No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mbzuai-oryx · Agent Tool · ★ 104
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🔒 Is OpenEarthAgent safe to install? View the security audit →
A Unified Framework for Tool-Augmented Geospatial Agents Akashah Shabbir\, [Muhammad Umer Sheikh]()\, [Muhammad Akhtar Munir](),[Hiyam Debary](),[Mustansar Fiaz](),[Muhammad Zaigham Zaheer](), [Paolo Fraccaro](), [Fahad Shahbaz Khan](), [Muhammad Haris Khan](), [Xiao Xiang Zhu]() , [Salman Khan]() Mohamed bin Zayed University of Artificial Intelligence, IBM Research, Linköping University, Technical University Munich, Australian National University Equal Contribution OpenEarthAgent is a unified framework for building tool-augmented geospatial agents capable of structured, multi-step reasoning over satellite imagery and GIS data. Designed for remote sensing applications, it integrates multispectral analysis, geospatial operations, and n
| Stars | 104 |
| Forks | 11 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64.8034753779669/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-05 |
| Created | 2026-02-20 |
| Platforms | python |
| Est. Tokens | ~16k |
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OpenEarthAgent is [ECCV 2026 🔥] OpenEarthAgent is a unified framework for tool-augmented geospatial agents.. It is categorized as a Agent Tool with 104 GitHub stars.
OpenEarthAgent is primarily written in Python.
You can find installation instructions and usage details in the OpenEarthAgent GitHub repository at github.com/mbzuai-oryx/OpenEarthAgent. The project has 104 stars and 11 forks, indicating an active community.
The top alternatives to OpenEarthAgent on Agent Skills Hub include tutor-skills, repren. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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