--- title: "vega vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huawei-noah-vega-vs-windmaple-awesome-automl" tools: ["huawei-noah-vega", "windmaple-awesome-automl"] --- # vega vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick vega if vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [vega](http://www.noahlab.com.hk/opensource/vega/) reports 849 GitHub stars, 177 forks, and 53 open issues, last pushed Feb 15, 2023. [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 [vega's repository](https://github.com/huawei-noah/vega) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [vega](/tools/huawei-noah-vega.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | AutoML tools chain | Curating AutoML research and resources | | Stars | 849 | 941 | | Forks | 177 | 156 | | Open issues | 53 | 1 | | Language | Python | - | | Adopt for | Vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Other | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vega](/tools/huawei-noah-vega.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1266d | 133d | | Open issues (now) | 53 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/huawei-noah-vega/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: vega - **Adopt for:** Vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose vega if… - License: vega is Other, awesome-AutoML is GPL-3.0. - When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, vega is Other. - Tags unique to awesome-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 vega - If dependency on proprietary solutions, such as those from a single vendor like Huawei, needs to be avoided - When you require an extensive open community support or the flexibility traditionally offered by more established open-source AutoML tools ## 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 vega and awesome-AutoML? vega: AutoML tools chain. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose vega over awesome-AutoML? Choose vega over awesome-AutoML when License: vega is Other, awesome-AutoML is GPL-3.0; When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows. ### When should I choose awesome-AutoML over vega? Choose awesome-AutoML over vega when License: awesome-AutoML is GPL-3.0, vega is Other; Tags unique to awesome-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 vega? If dependency on proprietary solutions, such as those from a single vendor like Huawei, needs to be avoided When you require an extensive open community support or the flexibility traditionally offered by more established open-source AutoML tools ### 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 vega or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 849). Stars measure visibility, not whether either tool fits your constraints. ### Are vega and awesome-AutoML open source? Yes - both are open-source projects on GitHub (vega: Other, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to vega or awesome-AutoML? GraphCanon lists graph-backed alternatives at [vega alternatives](/tools/huawei-noah-vega/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([vega markdown twin](/tools/huawei-noah-vega/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/huawei-noah-vega-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, vega or awesome-AutoML? vega: Dormant. 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 vega and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vega trust report](/tools/huawei-noah-vega/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huawei-noah-vega`](/api/graphcanon/graph?tool=huawei-noah-vega) - 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/_