# Website RAG & Lead Intelligence Crawler (`datavaultlabs/website-rag-lead-intelligence`) Actor

Convert public websites to clean Markdown for RAG and AI. Crawl pages and extract emails, phones, social profiles, metadata, technologies, headings and links.

- **URL**: https://apify.com/datavaultlabs/website-rag-lead-intelligence.md
- **Developed by:** [Samuel Huirau Atutahi](https://apify.com/datavaultlabs) (community)
- **Categories:** Automation, Developer tools, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **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).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

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).

# README

## Website RAG & Lead Intelligence Crawler

Turn public websites into structured, AI-ready data.

This Actor crawls public website pages and produces clean Markdown together with useful business and technical intelligence.

### What it extracts

For every successfully crawled page:

- Clean Markdown for RAG, LLM, and knowledge-base workflows
- Page title and meta description
- Canonical URL
- H1/H2/H3 headings
- Publicly displayed email addresses
- Publicly displayed phone numbers
- Linked social profiles
- Basic website technology detection
- Internal and external link counts
- HTTP status
- Crawl depth
- Word and character counts
- Fetch timestamp

### Useful for

#### RAG and AI agents

Convert website content into compact Markdown suitable for embeddings, retrieval systems, AI agents, summarization, and knowledge bases.

#### Business research

Collect publicly displayed business contact information, website metadata, social links, and technology signals alongside page content.

#### Website intelligence

Discover page structure, technologies, canonical URLs, headings, and internal-link relationships.

#### Data pipelines

Export results as JSON, CSV, Excel, XML, JSONL, or retrieve them through the Apify API.

### Crawl controls

Configure:

- Multiple start URLs
- Maximum page count
- Crawl depth
- Same-domain restriction
- robots.txt compliance
- Include URL patterns
- Exclude URL patterns
- Concurrency
- Request timeout
- Maximum Markdown size per page

### Output

Each dataset item represents one page.

Typical fields include:

`url`, `final_url`, `title`, `description`, `markdown`, `emails`, `phones`, `social_profiles`, `technologies`, `headings`, `word_count`, `internal_link_count`, and `external_link_count`.

A run-level `SUMMARY` record also contains aggregate crawl statistics and unique discovered contacts and technologies.

### Responsible use

This Actor is intended for publicly accessible website content.

`respect_robots` is enabled by default. Users are responsible for ensuring their use complies with applicable website terms, permissions, privacy rules, and laws.

The Actor does not log into websites, bypass access controls, solve CAPTCHAs, or access private content.

### Cost-efficient architecture

The Actor uses lightweight HTTP requests rather than a browser by default, keeping runs fast and inexpensive.

### DataVault Labs

Built by DataVault Labs for practical automation, structured data, RAG, and AI-agent workflows.

# Actor input Schema

## `start_urls` (type: `array`):

One or more public website URLs to crawl.

## `max_pages` (type: `integer`):

Maximum number of pages saved across the run.

## `max_depth` (type: `integer`):

0 means only start URLs. 1 follows links from start pages, and so on.

## `same_domain_only` (type: `boolean`):

Only crawl links on the same domain as each start URL.

## `respect_robots` (type: `boolean`):

Check robots.txt before fetching pages.

## `include_patterns` (type: `array`):

Optional glob patterns. When supplied, only matching URLs are crawled.

## `exclude_patterns` (type: `array`):

Optional glob patterns for URLs that should never be crawled.

## `concurrency` (type: `integer`):

Number of pages that may be fetched concurrently.

## `timeout_seconds` (type: `integer`):

Maximum seconds allowed for each HTTP request.

## `max_content_chars` (type: `integer`):

Limits the amount of cleaned Markdown stored for each page.

## `user_agent` (type: `string`):

HTTP User-Agent used for requests and robots.txt checks.

## Actor input object example

```json
{
  "start_urls": [
    {
      "url": "https://example.com"
    }
  ],
  "max_pages": 50,
  "max_depth": 2,
  "same_domain_only": true,
  "respect_robots": true,
  "include_patterns": [],
  "exclude_patterns": [],
  "concurrency": 8,
  "timeout_seconds": 20,
  "max_content_chars": 100000,
  "user_agent": "DataVaultLabs-RAGCrawler/1.0"
}
```

# Actor output Schema

## `results` (type: `string`):

Structured page records with Markdown, contact data, metadata, technologies, and links.

## `summary` (type: `string`):

Aggregate crawl statistics, discovered contacts, and detected technologies.

# 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 = {
    "start_urls": [
        {
            "url": "https://example.com"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("datavaultlabs/website-rag-lead-intelligence").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 = { "start_urls": [{ "url": "https://example.com" }] }

# Run the Actor and wait for it to finish
run = client.actor("datavaultlabs/website-rag-lead-intelligence").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 '{
  "start_urls": [
    {
      "url": "https://example.com"
    }
  ]
}' |
apify call datavaultlabs/website-rag-lead-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datavaultlabs/website-rag-lead-intelligence"
        }
    }
}

```

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/cTKqDmbkjQTbhr9g0/builds/pTXFN2FEgc2SpZB2o/openapi.json