用角色分工的智能体系统，让数据库监控更自适应、易部署。
ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
- 基于角色分工设计多智能体协作机制
- 支持自监控与自规划，降低开发维护成本
- 已在真实项目中验证，适合数据智能场景
近年来，大型语言模型（LLMs）与多智能体系统（MAS）结合在数据分析中展现出显著能力。然而，现有系统在涉及多样化功能需求和复杂数据处理任务时，常因缺乏通用性而需定制化方案；同时，当前多智能体系统难以模拟人类的自我规划、自我监控与协作能力，导致效率低下和资源浪费。为此，我们提出ROMAS——一种面向数据库监控与规划的角色驱动型多智能体系统，具备低代码开发与一键部署能力。该系统已成功应用于知名项目DB-GPT [Xue et al., 2023a, 2024b]，在真实场景中验证了其有效性。通过引入角色协同机制实现自我监控与规划，并利用现有多智能体能力增强数据库交互，ROMAS展现出更强的适应性与通用性。实验结果表明，其在多种场景下均表现优异，具有推动多智能体数据智能分析发展的潜力。
原文摘要 · Abstract (English)
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicability. Furthermore, current MAS fail to emulate essential human-like traits such as self-planning, self-monitoring, and collaborative work in dynamic environments, leading to inefficiencies and resource wastage. To address these limitations, we propose ROMAS, a novel Role-Based M ulti-A gent System designed to adapt to various scenarios while enabling low code development and one-click deployment. ROMAS has been effectively deployed in DB-GPT [Xue et al., 2023a, 2024b], a well-known project utilizing LLM-powered database analytics, showcasing its practical utility in real-world scenarios. By integrating role-based collaborative mechanisms for self-monitoring and self-planning, and leveraging existing MAS capabilities to enhance database interactions, ROMAS offers a more effective and versatile solution. Experimental evaluations of ROMAS demonstrate its superiority across multiple scenarios, highlighting its potential to advance the field of multi-agent data analytics.
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