--- title: "evalml vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-hibayesian-awesome-automl-papers" tools: ["alteryx-evalml", "hibayesian-awesome-automl-papers"] --- # evalml vs awesome-automl-papers *GraphCanon updated Aug 4, 2026* ## Verdict Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause 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. [evalml](https://evalml.alteryx.com) reports 852 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 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 [evalml's repository](https://github.com/alteryx/evalml) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [evalml](/tools/alteryx-evalml.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | A curated list of automated machine learning papers and resources. | | Stars | 852 | 4,155 | | Forks | 93 | 678 | | Open issues | 324 | 2 | | Language | Python | - | | Adopt for | EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause 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 | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 201d | 784d | | Open issues (now) | 324 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## 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 evalml if… - License: evalml is BSD-3-Clause, awesome-automl-papers is Apache-2.0. - Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.. - Requirements: Min 2 GB RAM. - Tags unique to evalml: data-science, feature-selection, hyperparameter-tuning, machine-learning. - You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### Choose awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, evalml is BSD-3-Clause. - Tags unique to awesome-automl-papers: hyperparameter-optimization, neural-architecture-search. - When you need a curated list of academic materials to research or learn about AutoML technologies ## When NOT to use evalml - You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. - Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ## 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 evalml and awesome-automl-papers? evalml: An AutoML library written in Python. 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 evalml over awesome-automl-papers? Choose evalml over awesome-automl-papers when License: evalml is BSD-3-Clause, awesome-automl-papers is Apache-2.0; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: data-science, feature-selection, hyperparameter-tuning, machine-learning; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### When should I choose awesome-automl-papers over evalml? Choose awesome-automl-papers over evalml when License: awesome-automl-papers is Apache-2.0, evalml is BSD-3-Clause; Tags unique to awesome-automl-papers: hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I avoid evalml? You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ### 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 evalml or awesome-automl-papers more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 852). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and awesome-automl-papers open source? Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, awesome-automl-papers: Apache-2.0). ### Where can I find alternatives to evalml or awesome-automl-papers? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/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/alteryx-evalml-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, evalml or awesome-automl-papers? evalml: Slowing. 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 evalml and awesome-automl-papers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - 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/_