---
title: "autokeras vs scikit-optimize"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-scikit-optimize-scikit-optimize"
tools: ["keras-team-autokeras", "scikit-optimize-scikit-optimize"]
---

# autokeras vs scikit-optimize

*GraphCanon updated Aug 4, 2026*

## Verdict

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+; pick scikit-optimize if scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.

[autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. [scikit-optimize](https://scikit-optimize.github.io) has 2.8k stars, 559 forks, and 318 open issues, last pushed Feb 23, 2024. Figures are from public GitHub metadata via [autokeras's repository](https://github.com/keras-team/autokeras) and [scikit-optimize's repository](https://github.com/scikit-optimize/scikit-optimize).

| | [autokeras](/tools/keras-team-autokeras.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Tagline | AutoML library for deep learning | Sequential model-based optimization library with scipy.optimize interface |
| Stars | 9,328 | 2,829 |
| Forks | 1,393 | 559 |
| Open issues | 161 | 318 |
| Language | Python | Python |
| 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+. | Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | BSD-3-Clause |
| Categories | Developer Tools, Model Training | Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [autokeras](/tools/keras-team-autokeras.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Archived (8%) |
| Days since push | 251d | 893d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 161 | 318 |
| Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/scikit-optimize-scikit-optimize/trust.md) |

## Shared compatibility

- **Python**: [autokeras](/tools/keras-team-autokeras.md) - Python runtime; [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) - Python runtime

## 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+.

## Decision facts: scikit-optimize

- **Adopt for:** Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.

## Choose when

### Choose autokeras if…

- License: autokeras is Apache-2.0, scikit-optimize is BSD-3-Clause.
- Tags unique to autokeras: autodl, automl, deep-learning, keras.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.

### Choose scikit-optimize if…

- License: scikit-optimize is BSD-3-Clause, autokeras is Apache-2.0.
- Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, scikit-learn.
- Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.

## 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.

## When NOT to use scikit-optimize

- Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient.
- Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments.
- Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.

## Common questions

### What is the difference between autokeras and scikit-optimize?

autokeras: AutoML library for deep learning. scikit-optimize: Sequential model-based optimization library with scipy.optimize interface. See the comparison table for live GitHub stats and shared categories.

### When should I choose autokeras over scikit-optimize?

Choose autokeras over scikit-optimize when License: autokeras is Apache-2.0, scikit-optimize is BSD-3-Clause; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.

### When should I choose scikit-optimize over autokeras?

Choose scikit-optimize over autokeras when License: scikit-optimize is BSD-3-Clause, autokeras is Apache-2.0; Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, scikit-learn; Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.

### 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.

### When should I avoid scikit-optimize?

Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient. Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments. Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.

### Is autokeras or scikit-optimize more popular on GitHub?

autokeras has more GitHub stars (9,328 vs 2,829). Stars measure visibility, not whether either tool fits your constraints.

### Are autokeras and scikit-optimize open source?

Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, scikit-optimize: BSD-3-Clause).

### Where can I find alternatives to autokeras or scikit-optimize?

GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [scikit-optimize alternatives](/tools/scikit-optimize-scikit-optimize/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/alternatives.md), [scikit-optimize markdown twin](/tools/scikit-optimize-scikit-optimize/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/keras-team-autokeras-vs-scikit-optimize-scikit-optimize.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, autokeras or scikit-optimize?

autokeras: Slowing. scikit-optimize: 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 autokeras and scikit-optimize?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autokeras trust report](/tools/keras-team-autokeras/trust); [scikit-optimize trust report](/tools/scikit-optimize-scikit-optimize/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras)
- 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/_