YouTube Comments & Replies Scraper

YouTube Comments Scraper — Text, Likes, Replies and Author Data

Extract YouTube comments and their full reply threads as structured JSON — comment text, voteCount (likes), author, authorChannelUrl, publishedAt, and replyCount for every top-level comment and reply. Paste a video URL, an 11-character video ID, or a channel URL and the scraper does the rest, including expanding a channel into its latest videos. No YouTube account, no Google Cloud project, no YouTube Data API key required. Configure the input below and hit Start to get your first dataset in minutes.

What is YouTube Comments Scraper with Replies?

YouTube Comments Scraper with Replies is an Apify Actor that reads a YouTube video's public comment section — top-level comments and their nested replies — and returns each one as a row of typed JSON with author, engagement, and timing data. It requires no YouTube account, no Google sign-in, and no API key; it reads the same public page and internal pagination endpoint your browser uses when you scroll through comments. It's built for marketers, social listening teams, researchers, and developers who need comment data in a database or LLM pipeline rather than read one by one in a browser.

What YouTube comment data is publicly available to scrape?

Anyone who opens a YouTube video page — logged in or not — can read its comments, reply threads, like counts, and commenter display names without further permission from Google or the video owner.

Data category	Publicly available (no login)	Restricted
Comment text and reply text	✅	—
Like count (voteCount) and reply count (replyCount)	✅	—
Commenter display name and channel link	✅	—
Commenter avatar, verified badge, "hearted by creator" flag	✅	—
Relative publish time ("2 years ago")	✅	—
Exact publish timestamp	⚠️ approximated	Only YouTube's own systems and the official API return a precise value
Comments on a video with comments turned off or a private/unlisted video the link owner hasn't shared	—	❌ not exposed on the public page
Commenter's email address or account details beyond their public channel	—	❌ never shown publicly

YouTube Comments Scraper with Replies only returns publicly visible data — what any visitor sees on the video page. Nothing behind a login wall.

What data can I extract with YouTube Comments Scraper with Replies?

The scraper returns one row per comment and one row per reply, both using the same 27-field schema (the dataset's default view lists all 27 — there is no hidden subset).

Comment & author identity

Field	Description
cid	Unique YouTube comment ID
type	"comment" for top-level comments, "reply" for replies
isReply	Boolean — true if this row is a reply
comment	The comment or reply text
author	Commenter's display name, normalized to start with @
authorChannelId	Commenter's YouTube channel ID
authorChannelUrl	Full URL to the commenter's channel, built from authorChannelId
avatar	URL of the commenter's avatar image
videoId	The 11-character ID of the video the comment belongs to
pageUrl	The video's watch URL
title	The video's title
commentUrl	Deep link to the specific comment (video URL + &lc=<cid>)
replyToCid	The parent comment's cid, present only on replies

Engagement, verification and timing

Field	Description
voteCount	Number of likes on the comment
replyCount	Number of replies YouTube reports for a top-level comment
commentsCount	Total comment count YouTube reports for the video, repeated on every row
authorIsChannelOwner	Boolean — true if the commenter owns the video's channel
isVerified	Boolean — true if the commenter's channel carries YouTube's verified badge
hasCreatorHeart	Boolean — true if the video's creator "hearted" this comment
publishedTimeText	YouTube's own relative time string, e.g. "2 years ago"
publishedAt	ISO-8601 UTC timestamp approximated from publishedTimeText (see the FAQ below)
scrapedAt	ISO-8601 UTC timestamp of when the scraper collected the row

🤖 AI enrichment fields (only present when aiEnhancement is on)

Field	Description
sentiment	"positive", "negative", or "neutral"
emotion	One of joy, anger, sadness, fear, surprise, disgust, neutral
language	ISO 639-1 language code detected in the comment
topics	Up to 3 short topic tags
toxicityScore	A 0.0 (clean) to 1.0 (toxic) score

🤖 Add-on: Need additional YouTube data?

If you also need channel-level metrics — subscriber counts, upload cadence, creator analytics — pair this Actor with YouTube Channel Finder With Creator Analytics (youtube-channel-finder-with-creator-analytics) from the same account. Run it first to shortlist channels, then feed the resulting video or channel URLs straight into this Actor's startUrls.

How does YouTube Comments Scraper with Replies differ from the official YouTube Data API?

The official YouTube Data API v3 can return comments through its commentThreads.list endpoint, but it requires a Google Cloud project, an API key, and counts every call against a shared daily quota — Google's own getting-started documentation states new projects get "a default quota allocation of... 10,000 units per day combined for all other endpoints" (as observed on Google's YouTube Data API documentation, checked 2026-07-26). YouTube Comments Scraper with Replies reads the same public comment data without any of that setup.

Feature	YouTube Data API v3	YouTube Comments Scraper with Replies
Setup	Google Cloud project + API key (OAuth for some calls)	Apify account only
Daily cap	Shared 10,000-unit default quota across all endpoints	No published unit cap — bounded only by your own maxComments / maxRepliesPerComment
Reply threads	Separate comments.list call per parent comment ID	Replies fetched automatically and interleaved under each parent
Channel-wide runs	You enumerate videos yourself via search.list or playlistItems.list	Paste a channel URL — maxVideosPerChannel expands it internally
Sentiment / topic tagging	Not offered — raw text only	Optional AI enrichment (aiEnhancement) across 7 providers
Keyword / date / like filtering	Not built in — filter client-side after fetching	keywords, excludeKeywords, minLikes, oldestCommentDate filter before rows are pushed

Use the official API if you're building a Google-verified app that needs OAuth-scoped write access (posting or moderating comments). Use YouTube Comments Scraper with Replies for read-only bulk comment extraction without Google credentials or quota management.

How to use YouTube Comments Scraper with Replies

The Actor runs on the Apify platform — no local install, no scraping infrastructure to maintain.

1. Open YouTube Comments Scraper with Replies on its Apify Store page.
2. Provide the required input: at least one entry in startUrls — a video URL, an 11-character video ID, or a channel URL.
3. Optionally set maxComments, sortOrder, includeReplies, and the keywords / excludeKeywords / minLikes / oldestCommentDate filters.
4. Click Start.
5. Open the Dataset tab when the run finishes and export as JSON, CSV, Excel, or XML — or pull results via the Apify API.

How to scale to bulk comment extraction

startUrls is an array, so a single run can process any number of video URLs, video IDs, and channel URLs together — the Actor loops through them one by one. To pull comments from an entire channel, add the channel URL (https://www.youtube.com/@handle, /channel/UC..., /c/name, or /user/name) and set maxVideosPerChannel to control how many of its latest videos get scraped.

What can you do with YouTube comment data?

• 📊 Social listening teams monitoring a launch use sentiment and topics (with aiEnhancement on) alongside publishedAt to track how audience reaction shifts over the days after a video goes live.
• 🎯 Marketers benchmarking a campaign use voteCount and replyCount to rank which comments resonated most and pull direct quotes with comment and commentUrl.
• 🔬 Researchers studying discourse use keywords and excludeKeywords at scrape time to isolate comment threads about a specific topic without downloading everything first.
• 🏢 Brand and reputation teams use toxicityScore and emotion to flag comment threads that need a moderation or support response.
• 🤖 AI engineers feed comment, author, and title into a RAG index or an LLM-based summarization agent to generate audience-sentiment reports without writing a YouTube parser.

How does YouTube Comments Scraper with Replies handle rate limits and blocking?

By default the Actor makes requests with no proxy at all. If YouTube responds with a 403 or 429, or a proxy connection fails, it automatically escalates: no proxy → Apify Proxy datacenter tier → Apify Proxy residential tier, retrying up to 3 times per request with a growing delay between attempts (roughly 2, 4, and 6 seconds). You can also pass your own proxyConfiguration to start on a specific proxy tier or group. If a video's page can't be fetched at all after retries, the Actor logs an error and moves on to the next URL in startUrls rather than failing the whole run.

⬇️ Input

Parameter	Required	Type	Description	Example value
startUrls	Yes	array	Video URLs, 11-character video IDs, or channel URLs (@handle, /channel/UC..., /c/name, /user/name). Channel URLs expand to their latest videos.	["https://www.youtube.com/watch?v=KrLj6nc516A"]
maxComments	No	integer (default 10, min 0)	Max top-level comments per video. Replies are additional, controlled by maxRepliesPerComment. 0 = no limit.	25
sortOrder	No	string (default "top", enum top / newest)	top = most liked first, newest = most recent first. Auto-switches to newest when oldestCommentDate is set.	"newest"
includeReplies	No	boolean (default true)	Collect reply (child) comments. Turn off to scrape only top-level comments.	true
maxRepliesPerComment	No	integer (default 0, min 0)	Cap replies collected per top-level comment. 0 = no limit.	5
keywords	No	array	Keep only comments containing at least one of these words/phrases (case-insensitive). Empty = keep all.	["pricing", "shipping"]
excludeKeywords	No	array	Drop comments containing any of these words/phrases (case-insensitive).	["spam"]
minLikes	No	integer (default 0, min 0)	Keep only comments with at least this many likes (voteCount).	5
oldestCommentDate	No	string	Keep only comments newer than this. Accepts an absolute date (2026-01-15) or a relative period (7 days, 3 months, 1 year). Auto-switches sort to newest.	"3 months"
maxVideosPerChannel	No	integer (default 10, min 1)	When a channel URL is provided, scrape comments from up to this many of its latest videos.	5
aiEnhancement	No	boolean (default false)	Add sentiment, emotion, language, topics, and toxicityScore to each comment via the AI provider you choose below. Requires an API key.	true
aiModel	No	string (default "claude-haiku-4-5"), enum of 31 models across 7 providers	Pick the model; provider is auto-detected from the name prefix (claude-* = Anthropic, gpt-*/o1/o3-* = OpenAI, gemini-* = Google, grok-* = xAI, deepseek-* = DeepSeek, sonar* = Perplexity, mistral-* = Mistral).	"claude-haiku-4-5"
aiApiKey	No	string (secret)	API key for the selected provider. Falls back to an environment variable: ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY/GOOGLE_API_KEY, XAI_API_KEY/GROK_API_KEY, DEEPSEEK_API_KEY, PERPLEXITY_API_KEY/PPLX_API_KEY, or MISTRAL_API_KEY.	"sk-ant-..."
proxyConfiguration	No	object (proxy editor)	Proxy settings. Default: no proxy, with automatic fallback to datacenter then residential if blocked.	{"useApifyProxy": false}

Example input

{
  "startUrls": [
    "https://www.youtube.com/watch?v=KrLj6nc516A",
    "https://www.youtube.com/@MrBeast"
  ],
  "maxComments": 25,
  "sortOrder": "top",
  "includeReplies": true,
  "maxRepliesPerComment": 5,
  "keywords": ["giveaway"],
  "excludeKeywords": ["spam"],
  "minLikes": 5,
  "oldestCommentDate": "3 months",
  "maxVideosPerChannel": 5,
  "aiEnhancement": true,
  "aiModel": "claude-haiku-4-5",
  "aiApiKey": "",
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

⬆️ Output

Every comment and reply is pushed as one typed, normalized JSON row with a consistent 27-field schema — including the AI fields, which are present (and populated) only when aiEnhancement is on. Replies are additionally mirrored into a dedicated per-run dataset named replies-<runId>, using the exact same schema, so you can pull only replies without filtering type. Export from the Dataset tab as JSON, CSV, Excel, or XML, or stream results through the Apify API.

Example output

{
  "cid": "UgzxS4_5UJwAI1xrNF14AaABAg",
  "type": "comment",
  "isReply": false,
  "publishedTimeText": "1 year ago",
  "publishedAt": "2025-07-26T09:12:00Z",
  "comment": "This challenge was insane, respect for actually finishing it!",
  "author": "@_Kevin_1",
  "authorChannelId": "UCyc0emnCfdnTEGr0A5sK2yA",
  "authorChannelUrl": "https://www.youtube.com/channel/UCyc0emnCfdnTEGr0A5sK2yA",
  "avatar": "https://yt3.ggpht.com/abc123=s176-c-k-c0x00ffffff-no-rj",
  "authorIsChannelOwner": false,
  "isVerified": false,
  "voteCount": 2043,
  "hasCreatorHeart": true,
  "replyCount": 1,
  "replyToCid": null,
  "commentUrl": "https://www.youtube.com/watch?v=KrLj6nc516A&lc=UgzxS4_5UJwAI1xrNF14AaABAg",
  "videoId": "KrLj6nc516A",
  "pageUrl": "https://www.youtube.com/watch?v=KrLj6nc516A",
  "title": "$1 vs $100,000,000 Car!",
  "commentsCount": 158342,
  "scrapedAt": "2026-07-26T10:00:00Z",
  "sentiment": "positive",
  "emotion": "joy",
  "language": "en",
  "topics": ["challenge", "cars"],
  "toxicityScore": 0.01
}

How does it work?

YouTube Comments Scraper with Replies makes plain HTTP requests to the same public video page and the same internal /youtubei/v1/next pagination endpoint your browser calls when you scroll through comments — there's no headless browser involved. It reads the ytInitialData JSON embedded in the video page to get a continuation token, client version, and visitor ID, then uses those to request further pages of comments and, separately, reply threads under each parent comment. Requests go out with no proxy by default and automatically escalate through Apify Proxy's datacenter and residential tiers if YouTube responds with a block. Only publicly visible comment data is ever returned, and the 27-field output schema stays the same regardless of changes to YouTube's page layout, since the Actor parses the underlying data structure rather than rendered HTML text.

Integrations

YouTube Comments Scraper with Replies runs on Apify, so it works with anything that can call the Apify API or trigger an Apify Actor run.

Calling YouTube Comments Scraper with Replies programmatically

from apify_client import ApifyClient

client = ApifyClient("<APIFY_API_TOKEN>")

run_input = {
    "startUrls": ["https://www.youtube.com/watch?v=KrLj6nc516A"],
    "maxComments": 25,
    "includeReplies": True,
}

run = client.actor("youtube-comments-scraper-with-replies").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["author"], item["comment"])

Works in Go, Ruby, Node.js, cURL — any language that can make an HTTP request against the Apify API.

No-code tools (n8n, Make, LangChain)

In n8n, use the Apify node (or a generic HTTP Request node against https://api.apify.com/v2/acts/youtube-comments-scraper-with-replies/runs) to start a run, then poll or use a webhook to fetch the dataset once it finishes. In Make, the Apify app module offers the same "Run an Actor" and "Get dataset items" actions, so a scenario can trigger a run on a schedule and feed rows into a Google Sheet or CRM. In LangChain, wrap the Apify Python client's call() and iterate_items() calls in a custom tool so an agent can pull fresh comment data as part of a larger chain.

Is it legal to scrape YouTube comments?

Scraping publicly available YouTube comments is generally legal, since this Actor collects only data any visitor can already see without logging in. Comment author names, avatars, and channel links can constitute personal data about the commenters, so if you store or process this data at scale you should have a lawful basis under GDPR (EU/UK users) or comply with the CCPA (California users) — for example, a documented legitimate interest for sentiment or brand-monitoring research, with a defined retention period. You are also responsible for complying with YouTube's own Terms of Service for your use case. Consult legal counsel if your use case involves bulk storage of personal data.

Frequently asked questions

What YouTube comment fields does YouTube Comments Scraper with Replies return?

The top fields are comment (the text), author, voteCount (likes), publishedAt, and replyCount. See the data fields section above for the complete 27-field schema.

Does YouTube Comments Scraper with Replies require a YouTube account or login?

No. The source code makes unauthenticated HTTP requests to YouTube's public video page and its public comment-pagination endpoint — no Google sign-in, cookies, or session are used.

How many comments can I extract in one run?

There's no fixed cap in the Actor itself — maxComments (default 10) controls top-level comments per video, and 0 removes the limit entirely; maxRepliesPerComment (default 0, meaning unlimited) controls replies per comment separately.

What happens if a video has comments disabled or the page can't be loaded?

The Actor logs an error for that specific video (❌ Failed to extract data from video page) and moves on to the next URL in startUrls — one bad video does not stop the rest of the run. If a URL can't be parsed into a valid video ID at all, it's skipped with a warning before any request is made.

Can I scrape multiple YouTube videos or channels at once?

Yes. startUrls is an array — mix video URLs, video IDs, and channel URLs in the same run, and the Actor processes them sequentially, expanding any channel URL into its latest videos first.

Does YouTube Comments Scraper with Replies work with Claude, ChatGPT, and other AI agent tools?

It's callable as an HTTP endpoint through the Apify API by any agent framework that can make requests. Separately, its own aiEnhancement option can call Claude, ChatGPT, Gemini, Grok, DeepSeek, Perplexity, or Mistral directly to classify each comment's sentiment, emotion, language, topics, and toxicity as part of the run.

How does YouTube Comments Scraper with Replies compare to other YouTube comment scrapers?

As observed on the Apify Store on 2026-07-26, memo23's youtube-comments-scraper documents comment and reply data but not channel-wide input, keyword/date/like filtering, or AI enrichment. delicious_zebu's youtube-comments-replies-scraper documents first- and second-level comments but not channel URLs, absolute timestamps, or filters. parseforge's youtube-comments-scraper documents optional AI sentiment analysis and up to 1,000,000 comments on a paid plan, but its listed 17 fields don't include vote/reply counts, verified badges, or "hearted by creator" flags. YouTube Comments Scraper with Replies combines channel-wide scraping, a dedicated replies dataset, keywords/excludeKeywords/minLikes/oldestCommentDate filtering, and 7-provider AI enrichment in one Actor — a combination none of the three competitor listings document together as of the date checked.

Does YouTube Comments Scraper with Replies return data in a format LLMs can use directly?

Yes. Every row is typed, normalized JSON with consistent field names across runs — no HTML parsing or CSS selectors required. Pass rows directly to an LLM, index comment text into a vector store, or feed the dataset to an agent tool.

What happens when YouTube changes its comment layout or anti-bot system?

The Actor parses YouTube's underlying data structures (ytInitialData and the /youtubei/v1/next JSON response) rather than rendered HTML, and the 27-field output schema is designed to stay stable across UI changes. No specific update turnaround time is published.

Can I use YouTube Comments Scraper with Replies without managing proxies or browser infrastructure?

Yes. By default it runs with no proxy and automatically escalates to Apify Proxy's datacenter, then residential, tier if YouTube blocks a request — you don't need to configure or rotate anything yourself unless you want to override the default with your own proxyConfiguration.

Why is publishedAt an approximation instead of an exact timestamp?

Because YouTube's public comment page only exposes a relative string like "2 years ago" (captured as-is in publishedTimeText), not an exact date. The Actor converts that string to an ISO-8601 UTC value in publishedAt by subtracting the stated period from the scrape time — treat publishedAt as an estimate accurate to the unit YouTube displayed (day, month, or year), not a precise second-level timestamp.

Which comment fields work best for AI training data and RAG indexing?

For RAG, index comment alongside title and author for context. For structured training data, voteCount, replyCount, isReply, and authorIsChannelOwner are the most consistently populated numeric and boolean fields across every run, and all fields return as typed primitives (strings, integers, booleans) rather than nested objects.

🔗 Related scrapers

Scraper	What it extracts
YouTube Channel Finder With Creator Analytics	Channel-level metrics and creator analytics for YouTube channels
Facebook Comments & Replies Scraper with AI Enrichment	Comments and reply threads from Facebook posts, with the same style of AI enrichment
Instagram Tagged Posts Scraper — Comments, Media & Metadata	Comments, media, and metadata from Instagram tagged posts
Instagram Followers And Following Scraper with AI Enrichment	Follower and following lists with optional AI enrichment

💬 Your feedback

Found a bug or missing a field? Let us know through the Actor's Issues tab on Apify Console, or leave a review on the Store listing — both are monitored and help prioritize fixes and new fields.