  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.
