Guardrails AI

Adding guardrails to large language models.

Security & Guardrails Python Grade A Listed Apache-2.0
81.8/100
Rank #61 of 1204
Compare Guardrails AI

Strong public trust posture, backed by multiple independent signals.

Open compare tool Suggest correction
Listing state
Listed
Evidence coverage
Grade A · 4/5 signals
Last push
2026-08-05 · 4d ago
Recent change
Provider Added

Is Guardrails AI safe? Guardrails AI scores 81.8/100 (Grade A), ranked #61 of 1204 tracked open-source AI agent projects, on evidence coverage A (4 of 5 independent signal types). The public evidence: its packages ship with cryptographic provenance; OSSF Scorecard rates its supply-chain practices 6.0/10; 100% of recent commits are signed; last pushed 2026-08-05. Every point is earned from checkable signals — never paid placement. How scoring works →

Ranked neighbours in Security & Guardrails

Quick Trust Read

What Would Improve It
Improve adoption to lift the weakest part of the trust profile.
Recent Changes
2026-08-07
Provider Added
Runtime surface grew — new detected provider dependency: Multi-provider (LiteLLM)
2026-07-19
Activity Score Changed
Activity score down 5pts (77 → 72)
Maintainer Checklist
Raise Scorecard signals Current OSSF Scorecard is 6.0/10. Tighten the weakest checks to improve public safety evidence.
76.6
Activity sub-score · out of 100
#1

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade A reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage A is separate — it grades how many independent signal types back the score (4 of 5), so a high score on thin evidence stays visible. Full methodology →

Signals refreshed 2026-08-09 19:01 UTC · Repo last pushed 4 days ago

Activity & Reach

Stars
7.3k
Forks
668
Last Push
2026-08-05
4 days ago
Commits (4 wk)
18
Downloads (7d)
41,090
pypi
HN mentions (30d)
34
Open Issues
37
Rank Change
=
was #61

Analysis

HVTrust Dimensions vs Security & Guardrails

81.8 / 100 · 100.0% confidence

Guardrails AI Security & Guardrails average (17 agents)

Safety / Integrity50% OSSF Scorecard · 30% provenance · 20% signed commits
20.0 / 25
12.4 above avg 7.6
Identity / Provenance60% listing status · 40% build provenance
18.0 / 18
6.8 above avg 11.2
Transparency50% declared license · 50% OSSF Scorecard
13.6 / 17
1.9 above avg 11.7
Maintenance60% last-push freshness · 40% commit activity
16.8 / 20
0.7 above avg 16.1
AdoptionLog-scaled stars · package downloads
15.4 / 20
5.2 above avg 10.2

Activity Inputs

76.6 / 100
StarsRepository reach
23.2 / 30
FreshnessLast push recency
24.4 / 25
ActivityRecent commits
15.9 / 25
CommunityFork signal
13.1 / 20

Supply Chain Trust

Package Provenance
Verified
pypi attestation
OSSF Scorecard
6.0 / 10
OpenSSF Scorecard · scanned Aug 8, 2026
Signed Commits
100%
of last 100 commits verified
Binary-Artifacts 10
Branch-Protection 5
CI-Tests 10
CII-Best-Practices 0
Code-Review 10
Contributors 10
Dangerous-Workflow 10
Dependency-Update-Tool 10
Fuzzing 0
License 10
Maintained 10
Packaging 10
Pinned-Dependencies 4
SAST 0
Security-Policy 0
Signed-Releases -1
Token-Permissions 0
Vulnerabilities 0

Is Guardrails AI safe?

Public supply-chain signals for Guardrails AI are strong: it has multiple independent trust indicators in place. This does not replace your own security review, but Guardrails AI carries less obvious unverified-evidence risk than projects with thin signals.
Does Guardrails AI publish package provenance?
Yes. Guardrails AI's package releases carry build provenance attestations, which cryptographically link the published package back to its source repository and CI workflow.
Does Guardrails AI have an OpenSSF Scorecard?
Guardrails AI has an OpenSSF Scorecard score of 6.0/10. The Scorecard checks for branch protection, signed releases, dependency updates, fuzzing, code review, and other supply-chain hygiene items. See the full check breakdown on this page.
Is Guardrails AI actively maintained?
Actively maintained. The repository was pushed to within the last 4 day(s).
What license does Guardrails AI use?
Guardrails AI ships under Apache-2.0. A declared, OSI-approved license is one of the transparency signals HVTrust scores.
Are Guardrails AI's commits signed?
100% of the last 100 commits to Guardrails AI are verified-signed (GPG, SSH, S/MIME, or GitHub's signing flow). Signed commits help confirm that code was authored by who the commit claims.

Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.

Compare Guardrails AI head-to-head

AI agent surface

MCP, providers, tool surface
Scored in HVTrust

These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →

MCP Server Support
None detected
No MCP server signal detected.
Detailed evidence is not shown in the public view.
External Service Dependencies
high confidence
3 detected
Public provider/service dependencies detected.
Credential signal: API keys or service config markers documented.
Tool / Plugin Surface
None detected
No clear plugin system or broad tool surface detected.
Detailed evidence is not shown in the public view.
Package Provenance Drift
low confidence
Unknown
Package source metadata is missing or inconclusive
Detailed evidence is not shown in the public view.
  • MCP signal live
  • External deps live
  • Tool / plugin surface live
  • Package provenance drift live
How this surface has changed

Detected changes to Guardrails AI's runtime surface and supply-chain posture, from daily public-signal snapshots. A change here means our detectors see something different — a genuinely changed capability, or better evidence of an existing one.

2026-08-07
Provider Added
Runtime surface grew — new detected provider dependency: Multi-provider (LiteLLM)
2026-06-05
Provider Added
Runtime surface grew — new detected provider dependencies: Anthropic, OpenAI
2026-06-05
Provenance Added
Package provenance attestation detected

Maintain Guardrails AI?

For maintainers

HVTrust scores Guardrails AI from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.

Reputation Timeline

Signal history
HVTrust 3Score 2Surface 2Listed 1Grade 1Scorecard 1Rank 1Provenance 1
2026-08-07
Provider Added
Runtime surface grew — new detected provider dependency: Multi-provider (LiteLLM)
2026-07-19
Activity Score Changed
Activity score down 5pts (77 → 72)
2026-06-18
HVTrust Changed
HVTrust up 3.2pts (80.2 → 83.4)
2026-06-05
Provider Added
Runtime surface grew — new detected provider dependencies: Anthropic, OpenAI
2026-06-05
Provenance Added
Package provenance attestation detected
2026-06-01
Rank Moved
Rank dropped 10 spots (#81 → #91)
2026-05-29
HVTrust Changed
HVTrust up 9.9pts (51.6 → 61.5)
2026-05-28
Activity Score Changed
Activity score up 11pts (61 → 72)
2026-05-27
Scorecard Added
OSSF Scorecard: 5.6/10
2026-05-27
Grade Changed
Trust grade C → B
2026-05-27
HVTrust Changed
HVTrust up 24.4pts (32.4 → 56.8)
2026-05-25
Newly Listed
First tracked at rank #110

Embed Badge Badge guide for maintainers →

For maintainers
HVTrust 81.8 Grade A
Markdown:
[![HVTrust](https://hvtracker.net/badge/guardrails-ai.svg)](https://hvtracker.net/agents/guardrails-ai)
HTML:
<a href="https://hvtracker.net/agents/guardrails-ai"><img src="https://hvtracker.net/badge/guardrails-ai.svg" alt="HVTrust"></a>

Other agents in Security & Guardrails

Data sources
GitHub REST API (repo, commits, stars, forks, license) · PyPI / pypistats (downloads, provenance) · OpenSSF Scorecard CLI · Algolia HN Search API
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.2 · Raw JSON