The Problem
YouTube's official Data API has strict quota limits that make bulk comment extraction impractical for research or monitoring. Scraping comments directly violates ToS, breaks when the DOM changes, and gets blocked by bot detection. Third-party tools either cost $50/mo for basic export features or output unstructured data that requires manual cleanup. Researchers, brand teams, and community managers need comment data in a clean tabular format, but every path to get there is either fragile, expensive, or both.
The Scavio Solution
Scavio's YouTube search endpoint returns structured comment data alongside video metadata. You search for a video URL or topic, get back comments with author, timestamp, like count, and reply threads in normalized JSON. From there, writing to CSV is five lines of code. No scraping, no quota gymnastics, no DOM parsing. The same API call that finds videos can pull their engagement data, giving you a complete research pipeline in one integration.
Before
Before Scavio, exporting YouTube comments meant either burning through official API quota in minutes, running fragile Selenium scripts, or paying $50/mo for a single-purpose export tool.
After
After Scavio, a Python script pulls structured comment data for any video and writes clean CSV in seconds. No quota limits to manage, no browser automation to maintain.
Who It Is For
YouTube researchers, brand community managers, and content creators who need bulk comment data in CSV format without managing API quotas or running scraping infrastructure.
Key Benefits
- Structured comment data with author, timestamp, and like count
- No YouTube Data API quota to manage or request increases for
- Clean JSON output converts to CSV in five lines of code
- Reply threads included with parent-child relationships
- Works for any public video without authentication complexity
Python Example
import requests
import csv
from pathlib import Path
API_KEY = "your_scavio_api_key"
def fetch_video_comments(video_query: str) -> list[dict]:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "youtube", "query": video_query},
timeout=15,
)
res.raise_for_status()
data = res.json()
comments = []
for video in data.get("organic", []):
for comment in video.get("comments", []):
comments.append({
"video_title": video.get("title", ""),
"author": comment.get("author", ""),
"text": comment.get("text", ""),
"likes": comment.get("likes", 0),
"published": comment.get("published", ""),
})
return comments
comments = fetch_video_comments("react server components tutorial")
if comments:
output = Path("comments_export.csv")
with output.open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=comments[0].keys())
writer.writeheader()
writer.writerows(comments)
print(f"Exported {len(comments)} comments to {output}")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function fetchVideoComments(videoQuery) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "youtube", query: videoQuery }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
const data = await res.json();
const comments = [];
for (const video of data.organic ?? []) {
for (const comment of video.comments ?? []) {
comments.push({
videoTitle: video.title ?? "",
author: comment.author ?? "",
text: comment.text ?? "",
likes: comment.likes ?? 0,
published: comment.published ?? "",
});
}
}
return comments;
}
const comments = await fetchVideoComments("react server components tutorial");
if (comments.length) {
const header = Object.keys(comments[0]).join(",");
const rows = comments.map((c) => Object.values(c).map((v) => `"${String(v).replace(/"/g, '""')}"`).join(","));
const fs = await import("fs/promises");
await fs.writeFile("comments_export.csv", [header, ...rows].join("\n"));
console.log(`Exported ${comments.length} comments`);
}Platforms Used
YouTube
Video search with transcripts and metadata