sgl-project vs TabbyML

sgl-project and TabbyML both appear on the Smoower Developer Ecosystem Index.

sgl-project (rank #278) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (46 vs 35). The gap of 11 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), sgl-project is clearly ahead of TabbyML. On education (docs, guides and learning material for developers), sgl-project is ahead of TabbyML. On community (issue response, PR reviews and discussion health), sgl-project is clearly ahead of TabbyML. On reach (how visible the ecosystem is beyond its own repos), TabbyML is clearly ahead of sgl-project. On momentum (release cadence and how fast the ecosystem moves), sgl-project is clearly ahead of TabbyML.

sgl-project carries 38,333 GitHub stars across 26 public repos, with 20 repositories active in the last 90 days and 349 external contributors on record. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors.

sgl-project is the stronger read for anyone weighting community. TabbyML looks better where reach 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.