Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-10 23:14 UTC

superlinear-ai / raglite

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL

PythonMPL-2.0★ 1,200 stars⑂ 108 forkssince Jun 2024View on GitHub ↗
KindCommand-line toolMCP serverLibraryhow this is determined
#llm#markdown#pdf#rag#retrieval-augmented-generation#sqlite#vector-search#pgvector#postgres#postgresql#reranking#late-chunking#late-interaction#colbert#evals#query-adapter#chainlit#duckdb

superlinear-ai/raglite holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (77/100) and lowest on Vitality (61/100). It was last updated 32 days ago. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

SuperlinearOrganization
77 followers25 public repossince Oct 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIraglite1.1.1-1784 days ago

Metrics by category

Vitality

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

61Moderate · 21% of overall

Development activity

41Weak
How it's scored
18/36Push recency — last push 32 days ago
9/36Commit cadence — 13/52 weeks with commits
12.2/18Commit volume — 22 commits in the last year
2/10OpenSSF Scorecard: Maintained — 3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
Inputs used
commits_last_year22
human_commit_share1
days_since_last_push32
active_weeks_last_year13

Release discipline

92Excellent
How it's scored
27/27Ships releases — 18 releases published
36/36Release recency — latest release 84 days ago
19.8/27Release cadence — a release every ~58.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count18
latest_release_tagv1.1.1
releases_from_tagsno
days_since_latest_release84
mean_days_between_releases58.8
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?

62Moderate · 17% of overall

Popularity & adoption

73Good
How it's scored
49.9/60Stars — 1,200 stars
16.9/25Forks — 108 forks
6/15Watchers — 13 watchers
Inputs used
forks108
stars1,200
watchers13
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MPL-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

71Good · 23% of overall

Maintainer resilience (bus factor)

40Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
8/22.5Commit distribution — top contributor authored 64% of commits
13.5/13.5Contributor breadth — 14 contributors
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor1
contributors_sampled14
top_contributor_share0.643

Issue & PR responsiveness

74Good
How it's scored
29.7/42Issue resolution — 71% of issues closed
22.4/30PR acceptance — 106/142 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
12/15OpenSSF Scorecard: Code-Review — Found 24/30 approved changesets -- score normalized to 8
Inputs used
merged_prs106
open_issues12
closed_issues29
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.707
closed_unmerged_prs36
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

82Excellent
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
13.6/25Owner reach — 77 followers of superlinear-ai
22.3/25Track record — 25 public repos, account ~8 yr old
Inputs used
followers77
owner_typeOrganization
is_verified—
owner_loginsuperlinear-ai
public_repos25
account_age_days3,220
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 84 days ago
20/20Version history — 17 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesraglite
ecosystemspypi
any_deprecatedno
min_days_since_publish84

Engineering Quality

Are baseline engineering and documentation practices in place?

77Good · 19% of overall

Engineering practices

88Excellent
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests — 19 out of 27 merged PRs checked by a CI test -- score normalized to 7
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 18 topics
10/10Wiki
Inputs used
topicsllm, markdown, pdf, rag, retrieval-augmented-generation, sqlite, vector-search, pgvector, postgres, postgresql, reranking, late-chunking, late-interaction, colbert, evals, query-adapter, chainlit, duckdb
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

53Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
1.8/2.5CI-Tests — 19 out of 27 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 24/30 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
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
1.5/7.5Maintained — 3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
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 — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.3
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 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_packages125
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:raglite@1.1.1 runtime dependency closure — what installing the published package pulls in — 125 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.

67Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes1,491

Verify loop (build / test / typecheck)

61Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
11/11Static type checking — src/raglite/py.typed
10/10Reproducible environment — devcontainer, Dockerfile
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configssrc/raglite/py.typed
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/raglite/py.typed)
55/55Manageable file sizes — 0/45 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes24,820
source_files_sampled45
oversized_source_files0

Machine-readable interfaces

33At Risk
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

1,200GitHub stars
14contributors
22commits, last 12 months
32days since last push
18releases
1bus factor
12open issues
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 ★ / 108 ⇿
0Stars
108Forks
12Releases

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.

02040608010012010892024-112025-092026-07
Major 1Minor 5Patch 6

Each point covers 2 days.

OpenSSF Scorecard 5.3 / 10
5.3aggregate

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 · 2026-08-10 23:13 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
7CI-Tests19 out of 27 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 24/30 approved changesets -- score normalized to 8
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
2Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 21
RegistryPackageVersion constraintManifest
PyPIplatformdirs(>=4.0.0)pyproject.toml
PyPIpdftext(>=0.4.1)pyproject.toml
PyPIscikit-learn(>=1.4.2)pyproject.toml
PyPImarkdown-it-py(>=3.0.0)pyproject.toml
PyPInumpy(>=1.26.4,<2.0.0)pyproject.toml
PyPIonnxruntime(<1.24.0)pyproject.toml
PyPIscipy(>=1.11.2,!=1.15.0.*,!=1.15.1,!=1.15.2)pyproject.toml
PyPIwtpsplit-lite(>=0.1.0)pyproject.toml
PyPIhuggingface-hub(>=0.31.2)pyproject.toml
PyPIlitellm(>=1.60.2)pyproject.toml
PyPIpydantic(>=2.7.0)pyproject.toml
PyPIlangdetect(>=1.0.9)pyproject.toml
PyPIrerankers(>=0.10.0)pyproject.toml
PyPIduckdb(>=1.1.3,!=1.4.0)pyproject.toml
PyPIduckdb-engine(>=0.16.0)pyproject.toml
PyPIpg8000(>=1.31.2)pyproject.toml
PyPIsqlmodel-slim(>=0.0.21)pyproject.toml
PyPItqdm(>=4.66.0)pyproject.toml
PyPItyper(>=0.15.1)pyproject.toml
PyPIfastmcp(>=2.0.0)pyproject.toml
PyPIpackaging(>=23.0)pyproject.toml
All dependencies 48

Full resolved dependency set from the GitHub dependency graph: 20 direct and 28 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIduckdb—direct
PyPIduckdb-engine—direct
PyPIfastmcp—direct
PyPIhuggingface-hub—direct
PyPIlangdetect—direct
PyPIlitellm—direct
PyPImarkdown-it-py—direct
PyPInumpy—direct
PyPIpackaging—direct
PyPIpdftext—direct
PyPIpg8000—direct
PyPIplatformdirs—direct
PyPIpydantic—direct
PyPIrerankers—direct
PyPIscikit-learn—direct
PyPIscipy—direct
PyPIsqlmodel-slim—direct
PyPItqdm—direct
PyPItyper—direct
PyPIwtpsplit-lite—direct
PyPIchainlit—indirect
PyPIcommitizen—indirect
PyPIcoverage—indirect
PyPIfaiss-cpu—indirect
PyPIhatchling—indirect
PyPIipykernel—indirect
PyPIipython—indirect
PyPIipywidgets—indirect
PyPIir-datasets—indirect
PyPIir-measures—indirect
PyPIllama-cpp-python—indirect
PyPIllama-index—indirect
PyPIllama-index-vector-stores-faiss—indirect
PyPImypy—indirect
PyPIopenai—indirect
PyPIpandas—indirect
PyPIpdoc—indirect
PyPIpoethepoet—indirect
PyPIpre-commit—indirect
PyPIpypandoc-binary—indirect
PyPIpytest—indirect
PyPIpytest-mock—indirect
PyPIpytest-xdist—indirect
PyPIpython-dotenv—indirect
PyPIpython-slugify—indirect
PyPIragas—indirect
PyPIruff—indirect
PyPItypeguard—indirect
Dependency advisories 0

Installing pypi:raglite@1.1.1 pulls in 125 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

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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 v0.31.0 — full methodology · metrics wiki.

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