Mage vs mlc-ai

Mage (Magical tools for data.) and mlc-ai both appear on the Smoower Developer Ecosystem Index.

mlc-ai (rank #209) holds a modest lead over Mage (rank #485) on the overall Smoower ecosystem score (49 vs 38). 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), mlc-ai is ahead of Mage. On education (docs, guides and learning material for developers), mlc-ai is slightly ahead of Mage. On community (issue response, PR reviews and discussion health), mlc-ai is clearly ahead of Mage. On reach (how visible the ecosystem is beyond its own repos), Mage is clearly ahead of mlc-ai. On momentum (release cadence and how fast the ecosystem moves), mlc-ai is ahead of Mage.

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. mlc-ai shows 51,532 stars across 38 public repos, 15 active in the last 90 days and 45 external contributors. The star gap on its own does not decide the comparison, but mlc-ai's footprint is roughly 5.7x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Mage is the stronger read for anyone weighting reach. mlc-ai 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.