AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.

Swarms operates as a Python-first framework and web-based interface, empowering users to configure individual agents with specific roles, memory management, and custom prompts. Users define agent interactions through a visual flow builder or YAML configuration, orchestrating complex decision trees, debates, and collaborative tasks. The platform supports plugin integration for data querying, knowledge base access, and third-party API calls. Upon deployment, Swarms provides real-time monitoring of agent activities, performance metrics, and logs. It scales horizontally using container orchestration tools, enabling large-scale AI simulations, robotic control architectures, or intelligent workflow automations. The open-source architecture ensures extensibility, community-driven enhancements, and self-hosting options for full data control.