The paper explores the potential of developing autonomous cooperation for conversational language models without relying heavily on human input. The proposed framework named role-playing utilizes inception prompting to direct chat agents toward tasks that align with human intentions while enhancing consistency. The role-playing framework produces conversational data for investigating the behaviors and capabilities of language models, resulting in a valuable resource for studying conversational language models. The authors' contributions include the introduction of a novel communicative agent framework, offering a scalable approach for investigating multi-agent systems, and making the library available for further research on communicative agents.