---
title: "automl-gs vs scikit-optimize"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-scikit-optimize-scikit-optimize"
tools: ["minimaxir-automl-gs", "scikit-optimize-scikit-optimize"]
---

# automl-gs vs scikit-optimize

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; 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.

[automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. [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 [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [scikit-optimize's repository](https://github.com/scikit-optimize/scikit-optimize).

| | [automl-gs](/tools/minimaxir-automl-gs.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Tagline | Automatically generate machine-learning models and code with input CSV and target field | Sequential model-based optimization library with scipy.optimize interface |
| Stars | 1,869 | 2,829 |
| Forks | 181 | 559 |
| Open issues | 28 | 318 |
| Language | Python | Python |
| Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | 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 | MIT | BSD-3-Clause |
| Categories | Data & Retrieval, Model Training | Model Training |

## Trust and health

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

| | [automl-gs](/tools/minimaxir-automl-gs.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Archived (8%) |
| Days since push | 2477d | 893d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 28 | 318 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/scikit-optimize-scikit-optimize/trust.md) |

## Decision facts: automl-gs

- **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data

## 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 automl-gs if…

- License: automl-gs is MIT, scikit-optimize is BSD-3-Clause.
- Tags unique to automl-gs: automl, keras, python, tensorflow.
- Also covers Data & Retrieval.
- Need to rapidly prototype models with limited ML expertise

### Choose scikit-optimize if…

- License: scikit-optimize is BSD-3-Clause, automl-gs is MIT.
- 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 automl-gs

- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code

## 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 automl-gs and scikit-optimize?

automl-gs: Automatically generate machine-learning models and code with input CSV and target field. 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 automl-gs over scikit-optimize?

Choose automl-gs over scikit-optimize when License: automl-gs is MIT, scikit-optimize is BSD-3-Clause; Tags unique to automl-gs: automl, keras, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.

### When should I choose scikit-optimize over automl-gs?

Choose scikit-optimize over automl-gs when License: scikit-optimize is BSD-3-Clause, automl-gs is MIT; 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 automl-gs?

Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code

### 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 automl-gs or scikit-optimize more popular on GitHub?

scikit-optimize has more GitHub stars (2,829 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.

### Are automl-gs and scikit-optimize open source?

Yes - both are open-source projects on GitHub (automl-gs: MIT, scikit-optimize: BSD-3-Clause).

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

GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [scikit-optimize alternatives](/tools/scikit-optimize-scikit-optimize/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/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/minimaxir-automl-gs-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, automl-gs or scikit-optimize?

automl-gs: Dormant. 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 automl-gs and scikit-optimize?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=minimaxir-automl-gs`](/api/graphcanon/graph?tool=minimaxir-automl-gs)
- 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/_