Java-Action-Shape is a dedicated action library designed to extend the LightJason multi-agent framework with advanced geometric capabilities. It provides agents with out-of-the-box actions to instantiate common shapes (circle, rectangle, polygon), apply transformations (translate, rotate, scale), and perform analytical computations (area, perimeter, centroid). Each action is thread-safe and integrates with LightJason’s asynchronous execution model, ensuring efficient parallel processing. Developers can define custom shapes by specifying vertices and edges, register them within the agent’s action registry, and include them in plan definitions. By centralizing shape-related logic, Java-Action-Shape reduces boilerplate code, enforces consistent APIs, and accelerates the creation of geometry-driven agent applications, from simulations to educational tools.

Java-Action-Storage is a core component of the LightJason multi-agent framework designed to handle the end-to-end persistence of agent actions. It defines a generic ActionStorage interface with adapters for popular databases and file systems, supports asynchronous and batched writes, and manages concurrent access from multiple agents. Users can configure storage strategies, query historical action logs, and replay sequences to audit system behavior or recover agent states after failures. The module integrates via simple dependency injection, enabling rapid Adoption in Java-based AI projects.

Poke-Env is designed to streamline the creation and evaluation of AI agents for Pokémon Showdown battles by providing a comprehensive Python interface. It handles communication with the Pokémon Showdown server, parses game state data, and manages turn-by-turn actions through an event-driven architecture. Users can extend base player classes to implement custom strategies using reinforcement learning or heuristic algorithms. The framework offers built-in support for battle simulations, parallelized matchups, and detailed logging of actions, rewards, and outcomes for reproducible research. By abstracting low-level networking and parsing tasks, Poke-Env allows AI researchers and developers to focus on algorithm design, performance tuning, and comparative benchmarking of battle strategies.