MGym is a specialized framework for crafting and managing multi-agent reinforcement learning (MARL) environments in Python. It enables users to define complex scenarios with multiple agents, each having customizable observation and action spaces, reward functions, and interaction rules. MGym supports both synchronous and asynchronous execution modes, providing parallel and turn-based agent simulations. Built with a familiar Gym-like API, MGym seamlessly integrates with popular RL libraries such as Stable Baselines, RLlib, and PyTorch. It includes utility modules for environment benchmarking, result visualization, and performance analytics, facilitating systematic evaluation of MARL algorithms. Its modular architecture allows rapid prototyping of cooperative, competitive, or mixed-agent tasks, empowering researchers and developers to accelerate MARL experimentation and research.

agent-steps is a Python step orchestration framework designed to streamline the development of AI agents by breaking complex tasks into discrete, reusable steps. Each step encapsulates a specific action—such as invoking a language model, performing data transformations, or external API calls—and can pass context to subsequent steps. The library supports synchronous and asynchronous execution, enabling scalable pipelines. Built-in logging and debugging utilities provide transparency into step execution, while its modular architecture promotes maintainability. Users can define custom step types, chain them into workflows, and integrate them easily into existing Python applications. agent-steps is suitable for building chatbots, automated data pipelines, decision support systems, and other multi-step AI-driven solutions.