Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 22:55 UTC

Project-MONAI / MONAI

AI Toolkit for Healthcare Imaging

PythonApache-2.0★ 8,591 stars⑂ 1,599 forkssince Oct 2019View on GitHub ↗
KindLibraryhow this is determined
#healthcare-imaging#deep-learning#medical-image-computing#medical-image-processing#pytorch#python3#monai

Project-MONAI/MONAI holds a health index of 100 out of 100, placing it in the Exceptional band. It scores highest on Security (100/100) and lowest on AI Readiness (75/100). It was last updated 2 days ago. 3 contributors account for most of its recent work.

100
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.

100
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 92 is calibrated to 100 on the published index scale (record calibration 2026-08-02).

Ownership

Project MONAIOrganization
2,804 followers37 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
PyPImonai1.6.0-4451 days ago

Metrics by category

Vitality

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

89Excellent · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 2 days ago
28.4/36Commit cadence — 41/52 weeks with commits
18/18Commit volume — 210 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year210
human_commit_share0.96
days_since_last_push2
active_weeks_last_year41

Release discipline

84Excellent
How it's scored
27/27Ships releases — 24 releases published
36/36Release recency — latest release 62 days ago
12.6/27Release cadence — a release every ~141 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count24
latest_release_tag1.6.0
releases_from_tagsno
days_since_latest_release62
mean_days_between_releases141

Community & Adoption

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

94Exceptional · 17% of overall

Popularity & adoption

96Exceptional
How it's scored
60/60Stars — 8,591 stars
25/25Forks — 1,599 forks
11.1/15Watchers — 100 watchers
Inputs used
forks1,599
stars8,591
watchers100
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_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com, readthedocs.org, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

86Excellent · 23% of overall

Maintainer resilience (bus factor)

73Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
16.5/22.5Commit distribution — top contributor authored 27% of commits
13.5/13.5Contributor breadth — 97 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled97
top_contributor_share0.268

Issue & PR responsiveness

80Excellent
How it's scored
37.5/42Issue resolution — 89% of issues closed
26.6/30PR acceptance — 3,395/3,834 decided PRs merged
3.7/13Newcomer PR acceptance — 2/7 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs3,395
open_issues351
closed_issues2,966
prs_merged_7d1
prs_decided_7d4
prs_merged_30d8
prs_decided_30d21
issue_closed_ratio0.894
closed_unmerged_prs439
first_time_authors_30d7
first_time_prs_merged_30d2
first_time_prs_decided_30d7

Ownership & stewardship

98Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
24.8/25Owner reach — 2,804 followers of Project-MONAI
23.5/25Track record — 37 public repos, account ~6 yr old
Inputs used
followers2,804
owner_typeOrganization
is_verified—
owner_loginProject-MONAI
public_repos37
account_age_days2,497
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 51 days ago
20/20Version history — 44 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmonai
ecosystemspypi
any_deprecatedno
min_days_since_publish51

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 15 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-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_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://project-monai.github.io/
10/10Repository description
10/10Topics — 7 topics
10/10Wiki
Inputs used
topicshealthcare-imaging, deep-learning, medical-image-computing, medical-image-processing, pytorch, python3, monai
has_wikiyes
homepagehttps://project-monai.github.io/
docs_sitehttps://project-monai.github.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

100Exceptional · 16% of overall

Security posture

100Exceptional
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocs/requirements.txt, pyproject.toml, requirements-dev.txt, requirements-min.txt, requirements.txt, setup.cfg, setup.py
has_codeql_workflowyes
has_security_policyyes
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
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:monai@1.6.0 runtime dependency closure — what installing the published package pulls in — 2 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.

75Good · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 96 of 96 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

100Exceptional
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config
11/11Static type checking — monai/py.typed
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 10 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 3 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_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsmonai/py.typed
agent_commit_share0.1
toolchain_manifests—
dependency_bot_commit_share0.03

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (monai/py.typed)
54.2/55Manageable file sizes — 19/1,349 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes193,412
source_files_sampled1,349
oversized_source_files19

Key facts

8,591GitHub stars
97contributors
210commits, last 12 months
2days since last push
24releases
3bus factor
351open 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)
  • Could not fetch pypi package 'monai._C' from its registry
  • OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 1,599 ⇿
0Stars
1,599Forks
11Releases

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.

4008001,2001,6001,599182022-092024-092026-08
Major 0Minor 6Patch 5

Each point covers 4 days.

Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPItorch>=2.8.0setup.cfg
PyPInumpy>=1.24,<3.0setup.cfg
All dependencies 82

Full resolved dependency set from the GitHub dependency graph: 2 direct and 80 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy—direct
PyPItorch—direct
PyPIbackports-tarfile—indirect
PyPIblack—indirect
PyPIclearml—indirect
PyPIcommonmark0.9.1indirect
PyPIcoverage—indirect
PyPIcucim-cu12—indirect
PyPIcucim-cu13—indirect
PyPIeinops—indirect
PyPIfilelock—indirect
PyPIfire—indirect
PyPIgdown—indirect
PyPIh5py—indirect
PyPIhuggingface-hub—indirect
PyPIimagecodecs—indirect
PyPIisort—indirect
PyPIitk—indirect
PyPIjsonschema—indirect
PyPIlmdb—indirect
PyPIlpips0.1.4indirect
PyPImatplotlib—indirect
PyPImccabe—indirect
PyPImetricsreloaded—indirect
PyPImlflow—indirect
PyPImore-itertools—indirect
PyPImypy—indirect
PyPInibabel—indirect
PyPIninja—indirect
PyPInni2.10.1indirect
PyPInvidia-ml-py—indirect
PyPIonnx—indirect
PyPIonnx-graphsurgeon—indirect
PyPIonnxruntime—indirect
PyPIonnxscript—indirect
PyPIopencv-python-headless—indirect
PyPIopenslide-bin—indirect
PyPIopenslide-python—indirect
PyPIoptuna—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIparameterized—indirect
PyPIpep8-naming—indirect
PyPIpillow—indirect
PyPIpolygraphy—indirect
PyPIpre-commit—indirect
PyPIpsutil—indirect
PyPIpyamg—indirect
PyPIpybind11—indirect
PyPIpycodestyle—indirect
PyPIpydata-sphinx-theme—indirect
PyPIpydicom—indirect
PyPIpyflakes—indirect
PyPIpynrrd—indirect
PyPIpytest—indirect
PyPIpytorch-ignite—indirect
PyPIpyyaml—indirect
PyPIrecommonmark0.6.0indirect
PyPIrequests—indirect
PyPIruff—indirect
PyPIscikit-image—indirect
PyPIscipy—indirect
PyPIsetuptools—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints1.11.1indirect
PyPIsphinxcontrib-applehelp—indirect
PyPIsphinxcontrib-devhelp—indirect
PyPIsphinxcontrib-htmlhelp—indirect
PyPIsphinxcontrib-jsmath—indirect
PyPIsphinxcontrib-qthelp—indirect
PyPIsphinxcontrib-serializinghtml—indirect
PyPItensorboard—indirect
PyPItensorboardx—indirect
PyPItifffile—indirect
PyPItorchio—indirect
PyPItorchvision—indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPItypeguard—indirect
PyPItypes-pyyaml—indirect
PyPItypes-setuptools—indirect
PyPIzarr—indirect
Dependency advisories 0

Installing pypi:monai@1.6.0 pulls in 2 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.

Related records

Inspected repositories sharing catalogue tags or ecosystems with Project-MONAI/MONAI. Read them side by side →

100ray-project/rayRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.Exceptional100wandb/wandbThe AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.Exceptional100microsoft/onnxruntimeONNX Runtime: cross-platform, high performance ML inferencing and training acceleratorExceptional100onnx/onnxOpen standard for machine learning interoperabilityExceptional99huggingface/transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. Exceptional99openvinotoolkit/nncfNeural Network Compression Framework for enhanced OpenVINO™ inferenceExceptional

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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.