Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-10 03:06 UTC

facebookresearch / neuroai

Python suite for neuroscience research across all modalities.

PythonMIT★ 322 stars⑂ 81 forkssince Apr 2026View on GitHub ↗
KindCommand-line toolLibraryPluginhow this is determined
#deep-learning#neuroscience

facebookresearch/neuroai holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (92/100) and lowest on Security (58/100). It was last updated today. 3 contributors account for most of its recent work.

93
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.

93
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 79 is calibrated to 93 on the published index scale (record calibration 2026-08-02).

Ownership

Meta ResearchOrganization · verified domain
37,941 followers1,455 public repossince Jan 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIneuralset0.4.05,414120 days ago
PyPIneuralbench0.4.01,72880 days ago
PyPIneuralfetch0.4.01,932110 days ago
PyPIneuraltrain0.4.04,775120 days ago

Metrics by category

Vitality

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

87Excellent · 21% of overall

Development activity

79Good
How it's scored
36/36Push recency — last push 0 days ago
15.2/36Commit cadence — 22/52 weeks with commits
18/18Commit volume — 190 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year190
human_commit_share1
days_since_last_push0
active_weeks_last_year22

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 9 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~21.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count9
latest_release_tagv0.4.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases21.9
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?

73Good · 17% of overall

Popularity & adoption

60Moderate
How it's scored
40.7/60Stars — 322 stars
15.9/25Forks — 81 forks
3.9/15Watchers — 6 watchers
Inputs used
forks81
stars322
watchers6
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 (MIT)
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_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

69Good
How it's scored
55.2/80Monthly downloads — 13,849 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesneuralset, neuralbench, neuralfetch, neuraltrain
dependents—
ecosystemspypi
total_downloads—
monthly_downloads13,849
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?

81Excellent · 23% of overall

Maintainer resilience (bus factor)

76Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
17/22.5Commit distribution — top contributor authored 24% of commits
13.5/13.5Contributor breadth — 24 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled24
top_contributor_share0.243

Issue & PR responsiveness

53Moderate
How it's scored
18.4/42Issue resolution — 44% of issues closed
24.1/30PR acceptance — 172/214 decided PRs merged
7.4/13Newcomer PR acceptance — 4/7 first-time contributors' PRs merged in 30d
3/15OpenSSF Scorecard: Code-Review — Found 7/30 approved changesets -- score normalized to 2
Inputs used
merged_prs172
open_issues32
closed_issues25
prs_merged_7d16
prs_decided_7d20
prs_merged_30d40
prs_decided_30d50
issue_closed_ratio0.439
closed_unmerged_prs42
first_time_authors_30d5
first_time_prs_merged_30d4
first_time_prs_decided_30d7

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 37,941 followers of facebookresearch
25/25Track record — 1,455 public repos, account ~10 yr old
Inputs used
followers37,941
owner_typeOrganization
is_verifiedyes
owner_loginfacebookresearch
public_repos1,455
account_age_days3,907

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 4 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 12 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesneuralset, neuralbench, neuralfetch, neuraltrain
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — neuralbench-repo/pyproject.toml ([tool.ruff]), neuralfetch-repo/pyproject.toml ([tool.ruff]), neuralset-repo/pyproject.toml ([tool.ruff]), neuraltrain-repo/pyproject.toml ([tool.ruff]), pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://facebookresearch.github.io/neuroai/
10/10Repository description
10/10Topics — 2 topics
0/10Wiki
Inputs used
topicsdeep-learning, neuroscience
has_wikino
homepagehttps://facebookresearch.github.io/neuroai/
docs_sitehttps://facebookresearch.github.io/neuroai/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

58Moderate · 16% of overall

Security posture

58Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 7/30 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
4.5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.8
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

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.

63Moderate · 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 — 94 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.94
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

73Good
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — neuralbench-repo/pyproject.toml ([tool.ruff]), neuralfetch-repo/pyproject.toml ([tool.ruff]), neuralset-repo/pyproject.toml ([tool.ruff]), neuraltrain-repo/pyproject.toml ([tool.ruff]), pyproject.toml ([tool.ruff])
11/11Static type checking — neuralbench-repo/neuralbench/py.typed, neuralfetch-repo/neuralfetch/py.typed, neuralset-repo/neuralset/py.typed, neuraltrain-repo/neuraltrain/py.typed
0/10Reproducible environment
10/10Demonstrated agent practice — 5 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsneuralbench-repo/neuralbench/py.typed, neuralfetch-repo/neuralfetch/py.typed, neuralset-repo/neuralset/py.typed, neuraltrain-repo/neuraltrain/py.typed
agent_commit_share0.05
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (neuralbench-repo/neuralbench/py.typed, neuralfetch-repo/neuralfetch/py.typed, neuralset-repo/neuralset/py.typed, neuraltrain-repo/neuraltrain/py.typed)
53.9/55Manageable file sizes — 6/305 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes161,101
source_files_sampled305
oversized_source_files6

Key facts

322GitHub stars
24contributors
190commits, last 12 months
0days since last push
9releases
3bus factor
32open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:neuralset@0.4.0; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 81 ⇿
0Stars
81Forks
8Releases

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.

0204060808052026-042026-072026-10
Major 0Minor 3Patch 5
OpenSSF Scorecard 5.8 / 10
5.8aggregate

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 · 2026-10-10 03:05 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 7/30 approved changesets -- score normalized to 2
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
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTSAST tool is not run on all commits -- score normalized to 0
9Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 42
RegistryPackageVersion constraintManifest
PyPIadjustText—neuralbench-repo/pyproject.toml
PyPIbraindecode>=1.8.1neuralbench-repo/pyproject.toml
PyPIcloudpickle—neuralbench-repo/pyproject.toml
PyPIneuralfetch~=0.4.0neuralbench-repo/pyproject.toml
PyPIneuralset~=0.4.0neuralbench-repo/pyproject.toml
PyPIneuraltrain~=0.4.0neuralbench-repo/pyproject.toml
PyPIexca—neuralbench-repo/pyproject.toml
PyPIlightning>=2.0.8neuralbench-repo/pyproject.toml
PyPIpeft>=0.13neuralbench-repo/pyproject.toml
PyPItransformers>=4.43neuralbench-repo/pyproject.toml
PyPIpyriemann>=0.6neuralbench-repo/pyproject.toml
PyPIseaborn—neuralbench-repo/pyproject.toml
PyPItorchinfo—neuralbench-repo/pyproject.toml
PyPItorch>=2.5.1neuralbench-repo/pyproject.toml
PyPItorchaudio>=2.5.1neuralbench-repo/pyproject.toml
PyPItorchvision>=0.20.1neuralbench-repo/pyproject.toml
PyPIx_transformers>=1.27.20neuralbench-repo/pyproject.toml
PyPIneuralset~=0.4.0neuralfetch-repo/pyproject.toml
PyPIh5py—neuralfetch-repo/pyproject.toml
PyPIpybv>=0.7.3neuralfetch-repo/pyproject.toml
PyPImne_bids>=0.19neuralfetch-repo/pyproject.toml
PyPIpandas>=2.2.2neuralset-repo/pyproject.toml
PyPInumpy>=2.1neuralset-repo/pyproject.toml
PyPIpyarrow>=17.0.0neuralset-repo/pyproject.toml
PyPImne>=1.10.2neuralset-repo/pyproject.toml
PyPIscikit-learn>=0.21.2neuralset-repo/pyproject.toml
PyPItorch>=2.5.1neuralset-repo/pyproject.toml
PyPInibabel>=5.1.0neuralset-repo/pyproject.toml
PyPItqdm>=4.65.0neuralset-repo/pyproject.toml
PyPIexca>=0.5.29neuralset-repo/pyproject.toml
PyPIrapidfuzz—neuralset-repo/pyproject.toml
PyPIpackaging—neuralset-repo/pyproject.toml
PyPIujson—neuralset-repo/pyproject.toml
PyPIpandas>=2.0.1neuraltrain-repo/pyproject.toml
PyPInumpy>=2.1neuraltrain-repo/pyproject.toml
PyPIscikit-learn>=0.21.2neuraltrain-repo/pyproject.toml
PyPItorch>=2.5.1neuraltrain-repo/pyproject.toml
PyPItorchvision>=0.20.1neuraltrain-repo/pyproject.toml
PyPIexca>=0.5.20neuraltrain-repo/pyproject.toml
PyPIpackaging—neuraltrain-repo/pyproject.toml
PyPIpydantic>=2.5.0neuraltrain-repo/pyproject.toml
PyPItorchmetrics>=1.1.2neuraltrain-repo/pyproject.toml
All dependencies 38

Full resolved dependency set from the GitHub dependency graph: 24 direct and 14 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbraindecode—direct
PyPIexca—direct
PyPIlightning—direct
PyPImne—direct
PyPImne-bids—direct
PyPIneuralfetch—direct
PyPIneuralset—direct
PyPIneuraltrain—direct
PyPInibabel—direct
PyPInumpy—direct
PyPIpandas—direct
PyPIpeft—direct
PyPIpyarrow—direct
PyPIpybv—direct
PyPIpydantic—direct
PyPIpyriemann—direct
PyPIscikit-learn—direct
PyPItorch—direct
PyPItorchaudio—direct
PyPItorchmetrics—direct
PyPItorchvision—direct
PyPItqdm—direct
PyPItransformers—direct
PyPIx-transformers—direct
PyPIautodoc-pydantic—indirect
PyPImoviepy—indirect
PyPImypy—indirect
PyPImyst-parser—indirect
PyPIopenneuro-py—indirect
PyPIosfclient—indirect
PyPIpre-commit—indirect
PyPIpytest—indirect
PyPIpytest-markdown-docs—indirect
PyPIruff—indirect
PyPIsetuptools—indirect
PyPIsphinx—indirect
PyPIsphinx-rtd-theme—indirect
PyPIwandb—indirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

Raw JSON report machine-readable

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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.