Reddit Comments Tree contains valuable data -- comment text, authors, scores, depth, and more. Scraping this data directly means dealing with anti-bot detection, CAPTCHAs, IP rotation, and constantly breaking selectors. The Scavio API handles all of that and returns clean, structured JSON from a single POST request.
This tutorial shows you how to scrape Reddit Comments Tree using Python and the Scavio API. By the end, you will have a working Python script that fetches real-time Reddit Comments Tree data and parses the results.
Prerequisites
- Python installed on your machine
- A Scavio API key (free tier includes 50 credits on signup -- no credit card required)
Step 1: Install Dependencies
Install requests to make HTTP requests:
pip install requestsStep 2: Make Your First Reddit Comments Tree Search
Send a POST request to the Scavio Reddit Comments Tree API endpoint with your query. The API returns structured JSON with comment text, authors, scores, and more.
# Comments are fetched per post: get a post_id from POST /api/v1/reddit/search, or resolve
# a URL with POST /api/v1/reddit/post. Deeper replies come from POST
# /api/v1/reddit/post/comments/replies using a row's reply_cursor.
import requests
API_KEY = "sk_live_your_key"
post_id = "t3_1u1143i"
response = requests.post(
"https://api.scavio.dev/api/v1/reddit/post/comments",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
},
json={"post_id": post_id, "sort": "TOP"},
timeout=60,
)
response.raise_for_status()
data = response.json()
rows = (data.get("data") or {}).get("comments") or []
for row in rows[:5]:
print(row.get("author"))
print(" ", row.get("score"), row.get("depth"), row.get("text"))Step 3: Example Response
The API returns structured JSON. Here is an example response for a Reddit Comments Tree search:
{
"data": {
"comments": [
{
"comment_id": "t1_oz1h216",
"author": "vscoderCopilot",
"text": "Seems like C# to me or maybe they make a new lang for AI usage",
"score": 2,
"depth": 0,
"created_at": "2026-07-22T09:02:17.303000+0000",
"reply_cursor": null
}
],
"next_cursor": null,
"has_more": false
},
"response_time": 2050,
"credits_used": 1,
"credits_remaining": 4810
}Every field is structured and typed -- no HTML parsing, no CSS selectors, no regex extraction. Your Python code can access any field directly.
Step 4: Full Working Example
Here is a complete, runnable Python script that searches Reddit Comments Tree and prints the results:
"""
Fetch Reddit Comments Tree data with the Scavio API.
POST /api/v1/reddit/post/comments - rows come back under data.comments, 1 credit per call.
"""
import json
import os
import requests
# Comments are fetched per post: get a post_id from POST /api/v1/reddit/search, or resolve
# a URL with POST /api/v1/reddit/post. Deeper replies come from POST
# /api/v1/reddit/post/comments/replies using a row's reply_cursor.
API_URL = "https://api.scavio.dev/api/v1/reddit/post/comments"
API_KEY = os.environ["SCAVIO_API_KEY"]
def fetch_reddit_comments_tree(post_id: str) -> dict:
response = requests.post(
API_URL,
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
},
json={"post_id": post_id, "sort": "TOP"},
timeout=60,
)
response.raise_for_status()
return response.json()
if __name__ == "__main__":
data = fetch_reddit_comments_tree("t3_1u1143i")
print(json.dumps(data, indent=2))
rows = (data.get("data") or {}).get("comments") or []
for row in rows[:5]:
print(row.get("author"))
print(" ", row.get("score"), row.get("depth"), row.get("text"))Why Use Scavio Instead of Scraping Reddit Comments Tree Directly?
- No proxy management. Direct scraping requires rotating proxies to avoid IP bans. Scavio handles all of this server-side.
- No CAPTCHA solving. Reddit Comments Tree aggressively blocks automated requests. Scavio returns clean data every time.
- Structured JSON output. No HTML parsing or CSS selector maintenance. Get typed, consistent data from every request.
- Multi-platform in one API. Search Google, Amazon, YouTube, and Walmart from the same API key with the same authentication pattern.
- Free tier included. 50 credits on signup with no credit card required. Each search costs 1 credit.