Comparison
xgboost vs awesome-AutoML
Verdict
Pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · xgboost alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | xgboost | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- xgboost
- Scalable, Portable and Distributed Gradient Boosting Library
- awesome-AutoML
- Curating AutoML research and resources
Stars
- xgboost
- 29k
- awesome-AutoML
- 941
Forks
- xgboost
- 8.9k
- awesome-AutoML
- 156
Open issues
- xgboost
- 416
- awesome-AutoML
- 1
Language
- xgboost
- C++
- awesome-AutoML
- -
Adopt for
- xgboost
- xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- xgboost
- -
- awesome-AutoML
- -
Runtime
- xgboost
- -
- awesome-AutoML
- -
License
- xgboost
- Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase
- awesome-AutoML
- GPL-3.0
Last pushed
- xgboost
- Aug 3, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- xgboost
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- xgboost
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- xgboost
- 0d
- awesome-AutoML
- 133d
Open issues (now)
- xgboost
- 416
- awesome-AutoML
- 1
Owner type
- xgboost
- Organization
- awesome-AutoML
- User
Full report
- xgboost
- Trust report
- awesome-AutoML
- Trust report
Choose xgboost if…
- License: xgboost is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt.
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
When NOT to use xgboost
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
- Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, xgboost is Apache-2.0.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dmlc/xgboost) · observed Aug 3, 2026
- GitHub forks (dmlc/xgboost) · observed Aug 3, 2026
- Last push (dmlc/xgboost) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: xgboost 29k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between xgboost and awesome-AutoML?
- xgboost: Scalable, Portable and Distributed Gradient Boosting Library. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose xgboost over awesome-AutoML?
- Choose xgboost over awesome-AutoML when License: xgboost is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt; Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- When should I choose awesome-AutoML over xgboost?
- Choose awesome-AutoML over xgboost when License: awesome-AutoML is GPL-3.0, xgboost is Apache-2.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid xgboost?
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability. Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed. Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is xgboost or awesome-AutoML more popular on GitHub?
- xgboost has more GitHub stars (28,620 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are xgboost and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to xgboost or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at xgboost alternatives and awesome-AutoML alternatives (xgboost markdown twin, awesome-AutoML markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, xgboost or awesome-AutoML?
- xgboost: Very active. awesome-AutoML: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for xgboost and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: xgboost trust report; awesome-AutoML trust report.