Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-18 17:33 UTC

autogluon / tabarena

A Living Benchmark for Machine Learning on Tabular Data

PythonApache-2.0★ 311 stars⑂ 74 forkssince May 2023View on GitHub ↗

autogluon/tabarena holds a health index of 87 out of 100, placing it in the Excellent band. It scores highest on Vitality (89/100) and lowest on Security (58/100). It was last updated today. A single contributor accounts for most of its recent work.

87
overall / 100
Excellent

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.

87
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 73 is calibrated to 87 on the published index scale (record calibration 2026-08-02).

Ownership

autogluonOrganization
255 followers16 public repossince Oct 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPItabarena0.1.03,0753515 days agotabularmachine-learningbenchmarkautomlautogluonensembledeep-learning
PyPIbencheval0.1.03,5503415 days agobenchmarkleaderboardelotabularmachine-learningtabarena

Metrics by category

Vitality

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

89Excellent · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
33.9/36Commit cadence49/52 weeks with commits
18/18Commit volume724 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 22 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year724
human_commit_share1
days_since_last_push0
active_weeks_last_year49
How it's scored
27/27Ships releases1 releases published
36/36Release recencylatest release 15 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count1
latest_release_tagv0.1.0
releases_from_tagsno
days_since_latest_release15
mean_days_between_releases

Community & Adoption

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

73Good · 17% of overall
How it's scored
40.4/60Stars311 stars
15.5/25Forks74 forks
5.8/15Watchers12 watchers
Inputs used
forks74
stars311
watchers12
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateyes
How it's scored
51/80Monthly downloads6,625 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagestabarena, bencheval
dependents
ecosystemspypi
total_downloads
monthly_downloads6,625
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

67Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
6.5/22.5Commit distributiontop contributor authored 71% of commits
13.5/13.5Contributor breadth25 contributors
10/10OpenSSF Scorecard: Contributorsproject has 4 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled25
top_contributor_share0.713
How it's scored
35.3/42Issue resolution84% of issues closed
27.4/30PR acceptance435/477 decided PRs merged
10.8/13Newcomer PR acceptance5/6 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 1/30 approved changesets -- score normalized to 0
Inputs used
merged_prs435
open_issues17
closed_issues90
prs_merged_7d53
prs_decided_7d57
prs_merged_30d55
prs_decided_30d59
issue_closed_ratio0.841
closed_unmerged_prs42
first_time_authors_30d6
first_time_prs_merged_30d5
first_time_prs_decided_30d6
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
17.3/25Owner reach255 followers of autogluon
18.8/25Track record16 public repos, account ~4 yr old
Inputs used
followers255
owner_typeOrganization
is_verifiedno
owner_loginautogluon
public_repos16
account_age_days1,802

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable2 package(s) on pypi
35/35Publish recencylatest publish 15 days ago
20/20Version history35 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagestabarena, bencheval
ecosystemspypi
any_deprecatedno
min_days_since_publish15

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter configruff.toml
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

55Moderate
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://tabarena.ai
10/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics
has_wikino
homepagehttps://tabarena.ai
docs_sitehttps://tabarena.ai
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

58Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
2.2/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 1/30 approved changesets -- score normalized to 0
2.5/2.5Contributorsproject has 4 contributing companies or organizations
0/10Dangerous-Workflowdangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 22 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
5/5SASTSAST tool is run on all commits
5/5Security-Policysecurity policy file detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
6/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.8
Excluded from scoring (no data or not applicable): Packaging. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages9
unassessed_packages22
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 9 resolved dependencies against OSV. 22 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

66Good · 4% of overall
How it's scored
45/45Agent instructionsAGENTS.md, CLAUDE.md, packages/tabflow_slurm/AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history92 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.92
agent_instruction_filesAGENTS.md, CLAUDE.md, packages/tabflow_slurm/AGENTS.md
agent_instruction_max_bytes54,027
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configruff.toml
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice95 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.95
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54.2/55Manageable file sizes10/681 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes172,354
source_files_sampled681
oversized_source_files10
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

311GitHub stars
25contributors
724commits, last 12 months
0days since last push
1releases
1bus factor
17open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'tabflow_slurm' from its registry

More detail

Star and fork history 0 ★ / 74 ⇿
0Stars
74Forks
1Releases

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.

01325385063757132023-112025-042026-09
Major 0Minor 1Patch 0

Each point covers 3 days.

OpenSSF Scorecard 4.8 / 10
4.8aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-18 17:33 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 1/30 approved changesets -- score normalized to 0
10Contributorsproject has 4 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 22 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
8Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 20
RegistryPackageVersion constraintManifest
PyPIscipypackages/bencheval/pyproject.toml
PyPInumpypackages/bencheval/pyproject.toml
PyPIpandaspackages/bencheval/pyproject.toml
PyPItabulatepackages/bencheval/pyproject.toml
PyPItqdmpackages/bencheval/pyproject.toml
PyPItyping-extensions>=4.11,<5packages/bencheval/pyproject.toml
PyPIscikit-learnpackages/bencheval/pyproject.toml
PyPIautogluon.tabular>=1.6.3b20260917,!=1.6.3,<1.7packages/tabarena/pyproject.toml
PyPIautogluon.core>=1.6,<1.7packages/tabarena/pyproject.toml
PyPIbenchevalpackages/tabarena/pyproject.toml
PyPIopenml>=0.14.1packages/tabarena/pyproject.toml
PyPIpyyamlpackages/tabarena/pyproject.toml
PyPItqdmpackages/tabarena/pyproject.toml
PyPItyping-extensions>=4.11,<5packages/tabarena/pyproject.toml
PyPIhuggingface-hubpackages/tabarena/pyproject.toml
PyPInumpypackages/tabarena/pyproject.toml
PyPIpandaspackages/tabarena/pyproject.toml
PyPItabulatepackages/tabarena/pyproject.toml
PyPIlogurupackages/tabarena/pyproject.toml
PyPItabarenapackages/tabflow_slurm/pyproject.toml
All dependencies 31

Full resolved dependency set from the GitHub dependency graph: 4 direct and 27 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIautogluon-coredirect
PyPIautogluon-tabulardirect
PyPIopenmldirect
PyPItyping-extensionsdirect
PyPIaplrindirect
PyPIautorank1.2.1indirect
PyPIcausilo1.0.0indirect
PyPIchimeraboostindirect
PyPIconfigspaceindirect
PyPIctboost0.1.61indirect
PyPIdata-foundryindirect
PyPIiltmindirect
PyPImatplotlib3.10.1indirect
PyPIplotlyindirect
PyPIpytabkitindirect
PyPIpytestindirect
PyPIrtdl-num-embeddingsindirect
PyPIruff0.14.0indirect
PyPIseaborn0.13.2indirect
PyPIsentence-transformersindirect
PyPIsetuptoolsindirect
PyPIskypilot0.13.0indirect
PyPIsynthefy-noriindirect
PyPItabdptindirect
PyPItabiclindirect
PyPItabmindirect
PyPItabpfnindirect
PyPItabpfn-extensionsindirect
PyPItabpfnwideindirect
PyPItabstar1.1.15indirect
PyPItabtune0.1.18indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 9 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 22 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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 v0.34.0 — full methodology · metrics wiki.

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