# GitHub and HuggingFace AI Research Monitor (`ghostgrid/github-huggingface-research-monitor`) Actor

Track trending AI repositories, models, datasets, and papers from GitHub and HuggingFace.

- **URL**: https://apify.com/ghostgrid/github-huggingface-research-monitor.md
- **Developed by:** [GhostGrid](https://apify.com/ghostgrid) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 ai research signals

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

## GitHub and HuggingFace AI Research Monitor

Track public AI research signals from GitHub and HuggingFace in one scheduled Actor. It uses public unauthenticated endpoints and does not require a GitHub token, HuggingFace token, private repository access, or user credentials.

### What it monitors

**GitHub**

- Public AI-related repositories from topic searches
- Public GitHub Trending pages for daily, weekly, or monthly momentum
- Stars, forks, issues, language, topics, license, and update timestamps

**HuggingFace**

- Trending public models
- Trending public datasets
- Trending daily papers
- Downloads, likes, tags, pipeline/library metadata, and publication timestamps

### Repeat monitoring

The Actor stores a compact public snapshot in a named Apify key-value store. Each record includes:

- `change_type`: `new`, `stars_changed`, `forks_changed`, `downloads_changed`, `likes_changed`, `metadata_changed`, or `unchanged`
- `change_detected`: whether the signal changed since the previous run
- `change_summary`: machine-readable reasons
- `previous_signal_hash`: the previous public metadata fingerprint
- `first_seen`: the first date this source identity was observed; it is preserved across runs

Set `onlyChanges` to `true` for a scheduled feed. If the scan succeeds but nothing changed, the dataset contains a `no_changes` status record instead of failing silently.

### Input

| Field | Type | Default | Description |
|---|---|---|---|
| `sources` | string\[] | `github`, `huggingface` | Public sources to query |
| `max_items_per_source` | integer | 50 | Maximum items per source category |
| `time_period` | string | `daily` | GitHub Trending window: `daily`, `weekly`, or `monthly` |
| `github_topics` | string\[] | AI topics | GitHub topic filters |
| `huggingface_categories` | string\[] | empty | Match public HF tags, pipeline names, or titles |
| `trackChanges` | boolean | true | Persist and compare snapshots |
| `onlyChanges` | boolean | false | Emit only new or changed records |

### Rate limits and limitations

GitHub Search is used without authentication and is subject to GitHub's public unauthenticated rate limits. The Actor catches individual source errors and still returns records from healthy sources. HuggingFace and GitHub data may change between runs; this is a monitoring feed, not a historical archive.

# Actor input Schema

## `sources` (type: `array`):

Which platforms to monitor.

## `max_items_per_source` (type: `integer`):

Maximum number of items to fetch from each source category.

## `time_period` (type: `string`):

Time period for trending items (used for GitHub trending page).

## `github_topics` (type: `array`):

Filter GitHub repos by these topics. Leave empty for default AI-related topics.

## `huggingface_categories` (type: `array`):

Filter HuggingFace items by these categories or tags.

## `trackChanges` (type: `boolean`):

Persist the last public signal snapshot and compare future runs.

## `onlyChanges` (type: `boolean`):

Emit only first-seen or materially changed repos, models, datasets, and papers. A status record is emitted when the scan succeeds but nothing changed.

## Actor input object example

```json
{
  "sources": [
    "github",
    "huggingface"
  ],
  "max_items_per_source": 50,
  "time_period": "daily",
  "github_topics": [],
  "huggingface_categories": [],
  "trackChanges": true,
  "onlyChanges": false
}
```

# 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 = {
    "sources": [
        "github",
        "huggingface"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ghostgrid/github-huggingface-research-monitor").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 = { "sources": [
        "github",
        "huggingface",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ghostgrid/github-huggingface-research-monitor").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 '{
  "sources": [
    "github",
    "huggingface"
  ]
}' |
apify call ghostgrid/github-huggingface-research-monitor --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/P5ewmz0n7vHgDsHnF/builds/fHSeP1sksqGbXgtgH/openapi.json