# RedNote / Xiaohongshu Search & Trend Scraper (`quanmatrix/rednote-commerce-trend-intelligence`) Actor

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

- **URL**: https://apify.com/quanmatrix/rednote-commerce-trend-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Social media, E-commerce, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.45 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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:

```json
{
  "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.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/rednote-commerce-trend-intelligence/changelog.md

# Actor input Schema

## `maxItems` (type: `integer`):

Maximum public explore-feed notes to return.

## `minLikes` (type: `integer`):

Optional minimum parsed like count.

## `creatorQuery` (type: `string`):

Optional case-insensitive substring filter on creator nickname.

## `titleQuery` (type: `string`):

Optional case-insensitive substring filter on note title.

## `previousSnapshot` (type: `array`):

Optional prior rows keyed by noteId for exact like and feed-rank deltas.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor\_title}}, {{result\_summary}}, or {{result\_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO\_SAFE\_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "maxItems": 20,
  "minLikes": 0,
  "creatorQuery": "",
  "titleQuery": "",
  "previousSnapshot": [],
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

## `results` (type: `string`):

Public RedNote explore notes enriched with engagement/rank deltas, momentum and agent-ready trend actions.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "previousSnapshot": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/rednote-commerce-trend-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "previousSnapshot": [] }

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/rednote-commerce-trend-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "previousSnapshot": []
}' |
apify call quanmatrix/rednote-commerce-trend-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/rednote-commerce-trend-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/fgLpGKj7I7uar3Iuj/builds/YfuxWKuyEd8JadFtK/openapi.json