FrameFetch

Any social-video URL → answers, transcript, metadata, insights, frames & on-screen text (OCR).
Agent-first video data API + MCP server. Pay per call, or with x402 (USDC) — no account.

FrameFetch turns one YouTube, YouTube Shorts, TikTok, Instagram Reels, Pinterest, or Reddit video URL into a single JSON response: a direct answer to a question about the video, metadata, engagement insights, a transcript (captions or Whisper), an LLM digest (text or spoken mp3), structured JSON (chapters/entities/products/claims), comments + sentiment, parametrically-sampled frames (every Nth / 1-per-second / a time range, at any width), and the on-screen text burned into those frames (OCR — captions, price tags, signage). Plus keyword search when you don't have a URL yet, and batch for up to 10 URLs in one call. Built API-first and MCP-first for AI agents.

This repo is the open-source client + docs. The service itself runs at framefetch.net — you bring a free API key (or pay per call with x402); the backend stays hosted.

Why

An LLM can't watch a video. To reason about one it needs the video turned into text and images first — an answer, a transcript, metadata, a few frames. FrameFetch returns all of that from a URL, across six platforms, through one schema.

Install

npm install framefetch

Node 18+ (uses built-in fetch). Get a free key: framefetch.net.

Version note. This repo is at 0.4.0. The newest version currently on npm is 0.3.0 — npm install framefetch still gives you that one, and it has only extract/metadata/transcript/frames/platforms/status/demo/createKey. Everything else documented below is live on the API today and available from this repo; from npm 0.3.0 you can reach the same data through extract({ fields: [...] }).

Ask a question — get an answer, not a transcript dump

A direct question about a video returns a short, grounded answer with timestamped quotes, instead of you parsing a 25,000-token transcript yourself.

import { FrameFetch } from 'framefetch';

const ff = new FrameFetch({ apiKey: process.env.FRAMEFETCH_API_KEY });

const { ask } = await ff.ask(
  'https://www.youtube.com/watch?v=jNQXAC9IVRw',
  'What does the presenter say to do first?',
);

console.log(ask.answer);          // short, direct answer
console.log(ask.confidence);      // 'high' | 'medium' | 'low'
for (const q of ask.quotes) {     // verbatim, timestamped supporting quotes
  console.log(`[${q.t_sec}s] ${q.text}`);
}
console.log(ask.coverage);        // which part of the transcript was analyzed

Charged only when an answer is actually produced. A repeat question about an already-extracted video reuses the cached transcript, so it answers fast without a re-download or re-transcription — but the answer itself is always freshly generated, never cache-served.

Frames-based answers: when a video has no transcript (e.g. Pinterest, or transcription failed), the answer is grounded in sampled keyframe images instead. Then coverage.mode is "frames", quotes is [] (no transcript text to quote), and confidence is capped at "medium".

Quick start

import { FrameFetch } from 'framefetch';

const ff = new FrameFetch({ apiKey: proc

... [View full README on GitHub](https://github.com/marvinrey7879/framefetch-client#readme)