Is Agentic Patterns Safe?
Agentic Patterns — Nerq Trust Score 51.2/100 (C- grade). Based on analysis of 2 trust dimensions, it is has notable safety concerns. Last updated: 2026-05-13.
Use Agentic Patterns with some caution. Agentic Patterns is a Python package with a Nerq Trust Score of 51.2/100 (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 Agentic Patterns safe?
CAUTION — Agentic Patterns has a Nerq Trust Score of 51.2/100 (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 Agentic Patterns's trust score?
Agentic Patterns has a Nerq Trust Score of 51.2/100, 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 Agentic Patterns?
Agentic Patterns'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 Agentic Patterns and who maintains it?
| Author | MichaelisTrofficus |
| Category | Python Packages |
| Source | N/A |
Similar Pypi by Trust Score
Safety Guide: Agentic Patterns
What is Agentic Patterns?
Agentic Patterns is a Python package.
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-patterns
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Agentic Patterns has a Nerq Trust Score of 51/100 (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
- Agentic Patterns has a Trust Score of 51/100 (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 | 55/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Agentic Patterns collect?
Privacy assessment for Agentic Patterns is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Agentic Patterns secure?
Security score: 90/100. Agentic Patterns has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under MIT, 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: Agentic Patterns Security Report
How we calculated this score
Agentic Patterns's trust score of 51.2/100 (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 (55/100), 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)
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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.