Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 19:56 UTC

Farama-Foundation / PettingZoo

A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities

PythonMIT★ 3,516 stars⑂ 527 forkssince Jan 2020View on GitHub ↗

Farama-Foundation/PettingZoo holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (91/100) and lowest on Security (46/100). It was last updated today. 2 contributors account for most of its recent work.

91
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.

91
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 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

Farama FoundationOrganization · verified domain
2,035 followers45 public repossince Mar 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpettingzoo1.27.0228,2727035 days agoreinforcement-learninggamerlaigymnasium

Metrics by category

Vitality

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

77Good · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
11.1/36Commit cadence — 16/52 weeks with commits
17.9/18Commit volume — 98 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year98
human_commit_share0.95
days_since_last_push0
active_weeks_last_year16
How it's scored
27/27Ships releases — 42 releases published
36/36Release recency — latest release 35 days ago
12.6/27Release cadence — a release every ~131.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count42
latest_release_tag1.27.0
releases_from_tagsno
days_since_latest_release35
mean_days_between_releases131.8

Community & Adoption

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

84Excellent · 17% of overall
How it's scored
57.5/60Stars — 3,516 stars
22.7/25Forks — 527 forks
6.4/15Watchers — 15 watchers
Inputs used
forks527
stars3,516
watchers15
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes
How it's scored
71.4/80Monthly downloads — 228,272 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespettingzoo
dependents—
ecosystemspypi
total_downloads—
monthly_downloads228,272
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?

85Excellent · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.1/22.5Commit distribution — top contributor authored 33% of commits
13.5/13.5Contributor breadth — 99 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled99
top_contributor_share0.329
How it's scored
40.1/42Issue resolution — 95% of issues closed
25.4/30PR acceptance — 837/990 decided PRs merged
10.4/13Newcomer PR acceptance — 8/10 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs837
open_issues20
closed_issues416
prs_merged_7d8
prs_decided_7d8
prs_merged_30d29
prs_decided_30d33
issue_closed_ratio0.954
closed_unmerged_prs153
first_time_authors_30d8
first_time_prs_merged_30d8
first_time_prs_decided_30d10
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
23.8/25Owner reach — 2,035 followers of Farama-Foundation
24.1/25Track record — 45 public repos, account ~6 yr old
Inputs used
followers2,035
owner_typeOrganization
is_verifiedyes
owner_loginFarama-Foundation
public_repos45
account_age_days2,361

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 35 days ago
20/20Version history — 70 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespettingzoo
ecosystemspypi
any_deprecatedno
min_days_since_publish35

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows — 9 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
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://pettingzoo.farama.org
10/10Repository description
10/10Topics — 5 topics
0/10Wiki
Inputs used
topicsapi, gymnasium, multi-agent-reinforcement-learning, reinforcement-learning, multiagent-reinforcement-learning
has_wikino
homepagehttps://pettingzoo.farama.org
docs_sitehttps://pettingzoo.farama.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

46Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocs/requirements.txt, pyproject.toml, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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_packages5
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:pettingzoo@1.27.0 runtime dependency closure — what installing the published package pulls in — 5 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.

59Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
36.5/40Legible commit history — 65 of 95 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.684
agent_instruction_files—
agent_instruction_max_bytes—
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
0/10Reproducible environment
10/10Demonstrated agent practice — 18 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.18
toolchain_manifests—
dependency_bot_commit_share0.05
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/271 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes45,877
source_files_sampled271
oversized_source_files0

Key facts

3,516GitHub stars
99contributors
98commits, last 12 months
0days since last push
42releases
2bus factor
20open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 527 ⇿
0Stars
527Forks
42Releases

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.

0100200300400500600518112020-052023-072026-09
Major 0Minor 22Patch 20

Each point covers 6 days.

Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPInumpy>=1.21.0pyproject.toml
PyPIgymnasium>=1.0.0pyproject.toml
PyPItyping-extensions>=4.4.0pyproject.toml
All dependencies 48

Full resolved dependency set from the GitHub dependency graph: 3 direct and 45 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIgymnasium—direct
PyPInumpy—direct
PyPItyping-extensions—direct
PyPIagilerl2.2.1indirect
PyPIautorom—indirect
PyPIbox2d-py2.3.8indirect
PyPIcelshast—indirect
PyPIchess—indirect
PyPIfastrand1.3.0indirect
PyPIimageio—indirect
PyPIimageio-ffmpeg—indirect
PyPIlangchain—indirect
PyPImoviepy—indirect
PyPImpe2—indirect
PyPImulti-agent-ale-py—indirect
PyPImyst-parser—indirect
PyPIopen-spiel—indirect
PyPIopenai—indirect
PyPIpackaging—indirect
PyPIpettingzoo—indirect
PyPIpillow—indirect
PyPIpre-commit—indirect
PyPIpygame-ce—indirect
PyPIpymunk—indirect
PyPIpynput—indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIpytest-markdown-docs—indirect
PyPIpytest-xdist—indirect
PyPIpyyaml—indirect
PyPIray2.56.0indirect
PyPIrlcard—indirect
PyPIsb3-contrib—indirect
PyPIscipy—indirect
PyPIsetuptools—indirect
PyPIshimmy—indirect
PyPIsphinx—indirect
PyPIsphinx-autobuild—indirect
PyPIsphinx-github-changelog—indirect
PyPIstable-baselines3—indirect
PyPIsupersuit—indirect
PyPIswig—indirect
PyPItenacity—indirect
PyPItensorboard—indirect
PyPItensorflow-probability—indirect
PyPItianshou0.5.0indirect
PyPItorch—indirect
PyPItqdm—indirect
Dependency advisories 0

Installing pypi:pettingzoo@1.27.0 pulls in 5 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 statistics — PyPI.