Comparison
awesome-AutoML vs MOE
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++.
Markdown twin · awesome-AutoML alternatives · MOE alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-AutoML | MOE |
|---|---|---|
| Maintenance | Slowing (133d since push) As of 2w · github_public_v1 | Archived (1228d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal 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 | Published findings 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
- awesome-AutoML
- Curating AutoML research and resources
- MOE
- A global, black box optimization engine for real world metric optimization
Stars
- awesome-AutoML
- 941
- MOE
- 1.3k
Forks
- awesome-AutoML
- 156
- MOE
- 139
Open issues
- awesome-AutoML
- 1
- MOE
- 175
Language
- awesome-AutoML
- -
- MOE
- C++
Adopt for
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
- MOE
- MOE optimizes real-world metrics via automated black-box processes. It is written in C++.
Persona
- awesome-AutoML
- -
- MOE
- -
Runtime
- awesome-AutoML
- -
- MOE
- -
License
- awesome-AutoML
- GPL-3.0
- MOE
- Licensed under the Apache License, Version 2.0.
Last pushed
- awesome-AutoML
- Mar 24, 2026
- MOE
- Mar 24, 2023
Categories
- awesome-AutoML
- Model Training
- MOE
- Model Training
Trust and health
Maintenance
- awesome-AutoML
- Slowing (36%)
- MOE
- Archived (8%)
Days since push
- awesome-AutoML
- 133d
- MOE
- 1228d
Archived on GitHub
- awesome-AutoML
- No
- MOE
- Yes
Open issues (now)
- awesome-AutoML
- 1
- MOE
- 175
Owner type
- awesome-AutoML
- User
- MOE
- Organization
OSV dependency advisories
- awesome-AutoML
- No lockfile (source not queried)
- MOE
- Published findings
Full report
- awesome-AutoML
- Trust report
- MOE
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (YelpArchive/MOE) · observed Aug 4, 2026
- GitHub forks (YelpArchive/MOE) · observed Aug 4, 2026
- Last push (YelpArchive/MOE) · observed Mar 24, 2023
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-AutoML 941 · MOE 1.3k (synced Aug 4, 2026).
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 and MOE alternatives (awesome-AutoML markdown twin, MOE 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, 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; MOE trust report.