# Hugging Face Scraper (`straightforward_hydra/huggingface-scraper`) Actor

AI model intelligence from the open Hugging Face Hub API: trending models, datasets and spaces by task, author and library. No API key.

- **URL**: https://apify.com/straightforward\_hydra/huggingface-scraper.md
- **Developed by:** [Dev D](https://apify.com/straightforward_hydra) (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 $1.00 / 1,000 items

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/platform/actors/running/actors-in-store#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

## Hugging Face Scraper 🤗

**AI model intelligence from the open Hugging Face Hub API — trending & most-downloaded models, datasets and spaces, by task, author and library. No API key, no login.**

Track the AI landscape with clean, structured data straight from the [Hugging Face Hub](https://huggingface.co) API: **trending** and **most-downloaded** models, **datasets** and **spaces** — filterable by task (text-generation, text-to-image, ASR…), **author/org** (Meta, Mistral, Google…) and library. Perfect for AI-trend monitoring, competitive intelligence on model labs, dataset discovery and building model catalogs.

> Uses the open Hugging Face Hub API — public data, no key required.

***

### What you can do with it

- 🔥 **Track trending models** — what's rising on the Hub right now, by task.
- 🏢 **Watch an AI lab** — everything `mistralai`, `meta-llama` or `google` ships, by downloads.
- 📚 **Discover datasets** — find training data by popularity, task or tag.
- 🚀 **Monitor Spaces** — the most-liked live demos and apps.
- 📊 **Build a model catalog** — downloads, likes, tags and update dates for a whole task area.

### Features

- ✅ **No API key** — open Hugging Face Hub endpoints, works out of the box.
- ✅ **Models, datasets & spaces** — one Actor, three resource types.
- ✅ **Smart sorts** — trending, downloads, likes, newest, recently updated.
- ✅ **Filters** — search, author/org, task/pipeline, library, tag.
- ✅ **Scales** — cursor-paginated, 100 items per page.

***

### Input

| Field | Description |
|---|---|
| **Resource** | models / datasets / spaces. |
| **Sort by** | trending / downloads / likes / newest / recently updated. |
| **Search** | Free-text name search. |
| **Author / organization** | e.g. `mistralai`, `meta-llama`, `google`. |
| **Task** | Models only, e.g. `text-generation`, `text-to-image`. |
| **Library / Tag** | Extra model/library/tag filters. |
| **Max results** | Result cap. |

#### Example — trending text-generation models

```json
{
  "resource": "models",
  "sort": "trending",
  "task": "text-generation",
  "maxResults": 100
}
```

#### Example — everything Mistral ships, by downloads

```json
{
  "resource": "models",
  "sort": "downloads",
  "author": "mistralai",
  "maxResults": 200
}
```

### Output

One row per item:

```json
{
  "type": "model",
  "id": "mistralai/Mistral-7B-Instruct-v0.3",
  "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3",
  "author": "mistralai",
  "downloads": 5438667,
  "likes": 2714,
  "trending_score": 12,
  "task": "text-generation",
  "library": "transformers",
  "tags": ["transformers", "safetensors", "mistral", "text-generation"],
  "last_modified": "2024-08-21T10:00:00.000Z"
}
```

Datasets and spaces carry the same core fields; spaces also include `sdk` (gradio/streamlit/docker), datasets include a short `description`.

### Run it on a schedule

Schedule the Actor to snapshot trending models or a lab's releases over time, and connect a **Google Sheets / Slack / webhook** integration to get alerted when new models drop or downloads spike.

### Notes & limitations

- Uses the open Hugging Face Hub API — no key needed (an optional token raises rate limits).
- Counts are point-in-time snapshots at run time.
- Public repository metadata (non-personal).
- Data source: Hugging Face Hub.

***

#### Keywords

Hugging Face, huggingface, HF Hub, AI models, machine learning models, LLM, model scraper, Hugging Face API, model downloads, trending models, AI datasets, ML datasets, Hugging Face spaces, model catalog, AI trends, transformers, text-generation, model intelligence, ML data, AI research.

# Actor input Schema

## `resource` (type: `string`):

What to pull from the Hub.

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

How to rank results.

## `search` (type: `string`):

Free-text search over names, e.g. "llama", "whisper", "stable-diffusion".

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

Limit to one org or user, e.g. meta-llama, mistralai, google, openai, stabilityai.

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

Filter models by task, e.g. text-generation, text-to-image, automatic-speech-recognition, image-text-to-text, feature-extraction.

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

Filter models by library, e.g. transformers, diffusers, gguf, sentence-transformers.

## `filterTag` (type: `string`):

Filter by a Hub tag, e.g. a language (en), license (license:mit) or dataset task.

## `token` (type: `string`):

Optional user access token (huggingface.co/settings/tokens) for higher rate limits. Not required for public data.

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

How many items to return (paged 100 at a time).

## Actor input object example

```json
{
  "resource": "models",
  "sort": "trending",
  "maxResults": 100
}
```

# 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("straightforward_hydra/huggingface-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("straightforward_hydra/huggingface-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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 straightforward_hydra/huggingface-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=straightforward_hydra/huggingface-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/S9lQ0sw1LlZWRWAb3/builds/R2L7Bzs4wKZXnzayl/openapi.json