# Hugging Face Insights Scraper — Models, Datasets & Spaces (`brilliant_gum/huggingface-insights-scraper`) Actor

Scrape Hugging Face models, datasets, spaces, and daily papers with downloads, likes, parameters, tags, and growth tracking between runs. Filter by pipeline, library, author, or keyword.

- **URL**: https://apify.com/brilliant\_gum/huggingface-insights-scraper.md
- **Developed by:** [Yuliia Kulakova](https://apify.com/brilliant_gum) (community)
- **Categories:** AI, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.005 / model scraped

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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 Insights Scraper

Scrape AI models, datasets, Spaces, and daily research papers from Hugging Face — with downloads, likes, parameters, growth tracking, and smart filters.

![Hugging Face Insights Scraper](https://i.imgur.com/oWOXSF9.png)

***

### Why this scraper

Hugging Face is where AI happens — 1M+ models, 300K+ datasets, trending research papers every day. But the site gives you a search bar and infinite scroll. No way to bulk-export, no way to compare models by parameter count, no way to track which models are gaining traction this week vs. last.

This scraper turns Hugging Face into a structured intelligence feed. Filter by pipeline task, ML library, author, or keyword. Get model sizes, architecture details, and popularity analytics. Track download and like growth between scheduled runs. Export to CSV, JSON, or pipe directly into your dashboard.

***

### What you get

**Models — the full picture**

- Name, author, downloads, likes, pipeline task, ML library
- Parameter count and size tier (tiny / small / medium / large / xlarge / massive)
- Architecture details (LlamaForCausalLM, MistralForCausalLM, etc.)
- License, language tags, base model, gated/private status
- Inference status (warm/cold)
- Popularity score, engagement ratio, downloads per day, model age

**Datasets — structured metadata**

- Name, author, downloads, likes, license
- Task categories (text-generation, question-answering, etc.)
- Size category (1K–10K, 10K–100K, 100K–1M, etc.)
- Language tags, creation date, last modified

**Spaces — AI demos and apps**

- Name, author, likes, SDK (Gradio, Streamlit, Docker)
- Runtime info, tags, creation date

**Daily Papers — cutting-edge research**

- Title, full abstract, AI-generated summary and keywords
- Authors, upvotes, comment count
- GitHub repo link and star count
- Arxiv URL, thumbnail, publication date

**Smart filters — get exactly what you need**

- Filter by keyword, author/org, pipeline task, ML library
- Minimum downloads and likes thresholds
- Parameter range (e.g., only 1B–10B models)
- Exclude gated or private items
- Sort by downloads, likes, trending, recently created, or recently modified

**Growth tracking between runs**

- Persistent snapshot store tracks downloads and likes over time
- On subsequent runs: downloadsDelta, downloadsPerHour, likesDelta, trend (up/down/flat)
- See which models are gaining or losing momentum
- Perfect for scheduled monitoring of AI model trends

**Detailed enrichment (optional)**

- Fetch full model details: exact parameter count, architectures, model type
- Size tier classification: tiny (<500M) → massive (100B+)
- Popularity score combining downloads and community engagement
- Downloads per day normalized by model age

***

### Example use cases

- **AI researchers**: Track trending models in your field, monitor new papers daily
- **ML engineers**: Find the best model for your task — filter by pipeline, size, and popularity
- **Investors**: Monitor which AI companies are gaining traction on Hugging Face
- **Data teams**: Build a dataset catalog filtered by task, size, and license
- **Content creators**: Track what's hot in AI this week for newsletters and reports
- **Competitive intelligence**: Monitor specific orgs (OpenAI, Meta, Google) and their model releases

***

### Input examples

**Trending models right now:**

```json
{
  "resourceType": "models",
  "sort": "trending",
  "maxResults": 50
}
```

**LLMs from Meta with full details:**

```json
{
  "resourceType": "models",
  "author": "meta-llama",
  "pipeline_tag": "text-generation",
  "sort": "downloads",
  "maxResults": 20,
  "fetchDetails": true
}
```

**Popular code datasets:**

```json
{
  "resourceType": "datasets",
  "search": "code",
  "sort": "likes",
  "minLikes": 50,
  "maxResults": 30
}
```

**Today's research papers:**

```json
{
  "resourceType": "papers",
  "maxResults": 50
}
```

**Image generation models with 10K+ downloads:**

```json
{
  "resourceType": "models",
  "pipeline_tag": "text-to-image",
  "sort": "downloads",
  "minDownloads": 10000,
  "maxResults": 20
}
```

***

### Output sample (model)

```json
{
  "type": "model",
  "id": "meta-llama/Llama-3.1-8B-Instruct",
  "author": "meta-llama",
  "downloads": 9980754,
  "likes": 6137,
  "pipeline": "text-generation",
  "library": "transformers",
  "parameters": 8030261248,
  "sizeTier": "medium (3B-10B)",
  "architectures": ["LlamaForCausalLM"],
  "modelType": "llama",
  "license": "llama3.1",
  "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"],
  "popularityScore": 3208,
  "downloadsPerDay": 14157,
  "engagementRatio": 61.49,
  "ageDays": 705,
  "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct"
}
```

***

### Integrations

Connect this scraper to any tool in your stack:

- **Google Sheets** — auto-sync model rankings weekly
- **Slack / Discord** — get alerts when a new trending model appears
- **Webhooks** — trigger your pipeline when new data lands
- **API** — fetch results programmatically from any language
- **Zapier / Make** — connect to 5000+ apps without code

***

### Cost

This actor uses pay-per-result pricing at **$5.00 per 1,000 results** ($0.005 per item). You only pay for the data you get — no platform usage fees on top.

| Example run | Results | Cost |
|---|---|---|
| Top 50 trending models | 50 | $0.25 |
| All meta-llama models with details | ~20 | $0.10 |
| 100 text-to-image models | 100 | $0.50 |
| Today's research papers | ~50 | $0.25 |
| 1,000 most downloaded models | 1,000 | $5.00 |

Platform compute costs are minimal — a typical 100-item run finishes in under 10 seconds.

***

### Limitations

- Hugging Face API rate limit: 500 requests per 5 minutes (handled automatically with throttling)
- Parameter count requires `fetchDetails: true` and is only available for models with safetensors weights
- Papers endpoint returns daily papers only (no historical archive search)

# Actor input Schema

## `resourceType` (type: `string`):

What to scrape: AI models, datasets, Spaces (demos), or daily research papers.

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

Filter by keyword (e.g. 'llama', 'sentiment', 'stable diffusion'). Leave empty to browse all.

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

Filter by author or org (e.g. 'meta-llama', 'openai', 'google'). Leave empty for all authors.

## `pipeline_tag` (type: `string`):

Filter models by task type.

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

Filter models by ML library (e.g. 'transformers', 'diffusers', 'sentence-transformers').

## `tags` (type: `string`):

Filter by tags like 'license:mit', 'language:en'. Comma-separated.

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

How to sort results.

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

Maximum number of items to return. Up to 10000 with pagination.

## `fetchDetails` (type: `boolean`):

Fetch detailed info for each item (parameters, architectures, files). Slower but richer data. Not needed for papers.

## `minDownloads` (type: `integer`):

Only include items with at least this many downloads.

## `minLikes` (type: `integer`):

Only include items with at least this many likes.

## `minParameters` (type: `integer`):

Only include models with at least this many parameters. E.g. 1000000000 for 1B+. Requires fetchDetails for accuracy.

## `maxParameters` (type: `integer`):

Only include models with at most this many parameters. 0 = no limit.

## `excludeGated` (type: `boolean`):

Exclude gated models/datasets that require access approval.

## `excludePrivate` (type: `boolean`):

Exclude private items (recommended).

## `enableHistory` (type: `boolean`):

Track downloads/likes changes between scheduled runs. Stores snapshots in a named KV store.

## `historyStoreName` (type: `string`):

Name of the Key-Value store for history snapshots.

## `proxyConfiguration` (type: `object`):

Optional proxy settings. HF API works without proxy, but can help with rate limits.

## Actor input object example

```json
{
  "resourceType": "models",
  "search": "",
  "author": "",
  "pipeline_tag": "",
  "library": "",
  "tags": "",
  "sort": "downloads",
  "maxResults": 100,
  "fetchDetails": false,
  "minDownloads": 0,
  "minLikes": 0,
  "minParameters": 0,
  "maxParameters": 0,
  "excludeGated": false,
  "excludePrivate": true,
  "enableHistory": false,
  "historyStoreName": "hf-history"
}
```

# Actor output Schema

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

Counts of scraped items and dataset link.

# 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("brilliant_gum/huggingface-insights-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("brilliant_gum/huggingface-insights-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 brilliant_gum/huggingface-insights-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/0U6DQb1i4Q0b09Ruw/builds/f0E3K5vgStiamGU0H/openapi.json