---
title: "AgentCoder — paper by Huang et al. (2023) — summary and ideas"
description: "LLM-based code generation improves when models can check and revise their own output. 44 references, cited by 64 papers here."
authors: ["Dong Huang", "Jie M.Zhang", "Michael Luck", "Qingwen Bu", "Yuhao Qing", "Heming Cui"]
year: 2023
venue: "arXiv:2312.13010"
source: "https://arxiv.org/abs/2312.13010"
doi: "10.48550/arxiv.2312.13010"
arxiv: "2312.13010"
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/huang-2023-agentcoder-multi-agent-based/"
---
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# AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

**Authors:** [Dong Huang](https://md.research.elamur.ai/free/en/authors/dong-huang/), [Jie M.Zhang](https://md.research.elamur.ai/free/en/authors/jie-m-zhang/), Michael Luck, Qingwen Bu, [Yuhao Qing](https://md.research.elamur.ai/free/en/authors/yuhao-qing/) and [Heming Cui](https://md.research.elamur.ai/free/en/authors/heming-cui/) · **Year:** 2023 · **Venue:** arXiv:2312.13010 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2312.13010) · [PDF](https://arxiv.org/pdf/2312.13010) · [DOI 10.48550/arxiv.2312.13010](https://doi.org/10.48550/arxiv.2312.13010) · [arXiv 2312.13010](https://arxiv.org/abs/2312.13010) · [OpenAlex](https://openalex.org/W4390092490)

**Citations:** 26 citations (OpenAlex, 2026-10-14) · [64](https://md.research.elamur.ai/free/en/papers/huang-2023-agentcoder-multi-agent-based/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM-based code generation improves when models can check and revise their own output. However, existing self-refinement and multi-agent approaches (MetaGPT, ChatDev, AgentVerse) have two weaknesses: their feedback relies on imprecise tests, and they coordinate many agents at a high token cost. AgentCoder addresses this with a lean framework of three roles. A programmer agent writes code from the requirement. A test designer agent, working separately and never seeing the code, produces basic, edge-case and large-scale tests. A test executor agent runs those tests locally and sends any errors back to the programmer, repeating until all tests pass or the iteration budget runs out. Keeping test creation apart from coding prevents the tests from inheriting the code's blind spots. The authors evaluated the framework on HumanEval, MBPP and their ET variants with 14 LLMs and 16 baselines. With GPT-4, AgentCoder reached 96.3% pass@1 on HumanEval and 91.8% on MBPP, beating the strongest prior method while using less than half its tokens. This makes reliable automated coding cheaper.

## Key points
- **Accuracy and cost with GPT-4:** pass@1 was 96.3% on HumanEval and 91.8% on MBPP, using about 56.9K and 66.3K tokens. The previous best reached 90.2% and 78.9% at 138.2K and 206.5K tokens. MetaGPT, ChatDev and AgentVerse all required substantially more tokens.
- **Test quality with GPT-4:** generated tests were 89.6% and 91.4% correct on HumanEval and MBPP, measured against the canonical solutions. MetaGPT scored 79.3% and 84.4%.
- **Test coverage with GPT-4:** line coverage was 91.7% on HumanEval and 92.3% on MBPP, compared with 81.7% and 80.5% for MetaGPT.
- **Ablation on separating roles (GPT-3.5-turbo):** one agent writing both code and tests did worse than separate agents.
  - pass@1: 71.3% vs 79.9% on HumanEval and 79.4% vs 89.9% on MBPP.
  - Test accuracy: 61.0% vs 87.8% on HumanEval and 51.8% vs 89.9% on MBPP.
  - Line coverage: 72.5% vs 87.5% on HumanEval and 75.9% vs 89.5% on MBPP.
- **Averages across all datasets:** AgentCoder scored 91.5% pass@1 with GPT-4 and 84.1% with GPT-3.5, against 86.8% and 75.3% for the prior state of the art.

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

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 212 works · 19,544 citations
- [University of Hong Kong](https://md.research.elamur.ai/free/en/universities/university-of-hong-kong/) — University, Pok Fu Lam, China · 118 works · 13,496 citations
- [University of Sussex](https://md.research.elamur.ai/free/en/universities/university-of-sussex/) — University, Brighton, United Kingdom · 24 works · 3,604 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works · [💻 Software](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) · 119 works · [🖥️ Computer Science Applications](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/) · 131 works

🧭 Topics (1): [💻 Code & programming languages](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · 168 works

## Cite

Cite as: Dong Huang, Jie M.Zhang, Michael Luck, Qingwen Bu, Yuhao Qing and Heming Cui. 2023. AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation. arXiv:2312.13010.

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

```bibtex
@misc{huang2023agentcoder,
  title = {{AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation}},
  author = {Dong Huang, Jie M.Zhang, Michael Luck, Qingwen Bu, Yuhao Qing and Heming Cui},
  year = {2023},
  doi = {10.48550/arxiv.2312.13010},
  eprint = {2312.13010},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.13010}
}
```

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## References

44 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*