Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With $891K spent on environment construction and an additional $576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.

翻译：训练具备能力的软件工程（SWE）智能体需要大规模、可执行且可验证的环境，这些环境应能提供动态反馈循环，支持迭代式代码编辑、测试执行与解决方案优化。然而，现有的开源数据集在规模和代码库多样性方面仍然有限，而工业解决方案则因其基础设施未公开而缺乏透明度，这为大多数学术研究团队设置了难以逾越的障碍。我们提出了OpenSWE，这是目前规模最大、完全透明的Python软件工程智能体训练框架，包含45,320个可执行的Docker环境，覆盖超过12.8k个代码库，所有Dockerfile、评估脚本及基础设施均已完全开源以确保可复现性。OpenSWE通过部署在64节点分布式集群上的多智能体合成流水线构建而成，自动化实现了代码库探索、Dockerfile构建、评估脚本生成以及迭代式测试分析。除了规模优势，我们还提出了一种以质量为中心的过滤流水线，用于表征每个环境的内在难度，过滤掉那些无法解决或挑战性不足的实例，仅保留能最大化学习效率的环境。该项目在环境构建上投入了89.1万美元，并在轨迹采样与难度感知筛选上额外投入了57.6万美元，总投资约147万美元，最终从约9,000个质量有保证的环境中获得了约13,000条精选轨迹。大量实验验证了OpenSWE的有效性：OpenSWE-32B和OpenSWE-72B在SWE-bench Verified上分别达到62.4%和66.0%的准确率，在Qwen2.5系列模型中创下了SOTA记录。此外，专注于软件工程的训练还带来了显著的领域外性能提升，包括在数学推理任务上最高提升12个百分点，在科学基准测试上提升5个百分点，且未损害事实召回能力。