Comparison
autogluon vs xgboost
Verdict
Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license.
Markdown twin · autogluon alternatives · xgboost alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | autogluon | xgboost |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization 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
- autogluon
- Fast and Accurate ML in 3 Lines of Code
- xgboost
- Scalable, Portable and Distributed Gradient Boosting Library
Stars
- autogluon
- 11k
- xgboost
- 29k
Forks
- autogluon
- 1.2k
- xgboost
- 8.9k
Open issues
- autogluon
- 388
- xgboost
- 416
Language
- autogluon
- Python
- xgboost
- C++
Adopt for
- autogluon
- AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
- xgboost
- xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
Persona
- autogluon
- -
- xgboost
- -
Runtime
- autogluon
- -
- xgboost
- -
License
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
- 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
Last pushed
- autogluon
- Aug 3, 2026
- xgboost
- Aug 3, 2026
Categories
- autogluon
- Developer Tools, Model Training
- xgboost
- Model Training
Trust and health
Open issues (now)
- autogluon
- 388
- xgboost
- 416
Full report
- autogluon
- Trust report
- xgboost
- Trust report
Choose autogluon if…
- autogluon is primarily Python; xgboost is C++.
- Tags unique to autogluon: automated-machine-learning, automl, computer-vision, data-science.
- Also covers Developer Tools.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
When NOT to use autogluon
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation.
- For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
Choose xgboost if…
- xgboost is primarily C++; autogluon is Python.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autogluon/autogluon) · observed Aug 4, 2026
- GitHub forks (autogluon/autogluon) · observed Aug 4, 2026
- Last push (autogluon/autogluon) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: autogluon 11k · xgboost 29k (synced Aug 4, 2026).
Common questions
- What is the difference between autogluon and xgboost?
- autogluon: Fast and Accurate ML in 3 Lines of Code. xgboost: Scalable, Portable and Distributed Gradient Boosting Library. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogluon over xgboost?
- Choose autogluon over xgboost when autogluon is primarily Python; xgboost is C++; Tags unique to autogluon: automated-machine-learning, automl, computer-vision, data-science; Also covers Developer Tools; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- When should I choose xgboost over autogluon?
- Choose xgboost over autogluon when xgboost is primarily C++; autogluon is Python; 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 avoid autogluon?
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
- 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.
- Is autogluon or xgboost more popular on GitHub?
- xgboost has more GitHub stars (28,620 vs 10,576). Stars measure visibility, not whether either tool fits your constraints.
- Are autogluon and xgboost open source?
- Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, xgboost: Apache-2.0).
- Where can I find alternatives to autogluon or xgboost?
- GraphCanon lists graph-backed alternatives at autogluon alternatives and xgboost alternatives (autogluon markdown twin, xgboost 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, autogluon or xgboost?
- autogluon: Very active. xgboost: Very active. 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 autogluon and xgboost?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogluon trust report; xgboost trust report.