Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 12:05 UTC

Nixtla / nixtla

TimeGPT-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★ 3,985 stars⑂ 332 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 92 out of 100, placing it in the Excellent band. It scores highest on Vitality (96/100) and lowest on Security (46/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.

92
overall / 100
Excellent

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.

92
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 78 is calibrated to 92 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization
2,834 followers40 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
PyPInixtla0.8.0-2823 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

93Exceptional
How it's scored
36/36Push recency — last push 2 days ago
29.8/36Commit cadence — 43/52 weeks with commits
18/18Commit volume — 142 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year142
human_commit_share0.65
days_since_last_push2
active_weeks_last_year43

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 49 releases published
36/36Release recency — latest release 23 days ago
27/27Release cadence — a release every ~29.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count49
latest_release_tagv0.8.0
releases_from_tagsno
days_since_latest_release23
mean_days_between_releases29.2

Community & Adoption

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

84Excellent · 17% of overall

Popularity & adoption

88Excellent
How it's scored
58.4/60Stars — 3,985 stars
21/25Forks — 332 forks
8.7/15Watchers — 38 watchers
Inputs used
forks332
stars3,985
watchers38
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

Sustainability & Governance

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

74Good · 23% of overall

Maintainer resilience (bus factor)

35Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
8.6/22.5Commit distribution — top contributor authored 62% of commits
13.5/13.5Contributor breadth — 24 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled24
top_contributor_share0.619

Issue & PR responsiveness

78Good
How it's scored
32.2/42Issue resolution — 77% of issues closed
24.3/30PR acceptance — 473/585 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_prs473
open_issues47
closed_issues154
prs_merged_7d6
prs_decided_7d8
prs_merged_30d15
prs_decided_30d17
issue_closed_ratio0.766
closed_unmerged_prs112
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

97Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
24.8/25Owner reach — 2,834 followers of Nixtla
22.6/25Track record — 40 public repos, account ~5 yr old
Inputs used
followers2,834
owner_typeOrganization
is_verified—
owner_loginNixtla
public_repos40
account_age_days1,989
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 23 days ago
20/20Version history — 28 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesnixtla
ecosystemspypi
any_deprecatedno
min_days_since_publish23

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 8 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

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?

46Weak · 16% of overall

Security posture

33At Risk
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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.

76Good · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 56 of 65 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.862
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

93Exceptional
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
11/11Static type checking — nixtla/py.typed
10/10Reproducible environment — devcontainer, lockfile
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 35 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
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_share0.02
toolchain_manifests—
dependency_bot_commit_share0.35

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Jupyter Notebook with type-check config (nixtla/py.typed)
53.9/55Manageable file sizes — 2/103 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes142,887
source_files_sampled103
oversized_source_files2

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

3,985GitHub stars
24contributors
142commits, last 12 months
2days since last push
49releases
1bus factor
47open 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 ★ / 332 ⇿
0Stars
332Forks
48Releases

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.

0125250375329302021-102024-032026-07
Major 0Minor 7Patch 32

Each point covers 5 days.

Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIannotated-types—pyproject.toml
PyPIhttpx—pyproject.toml
PyPInarwhals>=2.11.0pyproject.toml
PyPIorjson—pyproject.toml
PyPIpandas<3.0.0pyproject.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: 13 direct and 282 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIannotated-types0.7.0direct
PyPIhttpx0.28.1direct
PyPInarwhals2.16.0direct
PyPIorjson3.11.6direct
PyPIpandas—direct
PyPIpandas2.2.3direct
PyPIpydantic2.12.3direct
PyPIpydantic2.13.4direct
PyPItenacity9.1.2direct
PyPItqdm—direct
PyPItqdm4.67.1direct
PyPIutilsforecast—direct
PyPIutilsforecast0.2.15direct
PyPIadagio0.2.6indirect
PyPIaiohappyeyeballs2.6.1indirect
PyPIaiohttp3.14.3indirect
PyPIaiosignal1.4.0indirect
PyPIalembic1.17.0indirect
PyPIanyio4.11.0indirect
PyPIappnope0.1.4indirect
PyPIargon2-cffi25.1.0indirect
PyPIargon2-cffi-bindings25.1.0indirect
PyPIarrow1.4.0indirect
PyPIasn1crypto1.5.1indirect
PyPIasttokens3.0.0indirect
PyPIasync-lru2.0.5indirect
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PyPIattrs25.4.0indirect
PyPIazure-ai-ml—indirect
PyPIazure-identity—indirect
PyPIazureml-core—indirect
PyPIbabel2.17.0indirect
PyPIbeautifulsoup44.14.2indirect
PyPIbleach6.4.0indirect
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PyPIboto3—indirect
PyPIboto31.42.41indirect
PyPIbotocore1.42.41indirect
PyPIcertifi2025.10.5indirect
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PyPIcfgv3.4.0indirect
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PyPIjax—indirect
PyPIjedi0.19.2indirect
PyPIjinja23.1.6indirect
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PyPIjson50.12.1indirect
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PyPIlightgbm—indirect
PyPIlightgbm4.6.0indirect
PyPIlightning-utilities0.15.2indirect
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PyPImatplotlib-inline0.1.7indirect
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PyPImlforecast—indirect
PyPImlforecast1.0.2indirect
PyPImltable—indirect
PyPImpmath1.3.0indirect
PyPImsgpack1.2.1indirect
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PyPInbclient0.10.2indirect
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PyPInest-asyncio1.6.0indirect
PyPInetworkx3.4.2indirect
PyPInetworkx3.5indirect
PyPIneuralforecast—indirect
PyPIneuralforecast3.1.2indirect
PyPInixtla—indirect
PyPInixtla0.8.0indirect
PyPInixtlats0.5.2indirect
PyPInodeenv1.9.1indirect
PyPInotebook-shim0.2.4indirect
PyPInumba0.62.1indirect
PyPInumpy—indirect
PyPInumpy1.26.4indirect
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PyPInvidia-cusolver12.0.4.66indirect
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PyPIpolars-runtime-321.34.0indirect
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PyPIprettytable3.17.0indirect
PyPIprometheus-client0.23.1indirect
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PyPIprophet—indirect
PyPIprotobuf6.33.5indirect
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PyPIptyprocess0.7.0indirect
PyPIpure-eval0.2.3indirect
PyPIpy4j0.10.9.9indirect
PyPIpyarrow—indirect
PyPIpyarrow23.0.1indirect
PyPIpycparser2.23indirect
PyPIpydantic-core2.41.4indirect
PyPIpydantic-core2.46.4indirect
PyPIpygments2.20.0indirect
PyPIpyjwt2.13.0indirect
PyPIpyluach2.3.0indirect
PyPIpyopenssl26.4.0indirect
PyPIpyparsing3.2.5indirect
PyPIpyreadr0.5.2indirect
PyPIpyspark4.0.1indirect
PyPIpytest—indirect
PyPIpytest9.0.3indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-rerunfailures16.1indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv—indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-json-logger4.0.0indirect
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PyPIpytz2025.2indirect
PyPIpywinpty3.0.2indirect
PyPIpyyaml6.0.3indirect
PyPIpyzmq27.1.0indirect
PyPIquadprog0.1.13indirect
PyPIray2.56.1indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.33.0indirect
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PyPIrich—indirect
PyPIrich14.3.2indirect
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PyPIruff0.15.1indirect
PyPIs3fs—indirect
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PyPIscikit-learn1.7.2indirect
PyPIscipy—indirect
PyPIscipy1.15.3indirect
PyPIsend2trash1.8.3indirect
PyPIsetuptools83.0.0indirect
PyPIshap0.49.1indirect
PyPIsix1.17.0indirect
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PyPIsniffio1.3.1indirect
PyPIsnowflake-connector-python4.6.0indirect
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PyPIsoupsieve2.8.4indirect
PyPIsqlalchemy2.0.44indirect
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PyPIstatsforecast—indirect
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PyPItblib3.2.2indirect
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PyPItermcolor3.1.0indirect
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PyPIthreadpoolctl3.6.0indirect
PyPItinycss21.4.0indirect
PyPItomli2.3.0indirect
PyPItomlkit0.14.0indirect
PyPItoolz1.1.0indirect
PyPItorch—indirect
PyPItorch2.13.0indirect
PyPItorchmetrics1.8.2indirect
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PyPItriad1.0.1indirect
PyPItriton3.7.1indirect
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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.

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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 v0.31.0 — full methodology · metrics wiki.

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