--- title: "FEDOT vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-microsoft-archai" tools: ["aimclub-fedot", "microsoft-archai"] --- # FEDOT vs archai *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 archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [archai](https://microsoft.github.io/archai) has 485 stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [archai's repository](https://github.com/microsoft/archai). | | [FEDOT](/tools/aimclub-fedot.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 709 | 485 | | Forks | 94 | 93 | | Open issues | 82 | 4 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 7d | 252d | | Open issues (now) | 82 | 4 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [FEDOT](/tools/aimclub-fedot.md) - Python runtime; [archai](/tools/microsoft-archai.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: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## Choose when ### Choose FEDOT if… - License: FEDOT is BSD-3-Clause, archai is MIT. - Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, structural-learning. - For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types ### Choose archai if… - License: archai is MIT, FEDOT is BSD-3-Clause. - Tags unique to archai: automated-machine-learning, darts, deep-learning, model-compression. - Need rapid iteration in NAS projects while ensuring reproducibility ## 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 archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## Common questions ### What is the difference between FEDOT and archai? FEDOT: Automated modeling and machine learning framework FEDOT. archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FEDOT over archai? Choose FEDOT over archai when License: FEDOT is BSD-3-Clause, archai is MIT; Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, 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 archai over FEDOT? Choose archai over FEDOT when License: archai is MIT, FEDOT is BSD-3-Clause; Tags unique to archai: automated-machine-learning, darts, deep-learning, model-compression; Need rapid iteration in NAS projects while ensuring reproducibility. ### 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 archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### Is FEDOT or archai more popular on GitHub? FEDOT has more GitHub stars (709 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are FEDOT and archai open source? Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, archai: MIT). ### Where can I find alternatives to FEDOT or archai? GraphCanon lists graph-backed alternatives at [FEDOT alternatives](/tools/aimclub-fedot/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [archai markdown twin](/tools/microsoft-archai/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-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FEDOT or archai? FEDOT: Active. archai: 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 FEDOT and archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FEDOT trust report](/tools/aimclub-fedot/trust); [archai trust report](/tools/microsoft-archai/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/_