Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 18:15 UTC

dmlc / xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

C++ · Python · CudaApache-2.0★ 28,768 stars⑂ 8,899 forkssince Feb 2014View on GitHub ↗

dmlc/xgboost holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (100/100) and lowest on AI Readiness (74/100). It was last updated today. 2 contributors account for most of its recent work.

98
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.

98
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 87 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

1,783 followers52 public repossince Mar 2015

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIxgboost3.4.1-9332 days ago

Metrics by category

Vitality

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

99Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
35.3/36Commit cadence — 51/52 weeks with commits
18/18Commit volume — 476 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year476
human_commit_share0.93
days_since_last_push0
active_weeks_last_year51

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 80 releases published
36/36Release recency — latest release 1 days ago
27/27Release cadence — a release every ~36.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count80
latest_release_tagv3.3.1
releases_from_tagsno
days_since_latest_release1
mean_days_between_releases36.9

Community & Adoption

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

80Excellent · 17% of overall

Popularity & adoption

100Exceptional
How it's scored
60/60Stars — 28,768 stars
25/25Forks — 8,899 forks
15/15Watchers — 886 watchers
Inputs used
forks8,899
stars28,768
watchers886
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)
0/18CONTRIBUTING guide
0/13.5Code of conduct
7.2/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges12
has_contributingno
has_issue_templateyes
has_code_of_conductno
readme_badge_servicesapi.securityscorecards.dev, badge.fury.io, github.com, opencollective.com, readthedocs.org, shields.io
has_pull_request_templateno

Sustainability & Governance

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

79Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
16.1/22.5Commit distribution — top contributor authored 29% of commits
13.5/13.5Contributor breadth — 98 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled98
top_contributor_share0.286
How it's scored
39/42Issue resolution — 93% of issues closed
24.1/30PR acceptance — 5,452/6,774 decided PRs merged
7.8/13Newcomer PR acceptance — 6/10 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs5,452
open_issues404
closed_issues5,320
prs_merged_7d17
prs_decided_7d17
prs_merged_30d43
prs_decided_30d53
issue_closed_ratio0.929
closed_unmerged_prs1,322
first_time_authors_30d7
first_time_prs_merged_30d6
first_time_prs_decided_30d10
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.4/25Owner reach — 1,783 followers of dmlc
24.6/25Track record — 52 public repos, account ~11 yr old
Inputs used
followers1,783
owner_typeOrganization
is_verifiedno
owner_logindmlc
public_repos52
account_age_days4,201

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 32 days ago
20/20Version history — 93 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesxgboost
ecosystemspypi
any_deprecatedno
min_days_since_publish32

Engineering Quality

Are baseline engineering and documentation practices in place?

100Exceptional · 19% of overall

Engineering practices

100Exceptional
How it's scored
24/24CI workflows — 17 workflow(s)
24/24Tests present
16/16Linter config — python-package/pyproject.toml ([tool.ruff], [tool.pylint])
9.6/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://xgboost.readthedocs.io/
10/10Repository description
10/10Topics — 6 topics
10/10Wiki
Inputs used
topicsgbdt, gbrt, gbm, distributed-systems, xgboost, machine-learning
has_wikiyes
homepagehttps://xgboost.readthedocs.io/
docs_sitehttps://xgboost.readthedocs.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

78Good · 16% of overall
How it's scored
30/30Security 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
lockfiles—
manifestsdoc/requirements.txt, jvm-packages/pom.xml, python-package/pyproject.toml
has_codeql_workflowno
has_security_policyyes
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_packages3
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:xgboost@3.4.1 runtime dependency closure — what installing the published package pulls in — 3 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.

74Good · 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 — 88 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.946
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — demo/c-api/basic/Makefile, doc/Makefile, doc/R-package/Makefile
22/22Automated tests
11/11Lint / format config — python-package/pyproject.toml ([tool.ruff], [tool.pylint])
11/11Static type checking — python-package/xgboost/py.typed
0/10Reproducible environment
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 7 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageyes
bootstrap_filesdemo/c-api/basic/Makefile, doc/Makefile, doc/R-package/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configspython-package/xgboost/py.typed
agent_commit_share0.01
toolchain_manifestsjvm-packages/pom.xml, jvm-packages/xgboost4j-example/pom.xml, jvm-packages/xgboost4j-flink/pom.xml, jvm-packages/xgboost4j-spark-gpu/pom.xml, jvm-packages/xgboost4j-spark/pom.xml, jvm-packages/xgboost4j/pom.xml
dependency_bot_commit_share0.07
How it's scored
45/45Type-checkable code — C++ (statically typed)
54.5/55Manageable file sizes — 8/835 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes118,303
source_files_sampled835
oversized_source_files8
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — example
Inputs used
example_dirsexample
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

28,768GitHub stars
98contributors
476commits, last 12 months
0days since last push
80releases
2bus factor
404open issues
Maven, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 15 authors (cap 12)
  • pypi download statistics for 'xgboost' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/16 18:13:23 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 8,899 ⇿
0Stars
8,899Forks
36Releases

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.

7,5008,0008,5009,0008,89982022-072024-082026-09
Major 2Minor 6Patch 23

Each point covers 4 days.

Direct dependencies 5
RegistryPackageVersion constraintManifest
Mavencom.esotericsoftware:kryo5.6.2jvm-packages/pom.xml
Mavencommons-logging:commons-logging1.3.4jvm-packages/pom.xml
PyPInumpy—python-package/pyproject.toml
PyPIscipy—python-package/pyproject.toml
PyPInvidia-nccl-cu13—python-package/pyproject.toml
All dependencies 53

Full resolved dependency set from the GitHub dependency graph: 4 direct and 49 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
Mavencom.esotericsoftware:kryo5.6.2direct
Mavencommons-logging:commons-logging1.3.4direct
PyPInumpy—direct
PyPIscipy—direct
Mavencom.fasterxml.jackson.core:jackson-databind—indirect
Mavenjunit:junit—indirect
Mavenml.dmlc:xgboost4j-flink_2.123.5.0-SNAPSHOTindirect
Mavenml.dmlc:xgboost4j-spark_2.123.5.0-SNAPSHOTindirect
Mavenml.dmlc:xgboost4j_2.123.5.0-SNAPSHOTindirect
Mavennet.alchim31.maven:scala-maven-plugin4.9.2indirect
Mavenorg.apache.flink:flink-clients—indirect
Mavenorg.apache.flink:flink-ml-servable-core2.2.0indirect
Mavenorg.apache.hadoop:hadoop-common—indirect
Mavenorg.apache.hadoop:hadoop-hdfs—indirect
Mavenorg.apache.maven.plugins:maven-assembly-plugin—indirect
Mavenorg.apache.maven.plugins:maven-checkstyle-plugin3.6.0indirect
Mavenorg.apache.maven.plugins:maven-gpg-plugin3.2.7indirect
Mavenorg.apache.maven.plugins:maven-jar-plugin3.4.2indirect
Mavenorg.apache.maven.plugins:maven-javadoc-plugin3.11.3indirect
Mavenorg.apache.maven.plugins:maven-release-plugin3.1.1indirect
Mavenorg.apache.maven.plugins:maven-resources-plugin3.3.1indirect
Mavenorg.apache.maven.plugins:maven-shade-plugin—indirect
Mavenorg.apache.maven.plugins:maven-site-plugin3.21.0indirect
Mavenorg.apache.maven.plugins:maven-source-plugin3.3.1indirect
Mavenorg.apache.maven.plugins:maven-surefire-plugin—indirect
Mavenorg.apache.maven.plugins:maven-surefire-plugin3.5.2indirect
Mavenorg.codehaus.mojo:exec-maven-plugin3.5.0indirect
Mavenorg.scala-lang:scala-compiler—indirect
Mavenorg.scala-lang:scala-library—indirect
Mavenorg.scalactic:scalactic_2.123.2.19indirect
Mavenorg.scalatest:scalatest-maven-plugin—indirect
Mavenorg.scalatest:scalatest-maven-plugin2.2.0indirect
Mavenorg.scalatest:scalatest_2.123.2.19indirect
Mavenorg.sonatype.central:central-publishing-maven-plugin0.11.0indirect
PyPIbreathe—indirect
PyPIcloudpickle—indirect
PyPIdask—indirect
PyPIgraphviz—indirect
PyPImatplotlib—indirect
PyPImock—indirect
PyPImyst-parser—indirect
PyPIpandas—indirect
PyPIpyspark—indirect
PyPIray—indirect
PyPIscikit-build-core—indirect
PyPIscikit-learn—indirect
PyPIsetuptools—indirect
PyPIsh—indirect
PyPIsphinx—indirect
PyPIsphinx-gallery—indirect
PyPIsphinx-issues—indirect
PyPIsphinx-rtd-theme—indirect
PyPIsphinx-tabs—indirect
Dependency advisories 0

Installing pypi:xgboost@3.4.1 pulls in 3 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.