# AI Website to Clean Markdown & RAG Chunk Extractor (`kabeer099/ai-website-to-markdown-rag-scraper`) Actor

Converts websites, documentation portals, and articles into clean LLM-ready Markdown and token-sized RAG chunks for OpenAI, Claude, LangChain, and vector databases.

- **URL**: https://apify.com/kabeer099/ai-website-to-markdown-rag-scraper.md
- **Developed by:** [Kabeer Ahmed](https://apify.com/kabeer099) (community)
- **Categories:** AI, Developer tools, Open source
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

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 examples already wired to this Actor's own input schema, see the [API](#api) section below.

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`).

# README

## AI Website to Clean Markdown & RAG Chunk Extractor

Fast, lightweight converter that strips website boilerplate (navigation bars, footers, scripts, cookie banners, ads) and transforms web pages or technical documentation into clean **LLM-ready Markdown** and **token-sized RAG chunks**.

### Key Features

- **Clean Boilerplate Removal:** Automatically focuses on `<main>` / `<article>` content and strips noisy DOM nodes (`<script>`, `<style>`, `<nav>`, `<footer>`, cookie banners).
- **Structured Markdown Output:** Preserves heading hierarchies (`H1`-`H6`), bulleted/numbered lists, blockquotes, and fenced code blocks.
- **Ready-to-Embed RAG Chunks:** Splits long pages into configurable word/token chunks with `sectionHeading`, `wordCount`, and `estimatedTokens` metadata for direct ingestion into **OpenAI Embeddings**, **Pinecone**, **Qdrant**, **LangChain**, or **LlamaIndex**.
- **Optional Same-Domain Subpage Crawling:** Enable `crawlSubpages` to ingest entire documentation portals in a single run.

### Output Example

```json
{
  "url": "https://kabeer.site",
  "domain": "kabeer.site",
  "title": "Kabeer Ahmed | Full-Stack Developer & UI/UX Engineer",
  "wordCount": 420,
  "estimatedTokens": 559,
  "totalChunks": 2,
  "headingsCount": 8,
  "codeBlocksCount": 0,
  "markdown": "# Kabeer Ahmed\n\nFull-Stack Developer & UI/UX Engineer...",
  "ragChunks": [
    {
      "chunkIndex": 0,
      "sourceUrl": "https://kabeer.site",
      "sectionHeading": "Kabeer Ahmed",
      "wordCount": 280,
      "estimatedTokens": 373,
      "content": "..."
    }
  ]
}
```

### Built By

**Kabeer Ahmed** — Full-Stack Engineer & Automation Developer ([kabeer.site](https://kabeer.site))

# Actor input Schema

## `startUrls` (type: `array`):

List of web pages, blog posts, or documentation URLs to convert into clean Markdown and RAG chunks.

## `crawlSubpages` (type: `boolean`):

Automatically follow internal links on the same domain to extract multi-page documentation.

## `maxPagesPerRun` (type: `integer`):

Maximum number of pages to process in a single run.

## `chunkTargetWords` (type: `integer`):

Approximate word count per RAG chunk for vector database ingestion.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://kabeer.site"
    },
    {
      "url": "https://docs.apify.com/platform/actors"
    }
  ],
  "crawlSubpages": false,
  "maxPagesPerRun": 20,
  "chunkTargetWords": 300
}
```

# Actor output Schema

## `dataset` (type: `string`):

Dataset containing clean Markdown and RAG chunks

# 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("kabeer099/ai-website-to-markdown-rag-scraper").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("kabeer099/ai-website-to-markdown-rag-scraper").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 kabeer099/ai-website-to-markdown-rag-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kabeer099/ai-website-to-markdown-rag-scraper"
        }
    }
}
```

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/rh53jOYJgVUelJIQw/builds/xdfgyGH3FeK0fsDLg/openapi.json