# Xiaohongshu (RED) Explore Scraper — engagement by channel (`entrogix_works/xiaohongshu-explore-scraper`) Actor

Scrape Xiaohongshu explore feeds by channel and get notes with like counts plus a per-channel engagement breakdown. Unofficial; not affiliated with Xiaohongshu.

- **URL**: https://apify.com/entrogix\_works/xiaohongshu-explore-scraper.md
- **Developed by:** [Entrogix Works](https://apify.com/entrogix_works) (community)
- **Categories:** Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$4.00 / 1,000 note scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Xiaohongshu (RED) Explore Scraper — engagement by channel

Fetch Xiaohongshu explore feeds **channel by channel** and get notes with like counts, plus a breakdown that tells you how each channel actually behaves.

Most Xiaohongshu scrapers hand you a flat list of notes. The useful question is *which channel rewards what* — and that only shows up when you compare channels side by side.

### What you get per note

| Field | Example |
|---|---|
| `channel` | `fitness` |
| `title` | the note's display title |
| `likes` | `31000` |
| `isVideo`, `noteType` | `true`, `video` |
| `coverWidth`, `coverHeight`, `aspectRatio` | `1200`, `1600`, `1.333` |
| `authorNickname` | public display name |
| `noteId`, `url` | direct link to the note |

`aspectRatio` is included because taller covers occupy more of the feed on mobile — it is a usable explanatory variable for what travels.

### Channel breakdown (key-value store, `ENGAGEMENT_SUMMARY`)

Per channel and overall: `notes`, `notesWithLikes`, `medianLikes`, `maxLikes`, `topShare`, `videoShare`.

**`topShare`** is the share of all likes captured by the top 10% of notes. Close to 1 means a few viral notes dominate; lower means attention is spread.

A real measurement across three channels shows why this matters:

| Channel | median likes | topShare | videoShare |
|---|---|---|---|
| travel | 170 | 0.554 | 39% |
| food | 569 | 0.684 | 42% |
| fitness | 264 | 0.812 | 73% |

Fitness is video-first and winner-take-most; travel is neither. Same platform, different game.

### Input

```json
{
  "channels": ["travel", "food", "fitness"],
  "maxItemsPerChannel": 40
}
```

Eleven channels are available: fashion, food, cosmetics, movie, career, love, household, gaming, travel, fitness, video.

### Scope and limits — please read

**The explore feed is a recommendation feed, not an archive.** It returns roughly 24–35 notes per load, the set changes between runs, and this Actor does not scroll. Raising `maxItemsPerChannel` above that will not produce more notes — add channels instead.

This means the output is a **sample of what the channel is surfacing now**, which is what makes the per-channel comparison meaningful. It is not a complete list of notes in a category, and should not be used as one.

Keyword search is not supported: Xiaohongshu's search page loads results after mount rather than serving them with the page, so there is nothing to read without driving a logged-in session. This Actor does not log in.

**Display names only.** You get the public nickname shown on the note, but not the account's internal user id and not the avatar image. Engagement analysis does not need either, and leaving them out keeps the output free of fields that identify and track a person across handle changes.

`likes` counts are normalized from the site's own abbreviations (`4.1万` → `41000`). Where a count cannot be read it is returned as `null`, never `0`, so it cannot silently drag a median down.

### Pricing

Pay per result — charged only for notes actually delivered to your dataset.

### Disclaimer

**This is an unofficial tool and is not affiliated with, endorsed by, or sponsored by Xiaohongshu (RED) or its operators.** "Xiaohongshu" and "RED" are trademarks of their respective owners and are used here only to describe what this Actor reads. You are responsible for ensuring your use of the collected data complies with applicable law and with the platform's terms.

# Actor input Schema

## `channels` (type: `array`):

Explore channels to fetch. Each is fetched separately and tagged via the channel field.

## `maxItemsPerChannel` (type: `integer`):

The feed returns roughly 24-35 notes per load and this Actor does not scroll, so values above that will not produce more notes. To collect more, add channels rather than raising this.

## `minIntervalMs` (type: `integer`):

Pacing between channel fetches.

## `useProxy` (type: `boolean`):

Recommended. Vetted at 6/6 through residential proxy.

## `proxyType` (type: `string`):

Residential is the vetted setting for this target. Datacenter is cheaper on bandwidth but has not been measured here yet.

## Actor input object example

```json
{
  "channels": [
    "travel"
  ],
  "maxItemsPerChannel": 40,
  "minIntervalMs": 3000,
  "useProxy": true,
  "proxyType": "RESIDENTIAL"
}
```

# Actor output Schema

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

Every row this run produced, as JSON.

## `summary` (type: `string`):

Totals and medians for this run, so you can judge the rows without reading them all.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("entrogix_works/xiaohongshu-explore-scraper").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("entrogix_works/xiaohongshu-explore-scraper").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 '{}' |
apify call entrogix_works/xiaohongshu-explore-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,entrogix_works/xiaohongshu-explore-scraper"
        }
    }
}

```

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/RqUgIDrTcLaRLUjAa/builds/vlD3C37xHMri41EK0/openapi.json