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

# xgboost vs autokeras

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license; 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+.

[xgboost](https://xgboost.readthedocs.io/) reports 29k GitHub stars, 8.9k forks, and 416 open issues, last pushed Aug 3, 2026. [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 [xgboost's repository](https://github.com/dmlc/xgboost) and [autokeras's repository](https://github.com/keras-team/autokeras).

| | [xgboost](/tools/dmlc-xgboost.md) | [autokeras](/tools/keras-team-autokeras.md) |
| --- | --- | --- |
| Tagline | Scalable, Portable and Distributed Gradient Boosting Library | AutoML library for deep learning |
| Stars | 28,620 | 9,328 |
| Forks | 8,876 | 1,393 |
| Open issues | 416 | 161 |
| Language | C++ | Python |
| Adopt for | xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license | 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 | Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase | Apache-2.0 |
| Categories | Model Training | Developer Tools, Model Training |

## Trust and health

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

| | [xgboost](/tools/dmlc-xgboost.md) | [autokeras](/tools/keras-team-autokeras.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 251d |
| Open issues (now) | 416 | 161 |
| Full report | [trust report](/tools/dmlc-xgboost/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) |

## Decision facts: xgboost

- **Adopt for:** xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
- **License detail:** Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase

## 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 xgboost if…

- xgboost is primarily C++; autokeras is Python.
- Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt.
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.

### Choose autokeras if…

- autokeras is primarily Python; xgboost is C++.
- 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 NOT to use xgboost

- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
- Steer clear if your project does not require high-performance gradient boosting models for regression or classification.

## 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 xgboost and autokeras?

xgboost: Scalable, Portable and Distributed Gradient Boosting Library. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories.

### When should I choose xgboost over autokeras?

Choose xgboost over autokeras when xgboost is primarily C++; autokeras is Python; Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt; Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.

### When should I choose autokeras over xgboost?

Choose autokeras over xgboost when autokeras is primarily Python; xgboost is C++; 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 avoid xgboost?

Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability. Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed. Steer clear if your project does not require high-performance gradient boosting models for regression or classification.

### 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 xgboost or autokeras more popular on GitHub?

xgboost has more GitHub stars (28,620 vs 9,328). Stars measure visibility, not whether either tool fits your constraints.

### Are xgboost and autokeras open source?

Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, autokeras: Apache-2.0).

### Where can I find alternatives to xgboost or autokeras?

GraphCanon lists graph-backed alternatives at [xgboost alternatives](/tools/dmlc-xgboost/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([xgboost markdown twin](/tools/dmlc-xgboost/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/dmlc-xgboost-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, xgboost or autokeras?

xgboost: Very active. 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 xgboost and autokeras?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=dmlc-xgboost`](/api/graphcanon/graph?tool=dmlc-xgboost)
- 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/_