--- title: "FEDOT vs autoai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-blobcity-autoai" tools: ["aimclub-fedot", "blobcity-autoai"] --- # FEDOT vs autoai *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 autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [autoai](https://github.com/blobcity/autoai) has 186 stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [autoai's repository](https://github.com/blobcity/autoai). | | [FEDOT](/tools/aimclub-fedot.md) | [autoai](/tools/blobcity-autoai.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | | Stars | 709 | 186 | | Forks | 94 | 46 | | Open issues | 82 | 9 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [autoai](/tools/blobcity-autoai.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 7d | 496d | | Open issues (now) | 82 | 9 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/blobcity-autoai/trust.md) | ## Shared compatibility - **Python**: [FEDOT](/tools/aimclub-fedot.md) - Python runtime; [autoai](/tools/blobcity-autoai.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: autoai - **Adopt for:** Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. ## Choose when ### Choose FEDOT if… - License: FEDOT is BSD-3-Clause, autoai 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 autoai if… - License: autoai is Apache-2.0, FEDOT is BSD-3-Clause. - Tags unique to autoai: ai, autoai, codegen, deep-learning. - Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ## 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 autoai - Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. - Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ## Common questions ### What is the difference between FEDOT and autoai? FEDOT: Automated modeling and machine learning framework FEDOT. autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. See the comparison table for live GitHub stats and shared categories. ### When should I choose FEDOT over autoai? Choose FEDOT over autoai when License: FEDOT is BSD-3-Clause, autoai 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 autoai over FEDOT? Choose autoai over FEDOT when License: autoai is Apache-2.0, FEDOT is BSD-3-Clause; Tags unique to autoai: ai, autoai, codegen, deep-learning; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### 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 autoai? Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ### Is FEDOT or autoai more popular on GitHub? FEDOT has more GitHub stars (709 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are FEDOT and autoai open source? Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, autoai: Apache-2.0). ### Where can I find alternatives to FEDOT or autoai? GraphCanon lists graph-backed alternatives at [FEDOT alternatives](/tools/aimclub-fedot/alternatives) and [autoai alternatives](/tools/blobcity-autoai/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [autoai markdown twin](/tools/blobcity-autoai/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-blobcity-autoai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FEDOT or autoai? FEDOT: Active. autoai: 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 autoai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FEDOT trust report](/tools/aimclub-fedot/trust); [autoai trust report](/tools/blobcity-autoai/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/_