E vo A gent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025)

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 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 tasksolving 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/.

## introduction recently, the advent of large language models (llms) @xcite @xcite have shown remarkable capabilities in solving language understanding, reasoning, and generation tasks. based on the foundation of llms, many research works @xcite @xcite @xcite @xcite figure 1 : the illustration of evoagent. with the generated multiple specialized agents, evoagent can generate a better travel plan to meet user preferences. for ea operators, crossover can improve the results of parent agents by adjusting existing details (e.g., the information marked as blue). mutation can introduce new variations to refine the results of parent agents by generating child agents with new characteristics (e.g., the information marked as red). @xcite have discovered that by empowering multiple advanced skills (e.g., planning, tool, memory and so on), we can develop more powerful autonomous agents to solve more challenging tasks. therefore, how to design and leverage llmbased autonomous agents to tackle more diverse and complex real-world applications has attracted enormous interest. generally, many real-world scenarios are usually complex, encompassing a variety of challenging tasks that are beyond the capability of a single agent. to address this point, we notice that human society is composed of vast individuals, each possessing their unique characteristics. by selecting, orchestrating, and cooperating with different individuals, humans can form an efficient team group to handle complicated missions in the real world. therefore, there has been an increasing trend to develop multi-agent collaboration frameworks @xcite @xcite to simulate human behaviors for solving complex tasks. by developing a series of specialized agents with diverse settings, multiagent systems enable us to reveal emergent abilities among multiple agents and synergize their specialized expertise to achieve superior performance, akin to simulating human populations. nevertheless, it is worthy noting that, in most of (multi)agent frameworks, their designs heavily depend on handcrafted settings, including character roles, task scopes, skills, and prompt settings. although we admit that meticulous human design is quite useful for instructing llm-based agents to understand tasks, it also limits scaling up the number of agents to further improve performance due to expensive human labor. considering the increasing popularity of llm-based autonomous agents, how to create a generic agent generation paradigm to automatically build multi-agent systems is a critical challenge. in this paper, we introduce a novel method, evoagent, that formulates agent generation as the evolutionary processing @xcite in human society. specifically, to align human society, each agent can be considered as individuals that can procreate its population across successive generations. motivated by this mechanism, we can simulate such a human behavior to automatically generate multiple agents based on any pre-defined agents. therefore, as shown in figure 1 , evoagent can be considered as a one-shot agent generation method that starts from a specialized agent as the initial agent, and then considers its settings (e.g., role, skills, prompts, and so on) as the variables to be evolved. with a series operation of eas (e.g., selection, crossover, mutation), evoagent can automatically create multiple evolutionary agents based on the initial specialized agent. moreover, evoagent is not limited to the infrastructure of agent frameworks, as it is a generic multi-agent generation method. thus, it can be applied to any agent framework and expanded to multi-agent systems without any extra human effort. we conduct experiments on multiple datasets, including knowledge-based question answering and multi-modal reasoning ( § 4.1), interactive scientific solving ( § 4.2) and real-world complex planning ( § 4.3). experimental results indicate that evoa-gent can generate multiple agents with diverse skills and harness their capabilities to consistently improve the performance of the model in different scenarios. besides, to validate the scalability of evoagent in creating massive agents, we also apply our method to some conversational scenarios (e.g., debate), and the results also indicate the potential of evoagent in generating multiple diverse agents. overall, the contributions of this paper can be summarized as below: ## method generally, human society comprises a broad spectrum of individuals from diverse cultures, encompassing multiple generations. to solve specific tasks, human society usually involves a lot of expert individuals and aggregates their specialized expertise to achieve better answer. thus, it can be considered as the foundation to facilitate multiagent collaborations. to fulfill this point, how to automatically create multiple agents would be very critical. inspired by evolutionism, we formulate agent generation as an evolutionary process to generate multiple agents without any human labor. ## experiment in this section, we adopt evoagent to multiple applications to illustrate that evoagent can help llm-based agents better accomplish tasks with multi-agent generation. 1 we also demonstrate that evoagent can be applicable in supporting currently widely used multi-agent frameworks, such as metagpt, autogen, and camel in appendix d. ## conclusion in this paper, we propose evoagent, an automatic multi-agent generation system by leveraging evolutionary algorithms. evoagent is suitable to any existing agent framework and extends it to multi-agent systems with diverse and effective agents by using a series of evolutionary operations, including mutation, crossover, and selection. experiments on multiple tasks show that evoagent can significantly improve the capabilities of llmbased agents in solving complex tasks.