UnityMAS-O is a new reinforcement learning framework designed to optimize multi-agent systems that utilize large language models (LLMs), allowing for more efficient task decomposition and workflow management. This innovation enables users to define complex agent interactions and rewards without altering the underlying optimization infrastructure, potentially enhancing the performance of various applications in AI-driven environments.