Web data API for AI
Your Search Foundation, Supercharged! (acquired by @elastic 2025/10)
Firecrawl vs Jina AI
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
Firecrawl (Web data API for AI) and Jina AI (Your Search Foundation, Supercharged! (acquired by @elastic 2025/10)) both appear on the Smoower Developer Ecosystem Index.
Firecrawl (rank #122) holds a narrow lead over Jina AI (rank #189) on the overall Smoower ecosystem score (54 vs 50). The gap of 4 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), Jina AI is ahead of Firecrawl. On education (docs, guides and learning material for developers), Firecrawl is clearly ahead of Jina AI. On community (issue response, PR reviews and discussion health), Firecrawl is ahead of Jina AI. On reach (how visible the ecosystem is beyond its own repos), Jina AI is clearly ahead of Firecrawl. On momentum (release cadence and how fast the ecosystem moves), Firecrawl is ahead of Jina 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. Jina AI shows 72,580 stars across 266 public repos, 13 active in the last 90 days and 8 external contributors.
Firecrawl is the stronger read for anyone weighting education. Jina AI 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.
Side-by-side metrics
| Metric | Firecrawl | Jina AI |
|---|---|---|
| Ranking | ||
| Overall rank | #122 | #189 |
| Pillars | ||
| Overall | 54 | 50 |
| Code | 48 | 61 |
| Education | 87 | 57 |
| Community | 52 | 41 |
| Reach | 27 | 58 |
| Momentum | 37 | 26 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 199,181 | 72,580 |
| Forks | 17,907 | 7,652 |
| Public repos | 101 | 266 |
| Active repos (90d) | 28 | 13 |
| External contributors | 130 | 8 |
| Avg polish | 48 | 63 |
| Avg AI-readiness | 39 | 47 |