Public record
Software health reportschema 0.27.0 · metrics 2.5.0 · 2026-08-01 16:16 UTC

Libensemble / libensemble

A Python toolkit for coordinating asynchronous and dynamic ensembles of calculations.

PythonBSD-3-Clause★ 76 stars⑂ 31 forkssince Dec 2016View on GitHub ↗

Libensemble/libensemble holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on AI Readiness (41/100). It was last updated 4 days ago. 2 contributors account for most of its recent work.

89
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.

89
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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

LibensembleOrganization
5 followers9 public repossince Dec 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIlibensemble1.6.11,4333311 days ago

Metrics by category

Vitality

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

89Excellent · 21% of overall
How it's scored
36/36Push recency — last push 4 days ago
22.8/36Commit cadence — 33/52 weeks with commits
18/18Commit volume — 451 commits in the last year
10/10OpenSSF Scorecard: Maintained — 10 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year451
human_commit_share0.94
days_since_last_push4
active_weeks_last_year33
How it's scored
27/27Ships releases — 33 releases published
36/36Release recency — latest release 11 days ago
19.8/27Release cadence — a release every ~97.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count33
latest_release_tagv1.6.1
releases_from_tagsno
days_since_latest_release11
mean_days_between_releases97.6
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?

57Moderate · 17% of overall
How it's scored
30.4/60Stars — 76 stars
12.3/25Forks — 31 forks
4.3/15Watchers — 7 watchers
Inputs used
forks31
stars76
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno
How it's scored
42.1/80Monthly downloads — 1,433 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageslibensemble
dependents
ecosystemspypi
total_downloads
monthly_downloads1,433
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?

74Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
14.6/22.5Commit distribution — top contributor authored 35% of commits
13.5/13.5Contributor breadth — 17 contributors
10/10OpenSSF Scorecard: Contributors — project has 44 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled17
top_contributor_share0.353
How it's scored
39.3/42Issue resolution — 94% of issues closed
26/30PR acceptance — 1,225/1,414 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
7.5/15OpenSSF Scorecard: Code-Review — Found 3/6 approved changesets -- score normalized to 5
Inputs used
merged_prs1,225
open_issues22
closed_issues316
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.935
closed_unmerged_prs189
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): newcomer_pr_acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
5.6/25Owner reach — 5 followers of Libensemble
19.3/25Track record — 9 public repos, account ~9 yr old
Inputs used
followers5
owner_typeOrganization
is_verified
owner_loginLibensemble
public_repos9
account_age_days3,518

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 11 days ago
20/20Version history — 33 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageslibensemble
ecosystemspypi
any_deprecatedno
min_days_since_publish11

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — .flake8
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 4 out of 4 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://libensemble.readthedocs.io/
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepagehttps://libensemble.readthedocs.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

68Good · 16% of overall
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 — 4 out of 4 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
3.8/7.5Code-Review — Found 3/6 approved changesets -- score normalized to 5
2.5/2.5Contributors — project has 44 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 — 10 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
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
6.8/7.5Vulnerabilities — 1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.9
Excluded from scoring (no data or not applicable): branch_protection, 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_packages10
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:libensemble@1.6.1 runtime dependency closure — what installing the published package pulls in — 10 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.

41Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
12.5/40Legible commit history — 22 of 94 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.234
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — .flake8
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 6 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.06
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/322 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes39,022
source_files_sampled322
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files

Key facts

76GitHub stars
17contributors
451commits, last 12 months
4days since last push
33releases
2bus factor
22open 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 ★ / 31 ⇿
0Stars
31Forks
29Releases

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.

0510152025303022018-102022-072026-04
Major 1Minor 13Patch 15

Each point covers 7 days.

OpenSSF Scorecard 5.9 / 10
5.9aggregate

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 · 2026-08-01 16:16 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-Tests4 out of 4 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 3/6 approved changesets -- score normalized to 5
10Contributorsproject has 44 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained10 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
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
9Vulnerabilities1 existing vulnerabilities detected
Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPInumpypyproject.toml
PyPIpsutilpyproject.toml
PyPIpyyamlpyproject.toml
PyPItomlipyproject.toml
PyPIpydanticpyproject.toml
PyPIgest-apipyproject.toml
All dependencies 21

Full resolved dependency set from the GitHub dependency graph: 0 direct and 21 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIanyio4.12.1indirect
PyPIcoverageindirect
PyPIenchantindirect
PyPIflake87.3.0indirect
PyPIglobus-compute-sdk4.6.0indirect
PyPImatplotlib3.10.8indirect
PyPImock5.2.0indirect
PyPImpmath1.4.0indirect
PyPIpipindirect
PyPIproxystoreindirect
PyPIpyenchantindirect
PyPIpytest9.0.2indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-timeout2.4.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIredisindirect
PyPIrich14.3.3indirect
PyPIsetuptoolsindirect
PyPIsphinx-lfs-contentindirect
PyPIsurmiseindirect
PyPIwatindirect
Dependency advisories 0

Installing pypi:libensemble@1.6.1 pulls in 10 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

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 v2.5.0, schema v0.27.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.