EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

• URL: http://arxiv.org/abs/2406.14228v2
• Date: Thu, 11 Jul 2024 14:18:35 GMT
• Title: EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
• Authors: Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang,
• Abstract summary: EvoAgent is a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm. We show that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents.
• Score: 55.77492625524141
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse agent settings. EvoAgent can be generalized to any LLM-based agent framework, and can automatically extend the existing agent framework to multi-agent systems without any extra human designs. Experimental results across various tasks have shown that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents.

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