# Substack posts as clean text for AI and LLM pipelines

**Use case:** 

To feed a Substack newsletter to an AI model, set Content format to text. This example returns the 100 newest Astral Codex Ten posts as clean plain text, one row per post with title, date, word count and engagement, ready for retrieval, summaries or topic analysis.

## Input

```json
{
  "urls": [
    "https://www.astralcodexten.com"
  ],
  "maxPosts": 100,
  "audienceFilter": "all",
  "typeFilter": "all",
  "sortBy": "newest",
  "includeContent": true,
  "contentFormat": "text",
  "includeComments": false,
  "includeFacepile": false,
  "maxNotesPerHandle": 50
}
```

## Output

```json
{
  "title": {
    "label": "Title",
    "format": "string"
  },
  "author": {
    "label": "Author",
    "format": "string"
  },
  "publishedAt": {
    "label": "Published",
    "format": "string"
  },
  "url": {
    "label": "URL",
    "format": "string"
  },
  "wordCount": {
    "label": "Words",
    "format": "number"
  },
  "readingTimeMinutes": {
    "label": "Read mins",
    "format": "number"
  },
  "reactionCount": {
    "label": "Reactions",
    "format": "number"
  },
  "commentCount": {
    "label": "Comments",
    "format": "number"
  },
  "isPaywalled": {
    "label": "Paid",
    "format": "boolean"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Substack Scraper: Posts, Comments & Notes Actor is ready to run with the inputs already set for "Substack posts as clean text for AI and LLM pipelines". [Sign up or log in](https://console.apify.com/create-task-from-example/iRhi6UA0rXMSJKnU4) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Substack posts as clean text for AI and LLM pipelines" task

The Substack Scraper: Posts, Comments & Notes Actor will start running based on the input automatically. You can also change all the input fields, like newsletter, post or profile links, max posts per newsletter, and more, to match your specific use case.

### 3. Receive the "Substack posts as clean text for AI and LLM pipelines" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as title, author, published, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Substack posts as clean text for AI and LLM pipelines" task via API](https://apify.com/sourabhbgp/substack-scraper.md).

## About this Actor

This example demonstrates how to use [Substack Scraper: Posts, Comments & Notes](https://apify.com/sourabhbgp/substack-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/sourabhbgp/substack-scraper.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/sourabhbgp/substack-scraper.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`).