mlc-ai vs TabbyML

mlc-ai and TabbyML both appear on the Smoower Developer Ecosystem Index.

mlc-ai (rank #209) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (49 vs 35). The gap of 14 points reflects composite signals across code, docs, community and reach.

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

mlc-ai carries 51,532 GitHub stars across 38 public repos, with 15 repositories active in the last 90 days and 45 external contributors on record. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors.

mlc-ai 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.