Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 10:14 UTC

tenstorrent / tt-metal

:metal: TT-NN operator library, and TT-Metalium low level kernel programming model.

C++ · PythonApache-2.0★ 1,664 stars⑂ 656 forkssince Feb 2023View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#llama#llm#metal#stable-diffusion#tenstorrent#accelerator#scale-out#kernels#ai#deepseek#gpu#img-gen#video-gen#cuda

tenstorrent/tt-metal holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on Security (21/100). It was last updated today. 25 contributors account for most of its recent work.

94
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.

94
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 80 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

Tenstorrent AIOrganization
2,147 followers139 public repossince Apr 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIttnn0.78.03,649744 days agometaliumttnntt-metalmachine-learningtenstorrentlibnumalibhwloc

Metrics by category

Vitality

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

100Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 11,853 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year11,853
human_commit_share0.98
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~0.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.79.0-dev20260909
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases0.8

Community & Adoption

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

80Excellent · 17% of overall

Popularity & adoption

83Excellent
How it's scored
52.2/60Stars — 1,664 stars
23.5/25Forks — 656 forks
7.1/15Watchers — 20 watchers
Inputs used
forks656
stars1,664
watchers20
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code 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_conductyes
readme_badge_servicesgithub.com
has_pull_request_templateyes

Ecosystem adoption (downloads)

59Moderate
How it's scored
47.5/80Monthly downloads — 3,649 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesttnn
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,649
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?

89Excellent · 23% of overall

Maintainer resilience (bus factor)

98Exceptional
How it's scored
54/54Bus factor — 25 contributor(s) cover half of all commits
21.1/22.5Commit distribution — top contributor authored 6% of commits
13.5/13.5Contributor breadth — 100 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor25
contributors_sampled100
top_contributor_share0.061

Issue & PR responsiveness

80Excellent
How it's scored
34/42Issue resolution — 81% of issues closed
23.7/30PR acceptance — 24,402/30,914 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs24,402
open_issues4,540
closed_issues19,198
prs_merged_7d5
prs_decided_7d7
prs_merged_30d5
prs_decided_30d7
issue_closed_ratio0.809
closed_unmerged_prs6,512
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

79Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
24/25Owner reach — 2,147 followers of tenstorrent
25/25Track record — 139 public repos, account ~6 yr old
Inputs used
followers2,147
owner_typeOrganization
is_verifiedno
owner_logintenstorrent
public_repos139
account_age_days2,330

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 4 days ago
20/20Version history — 74 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesttnn
ecosystemspypi
any_deprecatedno
min_days_since_publish4

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 208 workflow(s)
24/24Tests present
16/16Linter config — .github/pyproject.toml ([tool.black]), pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
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://docs.tenstorrent.com/tt-metal/latest/ttnn/
10/10Repository description
10/10Topics — 14 topics
0/10Wiki
Inputs used
topicsllama, llm, metal, stable-diffusion, tenstorrent, accelerator, scale-out, kernels, ai, deepseek, gpu, img-gen, video-gen, cuda
has_wikino
homepagehttps://docs.tenstorrent.com/tt-metal/latest/ttnn/
docs_sitehttps://docs.tenstorrent.com/tt-metal/latest/ttnn/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21At Risk · 16% of overall

Security posture

1Critical
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifests.github/pyproject.toml, docs/requirements-docs.txt, infra/requirements-infra.txt, pyproject.toml, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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_packages21
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:ttnn@0.78.0 runtime dependency closure — what installing the published package pulls in — 21 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.

96Exceptional · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — .cursor/rules/diagnose-config-hash-mismatch.mdc, .cursor/rules/fix-sweep-trace-match.mdc, .cursor/rules/validate-sweep-trace.mdc, .github/copilot-instructions.md, .github/instructions/build-system.instructions.md, .github/instructions/ci-cd.instructions.md, .github/instructions/cpp.instructions.md, .github/instructions/fabric-distributed.instructions.md, .github/instructions/llk-kernels.instructions.md, .github/instructions/metal-api.instructions.md, .github/instructions/metal-dispatch.instructions.md, .github/instructions/metal-runtime.instructions.md, .github/instructions/models.instructions.md, .github/instructions/nanobind.instructions.md, .github/instructions/python.instructions.md, .github/instructions/tt-stl.instructions.md, .github/instructions/tt-train.instructions.md, .github/instructions/ttnn-core.instructions.md, .github/instructions/ttnn-ops.instructions.md, .github/instructions/ttnn.instructions.md, AGENTS.md, tt_metal/tt-llk/.claude/CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.tenstorrent.com/llms.txt)
40/40Legible commit history — 98 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.tenstorrent.com/llms.txt
legible_history_share1
agent_instruction_files.cursor/rules/diagnose-config-hash-mismatch.mdc, .cursor/rules/fix-sweep-trace-match.mdc, .cursor/rules/validate-sweep-trace.mdc, .github/copilot-instructions.md, .github/instructions/build-system.instructions.md, .github/instructions/ci-cd.instructions.md, .github/instructions/cpp.instructions.md, .github/instructions/fabric-distributed.instructions.md, .github/instructions/llk-kernels.instructions.md, .github/instructions/metal-api.instructions.md, .github/instructions/metal-dispatch.instructions.md, .github/instructions/metal-runtime.instructions.md, .github/instructions/models.instructions.md, .github/instructions/nanobind.instructions.md, .github/instructions/python.instructions.md, .github/instructions/tt-stl.instructions.md, .github/instructions/tt-train.instructions.md, .github/instructions/ttnn-core.instructions.md, .github/instructions/ttnn-ops.instructions.md, .github/instructions/ttnn.instructions.md, AGENTS.md, tt_metal/tt-llk/.claude/CLAUDE.md
agent_instruction_max_bytes10,291

Verify loop (build / test / typecheck)

91Excellent
How it's scored
18/18One-command bootstrap — docs/Makefile, tt_metal/tt-llk/tests/python_tests/Makefile
22/22Automated tests
11/11Lint / format config — .github/pyproject.toml ([tool.black]), pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
11/11Static type checking — tools/triage/mypy.ini, tools/triage/pyrightconfig.json
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 37 of the last 100 commits agent-authored or agent-credited
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_languageyes
bootstrap_filesdocs/Makefile, tt_metal/tt-llk/tests/python_tests/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configstools/triage/mypy.ini, tools/triage/pyrightconfig.json
agent_commit_share0.37
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

99Exceptional
How it's scored
45/45Type-checkable code — C++ (statically typed)
53.8/55Manageable file sizes — 373/17,078 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes771,575
source_files_sampled17,078
oversized_source_files373

Machine-readable interfaces

100Exceptional
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — tools/scaleout/cabling_descriptor/schemas/cluster_config.proto, tools/scaleout/cabling_descriptor/schemas/node_config.proto, tools/scaleout/deployment_descriptor/schemas/deployment.proto, tools/scaleout/factory_system_descriptor/schemas/factory_system_descriptor.proto, tools/scaleout/validation/schemas/ethernet_link_metrics.proto, tt_metal/fabric/protobuf/intermesh_connection_table.proto, tt_metal/fabric/protobuf/mesh_graph_descriptor.proto, tt_metal/fabric/protobuf/physical_grouping_descriptor.proto, tt_metal/fabric/protobuf/physical_system_descriptor.proto, tt_metal/fabric/protobuf/port_descriptor_table.proto, tt_metal/fabric/protobuf/router_port_directions.proto, tt_metal/impl/internal/disaggregation/protobuf/kv_chunk_address_table.proto
20/20MCP server
40/40Runnable examples — demos, example, examples, notebooks
Inputs used
example_dirsdemos, example, examples, notebooks
has_mcp_signalyes
api_schema_filestools/scaleout/cabling_descriptor/schemas/cluster_config.proto, tools/scaleout/cabling_descriptor/schemas/node_config.proto, tools/scaleout/deployment_descriptor/schemas/deployment.proto, tools/scaleout/factory_system_descriptor/schemas/factory_system_descriptor.proto, tools/scaleout/validation/schemas/ethernet_link_metrics.proto, tt_metal/fabric/protobuf/intermesh_connection_table.proto, tt_metal/fabric/protobuf/mesh_graph_descriptor.proto, tt_metal/fabric/protobuf/physical_grouping_descriptor.proto, tt_metal/fabric/protobuf/physical_system_descriptor.proto, tt_metal/fabric/protobuf/port_descriptor_table.proto, tt_metal/fabric/protobuf/router_port_directions.proto, tt_metal/impl/internal/disaggregation/protobuf/kv_chunk_address_table.proto
interfaces_expected_of—

Key facts

1,664GitHub stars
100contributors
11,853commits, last 12 months
0days since last push
100releases
25bus factor
4,540open 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 ★ / 656 ⇿
0Stars
656Forks
97Releases

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.

0125250375500625750650182024-012025-052026-09
Major 0Minor 5Patch 0

Each point covers 3 days.

Direct dependencies 9
RegistryPackageVersion constraintManifest
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PyPInetworkx>=3.1pyproject.toml
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PyPIpandas>=2.0.3pyproject.toml
PyPIseaborn>=0.13.2pyproject.toml
PyPIml_dtypes—pyproject.toml
All dependencies 231

Full resolved dependency set from the GitHub dependency graph: 21 direct and 210 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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Dependency advisories 0

Installing pypi:ttnn@0.78.0 pulls in 21 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.

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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 v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.