MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration
Keywords: self-evolving LLM agent, multi-agent collaboration
Abstract: 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 framework for $\textit{decentralized}$ multi-agent collaboration that enables agents to $\textit{dynamically evolve their roles and capabilities}$. 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 warm-up phase for initial 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 traditional static-role MAS in terms of task performance and adaptability to changing requirements, paving the way for more robust and versatile multi-agent collaborative systems.
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Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 10054
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