Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 17:01 UTC

jax-ml / jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

PythonApache-2.0★ 36,307 stars⑂ 3,779 forkssince Oct 2018View on GitHub ↗
#jax

jax-ml/jax holds a health index of 97 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on Security (64/100). It was last updated today. 5 contributors account for most of its recent work.

97
overall / 100
Exceptional

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.

97
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 85 is calibrated to 97 on the published index scale (record calibration 2026-08-02).

Ownership

jax-mlOrganization
1,041 followers15 public repossince Dec 2019

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

Metrics by category

Vitality

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

100Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 7,783 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year7,783
human_commit_share1
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 29 days ago
27/27Release cadence — a release every ~23.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagjax-v0.11.1
releases_from_tagsno
days_since_latest_release29
mean_days_between_releases23.2

Community & Adoption

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

89Excellent · 17% of overall

Popularity & adoption

99Exceptional
How it's scored
60/60Stars — 36,307 stars
25/25Forks — 3,779 forks
14/15Watchers — 334 watchers
Inputs used
forks3,779
stars36,307
watchers334
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

77Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

76Good · 23% of overall

Maintainer resilience (bus factor)

87Excellent
How it's scored
45.9/54Bus factor — 5 contributor(s) cover half of all commits
19/22.5Commit distribution — top contributor authored 16% of commits
13.5/13.5Contributor breadth — 99 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor5
contributors_sampled99
top_contributor_share0.157

Issue & PR responsiveness

67Good
How it's scored
31.9/42Issue resolution — 76% of issues closed
21.8/30PR acceptance — 21,114/29,089 decided PRs merged
3.2/13Newcomer PR acceptance — 1/4 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs21,114
open_issues1,750
closed_issues5,546
prs_merged_7d13
prs_decided_7d21
prs_merged_30d13
prs_decided_30d21
issue_closed_ratio0.76
closed_unmerged_prs7,975
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d4

Ownership & stewardship

72Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
21.7/25Owner reach — 1,041 followers of jax-ml
20.8/25Track record — 15 public repos, account ~6 yr old
Inputs used
followers1,041
owner_typeOrganization
is_verifiedno
owner_loginjax-ml
public_repos15
account_age_days2,478

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

100Exceptional
How it's scored
24/24CI workflows — 36 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://docs.jax.dev
10/10Repository description
10/10Topics — 1 topics
0/10Wiki
Inputs used
topicsjax
has_wikino
homepagehttps://docs.jax.dev
docs_sitehttps://docs.jax.dev
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall

Security posture

55Moderate
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsbuild/requirements_lock_3_12.txt, build/requirements_lock_3_13.txt, build/requirements_lock_3_14.txt, build/requirements_lock_3_14_ft.txt, build/requirements_lock_3_15.txt, build/requirements_lock_3_15_ft.txt, docs/requirements.txt, jaxlib/setup.py, pyproject.toml, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages76
unassessed_packages75
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 76 resolved dependencies against OSV. 75 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.

71Good · 4% of overall

Agent context & guidance

33At Risk
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
33.1/40Legible commit history — 62 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.62
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

86Excellent
How it's scored
18/18One-command bootstrap — build/rocm/docker/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — jax/py.typed
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesbuild/rocm/docker/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsjax/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

78Good
How it's scored
27/45Type-checkable code — Python with type-check config (jax/py.typed)
50.5/55Manageable file sizes — 102/1,252 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes2,365,934
source_files_sampled1,252
oversized_source_files102

Machine-readable interfaces

100Exceptional
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — jaxlib/gpu/triton.proto, jaxlib/mosaic/gpu/mosaic_gpu.proto, jaxlib/py_host_callback.proto, jaxlib/pytree.proto
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_filesjaxlib/gpu/triton.proto, jaxlib/mosaic/gpu/mosaic_gpu.proto, jaxlib/py_host_callback.proto, jaxlib/pytree.proto
interfaces_expected_of—
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

36,307GitHub stars
99contributors
7,783commits, last 12 months
0days since last push
100releases
5bus factor
1,750open 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 (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 3,779 ⇿
0Stars
3,779Forks
22Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

2,6002,8003,0003,2003,4003,6003,8003,779102025-022025-112026-09
Major 0Minor 0Patch 0

Each point covers 2 days.

All dependencies 151

Full resolved dependency set from the GitHub dependency graph: 0 direct and 151 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPI-—indirect
PyPIabsl-py—indirect
PyPIabsl-py2.5.0indirect
PyPIauditwheel—indirect
PyPIauditwheel6.8.2indirect
PyPIbuild—indirect
PyPIbuild1.6.1indirect
PyPIcloudpickle—indirect
PyPIcloudpickle3.1.2indirect
PyPIcmake—indirect
PyPIcolorama—indirect
PyPIcolorama0.4.6indirect
PyPIetils—indirect
PyPIetils1.14.0indirect
PyPIexecnet2.1.2indirect
PyPIfilelock—indirect
PyPIfilelock3.32.6indirect
PyPIflatbuffers—indirect
PyPIflatbuffers25.12.19indirect
PyPIflax—indirect
PyPIfsspec2026.7.0indirect
PyPIhypothesis—indirect
PyPIhypothesis6.168.0indirect
PyPIimportlib-metadata—indirect
PyPIiniconfig2.3.0indirect
PyPIipython—indirect
PyPIjax—indirect
PyPIjax0.11.1indirect
PyPIjax-cuda12-pjrt—indirect
PyPIjax-cuda12-pjrt0.11.1indirect
PyPIjax-cuda12-plugin—indirect
PyPIjax-cuda12-plugin0.11.1indirect
PyPIjax-cuda13-pjrt—indirect
PyPIjax-cuda13-pjrt0.11.1indirect
PyPIjax-cuda13-plugin—indirect
PyPIjax-cuda13-plugin0.11.1indirect
PyPIjaxlib—indirect
PyPIjaxlib0.11.1indirect
PyPIlibtpu—indirect
PyPIlibtpu0.0.47indirect
PyPImarkdown-it-py4.2.0indirect
PyPImatplotlib—indirect
PyPImdurl0.1.2indirect
PyPIml-dtypes—indirect
PyPIml-dtypes0.6.0indirect
PyPImpmath—indirect
PyPImpmath1.4.1indirect
PyPImyst-nb—indirect
PyPInumpy—indirect
PyPInumpy2.5.3indirect
PyPInumpy-typing-compat20260602.2.5indirect
PyPInvidia-cublas—indirect
PyPInvidia-cublas13.7.0.27indirect
PyPInvidia-cublas-cu12—indirect
PyPInvidia-cublas-cu1212.9.2.10indirect
PyPInvidia-cuda-cccl13.3.4.2.1indirect
PyPInvidia-cuda-cccl-cu1212.9.27indirect
PyPInvidia-cuda-crt—indirect
PyPInvidia-cuda-crt13.4.59indirect
PyPInvidia-cuda-cupti—indirect
PyPInvidia-cuda-cupti13.4.58indirect
PyPInvidia-cuda-cupti-cu12—indirect
PyPInvidia-cuda-cupti-cu1212.9.79indirect
PyPInvidia-cuda-nvcc—indirect
PyPInvidia-cuda-nvcc13.4.59indirect
PyPInvidia-cuda-nvcc-cu12—indirect
PyPInvidia-cuda-nvcc-cu1212.9.86indirect
PyPInvidia-cuda-nvrtc—indirect
PyPInvidia-cuda-nvrtc13.4.59indirect
PyPInvidia-cuda-nvrtc-cu12—indirect
PyPInvidia-cuda-nvrtc-cu1212.9.86indirect
PyPInvidia-cuda-runtime—indirect
PyPInvidia-cuda-runtime13.4.49indirect
PyPInvidia-cuda-runtime-cu12—indirect
PyPInvidia-cuda-runtime-cu1212.9.79indirect
PyPInvidia-cudnn-cu129.26.0.51indirect
PyPInvidia-cudnn-cu139.26.0.51indirect
PyPInvidia-cufft—indirect
PyPInvidia-cufft12.4.0.34indirect
PyPInvidia-cufft-cu12—indirect
PyPInvidia-cufft-cu1211.4.1.4indirect
PyPInvidia-cusolver—indirect
PyPInvidia-cusolver12.3.2.15indirect
PyPInvidia-cusolver-cu12—indirect
PyPInvidia-cusolver-cu1211.7.5.82indirect
PyPInvidia-cusparse—indirect
PyPInvidia-cusparse12.8.6.49indirect
PyPInvidia-cusparse-cu12—indirect
PyPInvidia-cusparse-cu1212.5.10.65indirect
PyPInvidia-nccl-cu12—indirect
PyPInvidia-nccl-cu122.31.2indirect
PyPInvidia-nccl-cu13—indirect
PyPInvidia-nccl-cu132.31.2indirect
PyPInvidia-nvjitlink—indirect
PyPInvidia-nvjitlink13.4.52indirect
PyPInvidia-nvjitlink-cu12—indirect
PyPInvidia-nvjitlink-cu1212.9.86indirect
PyPInvidia-nvshmem-cu12—indirect
PyPInvidia-nvshmem-cu123.7.2indirect
PyPInvidia-nvshmem-cu13—indirect
PyPInvidia-nvshmem-cu133.7.2indirect
PyPInvidia-nvvm—indirect
PyPInvidia-nvvm13.4.59indirect
PyPIopt-einsum—indirect
PyPIopt-einsum3.4.0indirect
PyPIoptype0.18.0indirect
PyPIpackaging26.3indirect
PyPIpillow—indirect
PyPIpillow12.3.0indirect
PyPIpluggy1.6.0indirect
PyPIpooch—indirect
PyPIportpicker—indirect
PyPIportpicker1.6.0indirect
PyPIprotobuf—indirect
PyPIpsutil7.2.2indirect
PyPIpydata-sphinx-theme0.16.1indirect
PyPIpyelftools0.33indirect
PyPIpygments2.21.0indirect
PyPIpyproject-hooks1.2.0indirect
PyPIpytest—indirect
PyPIpytest9.1.1indirect
PyPIpytest-xdist—indirect
PyPIpytest-xdist3.8.0indirect
PyPIrich—indirect
PyPIrich15.0.0indirect
PyPIscikit-learn—indirect
PyPIscipy—indirect
PyPIscipy1.18.1indirect
PyPIscipy-stubs—indirect
PyPIscipy-stubs1.18.1.0indirect
PyPIsetuptools—indirect
PyPIsetuptools84.0.0indirect
PyPIsnowballstemmer—indirect
PyPIsortedcontainers2.4.0indirect
PyPIsphinx—indirect
PyPIsphinx-book-theme1.1.1indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-design—indirect
PyPIsphinx-remove-toctrees—indirect
PyPIsphinxcontrib-mermaid—indirect
PyPIsphinxext-rediraffe—indirect
PyPItensorflow—indirect
PyPItensorflow-datasets—indirect
PyPItensorflow-hub—indirect
PyPItensorstore—indirect
PyPItensorstore0.1.85indirect
PyPItyping-extensions4.16.0indirect
PyPIwheel—indirect
PyPIwheel0.48.0indirect
PyPIxprof—indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 76 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 75 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

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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 statistics.