Firecrawl vs mlc-ai

Firecrawl (Web data API for AI) and mlc-ai both appear on the Smoower Developer Ecosystem Index.

Firecrawl (rank #122) holds a narrow lead over mlc-ai (rank #209) on the overall Smoower ecosystem score (54 vs 49). 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), Firecrawl is slightly ahead of mlc-ai. On education (docs, guides and learning material for developers), Firecrawl is ahead of mlc-ai. On community (issue response, PR reviews and discussion health), mlc-ai is clearly ahead of Firecrawl. On reach (how visible the ecosystem is beyond its own repos), Firecrawl is ahead of mlc-ai. On momentum (release cadence and how fast the ecosystem moves), Firecrawl is slightly ahead of mlc-ai.

Firecrawl carries 199,181 GitHub stars across 101 public repos, with 28 repositories active in the last 90 days and 130 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 Firecrawl's footprint is roughly 3.9x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Firecrawl is the stronger read for anyone weighting education. 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.