agentic-fleet 0.4.12

Adaptive Agentic AI Reasoning That Empower, Inform, and Integrate Seamlessly

AgenticFleet

AgenticFleet is an Adaptative Agentic System that leverages Chainlit for the frontend interface and FastAPI for the backend, built on the foundation of Autogen & Magentic-One.

[!CAUTION] This project is in early beta. Expect frequent updates (3-4 git pushes/week minimum) and breaking changes as we continue to enhance and stabilize the system.

Quick Links

• Join our Discord Community
• Follow us on Twitter
• Join Early Access Waitlist

Features

• Interactive Chainlit 2.0 chat interface
• FastAPI backend with structured logging and WebSocket support
• General Multi-tasking Agentic System based on Magentic-One
• Advanced prompt engineering with PromptFleet templates
• Dataset and prompt fabric tools for AI training
• Comprehensive error handling and connection management
• Environment-based configuration
• Extensible architecture for future enhancements

Roadmap

Current Progress:

• Implement core multi-agent architecture
• Add Multi-modal Surfer agent
• Add FileSurfer agent
• Integrate Chainlit 2.0 frontend
• Add OAuth authentication support
• Implement real-time streaming responses
• Add CogCache integration

Short-term Goals:

• Add Composio Agent
• Implement LLM model auto-selection
• Enhance agent coordination
• Add message persistence
• Improve file handling capabilities
• Release AgenticFabric
• Implement GraphFleet integration
• Develop AI training tools

Mid-term Goals:

• Launch cloud service with OAuth + Freetier
• Create comprehensive prompt engineering suite
• Build enterprise deployment options
• Establish agent marketplace
• Enable cross-platform interoperability
• Enhance UI/UX features
• Implement advanced monitoring
• Add automated error recovery

Prerequisites

• Python 3.10 or later
• uv package manager

Installation

From PyPI

The simplest way to install AgenticFleet is via pip:

pip install agentic-fleet

Or with optional dependencies:

# For development tools
pip install "agentic-fleet[dev,test]"

# For documentation tools
pip install "agentic-fleet[docs]"

From Source

1. Clone the repository:

git clone https://github.com/qredence/agenticfleet.git
cd agenticfleet

2. Create and activate a virtual environment using uv:

uv venv
. .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows

3. Install dependencies:

For basic installation:

uv pip install -e .
sudo playwright install-deps
sudo apt install -y nodejs npm
npx playwright install-deps

For development (includes testing, linting, and formatting tools):

uv pip install -e ".[dev,test]"

For documentation:

uv pip install -e ".[docs]"

4. Configure environment variables:

Copy the example environment file and update it with your settings:

cp .env.example .env

The .env file contains all necessary configuration for both backend and frontend:

• Azure Services configuration (OpenAI, Key Vault, etc.)
• External AI Services API keys
• Backend server settings
• Frontend (Chainlit) configuration

Development

To start the application in development mode:

# Ensure you're in the virtual environment
. .venv/bin/activate

# Start the Chainlit application
chainlit run src/app/app.py

This will:

• Launch the Chainlit interface at http://localhost:8001
• Enable real-time agent communication
• Provide colored logging output
• Handle graceful shutdown

Project Structure

agenticfleet/
├── src/
│   ├── app/                  # Main application directory
│   │   ├── .chainlit/       # Chainlit configuration
│   │   ├── models/          # Data models
│   │   ├── public/          # Static assets and styling
│   │   ├── app.py          # Main Chainlit application
│   │   ├── _app.py         # Alternative implementation
│   │   └── chainlit.md     # Welcome documentation
│   └── backend/            # Backend components (legacy)
│       ├── agents/         # Agent implementations
│       ├── models/         # Backend models
│       └── main.py         # FastAPI application
├── docs/                   # Documentation
│   ├── agentic-fleet.md   # Technical documentation
│   └── useful-snippet.md  # Code snippets and examples
├── labs/                  # Experimental features
│   ├── promptfleet/      # Prompt templates
│   ├── promptwizard/     # Prompt optimization
│   └── trace/            # Debugging tools
└── README.md

Development

Code Style and Quality

The project uses several tools to maintain code quality:

• Black: Code formatting

  black src/
  

• isort: Import sorting

  isort src/
  

• flake8: Code linting

  flake8 src/
  

• mypy: Static type checking

  mypy src/
  

Testing

Run tests with pytest:

pytest

Run tests with coverage report:

pytest --cov

Error Handling

The application implements comprehensive error handling:

• Connection errors with automatic retry
• Input validation errors
• Server-side errors with proper status codes
• User-friendly error messages in the UI
• Agent-specific error handling and recovery

Logging

Structured logging is implemented with:

• Different log levels (DEBUG, INFO, WARNING, ERROR)
• JSON-formatted log output
• Timestamp and context information
• Error tracking with stack traces
• Agent activity monitoring
• Performance metrics

Contributing

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

Please ensure your PR:

• Includes appropriate tests
• Updates documentation as needed
• Follows the existing code style
• Includes proper error handling
• Has meaningful commit messages

Citation

@misc{fourney2024magenticonegeneralistmultiagentsolving,
    title={Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks},
    author={Adam Fourney and Gagan Bansal and Hussein Mozannar and Cheng Tan and Eduardo Salinas 
            and Erkang and Zhu and Friederike Niedtner and Grace Proebsting and Griffin Bassman 
            and Jack Gerrits and Jacob Alber and Peter Chang and Ricky Loynd and Robert West 
            and Victor Dibia and Ahmed Awadallah and Ece Kamar and Rafah Hosn and Saleema Amershi},
    year={2024},
    eprint={2411.04468},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2411.04468}
}

For more information about Autogen, visit their documentation.

License

This project is licensed under the Apache 2.0 License.