The mario-ai project offers a comprehensive pipeline for developing AI agents to master Super Mario Bros. using neuroevolution. By integrating a Python-based NEAT implementation with the OpenAI Gym SuperMario environment, it allows users to define custom fitness criteria, mutation rates, and network topologies. During training, the framework evaluates generations of neural networks, selects high-performing genomes, and provides real-time visualization of both gameplay and network evolution. Additionally, it supports saving and loading trained models, exporting champion genomes, and generating detailed performance logs. Researchers, educators, and hobbyists can extend the codebase to other game environments, experiment with evolutionary strategies, and benchmark AI learning progress across different levels.

Evolving Agents provides a genetic programming–based framework for constructing and evolving modular AI agents. Users assemble agent architectures from interchangeable components, define environment simulations and fitness metrics, then run evolutionary cycles to automatically generate improved agent behaviors. The library includes tools for mutation, crossover, population management, and evolution monitoring, allowing researchers and developers to prototype, test, and refine autonomous agents in diverse simulated environments.