Is Agentic Ai Safe?
Agentic Ai — Nerq Trust Score 50.8/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-21
Agentic Ai is a Python package with a Nerq Trust Score of 50.8/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-21 Security: 90/100. Popularity: 15/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-21. Machine-readable data (JSON).
Is Agentic Ai safe?
Trust Score Breakdown — Agentic Ai has a Nerq Trust Score of 50.8/100 (C-). Measured across 2 independent trust signals (as of 2026-03-21).
What is Agentic Ai's trust score?
Agentic Ai has a Nerq Trust Score of 50.8/100, 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 Agentic Ai?
Agentic Ai's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Agentic Ai and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Agentic Ai
What is Agentic Ai?
Agentic Ai is a Python package — Pydantic agents for interacting with various backends..
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=agentic-ai
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Agentic Ai has a Nerq Trust Score of 51/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Agentic Ai has a Trust Score of 51/100 (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.