Multi-Agent Systems provides a comprehensive toolkit for creating, controlling, and observing interactions among autonomous agents. Developers can define agent classes with custom decision-making logic, set up complex environments with configurable resources and rules, and implement communication channels for information exchange. The framework supports synchronous and asynchronous scheduling, event-driven behaviors, and integrates logging for performance metrics. Users can extend core modules or integrate external AI models to enhance agent intelligence. Visualization tools render simulations in real-time or post-process, helping analyze emergent behaviors and optimize system parameters. From academic research to prototype distributed applications, Multi-Agent Systems simplifies end-to-end multi-agent simulations.

Skeernir is an open-source AI agent framework designed to accelerate the development of puppet master agents for game automation and process orchestration. The project includes a base template, core APIs, and sample modules that demonstrate how to connect agent logic to target environments, whether simulating gameplay or controlling operating system tasks. Its extensible architecture allows users to implement custom decision-making strategies, plug in machine learning models, and manage agent lifecycles across Windows, Linux, and macOS. With built-in logging and configuration support, Skeernir streamlines testing, debugging, and deployment of autonomous AI agents.