agentic-fleet 0.3.2

Autonomous agent fleet management system

AgenticFleet

An advanced multi-agent system framework building upon Magentic-One concepts, focusing on agent collaboration and autonomous interactions. AgenticFleet enables the creation and orchestration of AI agent fleets for complex task completion.

Features

• Multi-Agent Orchestration: Coordinate multiple AI agents working together on complex tasks
• Azure OpenAI Integration: First-class support for Azure OpenAI models and API
• Flexible Agent Types: Built-in support for various agent roles (Planner, Executor, Critic, Researcher)
• PydanticAI Integration: Strong type validation and model management
• Chainlit UI: Built-in support for interactive chat interface
• Extensible Architecture: Easy to add new agent types and behaviors

Quick Start

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/agenticfleet.git
cd agenticfleet

# Create and activate virtual environment
uv venv .venv
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows

# Install dependencies
uv pip install -e ".[all]"

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the Chainlit UI
python run_chainlit.py

Project Structure

agenticfleet/
├── src/agenticfleet/          # Main package directory
│   ├── agents/               # Agent implementations
│   │   ├── planner.py       # Strategic planning agent
│   │   ├── executor.py      # Task execution agent
│   │   ├── critic.py        # Result evaluation agent
│   │   └── researcher.py    # Information gathering agent
│   ├── api/                 # API endpoints
│   ├── core/                # Core functionality
│   ├── llm/                 # LLM integrations
│   └── frontend/            # UI components
├── tests/                    # Test files
├── docs/                     # Documentation
└── .github/                  # GitHub specific files
    └── workflows/           # CI/CD workflows

Configuration

The project uses environment variables for configuration. Copy .env.example to .env and set the following variables:

• AZURE_OPENAI_API_KEY: Your Azure OpenAI API key
• AZURE_OPENAI_API_BASE: Azure OpenAI API base URL
• AZURE_OPENAI_API_VERSION: API version to use
• AZURE_OPENAI_DEPLOYMENT: Model deployment name

Development

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
isort .

# Type checking
mypy src/

# Update dependencies
uv pip compile pyproject.toml -o requirements.txt

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - See LICENSE file for details