# Sitemap to Clean Markdown & RAG Chunks Crawler (`knightwwang/ai-sitemap-rag-crawler`) Actor

Feed an entire website via XML Sitemap (or domain) and automatically generate structured, clean Markdown documents and vector chunks for AI embeddings.

- **URL**: https://apify.com/knightwwang/ai-sitemap-rag-crawler.md
- **Developed by:** [knightwwang Wang](https://apify.com/knightwwang) (community)
- **Stats:** 2 total users, 1 monthly users, 80.0% 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

## 🗺️ Sitemap to Clean Markdown & RAG Chunks Crawler

Crawl an **entire website's XML sitemap** (or documentation site), convert all pages into **clean, structured Markdown**, and automatically split them into **semantic, vector-ready RAG chunks with exact token counts**.

***

### ⚡ Ideal For

- **Enterprise AI Knowledge Bases**: Ingest entire product docs (e.g. `docs.stripe.com/sitemap.xml`) into Pinecone / Weaviate / Qdrant in 1 click.
- **Competitor Content Auditing**: Extract thousands of clean blog posts from any competitor's sitemap without headers, footers, or cookie banners.
- **RAG Preprocessing**: Eliminate token-counting bottlenecks and complex text-splitters in your Python backend.

***

### 📥 Key Inputs

- `sitemapUrls`: e.g. `https://crawlee.dev/sitemap.xml`
- `urlPattern`: Filter URLs using regex (e.g. `/docs/` or `/blog/`)
- `maxPages`: Max number of pages to crawl
- `maxChunkTokens`: Token size per chunk (default: 800)
- `removeImages`: Strip images to save embedding tokens

***

### 📤 Output Dataset

Returns structured records with full markdown and individual chunk arrays:

```json
{
  "url": "https://crawlee.dev/docs/introduction",
  "title": "Introduction | Crawlee",
  "totalTokens": 1420,
  "chunksCount": 2,
  "chunks": [
    { "chunkIndex": 0, "tokens": 780, "text": "## Overview\nCrawlee is..." },
    { "chunkIndex": 1, "tokens": 640, "text": "## Quick Start\nTo install..." }
  ]
}
```

# Actor input Schema

## `sitemapUrls` (type: `array`):

List of XML sitemap URLs (e.g., https://example.com/sitemap.xml).

## `maxPages` (type: `integer`):

Maximum number of pages to crawl from the sitemap.

## `urlPattern` (type: `string`):

Optional regular expression to filter which URLs to scrape (e.g. /docs/ or /blog/).

## `maxChunkTokens` (type: `integer`):

Size limit per RAG chunk for OpenAI / Cohere embeddings.

## `removeImages` (type: `boolean`):

Remove images from Markdown to save token usage.

## `proxyConfiguration` (type: `object`):

Use Apify Proxy to bypass geo-restrictions.

## Actor input object example

```json
{
  "sitemapUrls": [
    {
      "url": "https://crawlee.dev/sitemap.xml"
    }
  ],
  "maxPages": 50,
  "urlPattern": "",
  "maxChunkTokens": 800,
  "removeImages": false,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

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

Dataset containing all parsed pages and 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 = {
    "sitemapUrls": [
        {
            "url": "https://crawlee.dev/sitemap.xml"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("knightwwang/ai-sitemap-rag-crawler").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 = { "sitemapUrls": [{ "url": "https://crawlee.dev/sitemap.xml" }] }

# Run the Actor and wait for it to finish
run = client.actor("knightwwang/ai-sitemap-rag-crawler").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 '{
  "sitemapUrls": [
    {
      "url": "https://crawlee.dev/sitemap.xml"
    }
  ]
}' |
apify call knightwwang/ai-sitemap-rag-crawler --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,knightwwang/ai-sitemap-rag-crawler"
        }
    }
}
```

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/dKhLZiknPlgGudNmz/builds/h1bUURf36GzHchGRt/openapi.json