YouTube Comments Scraper

Extract public comments from one or many YouTube videos and Shorts and turn them into clean, structured data. This YouTube comments scraper collects comment text, authors, likes, reply counts, publish dates, pinned and hearted status, and optional reply threads.

Use the Actor in the Apify Console or connect it to your workflow through the YouTube comments scraper API. Results are saved to an Apify dataset for download, filtering, analysis, or automated processing.

No limits, no quotas. This Actor has no request limits, comment caps, or hard quotas. You can scrape a single video or hundreds, one comment or as many as a video holds — every comment, every reply.

What can this YouTube comments scraper do?

This Actor can:

• Collect public comments from a YouTube video ID or full URL — including YouTube Shorts.
• Process multiple videos and Shorts in a single run — scraped concurrently at ~12 requests/second for speed.
• Return the newest comments or YouTube's Top comments.
• Collect every available comment or stop at a limit you choose.
• Keep only comments published after an absolute or relative date.
• Fetch complete reply threads or limit the replies collected per comment.
• Save structured results to a dataset and a run summary under OUTPUT.

Each result includes the video ID, comment ID, author details, comment text, likes, replies, relative and exact publish times, moderation signals, and source feed. Comments and replies are supported for both regular YouTube videos and Shorts.

📤 What the Actor returns

The Actor writes one dataset item per top-level comment to the run's default dataset, and a short run summary to the key–value store under OUTPUT.

• Dataset items (one per top-level comment) — described in the section YouTube comment data output. Each item has the fields below. When replies: true, replies are nested in each item's replies array (not separate dataset items).
• OUTPUT summary — { videos_requested, comments_pushed, replies_pushed, sort }, written to the key–value store at the end of the run.

Each comment record contains:

Common use cases

• Audience research: understand what viewers like, dislike, ask, or recommend.
• Sentiment analysis: prepare comment text for NLP, LLM, or BI workflows.
• Brand monitoring: find recurring feedback and discussions around products or campaigns.
• Competitor research: compare audience reactions across several videos.
• Content strategy: identify questions, objections, ideas, and high-engagement topics.
• Community analysis: study popular threads and conversations between viewers.
• Comment archiving: preserve public discussion data in JSON, CSV, Excel, or other Apify-supported formats.

📥 Input reference

Input examples

Choose the example closest to your goal and adjust the values.

Get the newest 100 comments from one video

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest",
  "limit": 100
}

Get comments from a YouTube Short

Pass a Short's full URL (or its video ID). Comments and replies are collected the same way as for regular videos.

{
  "videos": ["https://youtube.com/shorts/VIDEOID"],
  "sort": "newest",
  "limit": 100
}

Scrape comments from several videos

The limit applies separately to each video.

{
  "videos": ["dQw4w9WgXcQ", "9bZkp7q19f0"],
  "sort": "newest",
  "limit": 500
}

Collect every available comment

Leave out limit to continue until the selected YouTube feed has no more results.

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest"
}

Get Top comments

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "top",
  "limit": 200
}

Get comments from the last 7 days

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest",
  "published_after": "7 days"
}

Get comments published after a fixed date

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest",
  "published_after": "2026-01-01"
}

Get comments with all available replies

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest",
  "replies": true,
  "max_replies": 0
}

Limit replies to 20 per comment

{
  "videos": ["dQw4w9WgXcQ"],
  "sort": "newest",
  "replies": true,
  "max_replies": 20
}

Combine date filtering, replies, and multiple videos

{
  "videos": ["dQw4w9WgXcQ", "9bZkp7q19f0"],
  "sort": "newest",
  "limit": 300,
  "published_after": "30 days",
  "replies": true,
  "max_replies": 10
}

🔌 YouTube Comments Scraper API

You can run the Actor programmatically with the Apify API. Send the same JSON input used in the Console and receive the dataset items in the response.

Replace <ACTOR_ID> and <APIFY_API_TOKEN> with the values shown in the Actor's API tab:

curl -X POST \
  "https://api.apify.com/v2/actors/<ACTOR_ID>/run-sync-get-dataset-items?token=<APIFY_API_TOKEN>&format=json&clean=true" \
  -H "Content-Type: application/json" \
  -d '{
    "videos": ["dQw4w9WgXcQ"],
    "sort": "newest",
    "limit": 100,
    "replies": true,
    "max_replies": 10
  }'

For large jobs, start the Actor asynchronously, monitor the run, and retrieve items from its default dataset after completion. This avoids keeping one HTTP connection open for a long-running scrape.

📤 YouTube comment data output

Each dataset item represents a top-level comment. When reply collection is enabled, its replies appear in the replies array.

{
  "video_id": "dQw4w9WgXcQ",
  "comment_id": "Ugzge340dBgB75hWBm54AaABAg",
  "author": "@YouTube",
  "author_channel_id": "UCBR8-60-B28hp2BmDPdntcQ",
  "author_avatar": "https://yt3.ggpht.com/...",
  "author_is_channel_owner": true,
  "text": "This is the comment text.",
  "published": "1 year ago",
  "published_at": "2025-08-29T19:41:32Z",
  "like_count": 1234,
  "reply_count": 1000,
  "is_pinned": false,
  "pinned_by": "",
  "reply_level": 0,
  "is_hearted": false,
  "replies": [],
  "source": "newest"
}

Output fields explained

Reply structure

Replies use the same core fields as top-level comments and have reply_level: 1.

{
  "comment_id": "Ugzge340dBgB75hWBm54AaABAg",
  "reply_level": 0,
  "text": "Top-level comment",
  "replies": [
    {
      "comment_id": "Ugz...reply1",
      "author": "@someone",
      "text": "A reply to the comment.",
      "published_at": "2026-08-30T01:38:32Z",
      "like_count": 7,
      "reply_count": 0,
      "reply_level": 1
    }
  ]
}

Run summary in OUTPUT

The Actor stores a compact summary in the default key-value store:

{
  "videos_requested": 2,
  "comments_pushed": 300,
  "replies_pushed": 120,
  "sort": "newest"
}

The run log also reports the collected totals and an estimated cost at the end, e.g.

done: 300 comments, 120 replies (est. cost $0.1350 = $0.0750 comments + $0.0600 replies)

Export and integrate the results

Open the run's Storage tab to preview or download the dataset. Apify datasets can be exported in formats such as JSON, CSV, Excel, XML, and HTML.

You can also connect the results to databases, dashboards, AI pipelines, webhooks, scheduled tasks, or automation platforms supported by Apify.

Important behavior and limitations

• Comment totals: YouTube's displayed total can include both top-level comments and replies. The main dataset contains top-level comments; replies are included only when replies is enabled.
• Feed behavior: The Top and Newest orderings come from YouTube. If the requested feed is unavailable, the Actor returns no results instead of silently switching to another order.
• Date filtering: published_after works with the chronological Newest feed. It is ignored when sort is set to top.
• Public data: The Actor collects publicly visible comments. Videos that are unavailable, restricted, deleted, or have comments disabled may not return data.
• Concurrent scraping: When you provide several videos, they're scraped in parallel — up to 12 videos at a time (any extra videos queue and process as earlier ones finish), with the total request rate capped at ~12 requests/second. This keeps a batch run fast while staying inside the service's rate budget, and each video is processed independently — one unavailable video doesn't stop the rest. The video concurrency cap is configurable via the MAX_CONCURRENT_VIDEOS environment variable (default 12; the Actor clamps it to 1–500).
• Memory efficient: Comments are streamed to the dataset batch-by-batch (100 at a time), not held in memory, and only a bounded number of videos are scraped at once. Peak memory stays flat no matter how many videos or comments you request, so it works on a small, low-RAM run (typically well under 128 MB).
• Responsible use: Follow applicable laws, YouTube's terms, and data-protection requirements when collecting or processing public comment data.

There is no quota or cap imposed by the Actor itself — you can request any number of videos or comments in a single run, and the Actor pages through what YouTube exposes. The only ceiling is what the platform returns for a given video (for example, how many comments YouTube makes available, or whether a video has comments enabled).

Pricing

This Actor uses Apify's pay-per-event (PPE) monetization. Each comment pushed to the default dataset is charged as apify-default-dataset-item (billed automatically by Apify — no charging code needed). One custom event is additionally charged from the Actor and must be configured in Apify Console → Actor → Publication → Monetization (custom events → add):

Set a per-run spending limit, pick a Primary event, then review. Charging a custom event that isn't configured is a safe no-op (it logs a warning and bills nothing), so the reply event is an optional extra — the default dataset-item charge always applies for every comment.

A flat monthly fee is another option when the Actor is offered as a fixed-capacity service.

Frequently asked questions

What is a YouTube comments scraper?

A YouTube comments scraper automatically collects public comments from YouTube videos and converts them into structured records. The data can include comment text, authors, likes, dates, reply counts, and reply threads for research, reporting, moderation, or analysis.

How do I scrape comments from a specific YouTube video?

Add the video's full URL or ID to the videos input and run the Actor. The extracted comments will appear in the default dataset.

Can I scrape comments from YouTube Shorts?

Yes. The Actor accepts YouTube Shorts too — paste a Short's full URL (for example https://youtube.com/shorts/VIDEOID) or its video ID into the videos input. Comments and replies are collected exactly as they are for regular YouTube videos.

Can I scrape comments from multiple YouTube videos at once?

Yes. Add every video URL or ID to the videos array. The Actor processes the list in sequence, and limit applies to each video separately.

Can I collect all comments from a video?

Yes. Leave limit empty to continue through the selected feed until the Actor has returned everything YouTube makes available.

How do I scrape only recent YouTube comments?

Set sort to newest, then use published_after with a relative value such as 7 days or an absolute date such as 2026-01-01.

Can this Actor collect YouTube comment replies?

Yes. Set replies to true. Use max_replies to cap replies per comment, or set it to 0 to request all available replies.

What is the difference between Top and Newest comments?

newest returns YouTube's chronological comment feed. top uses YouTube's ranked Top comments feed. Because the order comes from YouTube, Top comments should not be treated as a strict sort by like count alone.

How does the YouTube comments scraper API work?

Send the Actor input as JSON to Apify's Run Actor API. For shorter jobs, the synchronous dataset endpoint can return comment records in the same response. For larger jobs, use an asynchronous run and retrieve the default dataset when processing finishes.

Do I need the official YouTube Data API?

No official YouTube Data API key is entered in the Actor input.

🤖 MCP support (Model Context Protocol)

This Actor is compatible with Apify's hosted MCP server (mcp.apify.com), so AI agents can call it as a tool — no extra code is needed. Apify automatically exposes eligible Actors as MCP tools built from their input schema.

How it works

Apify's MCP server (@apify/actors-mcp-server) discovers this Actor via its name and turns its input schema into a single "run this Actor" tool. When an agent calls the tool, it runs the Actor and returns the dataset items.

Connect it to an MCP client

Add Apify's hosted server to your MCP client (e.g. Claude Desktop, Cursor, or the mcp CLI), pointing at this Actor's tool.

Claude Desktop / claude_desktop_config.json:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=you/youtube-comments-scraper"
    }
  }
}

Command line:

$npx @apify/actors-mcp-server --actors you/youtube-comments-scraper

Replace you with your Apify account username. With the server connected, an agent can ask things like: "Get the 100 newest comments from this YouTube video" — the tool runs the Actor with the matching input fields and returns the comments.

What the tool exposes

The MCP tool accepts the Actor's input fields as tool arguments (all with sensible defaults except videos, which is required):

Requirements

• The Actor must be accessible to the account whose token the MCP client uses. If you want third parties to invoke it through a shared MCP server, publish it so it can be run by others (e.g. make it publicly runnable, or grant the calling account access). Private, full-permission, and rented Actors are excluded from the hosted MCP server.
• The MCP client connects with an Apify token that has permission to run this Actor.
• An MCP run behaves exactly like a normal run: it writes to the default dataset (one item per comment), charges the PPE events described above, and produces the OUTPUT summary.