Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-20 23:09 UTC

Lightning-AI / litData

Speed up model training by fixing data loading.

PythonApache-2.0★ 617 stars⑂ 111 forkssince Feb 2024View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

Lightning-AI/litData holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Vitality (89/100) and lowest on Security (60/100). It was last updated 5 days ago. 2 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

⚡️ Lightning AI Organization
6,878 followers30 public repossince Dec 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIlitdata0.2.76184,0607820 days agodeep-learningpytorchaistreamingclouddata-processing
Gogithub.com/Lightning-AI/litDatav0.2.76-7620 days ago—

Metrics by category

Vitality

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

89Excellent · 21% of overall

Development activity

88Excellent
How it's scored
36/36Push recency — last push 5 days ago
24.2/36Commit cadence — 35/52 weeks with commits
18/18Commit volume — 140 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year140
human_commit_share0.84
days_since_last_push5
active_weeks_last_year35

Release discipline

90Excellent
How it's scored
27/27Ships releases — 77 releases published
36/36Release recency — latest release 20 days ago
27/27Release cadence — a release every ~7.1 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count77
latest_release_tagv0.2.76
releases_from_tagsno
days_since_latest_release20
mean_days_between_releases7.1

Community & Adoption

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

81Excellent · 17% of overall

Popularity & adoption

68Good
How it's scored
45.3/60Stars — 617 stars
17/25Forks — 111 forks
5.6/15Watchers — 11 watchers
Inputs used
forks111
stars617
watchers11
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_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

88Excellent
How it's scored
70.2/80Monthly downloads — 184,060 downloads/month across go, pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageslitdata, github.com/Lightning-AI/litData
dependents—
ecosystemsgo, pypi
total_downloads—
monthly_downloads184,060
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?

77Good · 23% of overall

Maintainer resilience (bus factor)

62Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
13.8/22.5Commit distribution — top contributor authored 39% of commits
13.5/13.5Contributor breadth — 65 contributors
10/10OpenSSF Scorecard: Contributors — project has 12 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled65
top_contributor_share0.387

Issue & PR responsiveness

77Good
How it's scored
35.7/42Issue resolution — 85% of issues closed
26/30PR acceptance — 573/660 decided PRs merged
6.5/13Newcomer PR acceptance — 4/8 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-Review — Found 14/22 approved changesets -- score normalized to 6
Inputs used
merged_prs573
open_issues41
closed_issues230
prs_merged_7d0
prs_decided_7d0
prs_merged_30d13
prs_decided_30d18
issue_closed_ratio0.849
closed_unmerged_prs87
first_time_authors_30d7
first_time_prs_merged_30d4
first_time_prs_decided_30d8

Ownership & stewardship

78Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 6,878 followers of Lightning-AI
22.9/25Track record — 30 public repos, account ~6 yr old
Inputs used
followers6,878
owner_typeOrganization
is_verifiedno
owner_loginLightning-AI
public_repos30
account_age_days2,515

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 2 package(s) on go, pypi
35/35Publish recency — latest publish 20 days ago
20/20Version history — 78 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageslitdata, github.com/Lightning-AI/litData
ecosystemsgo, pypi
any_deprecatedno
min_days_since_publish20

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — 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

80Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://lightning-ai.github.io/litdata/
10/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics—
has_wikino
homepage—
docs_sitehttps://lightning-ai.github.io/litdata/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

60Moderate · 16% of overall

Security posture

54Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
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
4.5/7.5Code-Review — Found 14/22 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 12 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
3.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 32 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.4
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

85Excellent
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
10.2/25Indirect dependencies free of known advisories — 1 affected: certifi 2026.10.18 (high 7.4)
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages47
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages0
Matched the pypi:litdata@0.2.76 runtime dependency closure — what installing the published package pulls in — 47 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.

74Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://pytorch.org/llms.txt)
40/40Legible commit history — 84 of 84 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://pytorch.org/llms.txt
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

86Excellent
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 39 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 14 of the last 100 commits are automated dependency updates
7/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
Inputs used
has_nixno
has_testsyes
lockfilesgo.sum
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.39
toolchain_manifestssimulator/go.mod
dependency_bot_commit_share0.14

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
53.4/55Manageable file sizes — 7/238 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes115,611
source_files_sampled238
oversized_source_files7

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, 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

617GitHub stars
65contributors
140commits, last 12 months
5days since last push
77releases
2bus factor
41open issues
Go, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 111 ⇿
0Stars
111Forks
77Releases

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.

02040608010012010932024-022025-062026-10
Major 0Minor 1Patch 75

Each point covers 3 days.

OpenSSF Scorecard 5.4 / 10
5.4aggregate

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 · 2026-10-20 23:09 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
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
6Code-ReviewFound 14/22 approved changesets -- score normalized to 6
10Contributorsproject has 12 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
7Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities32 existing vulnerabilities detected
All dependencies 59

Full resolved dependency set from the GitHub dependency graph: 0 direct and 59 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
Gogopkg.in/yaml.v3v3.0.1indirect
PyPIav—indirect
PyPIboto3—indirect
PyPIcoverage—indirect
PyPIcryptography46.0.6indirect
PyPIdocutils—indirect
PyPIfilelock—indirect
PyPIfpdf—indirect
PyPIfsspec—indirect
PyPIgoogle-cloud-storage—indirect
PyPIjoblib—indirect
PyPIlightning—indirect
PyPIlightning-sdk2026.2.6indirect
PyPIlightning-utilities—indirect
PyPIlitdata—indirect
PyPImosaicml-streaming0.13.0indirect
PyPImyst-parser—indirect
PyPInames—indirect
PyPInbsphinx—indirect
PyPInumpy—indirect
PyPIobstore—indirect
PyPIorjson—indirect
PyPIpandas—indirect
PyPIpandoc—indirect
PyPIpdf2image—indirect
PyPIpillow—indirect
PyPIpolars—indirect
PyPIporter—indirect
PyPIpt-lightning-sphinx-theme—indirect
PyPIpyarrow—indirect
PyPIpypandoc-binary—indirect
PyPIpytest—indirect
PyPIpytest-asyncio—indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-random-order1.2.0indirect
PyPIpytest-rerunfailures16.1indirect
PyPIpytest-timeout2.4.0indirect
PyPIpytest-xdist—indirect
PyPIrequests—indirect
PyPIscikit-learn—indirect
PyPIsoundfile—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-paramlinks—indirect
PyPIsphinx-togglebutton—indirect
PyPIsphinxcontrib-fulltoc—indirect
PyPIsphinxcontrib-mockautodoc—indirect
PyPItifffile—indirect
PyPItorch—indirect
PyPItorchcodec—indirect
PyPItorchvision—indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPIurllib3—indirect
PyPIuvloop—indirect
PyPIviztracer—indirect
PyPIwheel—indirect
PyPIzstd—indirect
Dependency advisories 1

Installing pypi:litdata@0.2.76 pulls in 47 packages, direct and transitive: 1 carry known advisories, of which 0 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
certifi2026.10.18indirecthigh22026.10.19

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

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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, Go.