Comparison
FEDOT vs autogluon
Verdict
Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
Markdown twin · FEDOT alternatives · autogluon alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | FEDOT | autogluon |
|---|---|---|
| Maintenance | Active (7d since push) As of 3w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) 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
- autogluon
- Fast and Accurate ML in 3 Lines of Code
Stars
- FEDOT
- 709
- autogluon
- 11k
Forks
- FEDOT
- 94
- autogluon
- 1.2k
Open issues
- FEDOT
- 82
- autogluon
- 388
Language
- FEDOT
- Python
- autogluon
- Python
Adopt for
- FEDOT
- FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
- autogluon
- AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
Persona
- FEDOT
- -
- autogluon
- -
Runtime
- FEDOT
- -
- autogluon
- -
License
- FEDOT
- BSD-3-Clause
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
Last pushed
- FEDOT
- Jul 27, 2026
- autogluon
- Aug 3, 2026
Categories
- FEDOT
- Model Training
- autogluon
- Developer Tools, Model Training
Trust and health
Maintenance
- FEDOT
- Active (82%)
- autogluon
- Very active (96%)
Days since push
- FEDOT
- 7d
- autogluon
- 0d
Open issues (now)
- FEDOT
- 82
- autogluon
- 388
OSV dependency advisories
- FEDOT
- Published findings
- autogluon
- No lockfile (source not queried)
Full report
- FEDOT
- Trust report
- autogluon
- Trust report
Shared compatibility
- Python · FEDOT: Python runtime · autogluon: Python runtime
Choose FEDOT if…
- License: FEDOT is BSD-3-Clause, autogluon is Apache-2.0.
- 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 autogluon if…
- License: autogluon is Apache-2.0, FEDOT is BSD-3-Clause.
- Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, deep-learning.
- Also covers Developer Tools.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
When NOT to use autogluon
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation.
- For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aimclub/FEDOT) · observed Aug 4, 2026
- GitHub forks (aimclub/FEDOT) · observed Aug 4, 2026
- Last push (aimclub/FEDOT) · observed Jul 27, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (autogluon/autogluon) · observed Aug 4, 2026
- GitHub forks (autogluon/autogluon) · observed Aug 4, 2026
- Last push (autogluon/autogluon) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FEDOT 709 · autogluon 11k (synced Aug 4, 2026).
Common questions
- What is the difference between FEDOT and autogluon?
- FEDOT: Automated modeling and machine learning framework FEDOT. autogluon: Fast and Accurate ML in 3 Lines of Code. See the comparison table for live GitHub stats and shared categories.
- When should I choose FEDOT over autogluon?
- Choose FEDOT over autogluon when License: FEDOT is BSD-3-Clause, autogluon is Apache-2.0; 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 autogluon over FEDOT?
- Choose autogluon over FEDOT when License: autogluon is Apache-2.0, FEDOT is BSD-3-Clause; Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, deep-learning; Also covers Developer Tools; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- 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 autogluon?
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
- Is FEDOT or autogluon more popular on GitHub?
- autogluon has more GitHub stars (10,576 vs 709). Stars measure visibility, not whether either tool fits your constraints.
- Are FEDOT and autogluon open source?
- Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, autogluon: Apache-2.0).
- Where can I find alternatives to FEDOT or autogluon?
- GraphCanon lists graph-backed alternatives at FEDOT alternatives and autogluon alternatives (FEDOT markdown twin, autogluon 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 autogluon?
- FEDOT: Active. autogluon: Very active. 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 autogluon?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FEDOT trust report; autogluon trust report.