Can ROMAS improve LLM-based database monitoring?
ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
This paper introduces ROMAS, a new role-based multi-agent system for database monitoring and planning within the DB-GPT framework. ROMAS uses LLMs and assigns agents specific roles (planner, monitor, worker) to improve flexibility, self-monitoring, self-planning, and collaboration in complex data analysis tasks. Key features relevant to LLM-based multi-agent systems include role-based collaboration, self-reflection/planning mechanisms, low-code development within DB-GPT, enhanced database interaction, and the use of memory, error handling, and gap-narrowing strategies for optimized performance. It's tested on financial and general knowledge question answering datasets showing improvements over other LLM and Multi-agent approaches.