributions to achieve these goals.

v0.0.24 Roadmap

Version 0.0.24 builds upon the solid backend refactoring of v0.0.23 by focusing on local model support, orchestrator improvements, and enhanced agent communication. Key priorities include:

Required Features

  • VLLM Local Model Support: Add support for VLLM backends with local models for better performance and cost efficiency
  • Agent System Prompt Fixes: Fix the problem where the final agent expects human feedback through system prompt changes

Optional Features

  • Refactor Orchestrator: Streamline orchestrator code for better maintainability and performance
  • MCP Marketplace Integration: Integrate MCP Marketplace support for expanded tool ecosystem

Key technical approach:

  • Local Model Infrastructure: Enable high-performance local model inference through VLLM with OpenAI-compatible API
  • Autonomous Agent Behavior: Ensure final agents complete tasks without expecting human feedback
  • Code Maintainability: Streamline orchestrator code for improved performance and maintainability
  • Tool Ecosystem: Expand capabilities through MCP Marketplace discovery and installation

For detailed milestones and technical specifications, see the full v0.0.24 roadmap.


🀝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.


πŸ“„ License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.


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