Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 22:05 UTC

triton-inference-server / server

The Triton Inference Server provides an optimized cloud and edge inferencing solution.

Python · Shell · C++BSD-3-Clause★ 10,997 stars⑂ 1,837 forkssince Oct 2018View on GitHub ↗
KindNetwork servicehow this is determined

triton-inference-server/server holds a health index of 97 out of 100, placing it in the Exceptional band. It scores highest on Vitality (94/100) and lowest on AI Readiness (59/100). It was last updated 2 days ago. 4 contributors account for most of its recent work.

97
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.

97
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 85 is calibrated to 97 on the published index scale (record calibration 2026-08-02).

Ownership

2,052 followers36 public repossince Jul 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItritonfrontendpoints to another repo — not scored2.72.0185319 days ago

Metrics by category

Vitality

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

94Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 2 days ago
33.2/36Commit cadence — 48/52 weeks with commits
18/18Commit volume — 225 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year225
human_commit_share1
days_since_last_push2
active_weeks_last_year48
How it's scored
27/27Ships releases — 94 releases published
36/36Release recency — latest release 19 days ago
27/27Release cadence — a release every ~31 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count94
latest_release_tagv2.72.0
releases_from_tagsno
days_since_latest_release19
mean_days_between_releases31

Community & Adoption

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

88Excellent · 17% of overall
How it's scored
60/60Stars — 10,997 stars
25/25Forks — 1,837 forks
12/15Watchers — 146 watchers
Inputs used
forks1,837
stars10,997
watchers146
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
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

80Excellent · 23% of overall
How it's scored
43.2/54Bus factor — 4 contributor(s) cover half of all commits
18.2/22.5Commit distribution — top contributor authored 19% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Inputs used
bus_factor4
contributors_sampled100
top_contributor_share0.19
How it's scored
34.1/42Issue resolution — 81% of issues closed
27.3/30PR acceptance — 4,034/4,439 decided PRs merged
0/13Newcomer PR acceptance — 0/1 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs4,034
open_issues780
closed_issues3,385
prs_merged_7d7
prs_decided_7d7
prs_merged_30d28
prs_decided_30d31
issue_closed_ratio0.813
closed_unmerged_prs405
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.8/25Owner reach — 2,052 followers of triton-inference-server
23.4/25Track record — 36 public repos, account ~6 yr old
Inputs used
followers2,052
owner_typeOrganization
is_verifiedno
owner_logintriton-inference-server
public_repos36
account_age_days2,262

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
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://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicsinference, gpu, machine-learning, deep-learning, cloud, datacenter, edge
has_wikino
homepagehttps://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
docs_sitehttps://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

74Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
4.5/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.2/2.5CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 10 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 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
4.5/5SAST — SAST tool detected but not run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6.8/7.5Vulnerabilities — 1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.7
Excluded from scoring (no data or not applicable): Packaging. 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_packages6
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:tritonfrontend@2.72.0 runtime dependency closure — what installing the published package pulls in — 6 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)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile, docs/examples/jetson/concurrency_and_dynamic_batching/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.isort])
11/11Static type checking — src/python/tritonfrontend/py.typed
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 — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile, docs/examples/jetson/concurrency_and_dynamic_batching/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/python/tritonfrontend/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (src/python/tritonfrontend/py.typed)
53.8/55Manageable file sizes — 10/460 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes180,508
source_files_sampled460
oversized_source_files10
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

10,997GitHub stars
100contributors
225commits, last 12 months
2days since last push
94releases
4bus factor
780open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'tritonfrontend' points at a different repository (https://developer.nvidia.com/nvidia-triton-inference-server); excluded from ecosystem scoring
  • Could not fetch pypi package 'mlflow-triton' from its registry

More detail

Star and fork history 0 ★ / 1,837 ⇿
0Stars
1,837Forks
51Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

5001,0001,5002,0001,837112022-062024-082026-09
Major 0Minor 50Patch 1

Each point covers 4 days.

OpenSSF Scorecard 6.7 / 10
6.7aggregate

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 · 2026-09-19 22:04 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 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
9SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
9Vulnerabilities1 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPItritonserver—src/python/pyproject.toml
PyPIpydantic==2.10.6src/python/pyproject.toml
All dependencies 25

Full resolved dependency set from the GitHub dependency graph: 1 direct and 24 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpydantic2.10.6direct
PyPIboto3—indirect
PyPIdefusedxml—indirect
PyPIdistro—indirect
PyPIfastapi0.136.3indirect
PyPIgeventhttpclient—indirect
PyPIgrpcio—indirect
PyPIhatchling1.27.0indirect
PyPIhttpx0.27.2indirect
PyPImlflow—indirect
PyPInetworkx2.3indirect
PyPInumpy—indirect
PyPIonnx—indirect
PyPIopenai1.107.3indirect
PyPIpartial-json-parser—indirect
PyPIprotobuf—indirect
PyPIpytest—indirect
PyPIpytest8.1.1indirect
PyPIpytest-asyncio0.23.8indirect
PyPIpython-rapidjson—indirect
PyPIrequests—indirect
PyPIscipy1.16.3indirect
PyPIstarlette—indirect
PyPItest-generator0.1.1indirect
PyPItritonclient—indirect
Dependency advisories 0

Installing pypi:tritonfrontend@2.72.0 pulls in 6 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.