Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to ensure diverse expertise and prevent stereotypical outputs. It allows for a flexible yet structured approach to problem-solving and can help reduce redundancy and enhance output diversity. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods with 21.94% improvement in average accuracy, providing outstanding performance without requiring task-specific prompt engineering.

通过多个大型语言模型代理，我们提出了一种新的自适应团队构建范式，其中使用名为Captain Agent的新型代理设计来动态形成和管理任务求解过程中的团队。在六个真实场景的全面评估中，Captain Agent的平均准确率提高了21.94%，无需特定任务提示工程，表现出色且具有灵活的结构化问题解决方法。

面向语言模型代理的自适应对话团队建设