MLflow
Website
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Is MLflow safe? Strong public trust posture, backed by multiple independent signals.
Compare MLflow
How does it stack up against its Observability & Evaluation neighbours?
Pick any agent to compare →In detail: MLflow scores 89.5/100 (Grade A), ranked #29 of 1378 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; no published advisory affects its latest release (mlflow); last pushed 2026-10-17. Every point is earned from checkable signals — never paid placement. How scoring works →
How MLflow could raise its score
Each line changes one public signal and recomputes with the live scoring function. The gains don't add up exactly, because the score is capped near the top.
- Raise the OSSF Scorecard from 5.5 to 9.0lowest checks: CII-Best-Practices 0, Dangerous-Workflow 0, Fuzzing 0 +6.6 → 96.1 A · #1 of 27
Chain of custody
Scanners check what the code says. This traces who ships MLflow and whether that has changed: from the source repository, through how changes are reviewed and released, to the package you install. These are the checks for OWASP ASI04 Agentic Supply Chain Vulnerabilities.
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Source Signed
github.com/mlflow/mlflow, Apache-2.0, last pushed 2026-10-17. 100% of the last 100 commits carry a verified signature, so changes trace to a verified account.
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Review and release Mixed
OpenSSF Scorecard rates the repository's practices 5.5/10 (scanned Oct 17, 2026). The checks that decide who can get a change released:
- Code Review9
- Branch Protection3
- Signed Releases0
- Dangerous Workflow0
- Token Permissions10
- Pinned Dependenciesn/a
All 14 Scorecard checks
Binary-Artifacts 10Branch-Protection 3CII-Best-Practices 0Code-Review 9Dangerous-Workflow 0Fuzzing 0License 10Maintained 10Packaging 10Pinned-Dependencies -1SAST 0Security-Policy 10Signed-Releases 0Token-Permissions 10 -
Published packages Partly attested
- PyPI mlflow Source link isn't a GitHub repo Build provenance attested
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What you install today No known advisories
Latest release checked: mlflow 3.17.1. No published advisory affects it (OSV, checked 2026-10-17).
Changes to this chain
No change to package provenance, package source links, Scorecard coverage or license in HVTracker's daily snapshots of MLflow.
Evidence behind this score
Coverage A: 4 of 5 independent evidence types found.
- GitHub repository data
- Package downloads
- Supply-chain checks
- Public actions (missing)
- Community mentions
Verify this score yourself
Every build re-issues MLflow’s score as an Ed25519-signed credential, valid for 7 days. You can check it offline against HVTracker’s published key; if anyone changes a number after signing, verification fails.
What this score doesn’t check
It covers who ships the code, not what the code does. It doesn’t read tool descriptions or prompts for injected instructions, watch runtime behaviour, or find bugs nobody has disclosed yet. For that, run a content scanner before you connect it, such as Cisco MCP Scanner or Snyk Agent Scan.
How MLflow compares in Observability & Evaluation
- #1 Promptfoo 91.9 +2.4
- #2 MLflow 89.5 this agent
- #3 LangWatch 86.9 −2.6
- #4 OpenLLMetry 86.3 −3.2
- #5 Weights & Biases Weave 84.2 −5.3
Bars show each HVTrust score; the tick marks MLflow’s 89.5.
Where the 89.5 comes from
HVTrust dimensions vs the Observability & Evaluation average
89.5 / 100 · 100.0% confidenceMLflow Observability & Evaluation average (27 agents)
Quick Trust Read
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 →
Rank Trend
Activity & Reach
Analysis
Activity Inputs
94.4 / 100Common questions about MLflow
Does MLflow publish package provenance?
Does MLflow have an OpenSSF Scorecard?
Is MLflow actively maintained?
What license does MLflow use?
Are MLflow's commits signed?
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
AI agent surface
MCP, providers, tool surface
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 →
- code
- 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.
Embed Badge Badge guide for maintainers →
For maintainers
[](https://hvtracker.net/agents/mlflow)
<a href="https://hvtracker.net/agents/mlflow"><img src="https://hvtracker.net/badge/mlflow.svg" alt="HVTrust"></a>
Other agents in Observability & Evaluation
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 v4.4 · Raw JSON