mlc-ai vs Runway

mlc-ai and Runway (Building AI to Simulate the World) both appear on the Smoower Developer Ecosystem Index.

mlc-ai (rank #209) holds a narrow lead over Runway (rank #317) on the overall Smoower ecosystem score (49 vs 44). The gap of 5 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), mlc-ai is slightly ahead of Runway. On education (docs, guides and learning material for developers), mlc-ai is ahead of Runway. On community (issue response, PR reviews and discussion health), mlc-ai is clearly ahead of Runway. On reach (how visible the ecosystem is beyond its own repos), Runway is clearly 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. Runway shows 2,435 stars across 61 public repos, 16 active in the last 90 days and 0 external contributors. The star gap on its own does not decide the comparison, but mlc-ai's footprint is roughly 21.2x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

mlc-ai is the stronger read for anyone weighting community. Runway 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.