# Hugging Face Scraper — Models, Datasets & Spaces (`northbell/hugging-face-scraper`) Actor

Search the Hugging Face Hub for models, datasets and Spaces: downloads, likes, trending score, task, library, license, base model, languages and arXiv papers. Filter by author, task or library, sort by downloads or trending, and track new repos and download changes between runs.

- **URL**: https://apify.com/northbell/hugging-face-scraper.md
- **Developed by:** [Northbell](https://apify.com/northbell) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.24 / 1,000 repos

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

## Hugging Face Scraper — Models, Datasets & Spaces

List **models, datasets or Spaces** from the **Hugging Face Hub** as clean JSON — by search term, author, task or library, sorted by **downloads, likes, trending, newest or recently updated** — with **downloads, likes, trending score, task, library, license, base model, languages, training datasets and arXiv papers**. Schedule a search and each run shows **new repos** and **downloads and likes gained** since the last one.

### What you get

| Field | What it is |
|---|---|
| `type`, `id`, `owner`, `name`, `url` | the repo |
| `downloads`, `likes`, `trendingScore` | popularity (downloads = last 30 days, as the Hub counts them) |
| `task`, `library`, `sdk` | what it does and runs on |
| `license`, `languages`, `datasets`, `baseModels`, `arxiv` | from the repo's tags |
| `gated`, `createdAt`, `lastModified`, `tags`, `description` (datasets) | details |
| `search`, `rank`, `isNewSinceLastRun`, `downloadsChange`, `likesChange` | position in your list, and changes since your previous run |

Fixed 22 Oct 2026: repos below the part of the list your previous run read (it stopped at *max results*) counted as new, so with *Only new* a second run could return the next 100 older repos as new and charge for them. New now means it entered the ranks the previous run read, or was created after that run. The ranks counted are the top *max results* only, so an older repo that drifts up the downloads order from further down is not new either. With *Only new* and nothing new, the run now stops a little past the ranks it read last time; before, it read every page of the search and could run into Hugging Face's limit of 500 requests per 5 minutes (HTTP 429).

### Use it for

- **Model landscape tracking** — every new fine-tune of a base model, or the top GGUF builds, every week
- **AI market research** — which organizations and tasks get the downloads
- **Dataset discovery** — datasets for a topic with license and language
- **Trending watch** — the trending Spaces or models each morning

### Input

```json
{ "kind": "models", "searches": ["qwen"], "task": "text-generation", "library": "gguf", "sort": "downloads", "maxResults": 200 }
```

Measured on October 7, 2026: 1,200 text-generation models for "qwen" plus one model by ID in 1.3 seconds. Data comes from the public Hugging Face Hub API — no login, no token. The Hub allows 500 requests per 5 minutes; one request returns up to 1,000 repos.

### Pricing

**$0.30 per 1,000 repos**, plus $0.01 per run. Error rows are free. The $0.01 run-start fee is the one charge every run pays, even one that returns nothing.

### For AI agents

`{"kind": "spaces", "sort": "trending", "maxResults": 20}` returns today's 20 trending Spaces. Rows have `type`: `model`, `dataset`, `space` or `error`.

# Actor input Schema

## `kind` (type: `string`):

List models, datasets or Spaces.

## `searches` (type: `array`):

Matched against repo names.

## `author` (type: `string`):

e.g. "meta-llama", "google", "HuggingFaceH4".

## `task` (type: `string`):

Hugging Face pipeline tag, e.g. "text-generation", "image-text-to-text", "automatic-speech-recognition".

## `library` (type: `string`):

e.g. "transformers", "gguf", "diffusers", "mlx".

## `sort` (type: `string`):

Order of the results, as on the Hugging Face website.

## `maxResults` (type: `integer`):

How many models, datasets or Spaces to return for each search.

## `ids` (type: `array`):

Specific repos, e.g. "meta-llama/Llama-3.1-8B-Instruct".

## `onlyNew` (type: `boolean`):

From the second run on, return only repos that were not in the same list before. Every row also shows downloads and likes gained since then.

## Actor input object example

```json
{
  "kind": "models",
  "searches": [
    "qwen"
  ],
  "sort": "downloads",
  "maxResults": 100,
  "onlyNew": false
}
```

# Actor output Schema

## `repos` (type: `string`):

Models, datasets or Spaces with downloads, likes and metadata.

# 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 = {
    "searches": [
        "qwen"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("northbell/hugging-face-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 = { "searches": ["qwen"] }

# Run the Actor and wait for it to finish
run = client.actor("northbell/hugging-face-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 '{
  "searches": [
    "qwen"
  ]
}' |
apify call northbell/hugging-face-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,northbell/hugging-face-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/mwOw1ZFDr3uCYkUJP/builds/gfzzSpoxzqXuKLpTy/openapi.json