The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond. The GitHub repository of this project is made publicly available on: https://github.com/lightaime/camel.

本文提出了一种名为“角色扮演”的新型交际代理框架来实现自主协作，使用初始提示指导聊天代理完成任务并保持与人类意图的一致性，其贡献在于介绍了一种新颖的交际代理框架，提供了一种可扩展的研究多智能体系统合作行为和能力的方法，并开源了实现支持研究的库。

CAMEL: 大规模语言模型社群“心智”探索的沟通代理