CASA is designed as a modular, plug-and-play autonomy framework built on the Robot Operating System (ROS) ecosystem. It features a decentralized architecture where each robot runs local planners and behavior tree nodes, publishing to a shared blackboard for world-state updates. Task allocation is handled via auction-based algorithms that assign missions based on robot capabilities and availability. The communication layer uses standard ROS messages over multirobot networks to synchronize agents. Developers can customize mission parameters, integrate sensor drivers, and extend behavior libraries. CASA supports scenario simulation, real-time monitoring, and logging tools. Its extensible design allows research teams to experiment with novel coordination algorithms and deploy seamlessly on diverse robotic platforms, from unmanned ground vehicles to aerial drones.

AgentRpi transforms a Raspberry Pi into an edge AI agent hub by orchestrating language models alongside physical hardware interfaces. By combining sensor inputs (temperature, motion), camera feeds, and microphone audio, it processes contextual information through configured LLMs (OpenAI GPT, local Llama variants) to autonomously plan and execute actions. Users define behaviors using YAML configurations or Python scripts, enabling tasks like triggering alerts, adjusting GPIO pins, capturing images, or responding to voice instructions. Its plugin-based architecture allows seamless API integrations, custom skill additions, and support for Docker deployment. Ideal for low-power, privacy-sensitive environments, AgentRpi empowers developers to prototype intelligent automation scenarios without relying solely on cloud services.