# HuggingFace Trends Tracker — what is trending in AI models (`agent_muse/huggingface-trends-tracker`) Actor

- **URL**: https://apify.com/agent\_muse/huggingface-trends-tracker.md
- **Developed by:** [John Israel Lofamia](https://apify.com/agent_muse) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 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

## HuggingFace Trends Tracker

What's trending in AI models right now — as structured data.

Each run returns the top HuggingFace models ranked by **likes** or **downloads**,
with momentum status (`new`, `rising`, `steady`, `cooling`), like/download
deltas since the last run, task type, tags, and model links. Because the Actor
remembers what it saw last run, every row tells you whether the model is
**new**, **rising**, or cooling — schedule it daily and you get a live AI-model
trend feed.

### Inputs

- **Max models** (1–100, default 25)
- **Sort by**: likes or downloads (default likes)
- **Task filter** (optional, e.g. `text-generation`, `text-to-image`)

### Output

One dataset row per model: rank, model ID, author, likes, downloads, changes
since last run, momentum, task type, tags, last-modified time, and model URL.

### Notes

- Uses HuggingFace's free official API — no API key needed.
- Pricing: $0.02 per run + $0.002 per model row.
- Run it via the API, schedule it, or export the dataset to power dashboards,
  alerts, and research.

# Actor input Schema

## `limit` (type: `integer`):

Maximum models to return.

## `modelType` (type: `string`):

Only include models for this task (e.g. text-generation, text-to-image, feature-extraction). Leave empty for all tasks.

## `sortBy` (type: `string`):

Rank models by community likes or by download count.

## Actor input object example

```json
{
  "limit": 25,
  "modelType": "",
  "sortBy": "likes"
}
```

# Actor output Schema

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

Link to the dataset with HuggingFace trend rows.

# 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("agent_muse/huggingface-trends-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("agent_muse/huggingface-trends-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 '{}' |
apify call agent_muse/huggingface-trends-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,agent_muse/huggingface-trends-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/yHaH0dLDHFeIZ6479/builds/VR11CM6wqQn2qj8gx/openapi.json