Entire vs TabbyML

Entire (Entire is a new developer platform that hooks into your git workflow to capture AI agent sessions on every push, unifying your code with its context.) and TabbyML both appear on the Smoower Developer Ecosystem Index.

Entire (rank #103) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (55 vs 35). The gap of 20 points reflects composite signals across code, docs, community and reach.

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

Entire carries 5,422 GitHub stars across 26 public repos, with 23 repositories active in the last 90 days and 48 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 6.3x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Entire is the stronger read for anyone weighting momentum. 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.