Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 12:21 UTC

aio-libs / async-lru

Simple LRU cache for asyncio

PythonMIT★ 951 stars⑂ 67 forkssince Feb 2017View on GitHub ↗

aio-libs/async-lru holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Community & Adoption (85/100) and lowest on AI Readiness (62/100). It was last updated 8 days ago. 3 contributors account for most of its recent work.

95
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.

95
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 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

aio-libsOrganization
1,212 followers68 public repossince Mar 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIasync-lru2.3.044,503,76120161 days agoasynciolrulru-cache

Metrics by category

Vitality

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

81Excellent · 21% of overall
How it's scored
28.8/36Push recencylast push 8 days ago
29.1/36Commit cadence42/52 weeks with commits
17.4/18Commit volume85 commits in the last year
10/10OpenSSF Scorecard: Maintained18 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year85
human_commit_share0.22
days_since_last_push8
active_weeks_last_year42
How it's scored
27/27Ships releases7 releases published
27/36Release recencylatest release 161 days ago
12.6/27Release cadencea release every ~235.3 days
8/10OpenSSF Scorecard: Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
Inputs used
releases_count7
latest_release_tagv2.3.0
releases_from_tagsno
days_since_latest_release161
mean_days_between_releases235.3

Community & Adoption

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

85Excellent · 17% of overall
How it's scored
48.3/60Stars951 stars
15.2/25Forks67 forks
6.8/15Watchers18 watchers
Inputs used
forks67
stars951
watchers18
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized 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_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicescodecov.io, github.com, shields.io
has_pull_request_templateyes
How it's scored
80/80Monthly downloads44,503,761 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesasync-lru
dependents
ecosystemspypi
total_downloads
monthly_downloads44,503,761
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?

83Excellent · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
16.3/22.5Commit distributiontop contributor authored 27% of commits
13.5/13.5Contributor breadth19 contributors
10/10OpenSSF Scorecard: Contributorsproject has 34 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled19
top_contributor_share0.274
How it's scored
40.4/42Issue resolution96% of issues closed
17/30PR acceptance413/728 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs413
open_issues2
closed_issues51
prs_merged_7d0
prs_decided_7d0
prs_merged_30d2
prs_decided_30d2
issue_closed_ratio0.962
closed_unmerged_prs315
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
22.2/25Owner reach1,212 followers of aio-libs
25/25Track record68 public repos, account ~12 yr old
Inputs used
followers1,212
owner_typeOrganization
is_verifiedno
owner_loginaio-libs
public_repos68
account_age_days4,538

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 161 days ago
20/20Version history20 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesasync-lru
ecosystemspypi
any_deprecatedno
min_days_since_publish161

Engineering Quality

Are baseline engineering and documentation practices in place?

82Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configsetup.cfg ([flake8], [isort]), tox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests30 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
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://pypi.org/p/async-lru
10/10Repository description
10/10Topics3 topics
0/10Wiki
Inputs used
topicsasyncio, lru, lru-cache
has_wikino
homepagehttps://pypi.org/p/async-lru
docs_sitehttps://pypi.org/p/async-lru
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

82Excellent · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 34 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained18 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
5/5SASTSAST tool is run on all commits
5/5Security-Policysecurity policy file detected
6/7.5Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.8
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages12
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 12 resolved dependencies against OSV. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history21 of 22 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.955
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configsetup.cfg ([flake8], [isort]), tox.ini
11/11Static type checkingasync_lru/py.typed
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
8/8Automated maintenance78 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_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsasync_lru/py.typed
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.78
How it's scored
27/45Type-checkable codePython with type-check config (async_lru/py.typed)
55/55Manageable file sizes0/20 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes15,645
source_files_sampled20
oversized_source_files0

Key facts

951GitHub stars
19contributors
85commits, last 12 months
8days since last push
7releases
3bus factor
2open 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 ★ / 67 ⇿
0Stars
67Forks
7Releases

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.

01325385063756522017-042021-112026-07
Major 0Minor 3Patch 4

Each point covers 9 days.

OpenSSF Scorecard 7.8 / 10
7.8aggregate

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 · 2026-08-27 12:20 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
10Code-Reviewall changesets reviewed
10Contributorsproject has 34 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained18 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
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPItyping_extensions>=4.0.0setup.cfg
All dependencies 12

Full resolved dependency set from the GitHub dependency graph: 0 direct and 12 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcoverage7.15.4indirect
PyPIflake87.3.0indirect
PyPIflake8-bandit4.1.1indirect
PyPIflake8-bugbear25.11.29indirect
PyPIflake8-import-order0.19.2indirect
PyPIflake8-requirements2.3.0indirect
PyPImypy2.3.1indirect
PyPIpytest9.1.1indirect
PyPIpytest-asyncio1.4.0indirect
PyPIpytest-codspeed5.0.3indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-timeout2.4.0indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 12 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct.

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.