# Newsletter Content Dataset for AI & LLM Research

**Use case:** 

Build a structured dataset of Beehiiv newsletter articles with titles, URLs, publication dates, authors, descriptions, featured images, and metadata for AI research, NLP analysis, content intelligence, and LLM applications.

## Input

```json
{
  "urls": [
    "https://www.therundown.ai",
    "https://www.superhuman.ai",
    "https://milkroad.com"
  ],
  "keywords": [],
  "max_items": 60
}
```

## Output

```json
{
  "url": {
    "label": "Article URL",
    "format": "link"
  },
  "newsletter_name": {
    "label": "Newsletter",
    "format": "text"
  },
  "title": {
    "label": "Title",
    "format": "text"
  },
  "description": {
    "label": "Description",
    "format": "text"
  },
  "author": {
    "label": "Author",
    "format": "text"
  },
  "published_date": {
    "label": "Publication Date",
    "format": "date"
  },
  "image_url": {
    "label": "Image",
    "format": "image"
  },
  "word_count": {
    "label": "Word Count",
    "format": "number"
  },
  "reading_time_minutes": {
    "label": "Reading Time (min)",
    "format": "number"
  },
  "content_text": {
    "label": "Content (Text)",
    "format": "text"
  },
  "content_html": {
    "label": "Content (HTML)",
    "format": "html"
  },
  "matched_keyword": {
    "label": "Matched Keyword",
    "format": "text"
  },
  "source_sitemap_url": {
    "label": "Source Sitemap",
    "format": "link"
  },
  "scraped_at": {
    "label": "Scraped At",
    "format": "date"
  }
}
```

## About this Actor

This example demonstrates how to use [Beehiiv Newsletter Scraper - Low-cost 💲🔥📰📬](https://apify.com/delectable_incubator/beehiiv-newsletter-scraper-low-cost.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/delectable_incubator/beehiiv-newsletter-scraper-low-cost.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/delectable_incubator/beehiiv-newsletter-scraper-low-cost.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).