--- title: "harmonia vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ailabstw-harmonia-vs-windmaple-awesome-automl" tools: ["ailabstw-harmonia", "windmaple-awesome-automl"] --- # harmonia vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick harmonia if harmonia supports federated learning with differential privacy modules and GitOps-inspired architecture, designed for both research and production usage; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [harmonia](https://github.com/ailabstw/harmonia) reports 17 GitHub stars, 14 forks, and 0 open issues, last pushed Sep 21, 2020. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [harmonia's repository](https://github.com/ailabstw/harmonia) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [harmonia](/tools/ailabstw-harmonia.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Federated Learning Made Easy | Curating AutoML research and resources | | Stars | 17 | 941 | | Forks | 14 | 156 | | Open issues | 0 | 1 | | Language | Go | - | | Adopt for | Harmonia supports federated learning with differential privacy modules and GitOps-inspired architecture, designed for both research and production usage. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [harmonia](/tools/ailabstw-harmonia.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2143d | 133d | | Open issues (now) | 0 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/ailabstw-harmonia/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: harmonia - **Adopt for:** Harmonia supports federated learning with differential privacy modules and GitOps-inspired architecture, designed for both research and production usage. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose harmonia if… - License: harmonia is MPL-2.0, awesome-AutoML is GPL-3.0. - Tags unique to harmonia: differential privacy, federated-learning, gitops. - When needing frameworks that incorporate differential privacy directly into federated learning processes ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, harmonia is MPL-2.0. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use harmonia - If GitOps-inspired workflows are not aligned with your team's operational practices - In scenarios where the use of Go is less preferred among development teams ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between harmonia and awesome-AutoML? harmonia: Federated Learning Made Easy. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose harmonia over awesome-AutoML? Choose harmonia over awesome-AutoML when License: harmonia is MPL-2.0, awesome-AutoML is GPL-3.0; Tags unique to harmonia: differential privacy, federated-learning, gitops; When needing frameworks that incorporate differential privacy directly into federated learning processes. ### When should I choose awesome-AutoML over harmonia? Choose awesome-AutoML over harmonia when License: awesome-AutoML is GPL-3.0, harmonia is MPL-2.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid harmonia? If GitOps-inspired workflows are not aligned with your team's operational practices In scenarios where the use of Go is less preferred among development teams ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is harmonia or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 17). Stars measure visibility, not whether either tool fits your constraints. ### Are harmonia and awesome-AutoML open source? Yes - both are open-source projects on GitHub (harmonia: MPL-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to harmonia or awesome-AutoML? GraphCanon lists graph-backed alternatives at [harmonia alternatives](/tools/ailabstw-harmonia/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([harmonia markdown twin](/tools/ailabstw-harmonia/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/ailabstw-harmonia-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, harmonia or awesome-AutoML? harmonia: Dormant. awesome-AutoML: 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 harmonia and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [harmonia trust report](/tools/ailabstw-harmonia/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ailabstw-harmonia`](/api/graphcanon/graph?tool=ailabstw-harmonia) - 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/_