Public record
Software health reportschema 0.25.0 · metrics 2.10.0 · 2026-07-21 22:33 UTC

whylabs / whylogs

An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

Jupyter Notebook · Python · HTMLApache-2.0★ 2,828 stars⑂ 143 forkssince Aug 2020View on GitHub ↗

whylabs/whylogs holds a health index of 34 out of 100, placing it in the At Risk band. It scores highest on Community & Adoption (87/100) and lowest on Vitality (28/100). It was last updated 557 days ago. 2 contributors account for most of its recent work.

34
overall / 100
At Risk

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.

34
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

WhyLabsOrganization
189 followers40 public repossince Oct 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIwhylogspoints to another repo — not scored1.6.4157,722339594 days ago

Metrics by category

Vitality

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

28At Risk · 21% of overall
How it's scored
0/36Push recencylast push 557 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year0
human_commit_share0.99
days_since_last_push557
active_weeks_last_year0
How it's scored
27/27Ships releases100 releases published
7.2/36Release recencylatest release 594 days ago
27/27Release cadencea release every ~9.4 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count100
latest_release_tagv1.6.4
releases_from_tagsno
days_since_latest_release594
mean_days_between_releases9.4
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

87Excellent · 17% of overall
How it's scored
56/60Stars2,828 stars
17.9/25Forks143 forks
8.3/15Watchers32 watchers
Inputs used
forks143
stars2,828
watchers32
growth_stateorganic
growth_factor_pct100

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_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

82Excellent · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
15.7/22.5Commit distributiontop contributor authored 30% of commits
13.5/13.5Contributor breadth22 contributors
10/10OpenSSF Scorecard: Contributorsproject has 6 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled22
top_contributor_share0.304
How it's scored
41.9/42Issue resolution100% of issues closed
26.6/30PR acceptance1,034/1,167 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs1,034
open_issues1
closed_issues434
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.998
closed_unmerged_prs133
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
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
16.4/25Owner reach189 followers of whylabs
23.7/25Track record40 public repos, account ~6 yr old
Inputs used
followers189
owner_typeOrganization
is_verified
owner_loginwhylabs
public_repos40
account_age_days2,470
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows8 workflow(s)
24/24Tests present
16/16Linter config.flake8
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests0 out of 29 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://whylogs.readthedocs.io/
10/10Repository description
10/10Topics18 topics
10/10Wiki
Inputs used
topicsai-pipelines, approximate-statistics, statistical-properties, data-quality, calculate-statistics, python, logging, mlops, dataops, ml-pipelines, data-pipeline, dataset, machine-learning, data-science, analytics, constraints, data-constraints, model-performance
has_wikiyes
homepagehttps://whylogs.readthedocs.io/
docs_sitehttps://whylogs.readthedocs.io/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

54Moderate · 16% of overall
How it's scored
6.8/7.5Binary-Artifactsbinaries present in source code
6/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
0/2.5CI-Tests0 out of 29 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 6 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
3.5/5SASTSAST tool detected but not run on all commits
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities143 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.2
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

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_packages13
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:whylogs@1.6.4 runtime dependency closure — what installing the published package pulls in — 13 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.

60Moderate · 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 history94 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.949
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrappython/Makefile, python/docs/Makefile, python/examples/integrations/bentoml/Makefile
22/22Automated tests
11/11Lint / format config.flake8
0/11Static type checking
10/10Reproducible environmentDockerfile, lockfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
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
lockfilespoetry.lock
has_dockerfileyes
typed_languageno
bootstrap_filespython/Makefile, python/docs/Makefile, python/examples/integrations/bentoml/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifestsjava/build.gradle.kts, java/core-bundle/build.gradle.kts, java/core/build.gradle.kts, java/smoketest/build.gradle, java/spark-bundle/build.gradle.kts, java/spark/build.gradle.kts
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codeJupyter Notebook without a type-check config
54.7/55Manageable file sizes2/395 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes270,687
source_files_sampled395
oversized_source_files2
How it's scored
40/40API schema (OpenAPI/GraphQL/proto)proto/src/whylogs_messages.proto, proto/v0/v0_constraints.proto, proto/v0/v0_messages.proto, proto/v0/v0_summaries.proto, python/examples/integrations/flask_streaming/swagger.yaml
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_filesproto/src/whylogs_messages.proto, proto/v0/v0_constraints.proto, proto/v0/v0_messages.proto, proto/v0/v0_summaries.proto, python/examples/integrations/flask_streaming/swagger.yaml
interfaces_expected_of
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

2,828GitHub stars
22contributors
0commits, last 12 months
557days since last push
100releases
2bus factor
1open issues
Maven, PyPIpackage ecosystems

Data collection warnings

  • pypi package 'whylogs' points at a different repository (https://docs.whylabs.ai); excluded from ecosystem scoring

More detail

Star and fork history 2,828 ★ / 143 ⇿
2,828Stars
143Forks
100Releases

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.

05001,0001,5002,0002,5003,0002,828143552020-092023-082026-07
Major 0Minor 4Patch 96

Each point covers 6 days.

OpenSSF Scorecard 4.2 / 10
4.2aggregate

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 · 2026-07-21 22:32 UTC

9Binary-Artifactsbinaries present in source code
8Branch-Protectionbranch protection is not maximal on development and all release branches
0CI-Tests0 out of 29 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
7SASTSAST tool detected but not run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities143 existing vulnerabilities detected
Direct dependencies 35
RegistryPackageVersion constraintManifest
PyPIwhylogs-sketching>=3.4.1.dev3python/pyproject.toml
PyPIprotobuf>=3.19.4python/pyproject.toml
PyPIimportlib-metadata<4.3python/pyproject.toml
PyPItyping-extensions>=3.10python/pyproject.toml
PyPIwhylabs-client^0.6.15python/pyproject.toml
PyPIrequests^2.27python/pyproject.toml
PyPIbackoff^2.2.1python/pyproject.toml
PyPIplatformdirs^3.5.0python/pyproject.toml
PyPIpybars3^0.9python/pyproject.toml
PyPIipython*python/pyproject.toml
PyPIscipypython/pyproject.toml
PyPInumpypython/pyproject.toml
PyPIpandas*python/pyproject.toml
PyPIsphinx*python/pyproject.toml
PyPIsphinx-autoapi*python/pyproject.toml
PyPIsphinx-copybutton^0.5.0python/pyproject.toml
PyPImyst-parser^0.17.2python/pyproject.toml
PyPIfuro^2022.3.4python/pyproject.toml
PyPIsphinx-autobuild^2021.3.14python/pyproject.toml
PyPIsphinxext-opengraph^0.6.3python/pyproject.toml
PyPIsphinx-inline-tabs*python/pyproject.toml
PyPIipython_genutils^0.2.0python/pyproject.toml
PyPInbsphinx^0.8.9python/pyproject.toml
PyPInbconvert^7.0.0python/pyproject.toml
PyPIboto3^1.22.13python/pyproject.toml
PyPImlflow-skinnypython/pyproject.toml
PyPIdatabricks-cli^0.8.0python/pyproject.toml
PyPIgoogle-cloud-storage^2.5.0python/pyproject.toml
PyPIpyarrow>=8.0.0, <13python/pyproject.toml
PyPIpyspark^3.0.0python/pyproject.toml
PyPIfaster-fifo^1.4.5python/pyproject.toml
PyPIorjson^3.8.10python/pyproject.toml
PyPIPillowpython/pyproject.toml
PyPIscikit-learnpython/pyproject.toml
PyPIfugue^0.8.1python/pyproject.toml
All dependencies 245

Full resolved dependency set from the GitHub dependency graph: 46 direct and 199 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIdatabricks-cli0.8.7direct
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PyPIfuro2022.9.29direct
PyPIgoogle-cloud-storage2.16.0direct
PyPIimportlib-metadata4.2.0direct
PyPIipython7.34.0direct
PyPIipython-genutils0.2.0direct
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PyPInumpydirect
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PyPIpandas1.3.5direct
PyPIpillow10.3.0direct
PyPIpillow9.5.0direct
PyPIplatformdirs3.11.0direct
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PyPIpyarrow12.0.1direct
PyPIpybars30.9.7direct
PyPIpyspark3.4.3direct
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PyPIscikit-learn1.0.2direct
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PyPIbentomlindirect
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PyPIfs2.4.16indirect
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PyPIsphinxcontrib-devhelp1.0.2indirect
PyPIsphinxcontrib-htmlhelp2.0.0indirect
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PyPIthreadpoolctl3.5.0indirect
PyPItinycss21.2.1indirect
PyPItoml0.10.2indirect
PyPItomli2.0.1indirect
PyPItornado6.2indirect
PyPItqdm4.66.4indirect
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PyPItriad0.9.1indirect
PyPItwine4.0.2indirect
PyPItyped-ast1.5.5indirect
PyPItypes-protobuf4.24.0.4indirect
PyPItypes-python-dateutil2.8.19.14indirect
PyPItypes-pyyaml6.0.12.12indirect
PyPItypes-requests2.30.0.0indirect
PyPItypes-urllib31.26.25.14indirect
PyPIunidecode1.3.8indirect
PyPIurllib31.26.18indirect
PyPIutils1.0.1indirect
PyPIuvicornindirect
PyPIvirtualenv20.4.7indirect
PyPIwcwidth0.2.13indirect
PyPIwebencodings0.5.1indirect
PyPIwerkzeug2.2.3indirect
PyPIwhylogsindirect
PyPIwrapt1.16.0indirect
PyPIxmltodict0.13.0indirect
PyPIzipp3.15.0indirect
Dependency advisories 0

Installing pypi:whylogs@1.6.4 pulls in 13 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.

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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 v0.25.0 — full methodology · metrics wiki.

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