sgl-project vs vLLM
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
sgl-project and vLLM both appear on the Smoower Developer Ecosystem Index.
vLLM (rank #47) holds a modest lead over sgl-project (rank #278) on the overall Smoower ecosystem score (61 vs 46). The gap of 15 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), vLLM is clearly ahead of sgl-project. On education (docs, guides and learning material for developers), sgl-project is slightly ahead of vLLM. On community (issue response, PR reviews and discussion health), sgl-project is slightly ahead of vLLM. On reach (how visible the ecosystem is beyond its own repos), vLLM is ahead of sgl-project. On momentum (release cadence and how fast the ecosystem moves), vLLM is slightly ahead of sgl-project.
sgl-project carries 38,333 GitHub stars across 26 public repos, with 20 repositories active in the last 90 days and 349 external contributors on record. vLLM shows 115,730 stars across 43 public repos, 35 active in the last 90 days and 881 external contributors. The star gap on its own does not decide the comparison, but vLLM's footprint is roughly 3.0x larger, which usually shows up in downstream signals like inbound issues and third party integrations.
sgl-project is the stronger read for anyone weighting community. vLLM looks better where code quality 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 | sgl-project | vLLM |
|---|---|---|
| Ranking | ||
| Overall rank | #278 | #47 |
| Pillars | ||
| Overall | 46 | 61 |
| Code | 55 | 81 |
| Education | 74 | 71 |
| Community | 80 | 76 |
| Reach | 8 | 23 |
| Momentum | 40 | 42 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 38,333 | 115,730 |
| Forks | 8,904 | 26,361 |
| Public repos | 26 | 43 |
| Active repos (90d) | 20 | 35 |
| External contributors | 349 | 881 |
| Avg polish | 55 | 81 |
| Avg AI-readiness | 47 | 61 |