# Youtube Engagement Heatmap

**Use case:** 

Get detailed insights into audience engagement for any YouTube video. This task extracts the attention heatmap, highlighting moments of high interest and retention data when available. Perfect for analyzing viewer behavior and optimizing content strategy.

## Input

```json
{
  "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "type": "raw",
  "source_preference": "auto",
  "processing": {
    "time_unit": "millis",
    "make_for_llm": false
  }
}
```

## Output

```json
{
  "id": {
    "label": "Video ID",
    "format": "string"
  },
  "source_info": {
    "label": "Source Info",
    "format": "object"
  },
  "time_unit": {
    "label": "Time Unit",
    "format": "string"
  },
  "made_for_llm": {
    "label": "Made for LLM",
    "format": "boolean"
  }
}
```

## About this Actor

This example demonstrates how to use [ytHeatmap — YouTube Attention & Multitmodal Heatmap Engine](https://apify.com/alessiodev-it/ytheatmap.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/alessiodev-it/ytheatmap.md) to learn more, explore other use cases, and run it yourself.


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If asked about integration, you help developers integrate Actors into their projects.
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This Task's input is already configured above — use it as-is rather than inventing a new one.

- **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).
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For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/alessiodev-it/ytheatmap.md

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).