RedNote / Xiaohongshu Search & Trend Scraper

Search public RedNote/Xiaohongshu content by creator or title terms and return structured posts, creators, engagement, trend and commerce signals with optional snapshot comparison.

Quick start

Start with this working example and replace the target values with your own:

{
  "maxItems": 20,
  "minLikes": 0,
  "creatorQuery": "",
  "titleQuery": "",
  "previousSnapshot": []
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• maxItems — Maximum notes: Maximum public explore-feed notes to return.
• minLikes — Minimum likes: Optional minimum parsed like count.
• creatorQuery — Creator filter: Optional case-insensitive substring filter on creator nickname.
• titleQuery — Title filter: Optional case-insensitive substring filter on note title.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.003500 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Detailed documentation

Search and filter public RedNote/Xiaohongshu content by creator or title terms, returning structured trend and commerce signals with optional snapshot comparison.

Turn the public Xiaohongshu explore feed into repeatable trend intelligence instead of another static scrape.

Why use this Actor

RedNote is a strong early consumer-attention surface, but raw note exports do not tell you whether a note is gaining traction now. This Actor is deliberately optimized for scheduled monitoring: save one run as a snapshot, pass it into the next run, and receive exact like and feed-rank changes together with deterministic trend actions.

Key features

• Public explore-feed extraction without RedNote account credentials.
• Real note IDs, titles, creator identity, note type, likes, media, and current feed rank.
• Reusable previousSnapshot input keyed by noteId.
• Exact likeDelta and rankDelta calculations between runs.
• Deterministic momentumScore combining log-scaled engagement, like change, and feed-position movement.
• Agent-ready SURGING, RISING, WATCH, and BASELINE actions.
• Optional minimum-like, creator-name, and title filters.
• SNAPSHOT and INTELLIGENCE_SUMMARY key-value records for scheduled workflows.

Output

Each dataset row contains noteId, title, url, creatorId, creatorName, noteType, likeCount, feedRank, optional prior values and exact deltas, momentumScore, agentAction, media URL, transport, and scrape timestamp. The default dataset view emphasizes the fields needed for trend triage.

Example

A first run can use maxItems: 20 and an empty previousSnapshot. Save the returned SNAPSHOT. On the next scheduled run, pass those rows back as previousSnapshot; notes seen again receive exact engagement and position changes. A note moving from rank 18 to rank 6 while adding likes will score differently from a popular note whose position is fading.

Use cases

Use it for daily RedNote trend briefs, creator monitoring, viral-content discovery, campaign momentum checks, product-launch attention signals, consumer-research baselines, recurring category watchlists, and agentic market-intelligence pipelines. Commerce teams can use rising notes as demand signals and cross-check them against JD, Pinduoduo, Taobao, Tmall, or supplier marketplaces.

Pricing

The product is designed around one primary pay-per-result event: one enriched RedNote intelligence row. The factory target is USD 0.0035 per result before applicable Apify plan-tier discounts, subject to canonical pricing governance at release time.

Inputs

• maxItems: maximum public explore notes returned, from 1 to 100.
• minLikes: optional parsed-like threshold.
• creatorQuery: optional case-insensitive creator-name substring.
• titleQuery: optional case-insensitive title substring.
• previousSnapshot: optional prior output rows keyed by noteId.

Limitations

The hardened production surface is the public explore feed. The Actor does not pretend that anonymous explore data equals arbitrary keyword search, comments, or complete private creator analytics. Some RedNote search and detail surfaces can be login-gated, and this Actor does not bypass those gates. Public feed composition can also vary by time and network context.

Technical approach

The Actor reads Xiaohongshu's public server-rendered __INITIAL_STATE__ hydration payload. It tries direct transport first and then an Apify standard-proxy fallback. It fails closed when no validated public note rows are available rather than fabricating placeholder data.

Data ethics

The Actor is intended for public-content research, trend analysis, and monitoring. Users remain responsible for platform terms, applicable law, and appropriate treatment of creator/content data.