Advanced Speech-to-Text
Dify is an LLMOps platform that helps you build GPT-based applications easily and intuitively.
AssemblyAI vs LangGenius
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
AssemblyAI (Advanced Speech-to-Text) and LangGenius (Dify is an LLMOps platform that helps you build GPT-based applications easily and intuitively.) both appear on the Smoower Developer Ecosystem Index.
LangGenius (rank #152) holds a modest lead over AssemblyAI (rank #401) on the overall Smoower ecosystem score (52 vs 41). 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), LangGenius is ahead of AssemblyAI. On education (docs, guides and learning material for developers), LangGenius is slightly ahead of AssemblyAI. On community (issue response, PR reviews and discussion health), LangGenius is ahead of AssemblyAI. On reach (how visible the ecosystem is beyond its own repos), AssemblyAI is slightly ahead of LangGenius. On momentum (release cadence and how fast the ecosystem moves), LangGenius is clearly ahead of AssemblyAI.
AssemblyAI carries 1,025 GitHub stars across 67 public repos, with 8 repositories active in the last 90 days and 11 external contributors on record. LangGenius shows 153,705 stars across 39 public repos, 26 active in the last 90 days and 309 external contributors. The star gap on its own does not decide the comparison, but LangGenius's footprint is roughly 150.0x larger, which usually shows up in downstream signals like inbound issues and third party integrations.
AssemblyAI is the stronger read for anyone weighting reach. LangGenius looks better where momentum 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 | AssemblyAI | LangGenius |
|---|---|---|
| Ranking | ||
| Overall rank | #401 | #152 |
| Pillars | ||
| Overall | 41 | 52 |
| Code | 43 | 59 |
| Education | 76 | 77 |
| Community | 40 | 57 |
| Reach | 37 | 34 |
| Momentum | 16 | 41 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 1,025 | 153,705 |
| Forks | 268 | 29,341 |
| Public repos | 67 | 39 |
| Active repos (90d) | 8 | 26 |
| External contributors | 11 | 309 |
| Avg polish | 46 | 61 |
| Avg AI-readiness | 32 | 45 |