Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-28 09:52 UTC

vllm-project / tpu-inference

TPU inference for vLLM, with unified JAX and PyTorch support.

PythonApache-2.0★ 393 stars⑂ 271 forkssince May 2025View on GitHub ↗

vllm-project/tpu-inference holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Vitality (100/100) and lowest on Security (21/100). It was last updated today. 5 contributors account for most of its recent work.

75
overall / 100
Good

Software 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.

75
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)
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.

Ownership

vLLMOrganization
3,772 followers46 public repossince Jun 2023

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItpu_inference0.25.043,3732846 days ago

Metrics by category

Vitality

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

100Excellent · 22% of overall
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 3,251 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year3,251
human_commit_share1
days_since_last_push0
active_weeks_last_year52

Release discipline

100Excellent
How it's scored
27/27Ships releases — 10 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~24.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count10
latest_release_tagv0.25.0
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases24.6

Community & Adoption

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

73Good · 18% of overall
How it's scored
42.1/60Stars — 393 stars
20.3/25Forks — 271 forks
5.6/15Watchers — 11 watchers
Inputs used
forks271
stars393
watchers11
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingyes
has_issue_templateno
has_code_of_conductno
has_pull_request_templateyes
How it's scored
61.8/80Monthly downloads — 43,373 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagestpu_inference
dependents
ecosystemspypi
total_downloads
monthly_downloads43,373
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?

80Good · 24% of overall
How it's scored
45.9/54Bus factor — 5 contributor(s) cover half of all commits
14.8/22.5Commit distribution — top contributor authored 34% of commits
13.5/13.5Contributor breadth — 100 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor5
contributors_sampled100
top_contributor_share0.343
How it's scored
25.6/46.8Issue resolution — 55% of issues closed
33.5/38.3PR acceptance — 2,452/2,803 decided PRs merged
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs2,452
open_issues72
closed_issues87
issue_closed_ratio0.547
closed_unmerged_prs351
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 3,772 followers of vllm-project
18.4/25Track record — 46 public repos, account ~3 yr old
Inputs used
followers3,772
owner_typeOrganization
is_verified
owner_loginvllm-project
public_repos46
account_age_days1,135
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 6 days ago
20/20Version history — 284 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagestpu_inference
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

87Excellent · 20% of overall
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
16/16Linter config
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
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://docs.vllm.ai/projects/tpu
10/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics
has_wikino
homepagehttps://docs.vllm.ai/projects/tpu
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot 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, requirements.txt, requirements_benchmarking.txt, requirements_test.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.
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_packages141
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:tpu_inference@0.25.0 runtime dependency closure — what installing the published package pulls in — 141 packages. Reachability is not analyzed.

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.

41At risk · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
25.1/40Legible commit history — 47 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.47
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53.5/55Manageable file sizes — 16/601 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes231,672
source_files_sampled601
oversized_source_files16
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples, sample
Inputs used
example_dirsexamples, sample
has_mcp_signalno
api_schema_files

Key facts

393GitHub stars
100contributors
3,251commits, last 12 months
0days since last push
10releases
5bus factor
72open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 271 ⇿
0Stars
271Forks
10Releases

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.

05010015020025030026572025-102026-032026-07
Major 0Minor 8Patch 2
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 0

Installing pypi:tpu_inference@0.25.0 pulls in 141 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
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                "key": "wiki",
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        "description": "Are baseline engineering and documentation practices in place?"
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        "weight": 0.16,
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            "notes": [
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            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:tpu_inference@0.25.0 runtime dependency closure — what installing the published package pulls in — 141 packages. Reachability is not analyzed.",
            "notes": [
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            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
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                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 3
            },
            "components": [
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                "points": 100,
                "status": "met",
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                    "params": {}
                  }
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                "max_points": 100
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            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
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        "name": "AI Readiness",
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        "weight": 0,
        "metrics": [
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  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository",
    "OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks"
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}

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 v0.27.0 — full methodology · metrics wiki.

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