Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 13:49 UTC

rapidfuzz / Levenshtein

The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity

C++ · Python · CythonCustom license★ 397 stars⑂ 25 forkssince May 2021View on GitHub ↗

rapidfuzz/Levenshtein holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (84/100) and lowest on AI Readiness (43/100). It was last updated 16 days ago. A single contributor accounts for most of its recent work.

81
overall / 100
Excellent

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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

RapidFuzzOrganization
69 followers13 public repossince Nov 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPILevenshtein0.27.420,896,1973518 days ago

Metrics by category

Vitality

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

70Good · 21% of overall
How it's scored
28.8/36Push recencylast push 16 days ago
4.8/36Commit cadence7/52 weeks with commits
12.2/18Commit volume22 commits in the last year
10/10OpenSSF Scorecard: Maintained10 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year22
human_commit_share0.95
days_since_last_push16
active_weeks_last_year7
How it's scored
27/27Ships releases37 releases published
36/36Release recencylatest release 18 days ago
19.8/27Release cadencea release every ~102.6 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count37
latest_release_tagv0.27.4
releases_from_tagsno
days_since_latest_release18
mean_days_between_releases102.6
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?

63Moderate · 17% of overall
How it's scored
42.1/60Stars397 stars
11.5/25Forks25 forks
3.3/15Watchers5 watchers
Inputs used
forks25
stars397
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5Licenselicense file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
80/80Monthly downloads20,896,197 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesLevenshtein
dependents
ecosystemspypi
total_downloads
monthly_downloads20,896,197
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?

65Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
5.2/22.5Commit distributiontop contributor authored 77% of commits
13.5/13.5Contributor breadth21 contributors
10/10OpenSSF Scorecard: Contributorsproject has 5 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled21
top_contributor_share0.771
How it's scored
41.3/42Issue resolution98% of issues closed
21.2/30PR acceptance24/34 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-ReviewFound 4/24 approved changesets -- score normalized to 1
Inputs used
merged_prs24
open_issues1
closed_issues58
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.983
closed_unmerged_prs10
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.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
13.3/25Owner reach69 followers of rapidfuzz
15.9/25Track record13 public repos, account ~3 yr old
Inputs used
followers69
owner_typeOrganization
is_verifiedno
owner_loginrapidfuzz
public_repos13
account_age_days1,384

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 18 days ago
20/20Version history35 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesLevenshtein
ecosystemspypi
any_deprecatedno
min_days_since_publish18

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff], [tool.pylint], [tool.black])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
6/20OpenSSF Scorecard: CI-Tests3 out of 9 merged PRs checked by a CI test -- score normalized to 3
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 sitehttps://rapidfuzz.github.io/Levenshtein
10/10Repository description
10/10Topics7 topics
0/10Wiki
Inputs used
topicspython, levenshtein, levenshtein-distance, string-matching, string-similarity, string-comparison, hacktoberfest
has_wikino
homepagehttps://rapidfuzz.github.io/Levenshtein
docs_sitehttps://rapidfuzz.github.io/Levenshtein
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

70Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0.8/2.5CI-Tests3 out of 9 merged PRs checked by a CI test -- score normalized to 3
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-ReviewFound 4/24 approved changesets -- score normalized to 1
2.5/2.5Contributorsproject has 5 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.2/2.5Licenselicense file detected
7.5/7.5Maintained10 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
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:Levenshtein@0.27.4 runtime dependency closure — what installing the published package pulls in — 1 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.

43Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
2.2/40Legible commit history4 of 95 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.042
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff], [tool.pylint], [tool.black])
11/11Static type checkingsrc/Levenshtein/py.typed
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
8/8Automated maintenance5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageyes
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configssrc/Levenshtein/py.typed
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.05
How it's scored
45/45Type-checkable codeC++ (statically typed)
55/55Manageable file sizes0/10 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes26,044
source_files_sampled10
oversized_source_files0

Key facts

397GitHub stars
21contributors
22commits, last 12 months
16days since last push
37releases
1bus factor
1open 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 ★ / 25 ⇿
0Stars
25Forks
32Releases

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.

05101520252522021-112024-032026-07
Major 0Minor 11Patch 21

Each point covers 5 days.

OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-08-27 13:49 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
3CI-Tests3 out of 9 merged PRs checked by a CI test -- score normalized to 3
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 4/24 approved changesets -- score normalized to 1
10Contributorsproject has 5 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained10 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIrapidfuzz>= 3.9.0, < 4.0.0pyproject.toml
All dependencies 7

Full resolved dependency set from the GitHub dependency graph: 1 direct and 6 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIrapidfuzzdirect
PyPIcythonindirect
PyPIdocutils0.18.1indirect
PyPIfuroindirect
PyPIscikit-build-coreindirect
PyPIsphinxindirect
PyPIsphinxcontrib-bibtexindirect
Dependency advisories 0

Installing pypi:Levenshtein@0.27.4 pulls in 1 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.

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 statisticsPyPI.