# AI Launch Radar (`ghostgrid/ai-launch-radar`) Actor

Track AI launches and research signals from GitHub and HuggingFace, with optional experimental directory sources.

- **URL**: https://apify.com/ghostgrid/ai-launch-radar.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 $1.20 / 1,000 ai launch founds

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

## AI Launch Radar

Track AI launches and research signals from GitHub trending repos and HuggingFace models, datasets, and papers. Deduplicates results across sources to give you a unified view of what's new in AI.

### Why use it?

Stay on top of AI releases without visiting 5 different sites daily. Useful for AI researchers, investors, founders, and builders who need to know about new models, datasets, and open-source projects as they emerge.

### Key features

- **Multi-source aggregation** -- GitHub trending repos + HuggingFace models, datasets, and papers in one output
- **Deduplication** -- same project appearing on GH and HF is merged into one record
- **Trend scoring** -- each item gets a recency-weighted trend score for sorting
- **Period filtering** -- daily, weekly, or monthly time windows
- **Source-specific metadata** -- GitHub stars, HF downloads, paper citations per source

### Input

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `sources` | string\[] | \["github","huggingface"] | Sources to query: "github", "huggingface", or both |
| `max_items_per_source` | int | 20 | Max items per source (1-100) |
| `time_period` | select | "daily" | daily / weekly / monthly |
| `categories` | string\[] | \[] | Filter by category (e.g. "nlp", "computer-vision", "audio") |
| `include_papers` | bool | true | Include HuggingFace papers when source includes huggingface |

### Output fields

| Field | Type | Description |
|-------|------|-------------|
| `title` | string | Project/model/paper name |
| `source` | string | "github", "huggingface", or "merged" |
| `type` | string | "repository", "model", "dataset", or "paper" |
| `url` | string | Source URL |
| `description` | string | Description from the source |
| `stars` | int | GitHub stars (github only) |
| `downloads` | int | HF monthly downloads (huggingface only) |
| `author` | string | Creator or organization |
| `category` | string | AI category tag |
| `trend_score` | float | 0.0-1.0 recency-weighted trend score |
| `created_at` | string | ISO-8601 timestamp |

### Notes

- GitHub trending is refreshed daily by GitHub. Expect same-day data for new repos.
- HuggingFace data refreshes more frequently. New models appear within hours.
- Deduplication merges same-name items across sources; merged records show both URLs.

# Actor input Schema

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

Which sources to scrape. GitHub and HuggingFace are the reliable default sources. Product Hunt, Chrome Web Store, Futurepedia, and Toolify are experimental HTML sources.

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

Maximum number of items to collect from each source.

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

Time range for trending items. Applies to GitHub and some other sources.

## `categories` (type: `array`):

Optional filter for specific AI categories (e.g. 'llm', 'image-generation', 'code-assistant'). Leave empty to include all.

## `include_enrichment` (type: `boolean`):

Reserved for future detail fetches. Current output is source listing data.

## Actor input object example

```json
{
  "sources": [
    "github",
    "huggingface"
  ],
  "max_items_per_source": 50,
  "time_period": "daily",
  "categories": [],
  "include_enrichment": true
}
```

# 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("ghostgrid/ai-launch-radar").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("ghostgrid/ai-launch-radar").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 ghostgrid/ai-launch-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ghostgrid/ai-launch-radar"
        }
    }
}

```

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/jbpyb3hUGJEhscTk3/builds/hMDUBdLKmS69kzhMw/openapi.json