Is Ai Logger Safe?
Ai Logger — Nerq Trust Score 53.2/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25
Ai Logger is a Python package with a Nerq Trust Score of 53.2/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-25 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 Ai Logger safe?
Trust Score Breakdown — Ai Logger has a Nerq Trust Score of 53.2/100 (C-). Measured across 2 independent trust signals (as of 2026-03-25).
What is Ai Logger's trust score?
Ai Logger 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 Ai Logger?
Ai Logger's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Ai Logger and who maintains it?
| Author | VinnyVanGogh |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Ai Logger
What is Ai Logger?
Ai Logger is a Python package — A module to add logging in json format that an AI could easily understand..
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=ai-logger
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Ai Logger has a Nerq Trust Score of 53/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Ai Logger has a Trust Score of 53/100 (C-).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.