Why we built Stophy: YouTube data for AI agents
Agents need reliable text and structure—not brittle scrapers. Here is how we think about video URLs, transcripts, and JSON APIs.
Large language models and agent frameworks are only as good as the context you feed them. When your product touches YouTube, that context often means transcripts, titles, channel names, and timing—not a wall of HTML.
Scraping pages breaks when layouts change, when regions differ, or when you hit rate limits from opening tabs in the browser. A dedicated API turns a video URL into predictable JSON so your n8n flows, OpenClaw tools, or custom code can focus on reasoning—not parsing.
Stophy is built around credits, clear errors, and responses that match what you see in the playground. We are opinionated: one HTTP request should return one useful shape your stack can rely on.
What this unlocks
- Agents that cite moments in a video without brittle DOM parsing
- Workflows that branch on title, channel, or transcript length
- RAG pipelines that chunk on segments instead of random character cuts