# Open Source AI Models Scraper (`bornoo/open-source-ai-models-scraper`) Actor

Track the latest open-source AI models from popular repositories like Hugging Face and GitHub. Collect model names, organizations, descriptions, tags, licenses, popularity metrics, URLs, and timestamps. Export structured results to JSON, CSV, Excel, or XML for analysis and automation.

- **URL**: https://apify.com/bornoo/open-source-ai-models-scraper.md
- **Developed by:** [Biddut Hossain](https://apify.com/bornoo) (community)
- **Categories:** AI, Agents, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.01 / 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/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

## Open Source AI Models Tracker

Track the latest open-source AI models from popular repositories such as Hugging Face and GitHub. This Apify actor collects structured metadata about AI models and exports the results for analysis, monitoring, and automation.

### Features

- Track open-source AI models from multiple sources
- Collect model names and organizations
- Extract descriptions and tags
- Capture license information
- Record popularity metrics when available
- Export results to the Apify dataset
- Download data as JSON, CSV, Excel, XML, or HTML
- Easy integration with Apify schedules and webhooks

### Input

Example input:

```json
{
  "sources": [
    "https://huggingface.co/models",
    "https://github.com/trending"
  ],
  "maxItems": 50
}
```

#### Input Parameters

| Parameter | Type | Description |
|------------|------|-------------|
| sources | Array | List of source URLs to monitor |
| maxItems | Integer | Maximum number of models to collect |

### Output

Each dataset item contains fields similar to:

```json
{
  "modelName": "Example Model",
  "organization": "Example Organization",
  "description": "Example description",
  "tags": [
    "llm",
    "text-generation"
  ],
  "downloads": 125000,
  "likes": 3200,
  "license": "apache-2.0",
  "source": "Hugging Face",
  "url": "https://huggingface.co/example/model",
  "scrapedAt": "2026-07-16T00:00:00Z"
}
```

### Project Structure

```
.actor/
src/
    main.py
    tracker.py
requirements.txt
README.md
```

### Workflow

1. Read actor input
2. Load source URLs
3. Download each page
4. Detect the source platform
5. Extract model information
6. Normalize the data
7. Save results to the Apify dataset

### Use Cases

- Monitor newly released AI models
- Research open-source LLMs
- Build AI model databases
- Compare model popularity
- Track licensing information
- Feed AI dashboards
- Automation with Apify webhooks
- Market and technology research

### Requirements

- Python
- Apify SDK
- httpx
- BeautifulSoup4
- lxml

### Notes

- Public pages only are scraped.
- Available metadata depends on the source website.
- Websites may change their HTML structure over time, requiring parser updates.

### License

MIT License

# Actor input Schema

## `sort_by` (type: `string`):

Field to sort models by

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

How many models to fetch

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

Optional keyword to filter models, e.g. 'llama' or 'text-generation'

## Actor input object example

```json
{
  "sort_by": "downloads",
  "limit": 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("bornoo/open-source-ai-models-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("bornoo/open-source-ai-models-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 bornoo/open-source-ai-models-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/e4Ut7ExR4wIDtBlcL/builds/jX5E22iWb9PONWr2R/openapi.json