mlc-ai vs sgl-project

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

mlc-ai (rank #209) holds a narrow lead over sgl-project (rank #278) on the overall Smoower ecosystem score (49 vs 46). 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), sgl-project is slightly ahead of mlc-ai. On community (issue response, PR reviews and discussion health), sgl-project is slightly ahead of mlc-ai. On reach (how visible the ecosystem is beyond its own repos), mlc-ai is ahead of sgl-project. On momentum (release cadence and how fast the ecosystem moves), sgl-project is ahead of mlc-ai.

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. sgl-project shows 38,333 stars across 26 public repos, 20 active in the last 90 days and 349 external contributors.

mlc-ai is the stronger read for anyone weighting reach. sgl-project looks better where momentum 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.