mission-control-ai 0.2.0

AI Development Workflow Orchestration System

Mission Control

AI Development Workflow Orchestration System

Mission Control is a Python package that orchestrates AI agents to improve development workflows. It implements a human-in-the-loop terminal interface that acts as the communication point to a root agent, which builds detailed project plans, manages parallel and dependent tasks, and coordinates specialized agents.

Features

• Root Agent Orchestration: Intelligent project planning and task allocation
• Specialized Agent Profiles: Architecture, Development, Testing, Debugging, DevOps, and Security agents
• Memory Management: Individual and collective knowledge bases using Mem0
• Parallel Execution: Smart dependency analysis and parallel task execution
• Human-in-the-Loop: Interactive terminal interface for mission control
• Error Recovery: Automatic bug detection and specialized debugging agents
• Local Testing Environment: Automated setup for human review
• Flexible Model Support: Use Anthropic, OpenAI, or local models via Ollama
• Token Management: Configure limits and budgets for API usage

Architecture

Mission Control follows a staged execution model inspired by SPARC:

1. Analysis Phase: Analyze project requirements and identify components
2. Planning Phase: Create detailed project plan with task dependencies
3. Execution Phase: Deploy specialized agents in parallel groups
4. Validation Phase: Comprehensive testing and security audits
5. Deployment Phase: Setup local testing environment

Installation

pip install mission-control-ai

Or install from source:

git clone https://github.com/bluearchio/mission-control.git
cd mission-control
pip install -e .

Quick Start

1. Initial Setup

After installation, configure Mission Control with the interactive setup:

mission-control config

This will guide you through:

• Setting up API keys (Anthropic, OpenAI, Mem0)
• Configuring local models (Ollama)
• Setting token limits and budgets
• Customizing agent profiles
• Choosing memory storage options

2. Initialize a Project

mission-control init my-project
cd my-project

3. Start Mission Control

mission-control run

Configuration

Using the Configuration UI

Mission Control provides an interactive configuration interface:

mission-control config

Available configuration options:

1. API Keys: Configure Anthropic, OpenAI, and Mem0 API keys
2. Local Models: Set up Ollama for local inference
3. Token Limits: Set max tokens per request and total budgets
4. Memory Service: Choose between local storage, Mem0, or vector databases
5. Agent Profiles: Customize agent models, temperature, and capabilities
6. Import/Export: Save and share configurations

Using Ollama for Local Models

To use local models instead of API-based services:

1. Install Ollama from https://ollama.ai
2. Pull a model: ollama pull llama2
3. Configure in Mission Control: mission-control config
4. Select "Configure Local Models"

Environment Variables

You can also configure Mission Control using environment variables:

export ANTHROPIC_API_KEY=your-key
export LLM_PROVIDER=ollama
export OLLAMA_MODEL=llama2
export MAX_TOKENS=8192
export TOKEN_BUDGET=1000000

Usage

CLI Commands

mission-control --help          # Show all commands
mission-control config          # Interactive configuration
mission-control init <project>  # Initialize new project
mission-control run             # Start Mission Control
mission-control agents          # List available agents
mission-control version         # Show version

Terminal Commands

Once Mission Control is running:

• new - Start a new mission
• status - Show current mission status
• history - Show mission history
• agents - Show agent profiles
• memory - Show memory statistics
• help - Show help information
• exit - Exit Mission Control

Example Mission

mission-control> new
Mission description: Build a REST API with authentication, database integration, and comprehensive tests

Mission Control will:
1. Analyze your requirements
2. Create a detailed project plan
3. Deploy specialized agents to implement
4. Run tests and security audits
5. Setup local testing environment

Agent Types

Root Agent

• Project analysis and planning
• Task dependency analysis
• Agent assignment and orchestration

Architect Agent

• System design and architecture
• Technical specifications
• Design patterns and best practices

Developer Agent

• Code implementation
• API development
• Database integration

Tester Agent

• Unit test generation
• Integration testing
• Test coverage analysis

Debugger Agent

• Error analysis
• Bug fixing
• Performance optimization

DevOps Agent

• Environment setup
• Docker configuration
• CI/CD pipeline setup

Security Agent

• Security audits
• Vulnerability scanning
• Best practices enforcement

Memory System

Mission Control uses a dual-layer memory system:

Individual Agent Memory

• Each agent maintains its own knowledge base
• Learns from past experiences
• Improves decision making over time

Collective Memory

• Shared knowledge across all agents
• Best practices and patterns
• Cross-project learning

Configuration

Create a config/mission_config.json file to customize:

{
  "project_name": "my-project",
  "orchestrator": {
    "max_concurrent_agents": 10,
    "task_timeout": 3600
  },
  "agent_profiles": {
    "architect": {
      "name": "System Architect",
      "model": "claude-3-sonnet-20240229",
      "temperature": 0.7
    }
  }
}

Development

Project Structure

mission_control/
├── agents/          # Agent implementations
├── core/            # Core orchestration logic
├── memory/          # Memory service
├── terminal/        # Terminal interface
└── utils/           # Utilities and monitoring

Running Tests

pytest tests/

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details

Acknowledgments

• Inspired by the SPARC framework
• Memory system powered by Mem0
• Built with LangChain and LangGraph

Support

• GitHub Issues: https://github.com/bluearchio/mission-control/issues
• Documentation: https://github.com/bluearchio/mission-control/wiki