Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling

• URL: http://arxiv.org/abs/2507.23370v1
• Date: Thu, 31 Jul 2025 09:37:22 GMT
• Title: Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
• Authors: Trae Research Team, Pengfei Gao, Zhao Tian, Xiangxin Meng, Xinchen Wang, Ruida Hu, Yuanan Xiao, Yizhou Liu, Zhao Zhang, Junjie Chen, Cuiyun Gao, Yun Lin, Yingfei Xiong, Chao Peng, Xia Liu,
• Abstract summary: Trae Agent is the first agent-based ensemble reasoning approach for repository-level issue resolution.<n>We conduct experiments using three leading large language models (LLMs) on the widely-adopted SWE-bench benchmark.<n>Trae Agent consistently achieves superior performance, with an average improvement of 10.22% over all baselines in terms of Pass@1.
• Score: 18.390443362388623
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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