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The rapid advancement of chat-based language models has led to remarkableprogress in complex task-solving. However, their success heavily relies onhuman input to guide the conversation, which can be challenging andtime-consuming. This paper explores the potential of building scalabletechniques to facilitate autonomous cooperation among communicative agents, andprovides insight into their "cognitive" processes. To address the challenges ofachieving autonomous cooperation, we propose a novel communicative agentframework named role-playing. Our approach involves using inception promptingto guide chat agents toward task completion while maintaining consistency withhuman intentions. We showcase how role-playing can be used to generateconversational data for studying the behaviors and capabilities of a society ofagents, providing a valuable resource for investigating conversational languagemodels. In particular, we conduct comprehensive studies oninstruction-following cooperation in multi-agent settings. Our contributionsinclude introducing a novel communicative agent framework, offering a scalableapproach for studying the cooperative behaviors and capabilities of multi-agentsystems, and open-sourcing our library to support research on communicativeagents and beyond: https://github.com/camel-ai/camel.

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society