Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 05:41 UTC

onnx / onnxmltools

ONNXMLTools enables conversion of models to ONNX

PythonApache-2.0★ 1,167 stars⑂ 218 forkssince Feb 2018View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

onnx/onnxmltools holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Sustainability & Governance (81/100) and lowest on AI Readiness (50/100). It was last updated 11 days ago. 3 contributors account for most of its recent work.

78
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

78
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

2,733 followers30 public repossince Sep 2017

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIonnxmltools1.16.0-25194 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

57Moderate · 21% of overall
How it's scored
28.8/36Push recencylast push 11 days ago
6.2/36Commit cadence9/52 weeks with commits
13/18Commit volume27 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year27
human_commit_share0.91
days_since_last_push11
active_weeks_last_year9
How it's scored
27/27Ships releases27 releases published
16.2/36Release recencylatest release 194 days ago
12.6/27Release cadencea release every ~173.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count27
latest_release_tag1.16.0
releases_from_tagsno
days_since_latest_release194
mean_days_between_releases173.5

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

65Good · 17% of overall
How it's scored
49.7/60Stars1,167 stars
19.5/25Forks218 forks
8.8/15Watchers40 watchers
Inputs used
forks218
stars1,167
watchers40
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

81Excellent · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
16.5/22.5Commit distributiontop contributor authored 27% of commits
13.5/13.5Contributor breadth46 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor3
contributors_sampled46
top_contributor_share0.268
How it's scored
24.2/42Issue resolution58% of issues closed
22.4/30PR acceptance343/459 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs343
open_issues132
closed_issues180
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d3
issue_closed_ratio0.577
closed_unmerged_prs116
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
24.7/25Owner reach2,733 followers of onnx
22.9/25Track record30 public repos, account ~8 yr old
Inputs used
followers2,733
owner_typeOrganization
is_verified
owner_loginonnx
public_repos30
account_age_days3,263
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
26/35Publish recencylatest publish 194 days ago
20/20Version history25 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesonnxmltools
ecosystemspypi
any_deprecatedno
min_days_since_publish194

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://onnx.ai
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsmachine-learning, python-library, onnx, scikit-learn, keras
has_wikiyes
homepagehttps://onnx.ai
docs_sitehttps://onnx.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

56Moderate · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-dev.txt, requirements.txt
has_codeql_workflowyes
has_security_policyno
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages11
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:onnxmltools@1.16.0 runtime dependency closure — what installing the published package pulls in — 11 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

50Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history90 of 91 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.989
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
2/10Demonstrated agent practice1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance9 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0.01
toolchain_manifests
dependency_bot_commit_share0.09
How it's scored
0/45Type-checkable codePython without a type-check config
54.8/55Manageable file sizes1/280 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes113,027
source_files_sampled280
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

1,167GitHub stars
46contributors
27commits, last 12 months
11days since last push
27releases
3bus factor
132open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (2026/08/13 05:40:52 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 218 ⇿
0Stars
218Forks
27Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0408012016020024021442018-022022-052026-07
Major 0Minor 11Patch 10

Each point covers 8 days.

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPInumpypyproject.toml
PyPIonnx>=1.8.1pyproject.toml
PyPIprotobufpyproject.toml
PyPIskl2onnx>=1.4.9pyproject.toml
All dependencies 24

Full resolved dependency set from the GitHub dependency graph: 4 direct and 20 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpydirect
PyPIonnxdirect
PyPIprotobufdirect
PyPIskl2onnxdirect
PyPIblackindirect
PyPIcatboostindirect
PyPIcythonindirect
PyPIdillindirect
PyPIlibsvmindirect
PyPIlightgbmindirect
PyPImleapindirect
PyPIonnxruntimeindirect
PyPIopenpyxlindirect
PyPIpandasindirect
PyPIpysparkindirect
PyPIpytestindirect
PyPIpytest-covindirect
PyPIpytest-sparkindirect
PyPIruffindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIsetuptoolsindirect
PyPIwheelindirect
PyPIxgboostindirect
Dependency advisories 0

Installing pypi:onnxmltools@1.16.0 pulls in 11 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.