DvalinCode vs Guardrails AI
DvalinCode leads on trust: 83.9/100 (Grade A) against 76.7/100 (Grade B), a 7.2-point gap. DvalinCode leads on maintenance and transparency; Guardrails AI leads on adoption and rests on broader evidence. Note: DvalinCode's evidence coverage is thinner (coverage B vs A) — its score rests on fewer independent signal types.
Independent security verification for code written by humans and AI agents. Scan, repair, then prove it — Dvalin runs your project's own checks and issues a Verified Fix Record anyone can re-derive offline. Local-first, policy-bound, MIT.
Choose DvalinCode if maintenance and transparency matter most.
- +7.6Maintenance
- +0.5Transparency: OSSF Scorecard 6.6 against 6.0
Adding guardrails to large language models.
Choose Guardrails AI if adoption matters most.
- +7.6Adoption: 24.7k weekly downloads against 91
- A vs BEvidence coverage: 4 of 5 independent signal types, against 3
Where they differ
2 dimensions identical: Safety 20.0 · Identity 18.0 · Full evidence table
An independent, evidence-based trust comparison of DvalinCode and Guardrails AI, two Security & Guardrails projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.
Full evidence
| Signal | DvalinCodearthurpanhku/dvalincode | Guardrails AIguardrails-ai/guardrails |
|---|---|---|
| HVTrust score | 83.9 | 76.7 |
| Evidence grade | A | B |
| Coverage grade | B | A |
| Overall rank | #98 | #194 |
| Rank in Security & Guardrails | #2 | #3 |
| GitHub stars | 118 | 7.5k |
| Last updated | 4d ago | 1d ago |
| Build provenance | Yes | Yes |
| OSSF Scorecard | 6.6 / 10 | 6.0 / 10 |
| License | MIT | Apache-2.0 |
| Downloads | 91/wk | 25k/wk |
| Trust dimensions (points earned) | ||
| Safety / integrity / 25 | 20.0 | 20.0 |
| Identity & provenance / 18 | 18.0 | 18.0 |
| Transparency / 17 | 14.1 | 13.6 |
| Maintenance / 20 | 19.5 | 11.9 |
| Adoption / 20 | 7.6 | 15.2 |
| Runtime capability surface (full matrix) | ||
| MCP server | Implemented | — |
| External providers | 1 — Anthropic | 3 — Anthropic, Multi-provider (LiteLLM), OpenAI |
| Requires API keys | Yes | Yes |
| Plugin surface | plugins | — |
| Provenance drift | Match | Unknown |
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.3 →