Magical tools for data.
Mage vs TabbyML
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
Mage (Magical tools for data.) and TabbyML both appear on the Smoower Developer Ecosystem Index.
Mage (rank #485) holds a narrow lead over TabbyML (rank #569) on the overall Smoower ecosystem score (38 vs 35). The gap of 3 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), Mage is slightly ahead of TabbyML. On education (docs, guides and learning material for developers), Mage is ahead of TabbyML. On community (issue response, PR reviews and discussion health), TabbyML is ahead of Mage. On reach (how visible the ecosystem is beyond its own repos), TabbyML is slightly ahead of Mage. On momentum (release cadence and how fast the ecosystem moves), Mage is slightly ahead of TabbyML.
Mage carries 9,079 GitHub stars across 44 public repos, with 8 repositories active in the last 90 days and 22 external contributors on record. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors. The star gap on its own does not decide the comparison, but TabbyML's footprint is roughly 3.7x larger, which usually shows up in downstream signals like inbound issues and third party integrations.
Mage is the stronger read for anyone weighting education. TabbyML looks better where community is the deciding factor. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.
Side-by-side metrics
| Metric | Mage | TabbyML |
|---|---|---|
| Ranking | ||
| Overall rank | #485 | #569 |
| Pillars | ||
| Overall | 38 | 35 |
| Code | 34 | 33 |
| Education | 71 | 54 |
| Community | 38 | 47 |
| Reach | 40 | 44 |
| Momentum | 16 | 15 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 9,079 | 34,010 |
| Forks | 1,538 | 2,016 |
| Public repos | 44 | 18 |
| Active repos (90d) | 8 | 5 |
| External contributors | 22 | 12 |
| Avg polish | 42 | 31 |
| Avg AI-readiness | 15 | 30 |