--- title: "vega vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huawei-noah-vega-vs-keras-team-autokeras" tools: ["huawei-noah-vega", "keras-team-autokeras"] --- # vega vs autokeras *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 autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. [vega](http://www.noahlab.com.hk/opensource/vega/) reports 849 GitHub stars, 177 forks, and 53 open issues, last pushed Feb 15, 2023. [autokeras](http://autokeras.com/) has 9.3k stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [vega's repository](https://github.com/huawei-noah/vega) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [vega](/tools/huawei-noah-vega.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | AutoML tools chain | AutoML library for deep learning | | Stars | 849 | 9,328 | | Forks | 177 | 1,393 | | Open issues | 53 | 161 | | Language | Python | Python | | Adopt for | Vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python. | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vega](/tools/huawei-noah-vega.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1266d | 251d | | Open issues (now) | 53 | 161 | | Full report | [trust report](/tools/huawei-noah-vega/trust.md) | [trust report](/tools/keras-team-autokeras/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: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## Choose when ### Choose vega if… - License: vega is Other, autokeras is Apache-2.0. - When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows - Leaner open-issue backlog (53). ### Choose autokeras if… - License: autokeras is Apache-2.0, vega is Other. - Tags unique to autokeras: autodl, deep-learning, keras, machine-learning. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ## 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 autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## Common questions ### What is the difference between vega and autokeras? vega: AutoML tools chain. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose vega over autokeras? Choose vega over autokeras when License: vega is Other, autokeras is Apache-2.0; When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows; Leaner open-issue backlog (53). ### When should I choose autokeras over vega? Choose autokeras over vega when License: autokeras is Apache-2.0, vega is Other; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### 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 autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### Is vega or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 849). Stars measure visibility, not whether either tool fits your constraints. ### Are vega and autokeras open source? Yes - both are open-source projects on GitHub (vega: Other, autokeras: Apache-2.0). ### Where can I find alternatives to vega or autokeras? GraphCanon lists graph-backed alternatives at [vega alternatives](/tools/huawei-noah-vega/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([vega markdown twin](/tools/huawei-noah-vega/alternatives.md), [autokeras markdown twin](/tools/keras-team-autokeras/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-keras-team-autokeras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vega or autokeras? vega: Dormant. autokeras: 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 autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vega trust report](/tools/huawei-noah-vega/trust); [autokeras trust report](/tools/keras-team-autokeras/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/_