Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-18 01:04 UTC

Nixtla / nixtla

TimeGPT-2.1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

Jupyter NotebookCustom license★ 4,008 stars⑂ 336 forkssince Sep 2021View on GitHub ↗
KindLibraryhow this is determined
#time-series#time-series-forecasting#deep-learning#forecasting#gpt#generative-ai-time-series#timegpt#anomaly-detection#artificial-intelligence#gpts#llm#foundation-models#llms#agentic-ai#agent

Nixtla/nixtla holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (96/100) and lowest on Sustainability & Governance (76/100). It was last updated today. A single contributor accounts for most of its recent work.

96
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.

96
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 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization · verified domain
2,852 followers41 public repossince Mar 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPInixtla0.8.057,3053158 days agotime-seriesforecastinggpt

Metrics by category

Vitality

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

96Exceptional · 21% of overall

Development activity

94Exceptional
How it's scored
36/36Push recency — last push 0 days ago
30.5/36Commit cadence — 44/52 weeks with commits
18/18Commit volume — 159 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year159
human_commit_share0.71
days_since_last_push0
active_weeks_last_year44

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 52 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~24 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count52
latest_release_tagv0.9.0.dev2
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases24
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

83Excellent · 17% of overall

Popularity & adoption

88Excellent
How it's scored
58.4/60Stars — 4,008 stars
21/25Forks — 336 forks
8.7/15Watchers — 37 watchers
Inputs used
forks336
stars4,008
watchers37
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

79Good
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

79Good
How it's scored
63.4/80Monthly downloads — 57,305 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesnixtla
dependents—
ecosystemspypi
total_downloads—
monthly_downloads57,305
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?

76Good · 23% of overall

Maintainer resilience (bus factor)

41Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
8.9/22.5Commit distribution — top contributor authored 60% of commits
13.5/13.5Contributor breadth — 25 contributors
10/10OpenSSF Scorecard: Contributors — project has 11 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled25
top_contributor_share0.604

Issue & PR responsiveness

76Good
How it's scored
32.9/42Issue resolution — 78% of issues closed
24.3/30PR acceptance — 500/618 decided PRs merged
4.3/13Newcomer PR acceptance — 1/3 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs500
open_issues44
closed_issues159
prs_merged_7d11
prs_decided_7d13
prs_merged_30d26
prs_decided_30d30
issue_closed_ratio0.783
closed_unmerged_prs118
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d3

Ownership & stewardship

98Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
24.8/25Owner reach — 2,852 followers of Nixtla
22.9/25Track record — 41 public repos, account ~5 yr old
Inputs used
followers2,852
owner_typeOrganization
is_verifiedyes
owner_loginNixtla
public_repos41
account_age_days2,025

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 58 days ago
20/20Version history — 31 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesnixtla
ecosystemspypi
any_deprecatedno
min_days_since_publish58

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

75Good
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://www.nixtla.io/docs
10/10Repository description
10/10Topics — 15 topics
10/10Wiki
Inputs used
topicstime-series, time-series-forecasting, deep-learning, forecasting, gpt, generative-ai-time-series, timegpt, anomaly-detection, artificial-intelligence, gpts, llm, foundation-models, llms, agentic-ai, agent
has_wikiyes
homepagehttps://www.nixtla.io/docs
docs_sitehttps://www.nixtla.io/docs
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

77Good · 16% of overall

Security posture

71Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
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 11 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool is run on all commits
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
6/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 34 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.1
Excluded from scoring (no data or not applicable): Signed-Releases. 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_packages22
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:nixtla@0.8.0 runtime dependency closure — what installing the published package pulls in — 22 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.

81Excellent · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://www.nixtla.io/llms.txt)
40/40Legible commit history — 57 of 71 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://www.nixtla.io/llms.txt
legible_history_share0.803
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

94Exceptional
How it's scored
18/18One-command bootstrap — Makefile, experiments/azure-automl-forecasting/Makefile, experiments/foundation-time-series-arena/Makefile, experiments/lag-llama/Makefile, experiments/prophet/Makefile, experiments/vn1-competition/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — nixtla/py.typed
10/10Reproducible environment — devcontainer, lockfile
10/10Demonstrated agent practice — 9 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 29 of the last 100 commits are automated dependency updates
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, experiments/azure-automl-forecasting/Makefile, experiments/foundation-time-series-arena/Makefile, experiments/lag-llama/Makefile, experiments/prophet/Makefile, experiments/vn1-competition/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configsnixtla/py.typed
agent_commit_share0.09
toolchain_manifests—
dependency_bot_commit_share0.29

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Jupyter Notebook with type-check config (nixtla/py.typed)
53.7/55Manageable file sizes — 3/130 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes205,114
source_files_sampled130
oversized_source_files3

Machine-readable interfaces

100Exceptional
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — timegpt-docs/openapi.json
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_filestimegpt-docs/openapi.json
interfaces_expected_of—
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

4,008GitHub stars
25contributors
159commits, last 12 months
0days since last push
52releases
1bus factor
44open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 336 ⇿
0Stars
336Forks
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.

0125250375333302021-102024-042026-09
Major 0Minor 8Patch 32

Each point covers 5 days.

OpenSSF Scorecard 7.1 / 10
7.1aggregate

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 · 2026-09-18 01:01 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 11 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool is run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
8Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities34 existing vulnerabilities detected
Direct dependencies 10
RegistryPackageVersion constraintManifest
PyPIannotated-types—pyproject.toml
PyPIhttpx—pyproject.toml
PyPInarwhals>=2.11.0pyproject.toml
PyPIorjson—pyproject.toml
PyPIpandas<3.0.0pyproject.toml
PyPIpyarrow>=14.0.0,<24.0.0pyproject.toml
PyPIpydantic>=1.10pyproject.toml
PyPItenacity—pyproject.toml
PyPItqdm—pyproject.toml
PyPIutilsforecast>=0.2.15pyproject.toml
All dependencies 295

Full resolved dependency set from the GitHub dependency graph: 15 direct and 280 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIhttpx0.28.1direct
PyPInarwhals2.16.0direct
PyPIorjson3.11.6direct
PyPIpandas—direct
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PyPIpyarrow—direct
PyPIpyarrow23.0.1direct
PyPIpydantic2.12.3direct
PyPIpydantic2.13.4direct
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PyPItqdm—direct
PyPItqdm4.67.1direct
PyPIutilsforecast—direct
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PyPIs3transfer0.16.0indirect
PyPIscikit-learn1.7.2indirect
PyPIscipy—indirect
PyPIscipy1.15.3indirect
PyPIsend2trash1.8.3indirect
PyPIsetuptools83.0.0indirect
PyPIshap0.49.1indirect
PyPIsix1.17.0indirect
PyPIslicer0.0.8indirect
PyPIsniffio1.3.1indirect
PyPIsnowflake-connector-python4.7.1indirect
PyPIsnowflake-snowpark-python1.45.0indirect
PyPIsortedcontainers2.4.0indirect
PyPIsoupsieve2.8.4indirect
PyPIsqlalchemy2.0.44indirect
PyPIstack-data0.6.3indirect
PyPIstatsforecast—indirect
PyPIstatsforecast2.0.2indirect
PyPIstatsmodels0.14.5indirect
PyPIsympy1.14.0indirect
PyPItabulate0.9.0indirect
PyPItblib3.2.2indirect
PyPItensorboardx2.6.4indirect
PyPItermcolor3.1.0indirect
PyPIterminado0.18.1indirect
PyPIthreadpoolctl3.6.0indirect
PyPItinycss21.4.0indirect
PyPItomli2.3.0indirect
PyPItomlkit0.14.0indirect
PyPItoolz1.1.0indirect
PyPItorch—indirect
PyPItorch2.13.0indirect
PyPItorchmetrics1.8.2indirect
PyPItornado6.5.8indirect
PyPItraitlets5.14.3indirect
PyPItriad1.0.1indirect
PyPItriton3.7.1indirect
PyPItsdownsample0.1.4.1indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.2indirect
PyPItzlocal5.3.1indirect
PyPIuri-template1.3.0indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv20.36.1indirect
PyPIwandb—indirect
PyPIwcwidth0.2.14indirect
PyPIwebcolors24.11.1indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.9.0indirect
PyPIwerkzeug3.1.6indirect
PyPIwheel0.46.3indirect
PyPIwidgetsnbextension4.0.14indirect
PyPIxlrd2.0.2indirect
PyPIyarl1.22.0indirect
PyPIzict3.0.0indirect
PyPIzipp3.23.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:nixtla@0.8.0 pulls in 22 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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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.