Home/Compare/FEDOT vs automl-gs

Comparison

FEDOT vs automl-gs

Verdict

Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data.

Markdown twin · FEDOT alternatives · automl-gs alternatives

GraphCanon updated 2w

FEDOT logo

FEDOT

aimclub/FEDOT

709pushed Jul 27, 2026
vs
automl-gs logo

automl-gs

minimaxir/automl-gs

1.9kpushed Oct 22, 2019

Trust & integrity

SignalFEDOTautoml-gs
Maintenance
Active (7d since push)
As of 2w · github_public_v1
Dormant (2477d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Personal account
As of 2w · github_public_v1
OSV dependency advisories
Published findings
As of 1mo · osv@v1
Published findings
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

FEDOT
Automated modeling and machine learning framework FEDOT
automl-gs
Automatically generate machine-learning models and code with input CSV and target field

Stars

FEDOT
709
automl-gs
1.9k

Forks

FEDOT
94
automl-gs
181

Open issues

FEDOT
82
automl-gs
28

Language

FEDOT
Python
automl-gs
Python

Adopt for

FEDOT
FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
automl-gs
automl-gs: Python tool for automated machine-learning model creation from CSV data

Persona

FEDOT
-
automl-gs
-

Runtime

FEDOT
-
automl-gs
-

License

FEDOT
BSD-3-Clause
automl-gs
MIT

Last pushed

FEDOT
Jul 27, 2026
automl-gs
Oct 22, 2019

Categories

FEDOT
Model Training
automl-gs
Data & Retrieval, Model Training

Trust and health

Maintenance

FEDOT
Active (82%)
automl-gs
Dormant (18%)

Days since push

FEDOT
7d
automl-gs
2477d

Open issues (now)

FEDOT
82
automl-gs
28

Owner type

FEDOT
Organization
automl-gs
User

Full report

automl-gs
Trust report

Choose FEDOT if…

  • License: FEDOT is BSD-3-Clause, automl-gs is MIT.
  • Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning.
  • For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types

When NOT to use FEDOT

  • In scenarios with strict real-time requirements due to its optimization time
  • For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs
  • When the project specifically requires a non-evolutionary approach for pipeline design

Choose automl-gs if…

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

When NOT to use automl-gs

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

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: FEDOT 709 · automl-gs 1.9k (synced Aug 4, 2026).

Common questions

What is the difference between FEDOT and automl-gs?
FEDOT: Automated modeling and machine learning framework FEDOT. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories.
When should I choose FEDOT over automl-gs?
Choose FEDOT over automl-gs when License: FEDOT is BSD-3-Clause, automl-gs is MIT; Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning; For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types.
When should I choose automl-gs over FEDOT?
Choose automl-gs over FEDOT when License: automl-gs is MIT, FEDOT is BSD-3-Clause; Tags unique to automl-gs: keras, machine-learning, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.
When should I avoid FEDOT?
In scenarios with strict real-time requirements due to its optimization time For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs When the project specifically requires a non-evolutionary approach for pipeline design
When should I avoid automl-gs?
Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
Is FEDOT or automl-gs more popular on GitHub?
automl-gs has more GitHub stars (1,869 vs 709). Stars measure visibility, not whether either tool fits your constraints.
Are FEDOT and automl-gs open source?
Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, automl-gs: MIT).
Where can I find alternatives to FEDOT or automl-gs?
GraphCanon lists graph-backed alternatives at FEDOT alternatives and automl-gs alternatives (FEDOT markdown twin, automl-gs markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, FEDOT or automl-gs?
FEDOT: Active. automl-gs: Dormant. 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 FEDOT and automl-gs?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FEDOT trust report; automl-gs trust report.

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