Agent Examples est-il sûr ?

Agent Examples est-il sûr ?

Détail du score de confiance — Agent Examples has a Nerq Trust Score of 64.8/100 (C). Measured across 5 independent trust signals.

What Is Agent Examples?

Agent Examples is a software tool in the coding category: Collects examples of community agents and MCP tools.. It has 2 GitHub stars. Nerq Trust Score: 65/100 (C).

Nerq independently analyzes every software tool, app, and extension across multiple trust signals including sécurité vulnerabilities, maintenance activity, license conformité, and adoption par la communauté.

How Nerq Assesses Agent Examples's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Agent Examples performs in each:

• Sécurité (0/100): Agent Examples's sécurité posture is poor. This score factors in known CVEs, dependency vulnerabilities, sécurité policy presence, and code signing practices.
• Maintenance (1/100): Agent Examples is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
• Documentation (0/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
• Compliance (92/100): Agent Examples is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
• Community (0/100): Community adoption is limited. Basé sur GitHub stars, forks, download counts, and ecosystem integrations.

The overall Trust Score of 64.8/100 (C) is the weighted combination of these measured signals. It is a measurement, not a pass/fail or suitability judgment — weigh the individual signals against your own requirements.

Who Typically Evaluates Agent Examples?

Agent Examples is commonly evaluated by:

• Developers and teams working with coding tools
• Organizations evaluating AI tools for their stack
• Researchers exploring AI capabilities in this domain

How to read the signals: Agent Examples's measured signals (sécurité 0/100, maintenance 1/100, documentation 0/100, community 0/100) are shown above. These are measurements, not a suitability judgment — weigh each signal against the requirements of your own use case and risk tolerance.

How to Verify Agent Examples's Safety Yourself

While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:

1. Check the source code — Examiner le/la repository's sécurité policy, open issues, and recent commits for signs of active maintenance.
2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Agent Examples's dependency tree.
3. Avis permissions — Understand what access Agent Examples requires. Software tools should follow the principle of least privilege.
4. Test in isolation — Run Agent Examples in a sandboxed environment before granting access to production data or systems.
5. Monitor continuously — Use Nerq's API to set up automated trust checks: GET nerq.ai/v1/preflight?target=agent-examples
6. Examiner le/la license — Confirm that Agent Examples's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
7. Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses sécurité concerns openly. Low community engagement may indicate limited peer review of the codebase.

Common Safety Concerns with Agent Examples

When evaluating whether Agent Examples is safe, consider these category-specific risks:

Agent Examples and the EU AI Act

Agent Examples is classified as Minimal Risk under the EU AI Act. This is the lowest risk category, meaning it faces minimal regulatory requirements. However, transparency obligations still apply.

Nerq's conformité assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal conformité.

Best Practices for Using Agent Examples Safely

Whether you're an individual developer or an enterprise team, these practices will help you get the most from Agent Examples while minimizing risk:

Situations That Warrant Independent Review of Agent Examples

Nerq's signals are one input. In the following situations, evaluate Agent Examples's measured signals against your own requirements before making a decision:

• Environments handling sensitive or regulated data (healthcare, finance, government)
• Mission-critical systems where downtime has significant business impact
• Deployments with strict regulatory requirements that must be independently validated

For each situation, compare Agent Examples's measured trust score of 64.8/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Agent Examples is suitable for any particular use.

How Agent Examples Compares to Industry Standards

Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among coding tools, the average Trust Score is 62/100. Agent Examples's score of 64.8/100 is above the category average of 62/100.

This positions Agent Examples favorably among coding tools. While it outperforms the average, there is still room for improvement in certain trust dimensions.

Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks modéré in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.

Trust Score History

Nerq continuously monitors Agent Examples and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Agent Examples's score is updated within 24 hours.

Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to sécurité and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Agent Examples's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=agent-examples&include=history

Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — sécurité, maintenance, documentation, conformité, and community — has evolved independently, providing granular visibility into which aspects of Agent Examples are strengthening or weakening over time.

Agent Examples vs Alternatives

In the coding category, Agent Examples scores 64.8/100. There are higher-scoring alternatives available. For a detailed comparison, see:

• Agent Examples vs AutoGPT — Trust Score: 65.3/100
• Agent Examples vs ollama — Trust Score: 64.4/100
• Agent Examples vs langchain — Trust Score: 77.0/100

Points Essentiels

• Agent Examples has a measured Nerq Trust Score of 64.8/100 (C) — a composite of independent signals, not a suitability judgment.
• Among coding tools, Agent Examples scores above the category average of 62/100 (a positional measurement relative to peers).
• The individual signals — sécurité, maintenance, documentation, conformité, community — are shown above. Weigh them against your own requirements.
• Query the current measured values via Nerq's Preflight API.