Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 18:50 UTC

zenml-io / zenml

ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

PythonApache-2.0★ 5,582 stars⑂ 657 forkssince Nov 2020View on GitHub ↗
KindCommand-line toolLibraryNetwork servicehow this is determined
#mlops#machine-learning#data-science#production-ready#devops-tools#zenml#pipelines#metadata-tracking#deep-learning#pytorch#tensorflow#ml#ai#automl#workflow#llm#llmops#agentops#agents#genai

zenml-io/zenml holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on Security (75/100). It was last updated today. 3 contributors account for most of its recent work.

98
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.

98
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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

ZenMLOrganization
355 followers87 public repossince Aug 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIzenml0.96.4142,98719613 days agomachine-learningproductionpipelinemlopsdevopsaiagentsagentic-workflows

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 — 908 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year908
human_commit_share0.83
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 13 days ago
27/27Release cadence — a release every ~12.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tag0.96.4
releases_from_tagsno
days_since_latest_release13
mean_days_between_releases12.8

Community & Adoption

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

90Excellent · 17% of overall

Popularity & adoption

92Excellent
How it's scored
60/60Stars — 5,582 stars
23.5/25Forks — 657 forks
9/15Watchers — 43 watchers
Inputs used
forks657
stars5,582
watchers43
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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

Ecosystem adoption (downloads)

86Excellent
How it's scored
68.7/80Monthly downloads — 142,987 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageszenml
dependents—
ecosystemspypi
total_downloads—
monthly_downloads142,987
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?

78Good · 23% of overall

Maintainer resilience (bus factor)

75Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
18/22.5Commit distribution — top contributor authored 20% of commits
13.5/13.5Contributor breadth — 97 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled97
top_contributor_share0.2

Issue & PR responsiveness

71Good
How it's scored
34.3/42Issue resolution — 82% of issues closed
26.3/30PR acceptance — 3,956/4,506 decided PRs merged
0/13Newcomer PR acceptance — 0/4 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs3,956
open_issues129
closed_issues572
prs_merged_7d8
prs_decided_7d12
prs_merged_30d27
prs_decided_30d50
issue_closed_ratio0.816
closed_unmerged_prs550
first_time_authors_30d4
first_time_prs_merged_30d0
first_time_prs_decided_30d4

Ownership & stewardship

72Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
18.3/25Owner reach — 355 followers of zenml-io
23.2/25Track record — 87 public repos, account ~5 yr old
Inputs used
followers355
owner_typeOrganization
is_verifiedno
owner_loginzenml-io
public_repos87
account_age_days1,865

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 13 days ago
20/20Version history — 196 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageszenml
ecosystemspypi
any_deprecatedno
min_days_since_publish13

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 46 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://zenml.io
10/10Repository description
10/10Topics — 20 topics
0/10Wiki
Inputs used
topicsmlops, machine-learning, data-science, production-ready, devops-tools, zenml, pipelines, metadata-tracking, deep-learning, pytorch, tensorflow, ml, ai, automl, workflow, llm, llmops, agentops, agents, genai
has_wikino
homepagehttps://zenml.io
docs_sitehttps://zenml.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

75Good · 16% of overall

Security posture

75Good
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowyes
has_security_policyyes
has_dependabot_configyes

Dependency advisories

74Good
How it's scored
14.8/35Direct dependencies free of known advisories — 1 affected: click 8.2.1 (high 7.2)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
33.9/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 140 days ago
Inputs used
sourceosv
advisories1
affected_packages1
assessed_packages36
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:zenml@0.96.4 runtime dependency closure — what installing the published package pulls in — 36 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.

88Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — .cursor/rules/comments.mdc, .cursor/rules/zenml-domain-models.mdc, .cursor/rules/zenml-orm-schema.mdc, .github/CLAUDE.md, AGENTS.md, CLAUDE.md, docs/book/AGENTS.md, examples/rlm_document_analysis/CLAUDE.md, examples/software_factory/AGENTS.md, examples/software_factory/CLAUDE.md, src/zenml/cli/AGENTS.md, src/zenml/integrations/AGENTS.md, src/zenml/models/AGENTS.md, src/zenml/orchestrators/AGENTS.md, src/zenml/webhooks/AGENTS.md, src/zenml/zen_server/AGENTS.md, src/zenml/zen_stores/migrations/AGENTS.md, src/zenml/zen_stores/schemas/AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://zenml.io/llms.txt)
40/40Legible commit history — 74 of 83 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://zenml.io/llms.txt
legible_history_share0.892
agent_instruction_files.cursor/rules/comments.mdc, .cursor/rules/zenml-domain-models.mdc, .cursor/rules/zenml-orm-schema.mdc, .github/CLAUDE.md, AGENTS.md, CLAUDE.md, docs/book/AGENTS.md, examples/rlm_document_analysis/CLAUDE.md, examples/software_factory/AGENTS.md, examples/software_factory/CLAUDE.md, src/zenml/cli/AGENTS.md, src/zenml/integrations/AGENTS.md, src/zenml/models/AGENTS.md, src/zenml/orchestrators/AGENTS.md, src/zenml/webhooks/AGENTS.md, src/zenml/zen_server/AGENTS.md, src/zenml/zen_stores/migrations/AGENTS.md, src/zenml/zen_stores/schemas/AGENTS.md
agent_instruction_max_bytes21,466

Verify loop (build / test / typecheck)

100Exceptional
How it's scored
18/18One-command bootstrap — examples/e2e/Makefile, examples/e2e_nlp/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/zenml/py.typed
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 21 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 10 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesexamples/e2e/Makefile, examples/e2e_nlp/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/zenml/py.typed
agent_commit_share0.21
toolchain_manifests—
dependency_bot_commit_share0.1

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/zenml/py.typed)
54.6/55Manageable file sizes — 19/2,481 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes621,289
source_files_sampled2,481
oversized_source_files19

Machine-readable interfaces

40Weak
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

5,582GitHub stars
97contributors
908commits, last 12 months
0days since last push
100releases
3bus factor
129open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 14 authors (cap 12)
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 657 ⇿
0Stars
657Forks
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.

0125250375500625750651112020-122023-112026-09
Major 0Minor 45Patch 55

Each point covers 6 days.

Direct dependencies 17
RegistryPackageVersion constraintManifest
PyPIasgiref~=3.10.0pyproject.toml
PyPIclick>=8.0.1,<=8.2.1pyproject.toml
PyPIcloudpickle>=2.0.0pyproject.toml
PyPIdistro>=1.6.0,<2.0.0pyproject.toml
PyPIdocker~=7.1.0pyproject.toml
PyPIgitpython>=3.1.18,<4.0.0pyproject.toml
PyPIjsonref—pyproject.toml
PyPIopentelemetry-instrumentation-logging==0.64b0pyproject.toml
PyPIopentelemetry-sdk==1.43.0pyproject.toml
PyPIpackaging>=24.1pyproject.toml
PyPIpsutil>=5.0.0pyproject.toml
PyPIpydantic>=2.0,<=2.12.5pyproject.toml
PyPIpython-dateutil>=2.8.1,<3.0.0pyproject.toml
PyPIpyyaml>=6.0.1pyproject.toml
PyPIrich>=12.0.0pyproject.toml
PyPIsetuptools>=70.0.0pyproject.toml
PyPIstructlog>=25.5.0pyproject.toml
All dependencies 183

Full resolved dependency set from the GitHub dependency graph: 16 direct and 167 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIasgiref—direct
PyPIclick—direct
PyPIcloudpickle—direct
PyPIdistro—direct
PyPIdocker—direct
PyPIgitpython—direct
PyPIopentelemetry-instrumentation-logging0.64b0direct
PyPIopentelemetry-sdk1.43.0direct
PyPIpackaging—direct
PyPIpsutil—direct
PyPIpydantic—direct
PyPIpython-dateutil—direct
PyPIpyyaml—direct
PyPIrich—direct
PyPIsetuptools—direct
PyPIstructlog—direct
PyPIaccelerate—indirect
PyPIadlfs—indirect
PyPIalembic—indirect
PyPIautogen-core—indirect
PyPIautogen-ext—indirect
PyPIaws-profile-manager—indirect
PyPIazure-ai-ml—indirect
PyPIazure-identity—indirect
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PyPIseaborn—indirect
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PyPIsemantic-kernel—indirect
PyPIsession-info1.0.0indirect
PyPIsqlalchemy—indirect
PyPIsqlmodel0.0.38indirect
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PyPIstrands-agents-tools—indirect
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PyPIuvicorn—indirect
PyPIyamlfix—indirect
PyPIzenml—indirect
PyPIzizmor—indirect
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Dependency advisories 1

Installing pypi:zenml@0.96.4 pulls in 36 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
click8.2.1directhigh18.3.3

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 — PyPI.