Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/LinkSoul-AI/AutoAgents.

翻译：大语言模型（LLMs）在多智能体系统中的自动任务求解方面取得了显著进展。然而，现有基于LLM的多智能体方法大多依赖预定义智能体处理简单任务，限制了多智能体协作对不同场景的适应性。为此，我们提出AutoAgents这一创新框架，该框架能够根据任务自适应地生成并协调多个专业智能体，构建人工智能团队。具体而言，AutoAgents通过动态生成基于任务内容的多个所需智能体来耦合任务与角色之间的关系，并基于生成的专家智能体为当前任务规划解决方案。多个专业智能体相互协作，高效完成任务。同时，框架中融入了观察者角色，对指定计划和智能体响应进行反思与改进。我们在多个基准上的实验表明，AutoAgents相比现有基于多智能体的方法能生成更连贯、更准确的解决方案。这凸显了为不同任务分配不同角色及团队协作的重要性，为解决复杂任务提供了新视角。该项目代码仓库位于https://github.com/LinkSoul-AI/AutoAgents。