Is Agent Search Safe?
Agent Search — Nerq Trust Score 53.2/100 (C- grade). Based on analysis of 2 trust dimensions, it is has notable safety concerns. Last updated: 2026-05-13.
Use Agent Search with some caution. Agent Search is a Python package with a Nerq Trust Score of 53.2/100 (C-), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).
Is Agent Search safe?
CAUTION — Agent Search has a Nerq Trust Score of 53.2/100 (C-). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Agent Search's trust score?
Agent Search has a Nerq Trust Score of 53.2/100, earning a C- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Agent Search?
Agent Search's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Agent Search and who maintains it?
| Author | Owen Colegrove |
| Category | Python Packages |
| Source | N/A |
Similar Pypi by Trust Score
Safety Guide: Agent Search
What is Agent Search?
Agent Search is a Python package — AgentSearch: An open source framework and dataset for webscale local search..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=agent-search
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Agent Search has a Nerq Trust Score of 53/100 (C-) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Agent Search has a Trust Score of 53/100 (C-).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 50/100 |
| Popularity | 0/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Agent Search collect?
Privacy assessment for Agent Search is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Agent Search secure?
Security score: 90/100. Agent Search has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Apache-2.0, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Agent Search Security Report
How we calculated this score
Agent Search's trust score of 53.2/100 (C-) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (50/100), popularity (0/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on May 13, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.