YouTube Transcript Scraper: Channels, Playlists & AI Chunks

Turn any YouTube channel, playlist or search into clean, timestamped transcripts in one run. Schedule it, and each run delivers (and bills) only the new videos. AI-ready chunks with deep links, search inside transcripts, SRT/VTT/Markdown, full metadata. $0.003 per transcript, and videos without captions are free. Apify's free plan ($5/month credit) covers about 1,600 transcripts a month.

Why this one

	This Actor	Many transcript actors
Input	Videos, Shorts, channels, playlists and YouTube searches, mixed in one run	Video URLs (some: one channel)
Videos per channel / playlist	Up to 1,000 each, newest first, date filters stop paging early	Often capped at 100–200 per run
Scheduled runs	Only new videos: remembers what it delivered, so a daily run pays only for new uploads	Each run delivers and bills everything again
For AI / RAG	Chunks split at creator chapters, optional overlap, deep link to each second, estimatedTokens, one-row-per-chunk mode	Raw caption lines
Find mentions	Search inside transcripts: quoted passages with timestamps, or only videos that mention your words	Not available
Language	Your preference list; if missing, the video's original language	English-first or the first track listed
Failed / no captions	Listed with a machine-readable reasonCode and retryable, never billed	Error text, or missing from results
Formats	Chunks, chunk rows, segments, text, Markdown, SRT, WebVTT	1–5 formats

What it does

• Any input mix: video URLs or IDs, Shorts, @handles, channel URLs, playlist URLs, and YouTube search queries.
• Whole channels and playlists: up to 1,000 videos per source. Channel content type: long videos, Shorts, past live streams, or everything.
• Date filters: publishedAfter / publishedBefore as dates or relative (7 days, 3 months).
• Only new videos: turn it on and schedule the run daily or hourly. Each run delivers only uploads it hasn't sent you before. Give several inputs the same Memory name to share one memory.
• Search filters: sort by relevance/date/views/rating, upload date (last hour → year), duration (short/medium/long).
• Find words inside transcripts: every match comes back as a quoted passage with timestamp, chapter and a link to that second. Only videos that mention them turns the Actor into a brand/topic monitor: matching videos are delivered as usual; videos that don't match cost $0.001 each (we still had to fetch and search them).
• AI / RAG ready: ~60-second chunks (10–900 s), never crossing a creator chapter, optional overlap, each with start, end, chapter and a deep link. chunkRows gives one flat row per chunk (with video title, channel and date on every row), ready for Pinecone, Qdrant, Supabase, Google Sheets or an n8n loop.
• Clean timing: auto-captions overlap almost every line with the next; we clip them, so SRT/VTT show one line at a time and chunk times are exact.
• Readable documents: markdown gives title, chapters as headings and paragraphs, ready for Notion or Google Docs.
• Metadata: title, channel, @handle, publish date, duration, views, likes, category, keywords, description, thumbnail, chapters, word count. Best-effort: a field YouTube doesn't return for a video is left out rather than guessed.
• Built to keep working: YouTube's lightweight app API (not page scraping), automatic retries, cheapest network route first. Every video ends up in the results: delivered, or listed with the reason. A run that's moved between servers resumes without re-delivering or re-billing anything.

Use cases

• RAG chatbots and knowledge bases over a creator's, company's or course's whole channel, kept fresh with Only new videos.
• Brand and topic monitoring: search YouTube daily for your product or competitors and get the exact moments they're mentioned.
• Summaries of podcasts, lectures, earnings calls and conference talks.
• Content repurposing: blog posts, newsletters, threads and show notes from videos (Markdown output).
• Research: what are the top videos on a topic saying? Search + transcripts + quotes with timestamps.
• SEO and competitor analysis of what channels in your niche talk about.

Input

Field	What it does
Videos, channels or playlists	Any mix of URLs, IDs and @handles (max 200).
Search queries	Transcripts for the top video results of each search.
Max videos per source	Default 10, up to 1,000.
Channel content	videos (default), shorts, streams or all.
Published after / before	Dates (2026-01-31) or relative (7 days, 2 weeks, 3 months).
Only new videos / Memory name	Skip videos earlier runs already delivered.
Search: sort / upload date / duration	YouTube's own search filters.
Find words inside transcripts / Only videos that mention them	Quoted matches with timestamps; optional filter.
Preferred languages	Codes in order of preference. Default en.
Prefer auto-generated captions	Off by default (human captions are more accurate).
Transcript format	chunks (default), chunkRows, segments, text, markdown, srt, vtt.
Chunk length / overlap (seconds)	Default 60 / 0.
Include video metadata	On by default.

Switching from another transcript actor? videoUrls, urls, startUrls, youtube_url, channel_url, max_videos, language, start_date and end_date work as-is.

Example: monitor three channels daily for new videos, as RAG chunks:

{
  "sources": ["@lexfridman", "@hubermanlab", "https://www.youtube.com/playlist?list=PL..."],
  "maxVideosPerSource": 50,
  "onlyNewVideos": true,
  "transcriptFormat": "chunkRows",
  "chunkSeconds": 60,
  "chunkOverlapSeconds": 10
}

Example: who on YouTube talked about your product this week?

{
  "searchQueries": ["acme analytics review"],
  "searchSort": "date",
  "searchUploadDate": "week",
  "transcriptSearch": ["acme"],
  "onlyMatchingVideos": true
}

Output

One row per video (or one row per chunk with chunkRows). Real output for the default input:

{
  "type": "transcript",
  "videoId": "wjZofJX0v4M",
  "url": "https://www.youtube.com/watch?v=wjZofJX0v4M",
  "title": "Transformers, the tech behind LLMs | Deep Learning Chapter 5",
  "channelName": "3Blue1Brown",
  "channelHandle": "@3blue1brown",
  "publishedAt": "2024-04-01T12:13:57-07:00",
  "durationSeconds": 1634,
  "viewCount": 11389186,
  "likeCount": 237290,
  "category": "Education",
  "language": "en",
  "autoGenerated": false,
  "wordCount": 5137,
  "estimatedTokens": 7278,
  "availableLanguages": [
    "ar",
    "de",
    "en",
    "es",
    "fr",
    "hi",
    "..."
  ],
  "text": "The initials GPT stand for Generative Pretrained Transformer.  ...",
  "chapters": [
    {
      "start": 0,
      "title": "Predict, sample, repeat",
      "end": 183
    },
    {
      "start": 183,
      "title": "Inside a transformer",
      "end": 396
    },
    "..."
  ],
  "chunks": [
    {
      "start": 0,
      "end": 61.06,
      "text": "The initials GPT stand for Generative Pretrained T ...",
      "url": "https://www.youtube.com/watch?v=wjZofJX0v4M&t=0s",
      "chapter": "Predict, sample, repeat"
    },
    {
      "start": 61.06,
      "end": 122.02,
      "text": "that take in a text description and produce an ima ...",
      "url": "https://www.youtube.com/watch?v=wjZofJX0v4M&t=61s",
      "chapter": "Predict, sample, repeat"
    },
    "..."
  ]
}

With Find words inside transcripts (["attention"]), each row also gets:

{
  "matches": [
    {
      "start": 235.28,
      "matched": [
        "attention"
      ],
      "text": "This sequence of vectors then passes through an operation that's known as an attention block, and this allows the vectors to talk to each other and pass information back and forth to update their values.",
      "url": "https://www.youtube.com/watch?v=wjZofJX0v4M&t=235s",
      "chapter": "Inside a transformer"
    },
    "..."
  ]
}

Videos we can't deliver come as free rows you can branch on:

{"type": "no_transcript", "videoId": "...", "reasonCode": "no_captions", "retryable": false, "reason": "no captions on this video"}

reasonCode is one of no_captions, captions_restricted, age_restricted, private, members_only, region_blocked, removed, unavailable, live_or_upcoming, empty_transcript, temporary_error, blocked. retryable: true means a later run may succeed (e.g. a stream that's live now gets captions after it ends).

Download as JSON, CSV, Excel or HTML, or read the dataset over the Apify API.

Pricing

$0.003 per transcript delivered ($3 per 1,000), cheaper on higher Apify plans, whatever the video's length. No charge for videos without captions, failed videos, or videos outside your date range. No compute fee on top. The only other charge: with Only videos that mention them on, each video scanned without a match costs $0.001. Examples: one video $0.003 · a 30-video channel $0.09 · a daily only new videos run on 3 channels ≈ $0.01. Set a maximum cost per run and the Actor stops cleanly there, keeping everything already fetched.

Speed: a single video in seconds, about 20 videos a minute in bulk (a 1,000-video channel took 58 minutes in our test). The default run timeout is 3 hours; if a run reaches its timeout it stops cleanly, keeps everything, and tells you how to continue.

Integrations, schedules and AI agents

• Schedules: Apify Console → Schedules → run daily with Only new videos on.
• n8n, Make, Zapier: use the Apify integration ("Run Actor" + "Get dataset items"). Apify webhooks can trigger your flow when a run finishes.
• LangChain, LlamaIndex: Apify's document loaders read the dataset directly; chunkRows maps one row to one document.
• AI agents (MCP): available through Apify's MCP server (Claude, ChatGPT, Cursor). Tip for agents: start with maxVideosPerSource ≤ 10, read estimatedTokens before loading full text, and use transcriptSearch to get only the passages you need.

from apify_client import ApifyClient
client = ApifyClient("<TOKEN>")
run = client.actor("<this actor>").call(run_input={"sources": ["@lexfridman"], "maxVideosPerSource": 10, "transcriptFormat": "chunkRows"})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["title"], row["start"], row["text"][:80])

FAQ

Does every video have a transcript? No. The video needs captions (human-made or YouTube's automatic ones). Most spoken videos do; music-only, very new or very short videos may not. You're not charged for those.

Can it translate, or transcribe videos without captions? No. YouTube serves translated captions only to its own player, behind a token, and we don't fake that token. We also don't download audio. You get the caption languages a video actually has (availableLanguages); if yours is missing, you get the video's original-language transcript.

Speaker names? YouTube captions don't say who is speaking, so no transcript tool that reads captions can give reliable speaker labels.

Punctuation? Human-made captions are punctuated; YouTube's automatic captions often aren't. We don't rewrite the text.

Live streams and premieres? Finished streams with captions work. Upcoming or ongoing ones come back as live_or_upcoming, free and retryable.

Is it legal? It reads publicly available captions, the same text YouTube shows any viewer, and it never bypasses YouTube's access controls (no faked tokens, no logins, no private or members-only videos). You're responsible for how you use the content (copyright and YouTube's terms). Don't use it for personal data you have no right to process.

Something broke? Open an issue on the Issues tab with your run link and we'll fix it fast.

Related

Our YouTube Scraper (views, likes, dates, channel stats; fast mode) and YouTube Comments Scraper (comments, replies, filters, hot moments) take the same URLs.