Public record
Software health reportschema 0.27.0 · metrics 2.10.0 · 2026-07-30 22:09 UTC

google-deepmind / dm-haiku

JAX-based neural network library

PythonApache-2.0★ 3,267 stars⑂ 298 forkssince Feb 2020View on GitHub ↗

google-deepmind/dm-haiku holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (89/100) and lowest on Security (21/100). It was last updated 3 days ago. 2 contributors account for most of its recent work.

81
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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

Google DeepMindOrganization
25,814 followers399 public repossince Aug 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIdm-haikupoints to another repo — not scored0.0.17-203 days ago

Metrics by category

Vitality

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

79Good · 21% of overall
How it's scored
36/36Push recencylast push 3 days ago
17.3/36Commit cadence25/52 weeks with commits
15.3/18Commit volume50 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year50
human_commit_share1
days_since_last_push3
active_weeks_last_year25
How it's scored
27/27Ships releases19 releases published
36/36Release recencylatest release 3 days ago
12.6/27Release cadencea release every ~156.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count19
latest_release_tagv0.0.17
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases156.1

Community & Adoption

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

79Good · 17% of overall
How it's scored
57/60Stars3,267 stars
20.6/25Forks298 forks
8.4/15Watchers33 watchers
Inputs used
forks298
stars3,267
watchers33
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)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

69Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.2/22.5Commit distributiontop contributor authored 41% of commits
13.5/13.5Contributor breadth87 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor2
contributors_sampled87
top_contributor_share0.414
How it's scored
0/42Issue resolutionno issues or no data
15.7/30PR acceptance320/613 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs320
open_issues0
closed_issues0
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio
closed_unmerged_prs293
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
25/25Owner reach25,814 followers of google-deepmind
25/25Track record399 public repos, account ~11 yr old
Inputs used
followers25,814
owner_typeOrganization
is_verified
owner_logingoogle-deepmind
public_repos399
account_age_days4,352
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter config.pylintrc
0/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://dm-haiku.readthedocs.io
10/10Repository description
10/10Topics5 topics
0/10Wiki
Inputs used
topicsmachine-learning, neural-networks, jax, deep-learning, deep-neural-networks
has_wikino
homepagehttps://dm-haiku.readthedocs.io
docs_sitehttps://dm-haiku.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsdocs/requirements.txt, examples/requirements.txt, requirements-flax.txt, requirements-jax.txt, requirements-test.txt, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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_packages4
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:dm-haiku@0.0.17 runtime dependency closure — what installing the published package pulls in — 4 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.

61Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
21.3/40Legible commit history40 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.4
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format config.pylintrc
11/11Static type checkinghaiku/py.typed
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configshaiku/py.typed
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable codePython with type-check config (haiku/py.typed)
55/55Manageable file sizes0/156 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes48,717
source_files_sampled156
oversized_source_files0
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, notebooks
Inputs used
example_dirsexamples, notebooks
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

3,267GitHub stars
87contributors
50commits, last 12 months
3days since last push
19releases
2bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'dm-haiku' points at a different repository (https://github.com/deepmind/dm-haiku); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (2026/07/30 22:09:14 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 ★ / 298 ⇿
0Stars
298Forks
18Releases

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.

050100150200250300298172020-022023-052026-07
Major 0Minor 0Patch 16

Each point covers 6 days.

All dependencies 26

Full resolved dependency set from the GitHub dependency graph: 0 direct and 26 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIabsl-pyindirect
PyPIchexindirect
PyPIcloudpickleindirect
PyPIdillindirect
PyPIdm-envindirect
PyPIdm-treeindirect
PyPIflaxindirect
PyPIipykernelindirect
PyPIjaxindirect
PyPIjaxlibindirect
PyPIjmpindirect
PyPImockindirect
PyPInbsphinxindirect
PyPInumpyindirect
PyPIoptaxindirect
PyPIpackagingindirect
PyPIpytest-xdistindirect
PyPIsphinxindirect
PyPIsphinx-autodoc-typehintsindirect
PyPIsphinx-book-theme0.3.3indirect
PyPIsphinxcontrib-bibtexindirect
PyPIsphinxcontrib-katexindirect
PyPItabulateindirect
PyPItensorflowindirect
PyPItensorflow-datasetsindirect
PyPIvirtualenvindirect
Dependency advisories 0

Installing pypi:dm-haiku@0.0.17 pulls in 4 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 v0.27.0 — full methodology · metrics wiki.

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