fetchlayer.dev Sign in YouTube comments,
ready for your code and AI.
Extract public comments and nested replies from a YouTube video URL as structured JSON. Sort by top or newest, choose reply depth, and turn viewer feedback into research, content ideas, or AI analysis.
Free tier included · No credit card required · Use from any HTTP client
TypeScript / JavaScript? Use the open-source SDK: on npm · Source on GitHub
Request
$ curl -X POST https://api.fetchlayer.dev/youtube/comments \ -H "Authorization: Bearer ss-your-key" \ -H "Content-Type: application/json" \ -d '{"videoUrl":"https://youtube.com/watch?v=...","sort":"newest"}'
Response
[
{
"username": "@viewer",
"comment": "Please cover offline mode next.",
"likes": 42,
"hasReplies": true,
"date": "1 day ago",
"nestedReplies": [ ... ]
}
] Built for feedback workflows
Fetch the public signal. Use it wherever your team works.
Public video comments
Provide a public YouTube video URL and retrieve the visible comment conversation as structured JSON.
Top or newest
Prioritize the most engaged feedback or focus on the latest reaction to a new video, launch, or announcement.
Nested replies
Control reply depth when the valuable signal is in the conversation below a top-level comment.
Audience research
Find repeated questions, objections, praise, and content requests in the comments your audience leaves publicly.
AI-ready JSON
Send comments to an LLM or MCP agent to summarize viewer sentiment, group themes, and surface pain points.
Your workflow, your schedule
Call the endpoint from your own code, cron job, or automation when you need another fresh snapshot of the discussion.
Analyze YouTube comments with AI—then export the results to CSV.
Paste a video URL into FetchLayer Research Chat and ask what the audience is saying. It can surface recurring questions, pain points, sentiment, and content requests while keeping the source comments in a reusable file.
- ✓Ask questions in plain English
- ✓Analyze themes, sentiment, and pain points
- ✓Download the structured results as CSV or JSON
No code or setup required · Uses your FetchLayer credits
Analyze the comments on this YouTube video. Find recurring questions, pain points, and content requests, then export the comments and themes as CSV.
I found four recurring questions and three strong follow-up content ideas. The comment-level themes, engagement, and source context are organized in the export.
youtube-comment-analysis.csv
284 comments · 7 columns · ready to download
Solutions
Build the workflow around the feedback.
Analyze YouTube comments
Find recurring questions, objections, content requests, and audience sentiment from the source conversation.
Explore →Research your audience
Use public comment language to guide content ideas, market research, and competitor analysis.
Explore →Add App Store reviews
Bring public customer reviews into the same feedback and AI workflow.
Explore →From raw feedback to action
One request is the start of the workflow.
Store the structured response, run it on a schedule, pass it to an LLM, or feed it into the system your team already uses. FetchLayer handles retrieval so you can focus on the outcome.
- 01
Provide a video URL
Use the public URL for the video whose comments you want to understand.
- 02
Choose the view of the conversation
Set top or newest sorting, the reply depth you need, and how far to scroll for more comments.
- 03
Analyze or act on the signal
Store the comments, run your own monitoring workflow, or ask an AI agent to identify the useful patterns.
MCP & agents
Your AI already knows
how to use this.
Use the YouTube endpoint directly from your MCP-compatible agent. Ask it to retrieve public feedback, identify patterns, and turn the result into a useful brief without writing integration glue.
{
"mcpServers": {
"fetchlayer": {
"url": "https://mcp.fetchlayer.dev",
"headers": {
"Authorization": "Bearer sk-..."
}
}
}
} Read the newest comments on this product video. What questions keep appearing, what do viewers dislike, and what follow-up video should the creator make?
Using fetchlayer.youtube_comments
The discussion is broadly positive, but the repeated request is a step-by-step setup walkthrough. Viewers also ask whether the product supports offline use and team collaboration.
Works with every MCP-compatible tool
Use cases
What developers build with YouTube comment data.
Same API, endless applications. Here is what teams ship with public audience feedback.
Audience intelligence
Analyze public comments on your videos or competitors to find repeated questions, objections, and demand.
AI agent context
Give Claude, Codex, or your MCP agent structured comments and replies without copy-pasting threads.
Launch monitoring
Watch newest comments after a launch, announcement, or campaign and route emerging concerns quickly.
Content research
Turn repeated viewer questions and requests into the next video, FAQ, tutorial, or product explanation.
Product feedback
Capture objections, setup friction, and feature requests when a video introduces your product.
Competitor research
Study public comment sections to see where competing content leaves an audience unconvinced or unanswered.
Compare approaches
Spend time on the insight, not the collection layer.
| What you need | FetchLayer | Official API | Build it yourself |
|---|---|---|---|
| One URL to structured comments | ✓ | Project setup | You build it |
| No Google Cloud configuration | ✓ | Required | You manage it |
| Nested reply support | ✓ | Multiple methods | You parse it |
| Ready for AI and MCP workflows | ✓ | Your integration | Your integration |
FetchLayer provides access to publicly available data. It is not affiliated with, endorsed by, or sponsored by the platform.
Pricing
Start free. Pay for what you use, or lock in a flat rate.
Credits for flexible, no-commitment usage. Subscriptions for a fixed monthly rate with built-in savings. Subscriptions+ for teams that want volume pricing at scale.
Free plan
30 free requests
A small test drive to verify the API, inspect real responses, and decide whether you want to stay on credits or move to a subscription.
Pay as you go
$1.99 per 1,000 requests
$0.00199 per request
How credits are counted
One credit = one request. No matter how many results come back — replies, comments, search pages — you pay for the call, not the output size.
Your first successful payment unlocks unlimited API keys on the account, including pay-as-you-go credits.
- No monthly commitment — buy only what you need
- Credits never expire — they stay until you use them
- Same API, same MCP access, same data quality
- ∞ req/min
Credits never expire
Buy once, use whenever. There's no monthly reset, no pressure to hit a quota, no wasted credits at the end of a billing cycle.
No strings attached
No subscription to cancel, no seat minimums, no contract to negotiate. Start, pause, or scale whenever you want.
Full access from credit one
Every endpoint, MCP included, is available on credits. You're not on a limited tier — you get the same data as any subscriber.
FAQ
Common questions.
What can the YouTube Comments API retrieve?
FetchLayer extracts public top-level YouTube comments and nested replies from a video URL. Each result includes the author username, comment text, likes, relative date, and reply structure.
Do I need a Google Cloud project or YouTube credentials?
No. Authenticate with your FetchLayer API key and provide the public YouTube video URL you want to retrieve comments from.
Can I retrieve newest comments instead of top comments?
Yes. Set the sort option to top or newest depending on whether you need the most engaged feedback or the latest discussion.
Can an AI agent analyze the comments?
Yes. The endpoint returns structured comment data that you can pass to an LLM or use through FetchLayer’s MCP server to identify sentiment, recurring questions, content ideas, and customer feedback.
Can I analyze and export YouTube comments without code?
Yes. FetchLayer Research Chat can retrieve public comments from a YouTube video, analyze the discussion with AI, and export the structured results as a downloadable CSV or JSON file.
How do I retrieve more comments or nested replies?
Set a higher pages value to fetch more pages of top-level comments. Set a higher depth value to include more levels of nested replies for each comment thread.
Start pulling youtube comments today.
Get a free API key, make your first request, and put public feedback to work in your own product or research workflow.
Used by developers at
Individual developers or teams — not official partnerships