Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day

TL;DR

Deploy-Master automates large-scale discovery, validation, and deployment of over 50,000 scientific tools in a single day, enhancing reproducibility and integration into AI-driven workflows.

Contribution

It introduces a scalable, automated workflow for transforming open-source repositories into executable, containerized scientific tools with validation and registration at unprecedented scale.

Findings

Successfully deployed 50,112 tools in one day

Characterized deployment throughput, costs, and failure modes at scale

Enabled searchable registration of tools for reuse

Abstract

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.