EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
This addresses the scalability and functional limitations of human-designed multi-agent frameworks for LLM-based systems, representing an incremental improvement.
The paper tackles the challenge of automatically extending specialized LLM-based agents to multi-agent systems, introducing EvoAgent, which uses evolutionary algorithms to generate diverse agents and significantly enhances task-solving capability across various tasks.
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 specialized 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 settings. Experimental results across various tasks show that EvoAgent can significantly enhance the task-solving capability of LLM-based agents, and can be generalized to any LLM-based agent framework to extend them into multi-agent systems. Resources are available at https://evo-agent.github.io/.