How to Scrape Xiaohongshu (RedNote) Live Streams, 1080p Video & KOL Leads in 2026

Xiaohongshu (小红书 / RedNote) has evolved far beyond static photo carousels. In 2026, Xiaohongshu Live Commerce (live-room.xiaohongshu.com) and creator-led boutique shopping drive some of the highest average order values (AOV) and conversion rates in Asian e-commerce—outperforming traditional marketplace livestreams for luxury beauty, designer apparel, niche fragrances, and lifestyle home goods.

For global brands, MCN talent agencies, AI video analytics startups, and consumer research teams, capturing real-time Xiaohongshu live stream intelligence and KOL leads unlocks three high-ROI workflows:

• Live Commerce Benchmarking: Track which broadcasters are actively selling products (hasGoods: true), monitor concurrent viewer counts (viewersConcurrent), and capture real-time shopper questions (liveChatMessages).
• Automated KOL & Host Discovery: Discover rising creators across 13 lifestyle channels and 7 live categories (creatorLeads), enriched with exact follower counts, total likes & collects (totalLikesAndCollects), and verification badges (Pro Creator vs. Official Brand).
• Multimodal AI & ASR Video Pipelines: Stream real-time 1080p .flv and .m3u8 (HLS) broadcast feeds directly into Whisper ASR or Vision-Language Models (VLMs) to transcribe live sales pitches and detect featured SKUs.

What Data Can You Extract from Xiaohongshu Live Streams & KOLs?#

Quickstart: Scraping Xiaohongshu Live Streams & 1080p HLS/FLV Links in Python#

With the UnitBytes Xiaohongshu Live Stream & Commerce Scraper (or the unified RedNote All-in-One Scraper), you can extract active live rooms across all 7 categories with zero login cookies and zero upfront start fees ($0.00001):

import pandas as pd
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Discover active Talent & Beauty ("2") and Lifestyle ("3") live streams with e-commerce chat
run_input = {
    "mode": "live",
    "liveCategories": ["2", "3"],
    "maxItems": 54,
    "includeLiveChat": True,
    "includeAuthorProfile": True
}

run = client.actor("unitbytes/xiaohongshu-rednote-live-scraper").call(run_input=run_input)
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

# Filter for active e-commerce shopping broadcasts
shopping_rooms = [r for r in items if r.get("hasGoods")]
print(f"Discovered {len(items)} live streams ({len(shopping_rooms)} with active e-commerce carts)")

df = pd.DataFrame(items)
df.to_csv("xiaohongshu_live_commerce.csv", index=False, encoding="utf-8-sig")

Sample Live Stream Output JSON

{
  "itemType": "live_stream",
  "id": "570000000000000001",
  "roomId": "570000000000000001",
  "roomTitle": "秋冬高智感羊绒大衣专场｜显瘦穿搭实测",
  "url": "https://www.xiaohongshu.com/hina/livestream/570000000000000001",
  "liveStatus": "live",
  "hasGoods": true,
  "viewersTotalUv": 18420,
  "viewersConcurrent": 642,
  "roomPv": 24910,
  "hotValue": 51200,
  "streamInfo": {
    "flvUrl": "https://cdn.example.com/xhs/live/570000000000000001.flv",
    "hlsUrl": "https://cdn.example.com/xhs/live/570000000000000001.m3u8",
    "resolution": "1080x1920"
  },
  "buyerIntentKeywords": ["160能穿吗", "什么材质", "怎么洗"],
  "author": {
    "id": "600000000000000000000001",
    "nickname": "示範穿搭博主",
    "followerCount": 142000,
    "verification": {
      "badge": "Pro Creator",
      "isVerified": true,
      "desc": "时尚博主"
    }
  }
}

Automated KOL & Influencer Lead Discovery (creatorLeads)#

Instead of manually hunting for profile URLs, use RedNote Creator & KOL Leads Scraper (Docs) in creatorLeads mode to automatically sweep search keywords, 13 lifestyle channels (homefeed.fashion_v3, homefeed.cosmetics_v3, homefeed.food_v3), and active live broadcasts, deduplicate creator IDs, and enrich their full profiles starting at $0.0035 / profile ($3.50 / 1,000 on Gold+; $0.005 Free):

run_input = {
    "mode": "creatorLeads",
    "keywords": ["美妆测评", "OOTD"],
    "channels": ["homefeed.cosmetics_v3", "homefeed.fashion_v3"],
    "includeLiveHosts": True,
    "maxCreators": 50,
    "scrapePortfolio": True,
    "maxNotesPerCreator": 15
}

run = client.actor("unitbytes/xiaohongshu-rednote-profile-scraper").call(run_input=run_input)
kol_items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

V2 Upgrade: Bare Note URLs (No xsec_token), Keyword Search & 1,000+ Comments/Post#

When combining live stream & KOL discovery with post-level research across the UnitBytes RedNote suite, V2 removes the biggest historical Xiaohongshu scraping bottlenecks:

1. Bare /explore/<24-hex-id> URLs Work Without ?xsec_token: Both RedNote Search & Post Scraper (Docs) and RedNote Comments & Replies Scraper (Docs) hydrate plain https://www.xiaohongshu.com/explore/<24-hex-id> URLs, rednote.com item links, xhslink.com share URLs, and raw 24-character Note IDs directly—no ?xsec_token or cookies required.
2. Cookieless Keyword Search (300+ Notes / Query from $1.50/1K): Run mode: "search" with sort set to "popularity_descending", "general", or "time_descending" to benchmark up to 300+ brand or product posts per keyword.
3. Deep Multi-Page Post Comments (250–1,000+ / Post from $1.99/1K): Extract engagement-ranked root comments plus up to 3 top inline replies per comment (subCommentCount reports full thread totals) alongside commenter IP location and automated buyer-intent tagging:

comments_run = client.actor("unitbytes/xiaohongshu-rednote-comments-scraper").call(
    run_input={
        "mode": "comments",
        "startUrls": [{"url": "https://www.xiaohongshu.com/explore/660000000000000000000001"}],
        "maxCommentsPerNote": 250,
        "includeSubComments": True,
        "maxSubCommentsPerComment": 3
    }
)
comment_items = list(client.dataset(comments_run["defaultDatasetId"]).iterate_items())

Frequently Asked Questions (FAQ)