EvoGrok is an evolutionary AI framework that leverages insights from Grok-2 for continuous self-improvement and growth. The system uses an evolutionary algorithm to optimize agent parameters (alpha, gamma, epsilon, epsilon_decay) while querying the Grok-2 model for expert insights. It logs every generation's progress in an SQLite database.
- Evolutionary Algorithm: Agents evolve via selection, crossover, and mutation.
- Grok-2 Integration: Query the Grok-2 model for suggestions to improve agent parameters.
- Database Logging: Stores generation details and insights in a local SQLite database.
- Robust & Extensible: Designed with logging, error handling, and continuous operation for production use.
- Python 3.8+
- Environment variable `XAI_API_KEY` set to your xAI API key.
- Packages: `openai`, `numpy` (and optionally `torch` for GPU usage)
To start the evolutionary algorithm (which runs indefinitely), simply run:
python main.pyEvoGrok/
├── LICENSE
├── README.md
├── main.py
└── requirements.txt
EvoGrok is designed to run continuously on high-end GPUs (if available) or CPUs, evolving and learning over generations. You can adjust the parameters in `main.py` to tailor its behavior.
This project is licensed under the MIT License - see the LICENSE file for details.
EvoGrok/
├── LICENSE
├── README.md
├── main.py
└── requirements.txt