AI YouTube Transcript Analyzer

Turn one YouTube video into one sharp answer.

Video URL + Prompt = { "answer": "❤️‍🔥" }

No setup maze. No prompt engineering circus. No transcript cleanup work on your side.

Drop in a YouTube URL, tell the Actor what you want, and it will:

• fetch the transcript
• understand the full video
• chunk long videos safely behind the scenes
• return either plain text or strict JSON

Input

{
  "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "prompt": "Summarize the video in 3 concise bullet points.",
  "jsonschema": {
    "title": "video_summary",
    "type": "object",
    "additionalProperties": false,
    "properties": {
      "topic": { "type": "string" },
      "highlights": {
        "type": "array",
        "items": { "type": "string" }
      }
    },
    "required": ["topic", "highlights"]
  }
}

What You Get

• One YouTube video per run
• One custom prompt per run
• Plain text output, or strict JSON with your schema
• Clean dataset output shaped like { "result": ... }

Pricing, But Human

We price by transcript analysis.

• Most short videos usually fit into 1 transcript analysis
• A short video with jsonschema still usually fits into 1 transcript analysis
• If the video is long, we automatically split the transcript into chunks behind the scenes
• Each processed chunk counts as 1 transcript analysis
• After all chunks are processed, the final combined answer counts as 1 more transcript analysis

Big video? No panic.

We cut the transcript into safe pieces, analyze every piece, and stitch the final answer back together. The goal is simple: no lost context, no dropped data, no messy partial answer.

Example Dataset Item

{
  "result": {
    "topic": "Product strategy",
    "highlights": [
      "The speaker explains...",
      "The video compares...",
      "The conclusion emphasizes..."
    ]
  }
}

Example OUTPUT Record

{
  "summary": {
    "requested": 1,
    "succeeded": 1,
    "failed": 0,
    "chunkCount": 3,
    "fallbackUsed": false,
    "llmCallCount": 4,
    "aiCallsRequested": 4,
    "aiCallsCharged": 4,
    "chargeLimitReached": false,
    "finalModel": "mistralai/mistral-medium-3-instruct"
  },
  "error": null,
  "meta": {
    "pricing": {
      "eventName": "ai-call",
      "requestedAiCalls": 4,
      "chargedAiCalls": 4,
      "chargeLimitReached": false
    }
  }
}

Environment Variables

• WORKER_BASE_URL required
• WORKER_API_TOKEN optional
• REQUEST_TIMEOUT_MS optional

Apify Pricing Setup

Set this Actor to Pay per event and add an event named ai-call.

You choose the price of that event.

Inside Apify, the event name stays ai-call. For users, that event means 1 transcript analysis.

If you want pricing to be based on only real transcript analyses, remove the synthetic events you do not want:

• remove apify-default-dataset-item
• remove apify-actor-start

That means:

• 1 ai-call event = 1 transcript analysis
• you choose the price of each transcript analysis
• long videos cost more only because they truly require more analysis work