--- title: "awesome-AutoML vs MOE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/windmaple-awesome-automl-vs-yelparchive-moe" tools: ["windmaple-awesome-automl", "yelparchive-moe"] --- # awesome-AutoML vs MOE *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning; pick MOE if mOE optimizes real-world metrics via automated black-box processes. It is written in C++. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) reports 941 GitHub stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. [MOE](https://github.com/YelpArchive/MOE) has 1.3k stars, 139 forks, and 175 open issues, last pushed Mar 24, 2023. Figures are from public GitHub metadata via [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML) and [MOE's repository](https://github.com/YelpArchive/MOE). | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [MOE](/tools/yelparchive-moe.md) | | --- | --- | --- | | Tagline | Curating AutoML research and resources | A global, black box optimization engine for real world metric optimization | | Stars | 941 | 1,321 | | Forks | 156 | 139 | | Open issues | 1 | 175 | | Language | - | C++ | | Adopt for | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | MOE optimizes real-world metrics via automated black-box processes. It is written in C++. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | Licensed under the Apache License, Version 2.0. | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | [MOE](/tools/yelparchive-moe.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 133d | 1228d | | Archived on GitHub | No | Yes | | Open issues (now) | 1 | 175 | | Owner type | User | Organization | | Full report | [trust report](/tools/windmaple-awesome-automl/trust.md) | [trust report](/tools/yelparchive-moe/trust.md) | ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Decision facts: MOE - **Adopt for:** MOE optimizes real-world metrics via automated black-box processes. It is written in C++. - **License detail:** Licensed under the Apache License, Version 2.0. ## Choose when ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, MOE is Other. - 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. ### Choose MOE if… - License: MOE is Other, awesome-AutoML is GPL-3.0. - Tags unique to MOE: c++, docker, rest server. - MOE ships Docker support for self-hosted deployment. - When you require an optimization engine that operates as a global, isolated system through Docker containers. ## 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. ## When NOT to use MOE - If your team lacks the knowledge or experience to configure and run Docker environments. - Not suitable for projects where real-time interaction with optimization processes is needed, as MOE focuses on batch processing scenarios. ## Common questions ### What is the difference between awesome-AutoML and MOE? awesome-AutoML: Curating AutoML research and resources. MOE: A global, black box optimization engine for real world metric optimization. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-AutoML over MOE? Choose awesome-AutoML over MOE when License: awesome-AutoML is GPL-3.0, MOE is Other; 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 choose MOE over awesome-AutoML? Choose MOE over awesome-AutoML when License: MOE is Other, awesome-AutoML is GPL-3.0; Tags unique to MOE: c++, docker, rest server; MOE ships Docker support for self-hosted deployment; When you require an optimization engine that operates as a global, isolated system through Docker containers. ### 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. ### When should I avoid MOE? If your team lacks the knowledge or experience to configure and run Docker environments. Not suitable for projects where real-time interaction with optimization processes is needed, as MOE focuses on batch processing scenarios. ### Is awesome-AutoML or MOE more popular on GitHub? MOE has more GitHub stars (1,321 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-AutoML and MOE open source? Yes - both are open-source projects on GitHub (awesome-AutoML: GPL-3.0, MOE: Other). ### Where can I find alternatives to awesome-AutoML or MOE? GraphCanon lists graph-backed alternatives at [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) and [MOE alternatives](/tools/yelparchive-moe/alternatives) ([awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/alternatives.md), [MOE markdown twin](/tools/yelparchive-moe/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/windmaple-awesome-automl-vs-yelparchive-moe.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-AutoML or MOE? awesome-AutoML: Slowing. MOE: Archived. 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 awesome-AutoML and MOE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust); [MOE trust report](/tools/yelparchive-moe/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=windmaple-awesome-automl`](/api/graphcanon/graph?tool=windmaple-awesome-automl) - 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/_