  The rapid advancement of 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
provides 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 a society of
agents, providing a valuable resource for investigating conversational language
models. In particular, we conduct comprehensive studies on
instruction-following cooperation in multi-agent settings. 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: https://github.com/camel-ai/camel.
