# HuggingFace Intelligence Scraper (`primesieve/hf-intel-scraper`) Actor

Scrape HuggingFace models, datasets, spaces, and trending AI repos. No login, no key, no browser.

- **URL**: https://apify.com/primesieve/hf-intel-scraper.md
- **Developed by:** [Prime Sieve](https://apify.com/primesieve) (community)
- **Categories:**
- **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 and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#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

## HuggingFace Intelligence Scraper — Prime Sieve

**Scrape HuggingFace Models, Datasets, Spaces, and Trending AI repos — no login, no API key, no browser.**

Built by Prime Sieve. Uses HuggingFace's public `/api/models|datasets|spaces|trending` endpoints (fetch-clean). Ideal for the AI/dev ecosystem: model leaderboards, dataset research, and finding what's trending.

### What you get

Choose a `mode` and get structured rows per record:

**`models`** — id, author, pipeline tag, library, tags, gated/private, likes, downloads, trending score, parameter count, created/lastModified, URL.
**`datasets`** — id, author, pipeline tag, tags, gated/private, likes, downloads, trending score, description, lastModified, URL.
**`spaces`** — id, author, SDK (e.g. docker/streamlit), tags, likes, trending score, URL.
**`trending`** — what's actively trending on HuggingFace right now: repo type, ID, author, likes, downloads, param count, isLikedByUser, URL.

### Pricing

**$0.0015 per result** — pay per result, no subscriptions.

| | Prime Sieve (this) | david\_flagg/huggingface-models |
|---|---|---|
| Price | **$0.0015 / result** | $0.001 / result |
| Models | ✅ | ✅ |
| Datasets | ✅ | ❌ |
| Spaces | ✅ | ❌ |
| Trending | ✅ | ❌ |
| Sort (downloads/likes/trending) | ✅ | ❌ |
| Cursor pagination to 1,000 | ✅ | ❌ |
| Maintenance | active | stale |

Ships the full HuggingFace surface (all 4 modes + sorting) at a comparable unit price — more value per run, one actor instead of several.

### Use cases

1. **LLM watch / leaderboard** — track top models by downloads or trending score, monitor new releases
2. **Dataset research** — find candidate datasets by keyword, sort by downloads/likes for what's trusted
3. **AI startup intel** — see which frontier orgs are releasing models/spaces, parameter counts, gating
4. **Competitive monitoring** — scheduled runs diff likes/downloads/trending over time

### Input

```json
{
  "mode": "models",
  "search": "llama",
  "limit": 100,
  "sortBy": "downloads",
  "direction": -1,
  "full": true,
  "timeoutSeconds": 20
}
```

| Field | Default | Notes |
|---|---|---|
| `mode` | `models` | `models` / `datasets` / `spaces` / `trending` |
| `search` | ``| keyword filter |
| `limit` | `20` | max 1,000 |
| `sortBy` |`` | `downloads` / `likes` / `trendingScore` (or `createdAt` for spaces) |
| `direction` | \`\` | `-1` desc, `1` asc |
| `full` | `true` | include description + full tags |
| `timeoutSeconds` | `20` | per-request timeout |

### Output sample (`models`)

```json
{
  "mode": "models",
  "id": "meta-llama/Llama-3.1-8B-Instruct",
  "author": "meta-llama",
  "pipelineTag": "text-generation",
  "libraryName": "transformers",
  "tags": ["transformers", "safetensors", "llama", "text-generation"],
  "gated": "manual",
  "private": false,
  "likes": 6848,
  "downloads": 5644039,
  "trendingScore": 41,
  "numParameters": null,
  "createdAt": "2024-07-18T08:56:00.000Z",
  "lastModified": "2024-09-25T17:00:57.000Z",
  "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct"
}
```

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# Actor input Schema

## `mode` (type: `string`):

models = ML models; datasets = dataset repos; spaces = demo apps; trending = what's popular right now

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

Keyword to filter — e.g. llama, sentiment, llm

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

Number of records to return (max 1,000)

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

Sort key (trending mode ignores this). Blank = default sort.

## `direction` (type: `integer`):

1 = ascending, -1 = descending

## `full` (type: `boolean`):

Include description and full tag list (full=true on the API)

## `timeoutSeconds` (type: `integer`):

Timeout per API request

## Actor input object example

```json
{
  "mode": "models",
  "search": "",
  "limit": 100,
  "sortBy": "downloads",
  "direction": -1,
  "full": true,
  "timeoutSeconds": 20
}
```

# Actor output Schema

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

Dataset URL

# 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 = {
    "mode": "models",
    "search": "",
    "limit": 100,
    "sortBy": "downloads",
    "direction": -1,
    "full": true,
    "timeoutSeconds": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("primesieve/hf-intel-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 = {
    "mode": "models",
    "search": "",
    "limit": 100,
    "sortBy": "downloads",
    "direction": -1,
    "full": True,
    "timeoutSeconds": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("primesieve/hf-intel-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 '{
  "mode": "models",
  "search": "",
  "limit": 100,
  "sortBy": "downloads",
  "direction": -1,
  "full": true,
  "timeoutSeconds": 20
}' |
apify call primesieve/hf-intel-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,primesieve/hf-intel-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/UaZDtXHE896L0VIRF/builds/OL2tfStvXvFaymwNR/openapi.json