# AI Crawler & llms.txt Policy Auditor (`quanmatrix/ai-crawler-llmstxt-policy-auditor`) Actor

Audit robots.txt policies for major AI crawlers and inspect llms.txt discovery files for public websites with structured machine-readable results.

- **URL**: https://apify.com/quanmatrix/ai-crawler-llmstxt-policy-auditor.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Developer tools, SEO tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.94 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## AI Crawler & llms.txt Policy Auditor

Audit public website signals that communicate how AI-oriented crawlers may access content, and track adoption of the emerging `llms.txt` convention.

### Key features

- Fetches and parses `/robots.txt`.
- Summarizes explicit rules for major AI crawler user agents.
- Distinguishes explicit allow, explicit disallow, and no dedicated policy.
- Checks `/llms.txt` and `/.well-known/llms.txt`.
- Reports one structured policy record per website with score and issues.

### Input

```json
{"urls":["https://www.python.org","https://example.com"]}
```

### Output

Each dataset item contains the requested site, normalized origin, robots.txt status, AI crawler policy summary, llms.txt discovery status, content metadata, score, and issues.

### Example

A site may return `robotsFound: true`, `llmsTxtFound: false`, and a `crawlerPolicies` object showing whether user agents such as `GPTBot`, `ClaudeBot`, `Google-Extended`, `CCBot`, and `PerplexityBot` have explicit allow or disallow directives.

### Why use this Actor

1. **AI-specific policy summary** avoids hand-reading robots files across many domains.
2. **llms.txt discovery** tracks an emerging machine-readable publishing convention.
3. **Absent policy is explicit**, helping governance teams identify sites that need review.
4. **No paid monitoring API or AI dependency**, keeping recurring portfolio checks inexpensive.

### Pricing

Pay per dataset result. One website policy audit creates one primary chargeable result. Pricing is designed for recurring multi-site monitoring.

### Good use cases

- Publisher AI-access governance
- SEO and content policy audits
- Portfolio-wide crawler rule inventories
- Monitoring changes to robots.txt and llms.txt adoption

### Limitations

- Published directives do not guarantee crawler behavior or legal compliance.
- AI crawler user-agent conventions can change and should be reviewed periodically.
- `llms.txt` remains an emerging convention; absence is informational, not automatically a defect.

### Privacy and safety

The Actor reads only public HTTP(S) policy files, rejects private network targets, follows a small number of validated redirects, and requires no credentials.

### Automation and recurring checks

This Actor is designed for repeatable Apify workflows. Save a task when you need to run the same check regularly, schedule it from Apify Console, or call the Actor through the Apify API. Each run writes structured results to the default dataset so the output can be exported to JSON, CSV, Excel, or consumed by another automation.

For reliable monitoring, start with a small input and confirm the expected output before increasing the batch size. Public websites can change behavior over time, so recurring users should review issue flags and HTTP failures rather than assuming an unavailable value means the same thing as a passing result. The Actor uses limited Apify permissions and does not require access to unrelated account data.

# Actor input Schema

## `urls` (type: `array`):

Public HTTP(S) sites to inspect

## Actor input object example

```json
{
  "urls": [
    "https://example.com"
  ]
}
```

# Actor output Schema

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

No description

# 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("quanmatrix/ai-crawler-llmstxt-policy-auditor").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("quanmatrix/ai-crawler-llmstxt-policy-auditor").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 quanmatrix/ai-crawler-llmstxt-policy-auditor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/ai-crawler-llmstxt-policy-auditor"
        }
    }
}

```

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/HZkEGHfq5m8YL4qMy/builds/ayovq5Ge2daAcPw4t/openapi.json