--- title: "xgboost vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dmlc-xgboost-vs-hibayesian-awesome-automl-papers" tools: ["dmlc-xgboost", "hibayesian-awesome-automl-papers"] --- # xgboost vs awesome-automl-papers *GraphCanon updated Aug 4, 2026* ## Verdict Pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. [xgboost](https://xgboost.readthedocs.io/) reports 29k GitHub stars, 8.9k forks, and 416 open issues, last pushed Aug 3, 2026. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) has 4.2k stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. Figures are from public GitHub metadata via [xgboost's repository](https://github.com/dmlc/xgboost) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [xgboost](/tools/dmlc-xgboost.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | Scalable, Portable and Distributed Gradient Boosting Library | A curated list of automated machine learning papers and resources. | | Stars | 28,620 | 4,155 | | Forks | 8,876 | 678 | | Open issues | 416 | 2 | | Language | C++ | - | | Adopt for | xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | | 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 | Apache-2.0 | | Categories | Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [xgboost](/tools/dmlc-xgboost.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 784d | | Open issues (now) | 416 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/dmlc-xgboost/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/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-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## Choose when ### Choose xgboost if… - 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. - More GitHub stars (29k vs 4.2k) - visibility, not fit. ### Choose awesome-automl-papers if… - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - Also covers Evaluation & Observability. - When you need a curated list of academic materials to research or learn about AutoML technologies ## 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-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## Common questions ### What is the difference between xgboost and awesome-automl-papers? xgboost: Scalable, Portable and Distributed Gradient Boosting Library. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose xgboost over awesome-automl-papers? Choose xgboost over awesome-automl-papers when 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; More GitHub stars (29k vs 4.2k) - visibility, not fit. ### When should I choose awesome-automl-papers over xgboost? Choose awesome-automl-papers over xgboost when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Evaluation & Observability; When you need a curated list of academic materials to research or learn about AutoML technologies. ### 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-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### Is xgboost or awesome-automl-papers more popular on GitHub? xgboost has more GitHub stars (28,620 vs 4,155). Stars measure visibility, not whether either tool fits your constraints. ### Are xgboost and awesome-automl-papers open source? Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, awesome-automl-papers: Apache-2.0). ### Where can I find alternatives to xgboost or awesome-automl-papers? GraphCanon lists graph-backed alternatives at [xgboost alternatives](/tools/dmlc-xgboost/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([xgboost markdown twin](/tools/dmlc-xgboost/alternatives.md), [awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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-hibayesian-awesome-automl-papers.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-papers? xgboost: Very active. awesome-automl-papers: Dormant. 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-papers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [xgboost trust report](/tools/dmlc-xgboost/trust); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/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/_