multiagent-env is an open-source Python library designed to simplify the creation and evaluation of multi-agent reinforcement learning environments. Users can define both cooperative and adversarial scenarios by specifying agent count, action and observation spaces, reward functions, and environmental dynamics. It supports real-time visualization, configurable rendering, and easy integration with Python-based RL frameworks such as Stable Baselines and RLlib. The modular design allows rapid prototyping of new scenarios and straightforward benchmarking of multi-agent algorithms.

MultiAgentes provides a modular architecture for defining environments and agents, supporting synchronous and asynchronous multi-agent interactions. It includes base classes for environments and agents, predefined scenarios for cooperative and competitive tasks, tools for customizing reward functions, and APIs for agent communication and observation sharing. Visualization utilities allow real-time monitoring of agent behaviors, while logging modules record performance metrics for analysis. The framework integrates seamlessly with Gym-compatible reinforcement learning libraries, enabling users to train agents using existing algorithms. MultiAgentes is designed for extensibility, allowing developers to add new environment templates, agent types, and communication protocols to suit diverse research and educational use cases.