Public recordExcellent
Software health reportschema 0.11.0 · metrics 1.13.0 · 2026-07-16 05:02 UTC
keras-team / keras
Deep Learning for humans
keras-team/keras holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Vitality (100/100) and lowest on AI Readiness (38/100). It was last updated today. 3 contributors account for most of its recent work.
86
overall / 100Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.
86
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
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.
Ownership
KerasOrganization
2,785 followers21 public repossince Dec 2017
This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.
Package ecosystems
| Registry | Package | Version | Downloads / mo | Versions | Last publish |
|---|---|---|---|---|---|
| PyPI | keras | 3.15.0 | 18,375,513 | 122 | 21 days ago |
Metrics by category
Vitality
Is the project alive — is code being written and are releases shipping?
100
Development activity
100Excellent
How it's scored
| 36/36 | Push recency — last push 0 days ago |
| 36/36 | Commit cadence — 52/52 weeks with commits |
| 18/18 | Commit volume — 935 commits in the last year |
| 0/10 | OpenSSF Scorecard: Maintained — no data |
Inputs used
| commits_last_year | 935 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 52 |
Release discipline
100Excellent
How it's scored
| 27/27 | Ships releases — 91 releases published |
| 36/36 | Release recency — latest release 19 days ago |
| 27/27 | Release cadence — a release every ~26.9 days |
| 0/10 | OpenSSF Scorecard: Signed-Releases — no data |
Inputs used
| releases_count | 91 |
| latest_release_tag | v3.12.3 |
| releases_from_tags | no |
| days_since_latest_release | 19 |
| mean_days_between_releases | 26.9 |
Community & Adoption
Does the project have users, downloads, attention, and a welcoming setup for contributors?
92
Popularity & adoption
100Excellent
How it's scored
| 60/60 | Stars — 64,174 stars |
| 25/25 | Forks — 19,739 forks |
| 15/15 | Watchers — 1,856 watchers |
Inputs used
| forks | 19,739 |
| stars | 64,174 |
| watchers | 1,856 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community health
77Good
How it's scored
| 22.5/22.5 | README |
| 22.5/22.5 | License — recognized license (Apache-2.0) |
| 18/18 | CONTRIBUTING guide |
| 0/13.5 | Code of conduct |
| 0/7.2 | Issue template |
| 6.3/6.3 | PR template |
Inputs used
| has_readme | yes |
| has_license | yes |
| has_contributing | yes |
| has_issue_template | no |
| has_code_of_conduct | no |
| has_pull_request_template | yes |
Ecosystem adoption (downloads)
100Excellent
How it's scored
| 80/80 | Monthly downloads — 18,375,513 downloads/month across pypi |
| 0/20 | Registry dependents — not reported by this ecosystem |
Inputs used
| packages | keras |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 18,375,513 |
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?
80
How it's scored
| 36/54 | Bus factor — 3 contributor(s) cover half of all commits |
| 13.3/22.5 | Commit distribution — top contributor authored 41% of commits |
| 13.5/13.5 | Contributor breadth — 100 contributors |
| 0/10 | OpenSSF Scorecard: Contributors — no data |
Inputs used
| bus_factor | 3 |
| contributors_sampled | 100 |
| top_contributor_share | 0.408 |
How it's scored
| 46.3/46.8 | Issue resolution — 99% of issues closed |
| 20.6/38.3 | PR acceptance — 5,116/9,488 decided PRs merged |
| 0/15 | OpenSSF Scorecard: Code-Review — no data |
Inputs used
| merged_prs | 5,116 |
| open_issues | 127 |
| closed_issues | 12,697 |
| issue_closed_ratio | 0.99 |
| closed_unmerged_prs | 4,372 |
Ownership & stewardship
77Good
How it's scored
| 30/30 | Ownership backing — organization-owned |
| 0/20 | Verified domain |
| 24.8/25 | Owner reach — 2,785 followers of keras-team |
| 21.8/25 | Track record — 21 public repos, account ~8 yr old |
Inputs used
| followers | 2,785 |
| owner_type | Organization |
| is_verified | — |
| owner_login | keras-team |
| public_repos | 21 |
| account_age_days | 3,138 |
Package maintenance
100Excellent
How it's scored
| 25/25 | Published & resolvable — 1 package(s) on pypi |
| 35/35 | Publish recency — latest publish 21 days ago |
| 20/20 | Version history — 122 published versions |
| 20/20 | Not deprecated — active, not deprecated or yanked |
Inputs used
| packages | keras |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 21 |
Engineering Quality
Are baseline engineering and documentation practices in place?
85
Engineering practices
92Excellent
How it's scored
| 24/24 | CI workflows — 10 workflow(s) |
| 24/24 | Tests present |
| 16/16 | Linter config |
| 9.6/9.6 | Pre-commit hooks |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — no data |
Inputs used
| has_ci | yes |
| has_tests | yes |
| has_editorconfig | no |
| has_linter_config | yes |
| has_precommit_config | yes |
Documentation
75Good
How it's scored
| 30/30 | README |
| 0/25 | Documentation directory |
| 15/15 | Documentation / homepage site — http://keras.io/ |
| 10/10 | Repository description |
| 10/10 | Topics — 8 topics |
| 10/10 | Wiki |
Inputs used
| topics | deep-learning, tensorflow, neural-networks, machine-learning, data-science, python, jax, pytorch |
| has_wiki | yes |
| homepage | http://keras.io/ |
| has_readme | yes |
| has_docs_dir | no |
| has_description | yes |
Security
Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
73
Security posture
73Good
How it's scored
| 30/30 | Security policy (SECURITY.md) |
| 25/25 | Dependabot config |
| 0/25 | Dependency lockfiles — published library — lockfiles are an application concern, not expected |
| 0/20 | CodeQL workflow |
Inputs used
| source | file_signals |
| lockfiles | — |
| manifests | pyproject.toml, requirements-common.txt, requirements-jax-cuda.txt, requirements-jax-tpu.txt, requirements-tensorflow-cuda.txt, requirements-tensorflow-tpu.txt, requirements-torch-cuda.txt, requirements.txt |
| has_codeql_workflow | no |
| has_security_policy | yes |
| has_dependabot_config | yes |
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.
AI Readiness
How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.
38
Agent context & guidance
1Critical
How it's scored
| 0/45 | Agent instructions — no CLAUDE.md / AGENTS.md / editor rules |
| 0/15 | Machine-readable docs (llms.txt) |
| 0/40 | Legible commit history — no data |
Inputs used
| has_llms_txt | no |
| legible_history_share | — |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
Verify loop (build / test / typecheck)
60Moderate
How it's scored
| 0/18 | One-command bootstrap |
| 22/22 | Automated tests |
| 11/11 | Lint / format config |
| 0/11 | Static type checking |
| 10/10 | Reproducible environment — devcontainer |
| 0/10 | Demonstrated agent practice — no data |
| 5/8 | Automated maintenance — dependency automation configured, none observed in the sampled commits |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — no data |
Inputs used
| has_nix | no |
| has_tests | yes |
| lockfiles | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | — |
| has_devcontainer | yes |
| has_linter_config | yes |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
Code legibility for models
53Moderate
How it's scored
| 0/45 | Type-checkable code — Python without a type-check config |
| 53.3/55 | Manageable file sizes — 30/975 source files over 60KB |
Inputs used
| primary_language | Python |
| largest_source_bytes | 452,542 |
| source_files_sampled | 975 |
| oversized_source_files | 30 |
Machine-readable interfaces
40At risk
How it's scored
| 0/40 | API schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP server |
| 40/40 | Runnable examples — examples |
Inputs used
| example_dirs | examples |
| has_mcp_signal | no |
| api_schema_files | — |
Key facts
64,174GitHub stars
100contributors
935commits, last 12 months
0days since last push
91releases
3bus factor
127open issues
PyPIpackage ecosystems
Data collection warnings
- OpenSSF Scorecard timed out after 240s; skipping Scorecard checks
More detail
Direct dependencies 8
| Registry | Package | Version constraint | Manifest |
|---|---|---|---|
| PyPI | absl-py | — | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | rich | — | pyproject.toml |
| PyPI | namex | — | pyproject.toml |
| PyPI | h5py | — | pyproject.toml |
| PyPI | optree | — | pyproject.toml |
| PyPI | ml-dtypes | — | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
All dependencies 41
Full resolved dependency set from the GitHub dependency graph: 8 direct and 33 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.
| Registry | Package | Version | Relation |
|---|---|---|---|
| PyPI | absl-py | — | direct |
| PyPI | h5py | — | direct |
| PyPI | ml-dtypes | — | direct |
| PyPI | namex | — | direct |
| PyPI | numpy | — | direct |
| PyPI | optree | — | direct |
| PyPI | packaging | — | direct |
| PyPI | rich | — | direct |
| PyPI | ai-edge-litert | — | indirect |
| PyPI | build | — | indirect |
| PyPI | coverage | — | indirect |
| PyPI | dm-tree | — | indirect |
| PyPI | flax | — | indirect |
| PyPI | grain | — | indirect |
| PyPI | jax | — | indirect |
| PyPI | jax2onnx | — | indirect |
| PyPI | litert-torch | — | indirect |
| PyPI | onnxruntime | — | indirect |
| PyPI | onnxscript | — | indirect |
| PyPI | openvino | — | indirect |
| PyPI | orbax-checkpoint | — | indirect |
| PyPI | pandas | — | indirect |
| PyPI | pillow | — | indirect |
| PyPI | pre-commit | — | indirect |
| PyPI | protobuf | — | indirect |
| PyPI | pytest | — | indirect |
| PyPI | pytest-cov | — | indirect |
| PyPI | pytest-xdist | — | indirect |
| PyPI | requests | — | indirect |
| PyPI | ruff | — | indirect |
| PyPI | scikit-learn | — | indirect |
| PyPI | scipy | — | indirect |
| PyPI | setuptools | — | indirect |
| PyPI | tensorboard | — | indirect |
| PyPI | tensorboard-plugin-profile | — | indirect |
| PyPI | tensorflow | 2.20.0 | indirect |
| PyPI | tensorflow-cpu | — | indirect |
| PyPI | tensorflow-tpu | 2.19.1 | indirect |
| PyPI | tf2onnx | — | indirect |
| PyPI | torch | — | indirect |
| PyPI | torch-xla | — | indirect |
Raw JSON report machine-readable
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"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 7
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "at_risk",
"name": "AI Readiness",
"value": 38,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "critical",
"name": "Agent context & guidance",
"note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"legible_commit_history"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 1,
"inputs": {
"has_llms_txt": false,
"legible_history_share": null,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"demonstrated_agent_practice"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 60,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [],
"has_devcontainer": true,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": null,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": null,
"points": 11,
"status": "met",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": "devcontainer",
"points": 10,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "devcontainer"
}
}
],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "dependency automation configured, none observed in the sampled commits",
"points": 5,
"status": "partial",
"details": [
{
"code": "dependency_bot_config_only",
"params": {}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "OpenSSF Scorecard unavailable",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "moderate",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 53,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 452542,
"source_files_sampled": 975,
"oversized_source_files": 30
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python without a type-check config",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
"params": {
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "30/975 source files over 60KB",
"points": 53.3,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 975,
"oversized": 30
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "at_risk",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"examples"
],
"has_mcp_signal": false,
"api_schema_files": []
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "examples",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples"
}
}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
"OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
],
"report_type": "repository",
"generated_at": "2026-07-16T05:02:51.669843Z",
"schema_version": "0.11.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/k/keras-team/keras.svg",
"full_name": "keras-team/keras",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}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 v1.13.0, schema v0.11.0 — full methodology · metrics wiki.
How one result sits in the wider record: aggregate statistics — PyPI.