@inproceedings{hu-etal-2025-compileagent,
title = "{C}ompile{A}gent: Automated Real-World Repo-Level Compilation with Tool-Integrated {LLM}-based Agent System",
author = "Hu, Li and
Chen, Guoqiang and
Shang, Xiuwei and
Cheng, Shaoyin and
Wu, Benlong and
LiGangyang, LiGangyang and
Zhu, Xu and
Zhang, Weiming and
Yu, Nenghai",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.103/",
doi = "10.18653/v1/2025.acl-long.103",
pages = "2078--2091",
ISBN = "979-8-89176-251-0",
abstract = "With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10{\%} to 71{\%}. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects. The complete code and data are available at https://github.com/Ch3nYe/AutoCompiler."
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<abstract>With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10% to 71%. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects. The complete code and data are available at https://github.com/Ch3nYe/AutoCompiler.</abstract>
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%0 Conference Proceedings
%T CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System
%A Hu, Li
%A Chen, Guoqiang
%A Shang, Xiuwei
%A Cheng, Shaoyin
%A Wu, Benlong
%A LiGangyang, LiGangyang
%A Zhu, Xu
%A Zhang, Weiming
%A Yu, Nenghai
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F hu-etal-2025-compileagent
%X With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10% to 71%. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects. The complete code and data are available at https://github.com/Ch3nYe/AutoCompiler.
%R 10.18653/v1/2025.acl-long.103
%U https://aclanthology.org/2025.acl-long.103/
%U https://doi.org/10.18653/v1/2025.acl-long.103
%P 2078-2091
Markdown (Informal)
[CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System](https://aclanthology.org/2025.acl-long.103/) (Hu et al., ACL 2025)
ACL
- Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, and Nenghai Yu. 2025. CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2078–2091, Vienna, Austria. Association for Computational Linguistics.