mlsanityData to 7 Oct 2026

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

arXiv 2303.17760 ↗First posted 31 Mar 2023cs.AI cs.CL cs.CYcited by 1,348

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 1,348 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 97 of its 128 references that are in the map. First posted on arXiv 31 Mar 2023.

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97 of 128 references.

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  1. 2022Improving alignment of dialogue agents via targeted human judgements28 Sep

Other references · 96

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Citing papers per year: 2023: 29; 2024: 229; 2025: 471; 2026: 619.

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50 most cited of 1,348 All 1,348 (JSON)

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