--- title: "FEDOT vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-sindresorhus-awesome" tools: ["aimclub-fedot", "sindresorhus-awesome"] --- # FEDOT vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick FEDOT when license: FEDOT is BSD-3-Clause, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, FEDOT is BSD-3-Clause. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 92 forks, and 83 open issues, last pushed Jul 8, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [FEDOT](/tools/aimclub-fedot.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | 😎 Curated list of awesome topics including hardware resources | | Stars | 709 | 484,026 | | Forks | 92 | 35,799 | | Open issues | 83 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | CC0-1.0 | | Categories | Computer Vision, Data & Retrieval, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 3d | 11d | | Open issues (now) | 83 | 92 | | Owner type | Organization | User | | Security scan | 27 low (27 low) | No lockfile | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose FEDOT if… - License: FEDOT is BSD-3-Clause, awesome is CC0-1.0. - Tags unique to FEDOT: automated-machine-learning, automation, automl, evolutionary-algorithms. - Also covers Computer Vision, Data & Retrieval. ### Choose awesome if… - License: awesome is CC0-1.0, FEDOT is BSD-3-Clause. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 709) - visibility, not fit. ## When NOT to use FEDOT - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between FEDOT and awesome? FEDOT: Automated modeling and machine learning framework FEDOT. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose FEDOT over awesome? Choose FEDOT over awesome when License: FEDOT is BSD-3-Clause, awesome is CC0-1.0; Tags unique to FEDOT: automated-machine-learning, automation, automl, evolutionary-algorithms; Also covers Computer Vision, Data & Retrieval. ### When should I choose awesome over FEDOT? Choose awesome over FEDOT when License: awesome is CC0-1.0, FEDOT is BSD-3-Clause; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 709) - visibility, not fit. ### When should I avoid FEDOT? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is FEDOT or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 709). Stars measure visibility, not whether either tool fits your constraints. ### Are FEDOT and awesome open source? Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, awesome: CC0-1.0). ### Where can I find alternatives to FEDOT or awesome? GraphCanon lists graph-backed alternatives at [FEDOT alternatives](/tools/aimclub-fedot/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FEDOT or awesome? FEDOT: Very active. awesome: 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 awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FEDOT trust report](/tools/aimclub-fedot/trust); [awesome trust report](/tools/sindresorhus-awesome/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/_