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.

翻译：基于对话和聊天的语言模型的快速发展，在解决复杂任务方面取得了显著进展。然而，其成功在很大程度上依赖于人类输入来引导对话，这既具有挑战性又耗时。本文探讨了构建可扩展技术的潜力，以促进通信智能体之间的自主协作，并深入理解其“认知”过程。为解决实现自主协作的挑战，我们提出了一种名为角色扮演的新型通信智能体框架。我们的方法利用初始提示引导聊天智能体完成任务，同时保持与人类意图的一致性。我们展示了角色扮演如何用于生成对话数据，以研究聊天智能体的行为与能力，为探究对话语言模型提供了宝贵资源。我们的贡献包括：引入一种新型通信智能体框架，提供研究多智能体系统协作行为与能力的可扩展方法，并开源我们的库以支持通信智能体及其他相关研究。该项目GitHub仓库已公开，地址为：https://github.com/lightaime/camel。