Red Team MCP

Assemble elite AI agent teams to tackle any challenge

Quick Start • Features • Multi-Agent • MCP Integration • API

Red Team MCP is a multi-agent collaboration platform that connects to 68 providers and 1500+ models via models.dev. Build specialized agent teams, coordinate complex workflows, and integrate seamlessly with VS Code and Claude Desktop through the Model Context Protocol (MCP).

✨ Features

🎯 Universal Model Access

68 Providers: Anthropic, OpenAI, Google, Groq, Mistral, DeepSeek, and 60+ more
1500+ Models: Auto-synced from models.dev
Unified API: One interface for all providers

🤖 Multi-Agent Collaboration

5 Coordination Modes: Pipeline, Ensemble, Debate, Swarm, Hierarchical
Predefined Teams: Writing, Marketing, Research, Technical, Executive
Custom Teams: Build your own agent configurations

📡 MCP Integration

VS Code Ready: Works with GitHub Copilot
Claude Desktop: Native integration
Dynamic Tools: All agents exposed as MCP tools

🚀 Production Ready

FastAPI Backend: High-performance async API
Web Dashboard: HTMX-powered admin interface
Cost Tracking: Per-request usage analytics

🚀 Quick Start

Option A: Docker (Recommended)

git clone https://github.com/yourusername/red-team-mcp.git
cd red-team-mcp
cp .env.example .env
# Edit .env with your API keys

docker compose up -d
# Open http://localhost:8000/ui/

Option B: Local Install

git clone https://github.com/yourusername/red-team-mcp.git
cd red-team-mcp
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Configure API Keys

cp .env.example .env
# Edit .env with your API keys

Run

# Start the web server & dashboard
python main.py serve
# Open http://localhost:8000/ui/

# Or use the CLI
python main.py chat "What is machine learning?"

# Or start the MCP server
python main.py mcp

🤝 Multi-Agent Coordination

Red Team MCP excels at coordinating multiple AI agents on complex tasks. Choose from 5 coordination modes:

Mode	Description	Best For
Pipeline	Agents work sequentially, each building on the previous output	Document workflows, iterative refinement
Ensemble	Agents work in parallel, then synthesize results	Comprehensive analysis, multiple perspectives
Debate	Agents engage in back-and-forth argumentation	Critical thinking, finding flaws
Swarm	CrewAI-powered collaboration with delegation	Complex projects, dynamic task allocation
Hierarchical	Manager agent delegates to specialists	Large teams, structured workflows

Predefined Agent Teams

Team	Agents	Default Mode
Writing Team	Creative Writer, Editor, SEO Specialist	Pipeline
Marketing Team	Strategist, Brand Manager, Social Media	Hierarchical
Research Team	Researcher, Data Scientist, Analyst	Ensemble
Technical Team	Expert, Solutions Architect, Security	Debate
Executive Team	Strategy, Financial, Operations	Ensemble

Example: Multi-Agent Request

curl -X POST "http://localhost:8000/api/multi-agent" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Analyze the competitive landscape for AI startups",
    "coordination_mode": "ensemble",
    "agents": ["financial_analyst", "strategy_consultant", "technical_expert"]
  }'

📡 MCP Integration

Red Team MCP provides a Model Context Protocol server for seamless integration with AI assistants.

VS Code Setup

Create .vscode/mcp.json in your project:

{
  "servers": {
    "red-team-mcp": {
      "command": "python",
      "args": ["-m", "src.mcp_server_dynamic"],
      "cwd": "/path/to/red-team-mcp"
    }
  }
}

Claude Desktop Setup

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "red-team-mcp": {
      "command": "python",
      "args": ["/path/to/red-team-mcp/main.py", "mcp"]
    }
  }
}

Available MCP Tools

Tool	Description
`list_agents`	List all available agents
`list_teams`	List all predefined teams
`chat`	Chat with a specific agent
`run_team`	Execute a team on a task
`coordinate`	Run multi-agent coordination
`brainstorm`	Generate multiple perspectives

📖 API Reference

Chat Endpoint

POST /api/chat
Content-Type: application/json

{
  "agent_id": "creative_writer",
  "message": "Write a tagline for an AI product",
  "temperature": 0.8,
  "max_tokens": 500
}

Multi-Agent Endpoint

POST /api/multi-agent
Content-Type: application/json

{
  "query": "Develop a go-to-market strategy",
  "coordination_mode": "hierarchical",
  "agents": ["marketing_strategist", "sales_analyst"],
  "rebuttal_limit": 3
}

Run Team Endpoint

POST /api/team/{team_id}/run
Content-Type: application/json

{
  "query": "Create a blog post about AI trends",
  "coordination_mode": "pipeline"
}

Additional Endpoints

Endpoint	Method	Description
`/api/agents`	GET	List all agents
`/api/teams`	GET	List all teams
`/api/models`	GET	List available models
`/health`	GET	Health check
`/ws/chat`	WS	WebSocket streaming

🏗️ Architecture

red-team-mcp/
├── main.py                    # CLI entry point
├── config/config.yaml         # Configuration
├── src/
│   ├── api/                   # FastAPI application
│   │   ├── app.py            # App factory
│   │   ├── endpoints.py      # REST endpoints
│   │   └── websockets.py     # WebSocket handlers
│   ├── agents/               # Agent implementations
│   │   ├── configurable_agent.py
│   │   └── coordinator.py    # Multi-agent coordination
│   ├── web/                  # Dashboard UI
│   │   ├── routes.py
│   │   └── templates/        # HTMX templates
│   ├── providers/            # 68 provider implementations
│   ├── config.py             # Configuration management
│   ├── models.py             # Model selector
│   ├── db.py                 # SQLite persistence
│   └── mcp_server_dynamic.py # MCP server
└── mcp_servers/              # Generated MCP servers

⚙️ Configuration

Environment Variables

# Core providers
ANTHROPIC_API_KEY=your_key
OPENAI_API_KEY=your_key
GOOGLE_API_KEY=your_key
GROQ_API_KEY=your_key
DEEPSEEK_API_KEY=your_key

# And 60+ more providers supported!

config.yaml

api:
  host: "0.0.0.0"
  port: 8000
  rate_limit: "100/minute"

models:
  default: "claude-sonnet-4-20250514"

agents:
  predefined:
    - id: my_custom_agent
      name: Custom Agent
      model_id: gpt-4o
      provider: openai
      role: Specialist
      goal: Help with specific tasks

🧪 Development

# Run tests
python -m pytest tests/ -v

# Run with hot reload
python main.py serve --reload

# Generate MCP servers
python main.py generate-mcp --all

📊 Web Dashboard

Access the admin dashboard at http://localhost:8000/ui/ to:

💬 Chat with any agent interactively
👥 Manage Teams and agent configurations
📈 View Statistics on usage and costs
⚙️ Configure providers and settings
📤 Export configurations

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing)
Add tests for new functionality
Ensure all tests pass (python -m pytest)
Submit a pull request

📄 License

AGPL-3.0 License - see LICENSE for details.

🙏 Acknowledgments

models.dev - Comprehensive model database
CrewAI - Agent orchestration framework
FastAPI - High-performance web framework
All 68 AI providers making their models accessible

Ready to assemble your AI team?
Get Started →

Red Team MCP

Assemble elite AI agent teams to tackle any challenge

Quick Start • Features • Multi-Agent • MCP Integration • API

✨ Features

🎯 Universal Model Access

68 Providers: Anthropic, OpenAI, Google, Groq, Mistral, DeepSeek, and 60+ more
1500+ Models: Auto-synced from models.dev
Unified API: One interface for all providers

🤖 Multi-Agent Collaboration

5 Coordination Modes: Pipeline, Ensemble, Debate, Swarm, Hierarchical
Predefined Teams: Writing, Marketing, Research, Technical, Executive
Custom Teams: Build your own agent configurations

📡 MCP Integration

VS Code Ready: Works with GitHub Copilot
Claude Desktop: Native integration
Dynamic Tools: All agents exposed as MCP tools

🚀 Production Ready

FastAPI Backend: High-performance async API
Web Dashboard: HTMX-powered admin interface
Cost Tracking: Per-request usage analytics

🚀 Quick Start

Option A: Docker (Recommended)

git clone https://github.com/yourusername/red-team-mcp.git
cd red-team-mcp
cp .env.example .env
# Edit .env with your API keys

docker compose up -d
# Open http://localhost:8000/ui/

Option B: Local Install

git clone https://github.com/yourusername/red-team-mcp.git
cd red-team-mcp
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Configure API Keys

cp .env.example .env
# Edit .env with your API keys

Run

# Start the web server & dashboard
python main.py serve
# Open http://localhost:8000/ui/

# Or use the CLI
python main.py chat "What is machine learning?"

# Or start the MCP server
python main.py mcp

🤝 Multi-Agent Coordination

Red Team MCP excels at coordinating multiple AI agents on complex tasks. Choose from 5 coordination modes:

Mode	Description	Best For
Pipeline	Agents work sequentially, each building on the previous output	Document workflows, iterative refinement
Ensemble	Agents work in parallel, then synthesize results	Comprehensive analysis, multiple perspectives
Debate	Agents engage in back-and-forth argumentation	Critical thinking, finding flaws
Swarm	CrewAI-powered collaboration with delegation	Complex projects, dynamic task allocation
Hierarchical	Manager agent delegates to specialists	Large teams, structured workflows

Predefined Agent Teams

Team	Agents	Default Mode
Writing Team	Creative Writer, Editor, SEO Specialist	Pipeline
Marketing Team	Strategist, Brand Manager, Social Media	Hierarchical
Research Team	Researcher, Data Scientist, Analyst	Ensemble
Technical Team	Expert, Solutions Architect, Security	Debate
Executive Team	Strategy, Financial, Operations	Ensemble

Example: Multi-Agent Request

curl -X POST "http://localhost:8000/api/multi-agent" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Analyze the competitive landscape for AI startups",
    "coordination_mode": "ensemble",
    "agents": ["financial_analyst", "strategy_consultant", "technical_expert"]
  }'

📡 MCP Integration

Red Team MCP provides a Model Context Protocol server for seamless integration with AI assistants.

VS Code Setup

Create .vscode/mcp.json in your project:

{
  "servers": {
    "red-team-mcp": {
      "command": "python",
      "args": ["-m", "src.mcp_server_dynamic"],
      "cwd": "/path/to/red-team-mcp"
    }
  }
}

Claude Desktop Setup

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "red-team-mcp": {
      "command": "python",
      "args": ["/path/to/red-team-mcp/main.py", "mcp"]
    }
  }
}

Available MCP Tools

Tool	Description
`list_agents`	List all available agents
`list_teams`	List all predefined teams
`chat`	Chat with a specific agent
`run_team`	Execute a team on a task
`coordinate`	Run multi-agent coordination
`brainstorm`	Generate multiple perspectives

📖 API Reference

Chat Endpoint

POST /api/chat
Content-Type: application/json

{
  "agent_id": "creative_writer",
  "message": "Write a tagline for an AI product",
  "temperature": 0.8,
  "max_tokens": 500
}

Multi-Agent Endpoint

POST /api/multi-agent
Content-Type: application/json

{
  "query": "Develop a go-to-market strategy",
  "coordination_mode": "hierarchical",
  "agents": ["marketing_strategist", "sales_analyst"],
  "rebuttal_limit": 3
}

Run Team Endpoint

POST /api/team/{team_id}/run
Content-Type: application/json

{
  "query": "Create a blog post about AI trends",
  "coordination_mode": "pipeline"
}

Additional Endpoints

Endpoint	Method	Description
`/api/agents`	GET	List all agents
`/api/teams`	GET	List all teams
`/api/models`	GET	List available models
`/health`	GET	Health check
`/ws/chat`	WS	WebSocket streaming

🏗️ Architecture

red-team-mcp/
├── main.py                    # CLI entry point
├── config/config.yaml         # Configuration
├── src/
│   ├── api/                   # FastAPI application
│   │   ├── app.py            # App factory
│   │   ├── endpoints.py      # REST endpoints
│   │   └── websockets.py     # WebSocket handlers
│   ├── agents/               # Agent implementations
│   │   ├── configurable_agent.py
│   │   └── coordinator.py    # Multi-agent coordination
│   ├── web/                  # Dashboard UI
│   │   ├── routes.py
│   │   └── templates/        # HTMX templates
│   ├── providers/            # 68 provider implementations
│   ├── config.py             # Configuration management
│   ├── models.py             # Model selector
│   ├── db.py                 # SQLite persistence
│   └── mcp_server_dynamic.py # MCP server
└── mcp_servers/              # Generated MCP servers

⚙️ Configuration

Environment Variables

# Core providers
ANTHROPIC_API_KEY=your_key
OPENAI_API_KEY=your_key
GOOGLE_API_KEY=your_key
GROQ_API_KEY=your_key
DEEPSEEK_API_KEY=your_key

# And 60+ more providers supported!

config.yaml

api:
  host: "0.0.0.0"
  port: 8000
  rate_limit: "100/minute"

models:
  default: "claude-sonnet-4-20250514"

agents:
  predefined:
    - id: my_custom_agent
      name: Custom Agent
      model_id: gpt-4o
      provider: openai
      role: Specialist
      goal: Help with specific tasks

🧪 Development

# Run tests
python -m pytest tests/ -v

# Run with hot reload
python main.py serve --reload

# Generate MCP servers
python main.py generate-mcp --all

📊 Web Dashboard

Access the admin dashboard at http://localhost:8000/ui/ to:

💬 Chat with any agent interactively
👥 Manage Teams and agent configurations
📈 View Statistics on usage and costs
⚙️ Configure providers and settings
📤 Export configurations

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing)
Add tests for new functionality
Ensure all tests pass (python -m pytest)
Submit a pull request

📄 License

AGPL-3.0 License - see LICENSE for details.

🙏 Acknowledgments

models.dev - Comprehensive model database
CrewAI - Agent orchestration framework
FastAPI - High-performance web framework
All 68 AI providers making their models accessible

Ready to assemble your AI team?
Get Started →