---
title: "MetaAgents — paper by Sun; and Zhang (2023)"
description: "This paper asks whether LLM-based agents can build teams on their own by matching participants'… 70 references, cited by 36 papers here."
authors: ["Yuan Li; Lichao Sun;", "Yixuan Zhang"]
year: 2023
venue: "arXiv:2310.06500"
source: "https://arxiv.org/abs/2310.06500"
doi: "10.1145/3711032"
arxiv: "2310.06500"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2023-metaagents-large-language-model-based/"
---
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# MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming

**Authors:** Yuan Li; Lichao Sun; and [Yixuan Zhang](https://md.research.elamur.ai/free/en/authors/yixuan-zhang/) · **Year:** 2023 · **Venue:** [arXiv:2310.06500](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-human-computer-interaction/) ([Proceedings of the ACM on Human-Computer Interaction 2023](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-human-computer-interaction/2023/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1145/3711032) · [PDF](https://arxiv.org/pdf/2310.06500) · [DOI 10.1145/3711032](https://doi.org/10.1145/3711032) · [arXiv 2310.06500](https://arxiv.org/abs/2310.06500) · [OpenAlex](https://openalex.org/W4410049680)

**Citations:** 38 citations (OpenAlex, 2026-10-14) · [36](https://md.research.elamur.ai/free/en/papers/li-2023-metaagents-large-language-model-based/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM multi-agent systems such as ChatDev and MetaGPT depend on team structures and workflows that humans fix in advance. It remains unclear whether agents could organise themselves. This paper asks whether LLM-based agents can build teams on their own by matching participants' skills to suitable roles.

The authors create MetaAgents, a social simulation framework in which agents hold multi-turn dialogues and are linked through a chain of decisions. Their testbed is a simulated job fair, where agents must sketch a task workflow and recruit colleagues whose backgrounds fit each step. Four distinct scenarios are examined. Evaluation pairs task-oriented quantitative metrics with qualitative reading of the generated text.

Agents are reasonably good at outlining workflows and at using conversation content to pick partners. However, team formation worsens as the number of participants grows, partly because agents drift from their personas or overstate their abilities. The study is a step toward self-organising multi-agent systems and offers HCI/CSCW researchers a lens on LLM social behaviour.

## Key points
- Published in Proc. ACM Hum.-Comput. Interact. 9(2), article CSCW134 (April 2025); preprint arXiv:2310.06500.
- MetaAgents differs from ChatDev, MetaGPT and Auto-GPT by letting agents propose workflows and assemble teams themselves, rather than following predefined compositions.
- The case study is a job fair with four scenarios. The authors argue the setup transfers to roommate matching, networking sessions and collaborator search.
- Identified failure modes include behaviour misaligned with assigned personas, dishonesty about capabilities, and declining performance as scenarios scale up.
- Acknowledged limitations are static evaluation and subjective ground-truth labels. The authors suggest future work add human judgment, real recruitment data and comparisons with real-world experiments. They also flag ethical risks such as persona-induced bias and over-reliance on simulation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 287 works · 125,174 citations
- [Lehigh University](https://md.research.elamur.ai/free/en/universities/lehigh-university/) — University, Bethlehem, United States · 12 works · 1,652 citations
- [William & Mary](https://md.research.elamur.ai/free/en/universities/william-mary/) — University, Williamsburg, United States · 9 works · 895 citations

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## Cite

Cite as: Yuan Li; Lichao Sun; and Yixuan Zhang. 2023. MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming. arXiv:2310.06500.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sun2023metaagents,
  title = {{MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming}},
  author = {Yuan Li; Lichao Sun; and Yixuan Zhang},
  year = {2023},
  doi = {10.1145/3711032},
  eprint = {2310.06500},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3711032}
}
```

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- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/9/)

## References

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*Updated 3 November 2026*