Ist Knowledge Graph Memory sicher?
Knowledge Graph Memory — Nerq Trust Score 49.9/100 (Note D). Basierend auf der Analyse von 5 Vertrauensdimensionen wird es als bemerkenswerte Sicherheitsbedenken eingestuft. Zuletzt aktualisiert: 2026-04-01.
Vorsicht bei Knowledge Graph Memory. Knowledge Graph Memory is a software tool mit einer Nerq-Vertrauensbewertung von 49.9/100 (D). It is below the recommended threshold of 70. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-01. Maschinenlesbare Daten (JSON).
Ist Knowledge Graph Memory sicher?
NO — USE WITH CAUTION — Knowledge Graph Memory hat eine Nerq-Vertrauensbewertung von 49.9/100 (D). It has below-average trust signals with significant gaps in security, maintenance, or documentation. Not recommended for production use without thorough manual review and additional security measures.
Was ist die Vertrauensbewertung von Knowledge Graph Memory?
Knowledge Graph Memory hat eine Nerq-Vertrauensbewertung von 49.9/100 und erhält die Note D. Diese Bewertung basiert auf 5 unabhängig gemessenen Dimensionen.
Was sind die wichtigsten Sicherheitsergebnisse für Knowledge Graph Memory?
Das stärkste Signal von Knowledge Graph Memory ist gesamtvertrauen mit 49.9/100. Es wurden keine bekannten Schwachstellen erkannt. Hat die Nerq-Vertrauensschwelle von 70+ noch nicht erreicht.
Was ist Knowledge Graph Memory und wer pflegt es?
| Autor | https://github.com/evangstav/python-memory-mcp-server |
| Kategorie | AI tool |
| Sterne | 80,518 |
| Quelle | https://github.com/modelcontextprotocol/servers/tree/HEAD/src/memory |
Beliebte Alternativen in AI tool
What Is Knowledge Graph Memory?
Knowledge Graph Memory is a software tool in the AI tool category: Build and query persistent semantic networks for data management.. It has 80,518 GitHub stars. Nerq Trust Score: 50/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including security vulnerabilities, maintenance activity, license compliance, and community adoption.
How Nerq Assesses Knowledge Graph Memory's Safety
Nerq evaluates every software tool across 13+ independent trust signals drawn from public sources including GitHub, NVD, OSV.dev, OpenSSF Scorecard, and package registries. These signals are grouped into five core dimensions: Sicherheit (known CVEs, dependency vulnerabilities, security policies), Wartung (commit frequency, release cadence, issue response times), Documentation (README quality, API docs, examples), Compliance (license, regulatory alignment across 52 jurisdictions), and Community (stars, forks, downloads, ecosystem integrations).
Knowledge Graph Memory receives an overall Trust Score of 49.9/100 (D), which Nerq considers low. This is below the Nerq Verified threshold of 70. We recommend additional due diligence before production deployment. With 80,518 GitHub stars, Knowledge Graph Memory benefits from a large community that can identify and report issues quickly.
Nerq updates trust erzielt continuously as new data becomes available. To get the latest assessment, query the API: GET nerq.ai/v1/preflight?target=Knowledge Graph Memory
Each dimension is weighted according to its importance for the tool's category. For example, Security and Maintenance carry higher weight for tools that handle sensitive data or execute code, while Community and Documentation are weighted more heavily for developer-facing libraries and frameworks. This ensures that Knowledge Graph Memory's score reflects the risks most relevant to its actual usage patterns. The final score is a weighted average across all five dimensions, normalized to a 0-100 scale with letter grades from A (highest) to F (lowest).
Who Should Use Knowledge Graph Memory?
Knowledge Graph Memory is designed for:
- Developers and teams working with AI tool tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: We recommend caution with Knowledge Graph Memory. The low trust score suggests potential risks in security, maintenance, or community support. Consider using a more established alternative for any production or sensitive workload.
How to Verify Knowledge Graph Memory's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Review the repository security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Knowledge Graph Memory's dependency tree. - Bewertung permissions — Understand what access Knowledge Graph Memory requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Knowledge Graph Memory in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=Knowledge Graph Memory - Überprüfen Sie das/die license — Confirm that Knowledge Graph Memory'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.
- 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 security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Knowledge Graph Memory
When evaluating whether Knowledge Graph Memory is safe, consider these category-specific risks:
Understand how Knowledge Graph Memory processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Knowledge Graph Memory's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Knowledge Graph Memory. Security patches and bug fixes are only effective if you're running the latest version.
If Knowledge Graph Memory connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Knowledge Graph Memory's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Knowledge Graph Memory in violation of its license can expose your organization to legal liability.
Best Practices for Using Knowledge Graph Memory Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Knowledge Graph Memory while minimizing risk:
Periodically review how Knowledge Graph Memory is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Knowledge Graph Memory and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Knowledge Graph Memory only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Knowledge Graph Memory's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Knowledge Graph Memory is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Knowledge Graph Memory?
Even promising tools aren't right for every situation. Consider avoiding Knowledge Graph Memory in these scenarios:
- Production environments handling sensitive customer data
- Regulated industries (healthcare, finance, government) without additional compliance review
- Mission-critical systems where downtime has significant business impact
For each scenario, evaluate whether Knowledge Graph Memory von 49.9/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Knowledge Graph Memory Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among AI tool tools, the average Trust Score is 62/100. Knowledge Graph Memory's score of 49.9/100 is below the category average of 62/100.
This suggests that Knowledge Graph Memory trails behind many comparable AI tool tools. Organizations with strict security requirements should evaluate whether higher-scoring alternatives better meet their needs.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate 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 Knowledge Graph Memory 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, Knowledge Graph Memory'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 security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Knowledge Graph Memory's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Knowledge Graph Memory&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 — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Knowledge Graph Memory are strengthening or weakening over time.
Knowledge Graph Memory vs Alternatives
In the AI tool category, Knowledge Graph Memory erzielt 49.9/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Knowledge Graph Memory vs openclaw — Trust Score: 84.3/100
- Knowledge Graph Memory vs stable-diffusion-webui — Trust Score: 69.3/100
- Knowledge Graph Memory vs prompts.chat — Trust Score: 69.3/100
Wichtigste Punkte
- Knowledge Graph Memory hat eine Vertrauensbewertung von 49.9/100 (D) and is not yet Nerq Verified.
- Knowledge Graph Memory has significant trust gaps. Consider higher-rated alternatives unless specific requirements mandate its use.
- Among AI tool tools, Knowledge Graph Memory erzielt below the category average of 62/100, suggesting room for improvement relative to peers.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Häufig gestellte Fragen
Ist Knowledge Graph Memory sicher in der Verwendung?
Was ist Knowledge Graph Memory's trust score?
Was sind sicherere Alternativen zu Knowledge Graph Memory?
How often is Knowledge Graph Memory's safety score updated?
Can I use Knowledge Graph Memory in a regulated environment?
Disclaimer: Nerq-Vertrauensbewertungen sind automatisierte Bewertungen basierend auf öffentlich verfügbaren Signalen. Sie sind keine Empfehlungen oder Garantien. Führen Sie immer Ihre eigene Sorgfaltsprüfung durch.