--- title: "FEDOT vs autogluon" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-autogluon-autogluon" tools: ["aimclub-fedot", "autogluon-autogluon"] --- # FEDOT vs autogluon *GraphCanon updated Aug 4, 2026* ## 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. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [autogluon](https://auto.gluon.ai/) has 11k stars, 1.2k forks, and 388 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [autogluon's repository](https://github.com/autogluon/autogluon). | | [FEDOT](/tools/aimclub-fedot.md) | [autogluon](/tools/autogluon-autogluon.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Fast and Accurate ML in 3 Lines of Code | | Stars | 709 | 10,576 | | Forks | 94 | 1,171 | | Open issues | 82 | 388 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | 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 | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [autogluon](/tools/autogluon-autogluon.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 7d | 0d | | Open issues (now) | 82 | 388 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/autogluon-autogluon/trust.md) | ## Shared compatibility - **Python**: [FEDOT](/tools/aimclub-fedot.md) - Python runtime; [autogluon](/tools/autogluon-autogluon.md) - Python runtime ## Decision facts: FEDOT - **Adopt for:** FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. ## Decision facts: autogluon - **Adopt for:** 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. - **License detail:** Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/aimclub-fedot/alternatives) and [autogluon alternatives](/tools/autogluon-autogluon/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [autogluon markdown twin](/tools/autogluon-autogluon/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/aimclub-fedot-vs-autogluon-autogluon.md) 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](/tools/aimclub-fedot/trust); [autogluon trust report](/tools/autogluon-autogluon/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimclub-fedot`](/api/graphcanon/graph?tool=aimclub-fedot) - 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/_