Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915419567161344 |
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| author | Trae Research Team Gao, Pengfei Tian, Zhao Meng, Xiangxin Wang, Xinchen Hu, Ruida Xiao, Yuanan Liu, Yizhou Zhang, Zhao Chen, Junjie Gao, Cuiyun Lin, Yun Xiong, Yingfei Peng, Chao Liu, Xia |
| author_facet | Trae Research Team Gao, Pengfei Tian, Zhao Meng, Xiangxin Wang, Xinchen Hu, Ruida Xiao, Yuanan Liu, Yizhou Zhang, Zhao Chen, Junjie Gao, Cuiyun Lin, Yun Xiong, Yingfei Peng, Chao Liu, Xia |
| contents | Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models (LLMs), substantial progress has been made in addressing real-world software engineering tasks. Recent studies have introduced ensemble reasoning techniques to enhance the performance of LLM-based issue resolution. However, existing prompting-based methods still face limitations in effectively exploring large ensemble spaces and lack the capacity for repository-level understanding, both of which constrain their overall effectiveness. In this paper, we propose Trae Agent, the first agent-based ensemble reasoning approach for repository-level issue resolution. Trae Agent formulates our goal as an optimal solution search problem and addresses two key challenges, i.e., large ensemble spaces and repository-level understanding, through modular agents for generation, pruning, and selection. We conduct extensive experiments using three leading LLMs on the widely-adopted SWE-bench benchmark, comparing Trae Agent against four state-of-the-art ensemble reasoning techniques. Experimental results demonstrate that Trae Agent consistently achieves superior performance, with an average improvement of 10.22% over all baselines in terms of Pass@1. Trae Agent has achieved first place on the SWE-bench Verified leaderboard, with a notable Pass@1 score of 75.20%. We are pleased to release Trae Agent as an open-source project to support the research community, with all resources available at https://github.com/bytedance/trae-agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling Trae Research Team Gao, Pengfei Tian, Zhao Meng, Xiangxin Wang, Xinchen Hu, Ruida Xiao, Yuanan Liu, Yizhou Zhang, Zhao Chen, Junjie Gao, Cuiyun Lin, Yun Xiong, Yingfei Peng, Chao Liu, Xia Software Engineering Artificial Intelligence Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models (LLMs), substantial progress has been made in addressing real-world software engineering tasks. Recent studies have introduced ensemble reasoning techniques to enhance the performance of LLM-based issue resolution. However, existing prompting-based methods still face limitations in effectively exploring large ensemble spaces and lack the capacity for repository-level understanding, both of which constrain their overall effectiveness. In this paper, we propose Trae Agent, the first agent-based ensemble reasoning approach for repository-level issue resolution. Trae Agent formulates our goal as an optimal solution search problem and addresses two key challenges, i.e., large ensemble spaces and repository-level understanding, through modular agents for generation, pruning, and selection. We conduct extensive experiments using three leading LLMs on the widely-adopted SWE-bench benchmark, comparing Trae Agent against four state-of-the-art ensemble reasoning techniques. Experimental results demonstrate that Trae Agent consistently achieves superior performance, with an average improvement of 10.22% over all baselines in terms of Pass@1. Trae Agent has achieved first place on the SWE-bench Verified leaderboard, with a notable Pass@1 score of 75.20%. We are pleased to release Trae Agent as an open-source project to support the research community, with all resources available at https://github.com/bytedance/trae-agent. |
| title | Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2507.23370 |