--- title: "xgboost vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dmlc-xgboost-vs-windmaple-awesome-automl" tools: ["dmlc-xgboost", "windmaple-awesome-automl"] --- # xgboost vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## 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. [xgboost](https://xgboost.readthedocs.io/) reports 29k GitHub stars, 8.9k forks, and 416 open issues, last pushed Aug 3, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [xgboost's repository](https://github.com/dmlc/xgboost) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [xgboost](/tools/dmlc-xgboost.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Scalable, Portable and Distributed Gradient Boosting Library | Curating AutoML research and resources | | Stars | 28,620 | 941 | | Forks | 8,876 | 156 | | Open issues | 416 | 1 | | Language | C++ | - | | Adopt for | xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | 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 | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [xgboost](/tools/dmlc-xgboost.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 133d | | Open issues (now) | 416 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/dmlc-xgboost/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: xgboost - **Adopt for:** xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license - **License detail:** 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 ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/dmlc-xgboost/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([xgboost markdown twin](/tools/dmlc-xgboost/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/alternatives.md)), 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](/compare/dmlc-xgboost-vs-windmaple-awesome-automl.md) 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](/tools/dmlc-xgboost/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dmlc-xgboost`](/api/graphcanon/graph?tool=dmlc-xgboost) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_