# AI Video Model Radar (`dr_amp/ai-video-model-radar`) Actor

Monitor public official/docs/changelog URLs for AI video model releases, pricing/API changes and deprecations.

- **URL**: https://apify.com/dr\_amp/ai-video-model-radar.md
- **Developed by:** [Diego Jurado Garcia](https://apify.com/dr_amp) (community)
- **Categories:** Business, Videos, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 ai video signal 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/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

## AI Video Model Radar

Heuristic monitor for AI video/image model pages, changelogs, API docs and pricing pages.

It fetches public URLs, extracts visible text, matches model/change keywords, and emits structured signal items for teams that need to track generative-video tooling without reading every changelog manually.

### Use cases

- Monitor AI video model releases and API availability.
- Watch pricing/deprecation signals.
- Build a weekly digest for creative/production teams.
- Feed internal tooling with structured evidence items.

### Input

```json
{
  "sources": [
    {"name": "Runway", "url": "https://runwayml.com/research", "source_type": "official"}
  ],
  "keywords": ["runway", "api", "pricing", "deprecation"],
  "max_pages": 10,
  "include_raw_excerpt": false
}
```

If `sources` is omitted, a small safe default set of public official URLs is used. Failed fetches are returned as structured low-confidence items instead of crashing the run.

### Output

- `title`
- `date_seen`
- `source_url`
- `source_type`
- `models`
- `capability`
- `workflow_fit`
- `confidence`
- `risk`
- `recommended_action`
- `evidence_excerpt`
- `matched_keywords`

### Local offline smoke

```bash
python3 src/main.py --local --input samples/input.json --output /tmp/ai-video-model-radar-output.json
python3 -m pytest tests/
```

### Limitations

This is deterministic heuristic monitoring, not a truth oracle. It does not use LLMs, proxies, logins, social scraping, or paid APIs. Use official sources where possible and manually verify important alerts.

# Actor input Schema

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

Public URLs to monitor.

## `keywords` (type: `array`):

Terms to match.

## `max_pages` (type: `integer`):

Maximum number of source pages to process in one run.

## `include_raw_excerpt` (type: `boolean`):

Include the first 1000 characters of normalized page text in each item.

## Actor input object example

```json
{
  "sources": [
    {
      "name": "Fixture Runway",
      "url": "fixture://runway-changelog",
      "source_type": "changelog"
    },
    {
      "name": "Fixture Veo",
      "url": "fixture://veo-deprecation",
      "source_type": "official"
    }
  ],
  "max_pages": 10,
  "include_raw_excerpt": 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": [
        {
            "name": "Fixture Runway",
            "url": "fixture://runway-changelog",
            "source_type": "changelog"
        },
        {
            "name": "Fixture Veo",
            "url": "fixture://veo-deprecation",
            "source_type": "official"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("dr_amp/ai-video-model-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 = { "sources": [
        {
            "name": "Fixture Runway",
            "url": "fixture://runway-changelog",
            "source_type": "changelog",
        },
        {
            "name": "Fixture Veo",
            "url": "fixture://veo-deprecation",
            "source_type": "official",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("dr_amp/ai-video-model-radar").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": [
    {
      "name": "Fixture Runway",
      "url": "fixture://runway-changelog",
      "source_type": "changelog"
    },
    {
      "name": "Fixture Veo",
      "url": "fixture://veo-deprecation",
      "source_type": "official"
    }
  ]
}' |
apify call dr_amp/ai-video-model-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=dr_amp/ai-video-model-radar",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/FWio6bszwnkSukG9V/builds/rVGhmEUvW0jPU3kvt/openapi.json