# Hugging Face Scraper — Trending Models, Datasets & Spaces (`samwise.agency/huggingface-tracker`) Actor

Track the AI ecosystem: trending Hugging Face models, datasets, and Spaces with downloads, likes, trending scores, pipeline tags, and timestamps. Official HF Hub API — keyless, clean JSON/CSV.

- **URL**: https://apify.com/samwise.agency/huggingface-tracker.md
- **Developed by:** [samwise.agency](https://apify.com/samwise.agency) (community)
- **Categories:** AI, Developer tools
- **Features:** Limited permissions
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 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.

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 — Trending Models, Datasets & Spaces

Track the fastest-moving corner of AI. This Actor pulls **trending and top models, datasets, and Spaces** from the official Hugging Face Hub API — downloads, likes, trending scores, pipeline tags, and update timestamps in clean JSON/CSV.

**What you can extract:**

- 🔥 **Trending models** — what the AI community is adopting right now
- 📚 **Datasets & Spaces** — same metrics, same clean schema
- 🔍 **Search & filters** — by keyword, org (meta-llama, mistralai…), or tag
- 📈 **Sort options** — trending, downloads, likes, newest, recently updated

### Quick start

1. Click **Run** — default fetches the top 50 trending models
2. Add a search query ("llama", "OCR", "text-to-speech") or an org name
3. Schedule daily + diff runs = a new-model alert system for your niche

### Sample output

```json
{
    "assetType": "model",
    "id": "meta-llama/Llama-4-70B-Instruct",
    "url": "https://huggingface.co/meta-llama/Llama-4-70B-Instruct",
    "author": "meta-llama",
    "pipelineTag": "text-generation",
    "libraryName": "transformers",
    "downloads": 2841022,
    "likes": 15320,
    "trendingScore": 312,
    "gated": true,
    "tags": ["text-generation", "llama", "conversational"],
    "createdAt": "2026-04-05T16:00:12.000Z",
    "lastModified": "2026-06-28T09:31:44.000Z"
}
```

### Use cases

- **AI newsletters & content** — weekly "what's trending" data, automated
- **ML engineering** — monitor new models in your task category (e.g. ASR, OCR)
- **VC / market research** — org-level activity tracking across labs
- **Competitive intel** — watch competitors' orgs for new releases
- **RAG/agents** — MCP-ready: let your agent query the AI ecosystem live

### Input reference

| Field | Type | Notes |
|---|---|---|
| `assetTypes` | array | `models`, `datasets`, `spaces` |
| `searchQueries` | array | Keyword searches |
| `authors` | array | Org/user names |
| `filterTags` | array | HF tags like `text-generation` |
| `sort` | select | trending / downloads / likes / updated / newest |
| `maxItemsPerQuery` | integer | Cost cap |

### Pricing

Pay per result row delivered.

### FAQ

**Official data?** Yes — the public Hugging Face Hub API, no key needed.

**How fresh is "trending"?** It's HF's live trending score at run time — schedule daily to build a time series.

# Actor input Schema

## `assetTypes` (type: `array`):

Which Hugging Face Hub assets to fetch.

## `searchQueries` (type: `array`):

Keywords to search for (e.g. 'llama', 'text-to-speech', 'OCR'). Leave empty to fetch the overall top list.

## `authors` (type: `array`):

Fetch all assets from specific orgs (e.g. 'meta-llama', 'google', 'mistralai').

## `filterTags` (type: `array`):

HF filter tags, e.g. 'text-generation', 'transformers', 'en'.

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

Ranking for results (always descending).

## `maxItemsPerQuery` (type: `integer`):

Cap per asset type / query.

## Actor input object example

```json
{
  "assetTypes": [
    "models"
  ],
  "searchQueries": [],
  "authors": [],
  "filterTags": [],
  "sort": "trendingScore",
  "maxItemsPerQuery": 50
}
```

# 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 = {
    "searchQueries": [],
    "authors": [],
    "filterTags": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("samwise.agency/huggingface-tracker").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 = {
    "searchQueries": [],
    "authors": [],
    "filterTags": [],
}

# Run the Actor and wait for it to finish
run = client.actor("samwise.agency/huggingface-tracker").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 '{
  "searchQueries": [],
  "authors": [],
  "filterTags": []
}' |
apify call samwise.agency/huggingface-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,samwise.agency/huggingface-tracker"
        }
    }
}
```

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/Fpj7rrlfVeB3sxwUV/builds/uWXD5f1HVqbEorXBL/openapi.json