# AI Crawler Log Analyzer — GPTBot, Claude & Perplexity (`plenteous_humidifier/ai-crawler-log-analyzer`) Actor

Analyze Nginx, Apache, Cloudflare, and JSON server logs for AI search, user-fetch, and training crawlers. Get bot traffic, errors, bandwidth, top pages, and actionable recommendations without exposing raw lines in dataset output.

- **URL**: https://apify.com/plenteous\_humidifier/ai-crawler-log-analyzer.md
- **Developed by:** [Luigy Gabriel](https://apify.com/plenteous_humidifier) (community)
- **Categories:** AI, Developer tools, SEO tools
- **Stats:** 1 total users, 0 monthly users, 0.0% 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/platform/actors/running/actors-in-store#pay-per-usage

## 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 Log Analyzer

Turn Nginx, Apache, Cloudflare, or JSON-line server logs into an AI-crawler visibility report. The Actor separates search/discovery bots from user-triggered fetchers and training/dataset crawlers, then reports response health, bandwidth, top pages, and recommendations.

### What it detects

- OpenAI: OAI-SearchBot, ChatGPT-User, and GPTBot
- Anthropic: Claude-SearchBot, Claude-User, and ClaudeBot
- Perplexity: PerplexityBot and Perplexity-User
- Meta, Amazonbot, and Common Crawl tokens

### Privacy

The raw `logText` input is processed inside the Actor run. Raw lines, IP addresses, and full user-agent strings are not written to the output dataset. Only aggregate metrics are returned.

### Input

Paste combined-format access logs or one JSON object per line. For Cloudflare exports, the Actor recognizes `ClientRequestUserAgent`, `ClientRequestURI`, `ClientRequestMethod`, `EdgeResponseStatus`, and `EdgeResponseBytes`.

### Output

- Dataset item with aggregate metrics
- `REPORT` key-value record with the complete JSON analysis
- Bot breakdown, top paths, status codes, bandwidth, and prioritized recommendations

The Actor does not infer robots.txt permissions from traffic alone. Use the report to identify which policies and response paths need manual review.

# Actor input Schema

## `logText` (type: `string`):

Nginx/Apache combined logs or one JSON object per line. Supported JSON fields include Cloudflare ClientRequestUserAgent, ClientRequestURI, EdgeResponseStatus, and EdgeResponseBytes.

## `maxLines` (type: `integer`):

Safety limit for this run.

## Actor input object example

```json
{
  "maxLines": 250000
}
```

# Actor output Schema

## `analysis` (type: `string`):

No description

## `report` (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("plenteous_humidifier/ai-crawler-log-analyzer").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("plenteous_humidifier/ai-crawler-log-analyzer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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 plenteous_humidifier/ai-crawler-log-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=plenteous_humidifier/ai-crawler-log-analyzer",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/4XjcJrXM93g7fZTqe/builds/grwHRvhqLJuGr0M6b/openapi.json