How to use YouTube Transcript & Subtitles Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$2.48 per 1,000 transcripts on the Free plan, down to $1.80 on higher plans (live Apify price on 2026-10-31).

1. In the Apify Store (no code)

1. Open arcadium/youtube-transcripts and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "urls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ],
  "languages": [],
  "captionType": "any",
  "maxVideosPerSource": 10,
  "subtitleFormat": "none"
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~youtube-transcripts/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"urls":["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],"languages":[],"captionType":"any","maxVideosPerSource":10,"subtitleFormat":"none"}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/youtube-transcripts').call({
  "urls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ],
  "languages": [],
  "captionType": "any",
  "maxVideosPerSource": 10,
  "subtitleFormat": "none"
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/youtube-transcripts").call(run_input={
    "urls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
    "languages": [],
    "captionType": "any",
    "maxVideosPerSource": 10,
    "subtitleFormat": "none",
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/youtube-transcripts",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~youtube-transcripts/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"urls\":[\"https://www.youtube.com/watch?v=dQw4w9WgXcQ\"],\"languages\":[],\"captionType\":\"any\",\"maxVideosPerSource\":10,\"subtitleFormat\":\"none\"}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/youtube-transcripts",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/youtube-transcripts")
rows = tool.invoke({"run_input": {
    "urls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
    "languages": [],
    "captionType": "any",
    "maxVideosPerSource": 10,
    "subtitleFormat": "none",
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "transcript",
  "platform": "youtube",
  "videoId": "dQw4w9WgXcQ",
  "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "title": "Rick Astley - Never Gonna Give You Up (Official Video) (4K Remaster)",
  "channelName": "Rick Astley",
  "channelId": "UCuAXFkgsw1L7xaCfnd5JJOw",
  "channelHandle": "@RickAstleyYT",
  "channelUrl": "https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw",
  "durationSeconds": 213,
  "publishDate": "2009-10-24T23:57:33-07:00",
  "viewCount": 1829055386,
  "likeCount": 19491653,
  "category": "Music",
  "isLiveContent": false,
  "keywords": [
    "rick astley",
    "Never Gonna Give You Up"
  ],
  "description": "The official video for “Never Gonna Give You Up” by Rick Astley. Never: The Autobiography OUT NOW!...",
  "language": "en",
  "languageName": "English",
  "isAutoGenerated": false,
  "languageMatched": true,
  "translationStatus": "not_requested",
  "translatedFrom": null,
  "availableLanguages": [
    {
      "code": "en",
      "name": "English",
      "isAutoGenerated": false
    },
    {
      "code": "en",
      "name": "English",
      "isAutoGenerated": true
    }
  ],
  "transcript": "[♪♪♪] ♪ We're no strangers to love ♪ ♪ You know the rules and so do I ♪ ...",
  "segmentCount": 61,
  "wordCount": 487,
  "segments": [
    {
      "start": 1.36,
      "duration": 1.68,
      "text": "[♪♪♪]"
    },
    {
      "start": 18.64,
      "duration": 3.24,
      "text": "♪ We're no strangers to love ♪"
    }
  ],
  "source": null,
  "scrapedAt": "2026-10-22T08:00:00.000Z"
}

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.