How to Scrape Xiaohongshu (RedNote) Posts, Influencers & Comments in 2026 (Zero-Login Guide)

Xiaohongshu (小红书 — known globally as RedNote) has become the single most influential product discovery, beauty, luxury, and lifestyle search engine for Gen-Z and Millennial consumers across Asia and the global diaspora. Over 300 million monthly active users treat RedNote not just as a social feed, but as their primary product review and shopping search engine before buying cosmetics, apparel, electronics, travel, or wellness products.

For global consumer brands, hedge funds, market research teams, and KOL marketing agencies, Xiaohongshu data is pure gold:

• Early Trend Detection: Viral skincare ingredients, fashion silhouettes, and beverage trends break on Xiaohongshu 3 to 6 months before they hit TikTok or Amazon.
• KOL & Creator Vetting: Identifying high-conversion micro-influencers by analyzing real engagement-to-follower ratios (likes, collects, comments, shares) rather than vanity follower counts.
• Unfiltered Consumer Sentiment: Reading nested comment threads to see why buyers love—or return—a competitor's product.

The obstacle? Xiaohongshu does not offer a public data API, and its web and mobile interfaces enforce strict SMS login walls and aggressive rate limits after just a few clicks.

In this guide, we break down how teams extract clean, structured Xiaohongshu data at scale in 2026—whether you want a Done-For-You Excel/CSV dataset delivered to your inbox or a self-serve cloud API for your data pipeline.

What Data Can You Extract from Xiaohongshu (RedNote)?#

Depending on your business use case, Xiaohongshu extraction falls into four core data feeds:

Why Traditional Scraping Fails on Xiaohongshu#

If your engineering team has tried to scrape xiaohongshu.com using standard headless browsers or Python scripts, you have likely encountered three immediate roadblocks:

1. Mandatory +86 SMS Login & QR Code Walls

Unauthenticated visitors can view at most one or two landing pages before Xiaohongshu blocks search results and comment drawers behind a QR/SMS login modal. Even if you register an account with a phone number, automated browsing triggers immediate account restrictions or SMS re-verification prompts within a few dozen requests.

2. Strict Anti-Bot & Rate-Limit Controls

Xiaohongshu's web and mobile endpoints actively block basic automated scripts and unverified cloud requests within minutes, making DIY scrapers brittle and expensive to maintain.

3. Ephemeral Media & CDN Links

High-resolution carousel images and watermark-free video streams use expiring links if not properly normalized. Extracting broken or low-resolution thumbnail links ruins downstream computer vision and creative analysis pipelines.

Two Ways to Get Xiaohongshu Data Today#

At UnitBytes, we support both non-technical business teams (who just want clean spreadsheets) and engineering teams (who want automated API pipelines):

Option B: Self-Serve Cloud Extraction via Python & Apify#

If you want to run extractions on-demand or schedule daily feeds into Snowflake, BigQuery, or Amazon S3, UnitBytes maintains 6 specialized Xiaohongshu cloud engines on Apify with zero account or proxy setup required:

1. RedNote & Xiaohongshu Keyword Search Scraper (Docs): Dedicated pure 5-field cookieless Keyword Search API (keywords, sort, noteType, maxItems, enrichDetails) returning 300+ unique notes per query from $1.50 / 1,000 notes ($0.0015 Gold+; $0.0022 Free).
2. RedNote (Xiaohongshu) All-in-One Scraper (Docs): Unified 7-in-1 engine for cookieless keyword search (300+ notes/query), bare /explore/<id> post URLs, multi-page comments, KOL leads, and live rooms from $1.50 / 1,000 notes (Gold+; $0.0022 Free).
3. RedNote Search & Post Scraper (Docs): High-speed cookieless keyword search (popularity_descending, general, time_descending), bare post URL hydration, and 13 category trend feeds from $1.50 / 1,000 notes.
4. RedNote Creator & KOL Leads Scraper (Docs): Auto-discover niche KOLs by keyword or category and extract follower counts, total likes/collects, MCN badges, and ~30 recent portfolio notes from $3.50 / 1,000 profiles.
5. RedNote Comments & Replies Scraper (Docs): Paginate 250–1,000+ root comments plus up to 3 top inline replies per post from bare /explore/<id> URLs, keyword sweeps, or active liveChat rooms from $1.99 / 1,000 comments ($0.00199 Gold+).
6. RedNote Live Stream & Commerce Scraper (Docs): Sweep active live rooms across all 7 live categories with 1080p FLV/HLS (.m3u8) stream URLs, concurrent viewers, hasGoods e-commerce flags, and live chat from $5.40 / 1,000 live rooms.

Quickstart 1: Cookieless Keyword Search (300+ Posts / Query) in Python

Install the official Apify Python client:

pip install apify-client pandas

Run a cookieless keyword search sorted by popularity (popularity_descending, general, or time_descending)—or pass bare https://www.xiaohongshu.com/explore/<24-hex-id> post URLs directly in startUrls—and export to CSV:

import pandas as pd
from apify_client import ApifyClient

# Initialize the ApifyClient with your API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Cookieless keyword search (up to 300+ unique notes per keyword)
run_input = {
    "mode": "search",
    "keywords": ["早C晚A", "敏感肌护肤"],
    "sort": "popularity_descending",
    "noteType": "all",
    "maxItems": 100,
    "enrichNoteDetails": True
}

# Trigger the UnitBytes RedNote All-in-One Engine
run = client.actor("unitbytes/xiaohongshu-scraper").call(run_input=run_input)

# Load results into a Pandas DataFrame and save to Excel / CSV
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(items)
df.to_csv("xiaohongshu_brand_trends.csv", index=False, encoding="utf-8-sig")
print(f"Exported {len(df)} Xiaohongshu notes to xiaohongshu_brand_trends.csv")

Quickstart 2: Multi-Page Comments (250–1,000+ / Post) from Bare Note URLs

With the V2 RedNote Comments & Replies Scraper, you can pass plain xiaohongshu.com/explore/<24-hex-id> URLs, xhslink.com share links, raw 24-character Note IDs, or search keywords without needing ?xsec_token:

comment_input = {
    "mode": "comments",
    "startUrls": [
        {"url": "https://www.xiaohongshu.com/explore/660000000000000000000001"}
    ],
    "maxCommentsPerNote": 250,
    "includeSubComments": True,
    "maxSubCommentsPerComment": 3
}

comment_run = client.actor("unitbytes/xiaohongshu-rednote-comments-scraper").call(
    run_input=comment_input
)
comments = list(client.dataset(comment_run["defaultDatasetId"]).iterate_items())
high_intent = [c for c in comments if (c.get("sentiment") or {}).get("isBuyerIntent")]
print(f"Extracted {len(comments)} comments ({len(high_intent)} high buyer-intent inquiries)")

Sample Output Schema#

Every extracted Xiaohongshu record is normalized into a clean JSON structure ready for BI dashboards or LLM pipelines:

{
  "itemType": "note",
  "id": "660000000000000000000001",
  "noteId": "660000000000000000000001",
  "title": "2026早C晚A护肤公式｜敏感肌实测28天对比",
  "type": "normal",
  "likedCount": 14820,
  "collectedCount": 9430,
  "commentCount": 612,
  "shareCount": 1205,
  "author": {
    "id": "600000000000000000000001",
    "nickname": "示範護膚測評博主",
    "ipLocation": "上海",
    "followerCount": 184500
  },
  "tags": ["早C晚A", "敏感肌护肤", "精华测评"],
  "noteUrl": "https://www.xiaohongshu.com/explore/660000000000000000000001",
  "scrapedAt": "2026-11-14T09:30:00Z"
}

How Brands Calculate Engagement & Viral Velocity on RedNote#

Unlike Western platforms where Likes dominate, Xiaohongshu's recommendation algorithm weights Saves / Collects (collectedCount) and Comments (commentCount) heavily as purchase-intent signals:

1. Save-to-Like Ratio (collectedCount / likedCount): A ratio above 0.45 indicates high "reference value"—users are bookmarking the post as a shopping list or tutorial to buy later.
2. Creator Efficiency Score: Comparing a note's total interactions (likedCount + collectedCount + commentCount) against the creator's followerCount reveals whether a post achieved organic algorithmic breakout beyond the influencer's existing follower base.

Need a Custom Xiaohongshu Dataset or Automated Brand Monitor?#

Whether you need a one-time CSV export of 10,000 viral notes for a market research report, a monthly KOL leaderboard, or a daily automated feed delivered to your team's Google Sheets or Snowflake warehouse, you can request a custom dataset directly below.

Frequently Asked Questions (FAQ)