updated 2026-08-19 05:32 UTC

MLflow

mlflow/mlflow

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, eva

Observability & Evaluation Python Grade A Listed Apache-2.0
89.8/100
Rank #13 of 1308
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Strong public trust posture, backed by multiple independent signals.

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Listing state
Listed
Evidence coverage
Grade A · 4/5 signals
Last push
2026-08-19 · 0d ago
Recent change
No recent signal change

Is MLflow safe? MLflow scores 89.8/100 (Grade A), ranked #13 of 1308 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 5.5/10; 100% of recent commits are signed; last pushed 2026-08-19. Every point is earned from checkable signals — never paid placement. How scoring works →

Ranked neighbours in Observability & Evaluation

#2 Promptfoo — 88.7 (A) → #3 Weights & Biases Weave — 86.5 (A) → #4 OpenLLMetry — 84.9 (A) → #5 Langfuse — 78.1 (B) →

Quick Trust Read

What Would Improve It
Improve safety / integrity to lift the weakest part of the trust profile.
Maintainer Checklist
Raise Scorecard signals Current OSSF Scorecard is 5.5/10. Tighten the weakest checks to improve public safety evidence.
Compare with Promptfoo Get email alerts for MLflow View Observability & Evaluation Report missing data
94.2
Activity sub-score · out of 100
#1
In Observability & Evaluation

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 2026-08-19 05:32 UTC · Repo last pushed today

Rank Trend

2026-08-11 2026-08-19

Activity & Reach

Stars
27.6k
Forks
6.2k
Last Push
2026-08-19
today
Commits (4 wk)
358
Downloads (7d)
8,847,787
pypi
HN mentions (30d)
1
Open Issues
1450
Rank Change
=
was #13

Analysis

HVTrust Dimensions vs Observability & Evaluation

89.8 / 100 · 100.0% confidence

MLflow Observability & Evaluation average (23 agents)

Safety / Integrity50% OSSF Scorecard · 30% provenance · 20% signed commits
19.4 / 25
8.4 above avg 11.0
Identity / Provenance60% listing status · 40% build provenance
18.0 / 18
5.0 above avg 13.0
Transparency50% declared license · 50% OSSF Scorecard
13.2 / 17
1.3 above avg 11.9
Maintenance60% last-push freshness · 40% commit activity
20.0 / 20
4.9 above avg 15.1
AdoptionLog-scaled stars · package downloads
19.9 / 20
6.0 above avg 13.9

Activity Inputs

94.2 / 100
StarsRepository reach
26.6 / 30
FreshnessLast push recency
25.0 / 25
ActivityRecent commits
25 / 25
CommunityFork signal
17.6 / 20

Supply Chain Trust

Package Provenance
Verified
pypi attestation
OSSF Scorecard
5.5 / 10
OpenSSF Scorecard · scanned Jun 20, 2026 (refresh pending)
Signed Commits
100%
of last 100 commits verified
Code-Review 9
Maintained 10
CII-Best-Practices 0
Security-Policy 10
License 10
Dangerous-Workflow 0
Pinned-Dependencies -1
Signed-Releases 0
Token-Permissions 10
Branch-Protection 3
Packaging 10
Binary-Artifacts 10
SAST 0
Fuzzing 0

Is MLflow safe?

Public supply-chain signals for MLflow are strong: it has multiple independent trust indicators in place. This does not replace your own security review, but MLflow carries less obvious unverified-evidence risk than projects with thin signals.
Does MLflow publish package provenance?
Yes. MLflow's package releases carry build provenance attestations, which cryptographically link the published package back to its source repository and CI workflow.
Does MLflow have an OpenSSF Scorecard?
MLflow has an OpenSSF Scorecard score of 5.5/10. 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 MLflow actively maintained?
Actively maintained. The repository was pushed to within the last 1 day(s).
What license does MLflow use?
MLflow ships under Apache-2.0. A declared, OSI-approved license is one of the transparency signals HVTrust scores.
Are MLflow's commits signed?
100% of the last 100 commits to MLflow 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 MLflow head-to-head

MLflow vs Promptfoo → MLflow vs Weights & Biases Weave → MLflow vs OpenLLMetry →

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
high confidence
Implemented
MLflow appears to expose MCP server capabilities.
Detailed evidence is not shown in the public view.
External Service Dependencies
high confidence
3 detected
Public provider/service dependencies detected.
Credential signal: No explicit API-key/config marker detected.
Tool / Plugin Surface
high confidence
Declared
Declared plugin/integration surface detected.
  • code
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

Maintain MLflow?

For maintainers

HVTrust scores MLflow 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.

Submit a correction → Add the trust badge

Reputation Timeline

Signal history
Listed 1
2026-06-02
Newly Listed
First tracked at rank #9
GitHub → PyPI: mlflow

Embed Badge Badge guide for maintainers →

For maintainers
HVTrust 89.8 Grade A
Markdown:
[![HVTrust](https://hvtracker.net/badge/mlflow.svg)](https://hvtracker.net/agents/mlflow)
HTML:
<a href="https://hvtracker.net/agents/mlflow"><img src="https://hvtracker.net/badge/mlflow.svg" alt="HVTrust"></a>

Other agents in Observability & Evaluation

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 v4.3 · Raw JSON