MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration
This addresses the need for more robust and versatile multi-agent collaborative systems, though it appears incremental as it builds on existing multi-agent frameworks with novel self-evolving mechanisms.
The paper tackles the problem of limited adaptability in LLM-based multi-agent systems by introducing MorphAgent, a decentralized system where agents dynamically evolve their roles and capabilities, resulting in improved task performance and adaptability compared to existing frameworks.
Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting their adaptability to evolving challenges. This paper introduces MorphAgent, a novel Autonomous, Self-Organizing, and Self-Adaptive Multi-Agent System for decentralized agent collaboration that enables agents to dynamically evolve their roles and capabilities. Our approach employs self-evolving agent profiles, optimized through three key metrics, guiding agents in refining their individual expertise while maintaining complementary team dynamics. MorphAgent implements a two-phase process: a Profile Update phase for profile optimization, followed by a Task Execution phase where agents continuously adapt their roles based on task feedback. Our experimental results show that MorphAgent outperforms existing frameworks in terms of task performance and adaptability to changing requirements, paving the way for more robust and versatile multi-agent collaborative systems.