Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908750855536640 |
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| author | Wang, Yi Huang, Zhenting Ding, Zhaohan Liao, Ruoxue Huang, Yuan Liu, Xinzijian Xie, Jiajun Chen, Siheng Zhang, Linfeng |
| author_facet | Wang, Yi Huang, Zhenting Ding, Zhaohan Liao, Ruoxue Huang, Yuan Liu, Xinzijian Xie, Jiajun Chen, Siheng Zhang, Linfeng |
| contents | Open-source scientific software is abundant, yet most tools remain difficult to compile, configure, and reuse, sustaining a small-workshop mode of scientific computing. This deployment bottleneck limits reproducibility, large-scale evaluation, and the practical integration of scientific tools into modern AI-for-Science (AI4S) and agentic workflows.
We present Deploy-Master, a one-stop agentic workflow for large-scale tool discovery, build specification inference, execution-based validation, and publication. Guided by a taxonomy spanning 90+ scientific and engineering domains, our discovery stage starts from a recall-oriented pool of over 500,000 public repositories and progressively filters it to 52,550 executable tool candidates under license- and quality-aware criteria. Deploy-Master transforms heterogeneous open-source repositories into runnable, containerized capabilities grounded in execution rather than documentation claims. In a single day, we performed 52,550 build attempts and constructed reproducible runtime environments for 50,112 scientific tools. Each successful tool is validated by a minimal executable command and registered in SciencePedia for search and reuse, enabling direct human use and optional agent-based invocation.
Beyond delivering runnable tools, we report a deployment trace at the scale of 50,000 tools, characterizing throughput, cost profiles, failure surfaces, and specification uncertainty that become visible only at scale. These results explain why scientific software remains difficult to operationalize and motivate shared, observable execution substrates as a foundation for scalable AI4S and agentic science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03513 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day Wang, Yi Huang, Zhenting Ding, Zhaohan Liao, Ruoxue Huang, Yuan Liu, Xinzijian Xie, Jiajun Chen, Siheng Zhang, Linfeng Software Engineering Artificial Intelligence Open-source scientific software is abundant, yet most tools remain difficult to compile, configure, and reuse, sustaining a small-workshop mode of scientific computing. This deployment bottleneck limits reproducibility, large-scale evaluation, and the practical integration of scientific tools into modern AI-for-Science (AI4S) and agentic workflows. We present Deploy-Master, a one-stop agentic workflow for large-scale tool discovery, build specification inference, execution-based validation, and publication. Guided by a taxonomy spanning 90+ scientific and engineering domains, our discovery stage starts from a recall-oriented pool of over 500,000 public repositories and progressively filters it to 52,550 executable tool candidates under license- and quality-aware criteria. Deploy-Master transforms heterogeneous open-source repositories into runnable, containerized capabilities grounded in execution rather than documentation claims. In a single day, we performed 52,550 build attempts and constructed reproducible runtime environments for 50,112 scientific tools. Each successful tool is validated by a minimal executable command and registered in SciencePedia for search and reuse, enabling direct human use and optional agent-based invocation. Beyond delivering runnable tools, we report a deployment trace at the scale of 50,000 tools, characterizing throughput, cost profiles, failure surfaces, and specification uncertainty that become visible only at scale. These results explain why scientific software remains difficult to operationalize and motivate shared, observable execution substrates as a foundation for scalable AI4S and agentic science. |
| title | Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2601.03513 |