How to See Which AI Bots Are Crawling Your Site

Updated April 29, 2026 | Professional Guide | 45-60 minutes | Beginner

What You'll Learn

Understanding which AI bots are crawling your site is essential for managing your digital presence in 2026. This guide provides a complete walkthrough for how to see which AI bots are crawling your site through server log analysis, specialized tools, and automated monitoring systems. You'll discover how to identify crawlers from ChatGPT, Perplexity, Claude, and other AI platforms, verify legitimate bots from malicious ones, and make informed decisions about which bots to allow or block. Whether you're dealing with agentic traffic that's accelerating sharply, up 6900% year-over-year, or simply want visibility into your site's AI traffic, you'll gain the skills to monitor and control AI bot access to your content.

• How to identify specific AI crawlers accessing your site using multiple detection methods
• How to analyze server logs to track bot behavior patterns and verify legitimate crawlers
• How to set up automated monitoring systems to track AI bot activity over time
• How to make data-driven decisions about which AI bots to allow, block, or throttle

Prerequisites: Basic understanding of web server concepts and access to your website's server logs or hosting control panel.

Why Seeing Which AI Bots Are Crawling Your Site Matters in 2026

The web traffic landscape has fundamentally transformed. Artificial intelligence bots now account for over 51% of global internet traffic—a staggering shift that most website owners barely notice. Unlike traditional search crawlers that drive traffic back to your site, AI crawlers are automated programs that systematically browse websites to gather data for training artificial intelligence models or answering user queries directly within an AI application. These AI crawlers are much less likely to refer human user traffic to the pages they crawl. Instead, they use the pages they crawl to train AI models that respond to user queries without the user ever leaving the AI app or visiting a website.

This shift creates a critical blind spot for most website owners. Traditional analytics platforms like Google Analytics 4 and Matomo are designed to filter out bot traffic, which means AI crawler activity is largely invisible in your standard analytics dashboards. You could be serving massive volumes of AI requests while watching your human traffic decline, with no idea what's happening. That's why understanding which bots are accessing your content and why is no longer optional—it's essential.

The stakes get higher when you consider the security implications. In a recent analysis of traffic associated with 16 well-known AI crawlers and scrapers, 5.7% of requests presenting an AI crawler/scraper user agent were spoofed, meaning bad actors are impersonating legitimate bots. This makes proper detection and verification methods crucial for maintaining site security and performance. For supporting data, see We built a tool to see which AI bots are actually citing your ....

The Process at a Glance

Total estimated time: 85-140 minutes (1.5-2.5 hours)

Step 1: Access Your Server Logs

What You're Doing

Your server logs are the complete, unfiltered record of everything that happens on your website. Server logs are files automatically generated by your web server that record every single request made to your site, including those from AI bots. This provides the only complete record of bot activity, since server logs capture 100% of bot requests, making them the only reliable source for understanding how AI systems interact with your site. Without them, you're flying blind.

How to Do It

1. cPanel/WHM users: Navigate to the "Raw Access Logs" or "Log Files" section in your control panel and download the access.log file for the desired time period.
2. Apache users: Access logs are typically located at /var/log/apache2/access.log or /var/log/httpd/access_log. You will need SSH or file manager access to retrieve them.
3. Nginx users: Find logs at the default location of /var/log/nginx/access.log.
4. Cloud hosting (AWS/GCP/Azure): You must first enable access logging in your load balancer (like ELB), CDN (like CloudFront), or storage bucket (like S3) settings, as it is often disabled by default.
5. Shared hosting: If logs are not available in your control panel, contact your hosting provider's support team to request the files.

What Done Looks Like

You have successfully downloaded or opened one or more log files containing raw text entries, where each line represents a single request to your server and looks similar to this example: 192.168.1.1 - - [29/Apr/2026:10:15:30 +0000] "GET /page.html HTTP/1.1" 200 1234 "-" "Mozilla/5.0 (compatible; GPTBot/1.0; +https://openai.com/gptbot)" For a more detailed walkthrough, see How to Use Server Logs to See if AI Systems Are ....

Step 2: Search for Known AI Bot User Agents

What You're Doing

Now that you have your logs, it's time to find the bots. Every bot identifies itself with a unique fingerprint called a user agent string. A user agent string is a line of text that a browser or bot sends to identify itself to a web server. Legitimate bots use this to identify themselves, usually containing the name of the bot or the company that owns it, which allows you to find them in your logs and separate them from regular human traffic.

How to Do It

1. Search for OpenAI's bots: Use command-line tools like `grep` to search your log file for specific strings.
   • GPTBot: grep "GPTBot" access.log
   • ChatGPT-User: grep "ChatGPT-User" access.log
   • OAI-SearchBot: grep "OAI-SearchBot" access.log
2. Search for other major AI bots: Expand your search to include other prominent AI crawlers.
   • ClaudeBot: grep "ClaudeBot" access.log
   • PerplexityBot: grep "PerplexityBot" access.log
   • Google-Extended: grep "Google-Extended" access.log
   • Meta-ExternalAgent: grep "Meta-ExternalAgent" access.log
3. Count total requests per bot: Pipe the `grep` command to `wc -l` to get a count of matching lines. grep "GPTBot" access.log | wc -l
4. Extract specific bot activity: Redirect the output of your search into a new file for isolated analysis. grep "GPTBot" access.log > gptbot_requests.log

Example

Here's what you might find when searching for different AI bots, giving you a clear picture of their relative activity levels:

What Done Looks Like

You have a text file or spreadsheet listing each major AI bot and its total request count for the analysis period, allowing you to see their distinct activity patterns in your logs. For a more detailed walkthrough, see AI Crawler Access Checker.

Step 3: Verify Bot Authenticity

What You're Doing

Here's where things get real: just because a bot claims to be GPTBot doesn't mean it actually is. The purpose of this step is to confirm that bots claiming to be from legitimate AI companies are actually from those companies. User-agent strings can be easily spoofed, meaning a malicious actor could claim to be GPTBot when they're actually something else entirely. This is why IP verification is essential for confirming that traffic claiming to be from legitimate AI companies actually originates from their infrastructure.

How to Do It

1. Extract IP addresses for verification: Use a command to isolate the unique IP addresses associated with a specific user agent. grep "GPTBot" access.log | awk '{print $1}' | sort | uniq
2. Perform reverse DNS lookup: Use a reverse DNS lookup—a process that queries the Domain Name System (DNS) to determine the hostname associated with a given IP address—to verify the bot's origin. nslookup [IP_ADDRESS] or dig -x [IP_ADDRESS]
3. Check against known IP ranges: A legitimate bot's IP address should resolve to a domain owned by the parent company.
   • OpenAI: Look for hostnames ending in .openai.com or .openai.net.
   • Anthropic: Look for hostnames ending in .anthropic.com.
   • Google: Look for hostnames ending in .google.com or .googlebot.com.
   • Meta: Look for hostnames ending in .facebook.com or .meta.com.
4. Cross-reference with published IP ranges: For maximum certainty, check the official documentation from AI companies for their current, published IP ranges and compare them against your findings.

Best Practices

• Always verify any bot traffic exceeding 1,000 requests per day to prevent resource drain from spoofed agents.
• Maintain and review a whitelist of verified IP ranges on a quarterly basis for legitimate bots you wish to allow.
• Set up automated alerts for unusual traffic patterns, such as spikes exceeding 200% of the daily average from unverified IPs.

What Done Looks Like

You can confidently distinguish between legitimate AI bots and potential imposters, and you have a list of verified IP addresses for whitelisting and a separate list of suspicious IPs for potential blocking.

Key Takeaway: Verification is non-negotiable. You must use reverse DNS lookups on IP addresses to confirm a bot's identity, as user agent strings alone are unreliable and easily faked.

Step 4: Analyze Traffic Patterns and Behavior

What You're Doing

You've identified which bots are visiting. Now let's understand what they're actually doing. This step focuses on understanding how AI bots interact with your site by analyzing their crawling patterns, frequency, and the specific content they access most often. This analysis reveals their priorities and impact on your server resources.

How to Do It

1. Analyze crawl frequency: Determine how many requests a bot makes per day to understand its crawl rate. grep "GPTBot" access.log | awk '{print $4}' | cut -d: -f1-3 | sort | uniq -c
2. Identify most crawled pages: Find out which pages are most interesting to AI bots. grep "GPTBot" access.log | awk '{print $7}' | sort | uniq -c | sort -nr | head -20
3. Check response codes: See if bots are encountering errors (like 404s) or accessing content successfully (200s). grep "GPTBot" access.log | awk '{print $9}' | sort | uniq -c
4. Monitor bandwidth consumption: Calculate the total data transferred to a specific bot. grep "GPTBot" access.log | awk '{sum += $10} END {print "Total bytes: " sum}'
5. Track hourly patterns: Identify the peak activity hours for each bot to understand their crawling schedule. grep "GPTBot" access.log | cut -d[ -f2 | cut -d] -f1 | cut -d: -f2 | sort -n | uniq -c

Example

AI bot behavior analysis might reveal distinct patterns that inform your management strategy:

What Done Looks Like

You have a clear, data-backed understanding of which content AI bots prioritize, when they're most active, and whether they're successfully accessing your pages, with these findings documented in a report or dashboard for strategic review.

Key Takeaway: Analyzing bot behavior reveals not just *who* is crawling your site, but *how* and *why*, providing the necessary data to decide whether their activity is beneficial or detrimental.

Step 5: Use Specialized AI Bot Detection Tools

What You're Doing

Manual log analysis works, but it doesn't scale. The goal of this step is to implement automated tools designed specifically for AI bot detection and monitoring to supplement and simplify your manual work. These tools provide dashboards, alerts, and deeper insights that are difficult to achieve with command-line methods alone.

How to Do It

1. Install Indexly for AI Visibility tracking: This is a comprehensive platform for ongoing monitoring.
   • Connect your website to monitor real-time AI bot activity.
   • Track which specific AI crawlers are accessing your content.
   • Monitor AI visibility and Authority Stack metrics to see how you appear in AI results.
   • Generate detailed reports on AI bot behavior patterns.
2. Try CrawlerCheck for quick verification: A useful tool for spot-checking your site's accessibility.
   • Enter your URL to instantly see which bots are allowed or blocked.
   • Check robots.txt compliance across dozens of AI crawlers.
   • Verify meta tag and HTTP header settings that affect bot access.
3. Use MRS Digital's AI Crawler Access Checker: Another excellent tool for quick configuration checks.
   • Check access permissions for all major AI bots.
   • Identify any accidentally blocked crawlers that you want to allow.
   • Verify your robots.txt configuration against best practices.
4. Set up Screaming Frog Log File Analyser: A powerful desktop application for in-depth analysis.
   • Import your server logs for a detailed, user-friendly analysis.
   • Filter by AI bot user agents to isolate their activity.
   • Generate visual reports and charts on bot activity over time.

Best Practices

• Start with free tools for at least one week to understand your baseline bot activity before committing to a paid solution.
• Use a combination of a real-time monitoring tool (like Indexly) and a log file analyzer (like Screaming Frog) for comprehensive coverage.
• Set up automated email or Slack alerts for unusual bot behavior, such as a new high-volume crawler appearing.

What Done Looks Like

You have one or more automated systems in place that continuously monitor AI bot activity, providing you with dashboards and alerting you to significant changes or potential issues without requiring daily manual log checks.

Key Takeaway: While manual log analysis is foundational, specialized tools automate the process, provide richer visualizations, and enable real-time monitoring that is essential for effective, long-term AI bot management.

Step 6: Set Up Ongoing Monitoring

What You're Doing

One-time analysis is useful. Ongoing monitoring is essential. This step involves establishing a systematic, long-term approach to track AI bot activity over time. Effective ongoing monitoring enables you to spot trends, identify new bots as they emerge, and respond proactively to changes in crawling behavior before they impact your site's performance.

How to Do It

1. Create automated log analysis scripts: For a technical, low-cost solution.
   • Write shell scripts (e.g., bash) to automatically extract daily bot statistics from logs.
   • Set up cron jobs—time-based job schedulers in Unix-like operating systems—to run your analysis scripts automatically at set intervals (e.g., daily at midnight).
   • Configure the scripts to generate and email daily or weekly summary reports.
2. Set up monitoring with the ELK Stack: For a powerful, enterprise-grade solution.
   • Configure Logstash to parse and normalize your server logs as they are generated.
   • Use Elasticsearch to index all bot activity for fast searching and aggregation.
   • Create custom Kibana dashboards to visualize AI bot traffic, top crawled pages, and error rates in real-time.
3. Configure alerts in Datadog or Splunk: If you already use these platforms for infrastructure monitoring.
   • Set thresholds for unusual bot activity (e.g., requests per minute from a single bot).
   • Monitor bandwidth consumption by bot type and alert when it exceeds a daily budget.
   • Create alerts to track new or unknown user agents that appear in your logs.
4. Establish baseline metrics: Define what "normal" looks like for your site.
   • Document the average daily request volume and peak hours for each key bot.
   • Set acceptable request rate limits (e.g., no more than 60 requests per minute).
   • Define clear criteria for suspicious behavior, such as hitting login pages or a high rate of 404 errors.

Example

A typical monitoring dashboard or alert system might track these key performance indicators:

What Done Looks Like

You receive regular, automated reports showing AI bot activity trends and get automatically notified via email or Slack the moment unusual patterns or threshold breaches occur, allowing for immediate response.

Key Takeaway: One-time analysis is not enough. You must implement an automated, ongoing monitoring system with established baselines and alerts to manage the dynamic AI bot landscape effectively.

Step 7: Create Your AI Bot Management Strategy

What You're Doing

Now that you understand what's happening, it's time to decide what to do about it. The final step is to develop a comprehensive, documented plan for how to handle different types of AI bots based on your analysis. This strategy involves balancing the visibility benefits of being included in AI results with the costs of server resource consumption.

How to Do It

1. Categorize detected bots: Group bots by their primary function to apply different rules.
   • Training bots: GPTBot, ClaudeBot, Meta-ExternalAgent (High resource use, low direct referral value).
   • Search bots: ChatGPT-User, OAI-SearchBot, PerplexityBot (Lower resource use, high visibility value).
   • Research bots: Google-Extended, CCBot (Used for broad web analysis, moderate value).
   • Unknown/suspicious: Unverified or malicious bots that should be blocked by default.
2. Define access policies per category: Create clear rules for each group.
   • Fully allow search bots to maximize visibility in AI search results.
   • Consider blocking or throttling training bots if bandwidth or server load is a concern.
   • Immediately block all suspicious or unverified bots at the firewall level.
   • Set rate limits for high-volume but legitimate bots to prevent performance degradation.
3. Implement using robots.txt: Use robots.txt—a standard text file on your server that tells crawlers which pages they can or cannot request—for basic control.
   • Add specific `User-agent` rules for each bot type you want to manage.
   • Use the `Crawl-delay` directive to suggest a rate limit (though not all bots obey it).
   • Create separate policies for different site sections (e.g., disallow access to `/admin/`).
4. Set up server-level controls: For more robust and enforceable rules.
   • Configure firewall rules (e.g., using `iptables` or a WAF) for IP-based blocking.
   • Implement rate limiting at the web server level (e.g., Nginx `limit_req_zone`).
   • Use advanced bot management features offered by your CDN (like Cloudflare or Akamai).

Common Mistakes

• Blocking all AI bots indiscriminately and losing all visibility in AI search results, which is a rapidly growing traffic source.
• Allowing unlimited access to all bots and letting resource-intensive training bots overwhelm your server resources and increase costs.
• Not regularly reviewing and updating bot policies, at least quarterly, as new crawlers emerge and old ones change behavior.

What Done Looks Like

You have a documented strategy that clearly defines which bots are allowed, blocked, or throttled, and this strategy is actively implemented through a combination of an updated robots.txt file and server-level controls.

Key Takeaway: A successful AI bot strategy is not about simply allowing or blocking. It's about selectively managing access based on bot category and your business goals to maximize visibility while protecting resources.

What to Do After Detecting AI Bots

Immediate Actions (Week 1)

Focus on understanding your current AI bot landscape and making necessary immediate adjustments. Review your findings to identify any bots consuming excessive bandwidth or accessing sensitive areas of your site. Update your robots.txt file to implement basic access controls based on your initial analysis, such as blocking any clearly malicious or spoofed bots you discovered.

Optimization Phase (Weeks 2-4)

Fine-tune your AI bot management strategy based on ongoing monitoring data. Set up automated alerts for unusual activity patterns and establish regular reporting schedules (e.g., a weekly summary email). Begin optimizing the content on pages frequented by valuable search bots, ensuring proper structure and metadata to improve how your content is represented in AI answers.

Long-term Monitoring (Month 2+)

Develop a comprehensive AI visibility strategy that includes tracking how your content performs in AI-generated responses. Consider implementing Indexly's AI Visibility platform to monitor not just which bots are crawling but how your brand appears in AI search results. Regularly review and update your bot management policies every quarter as new AI crawlers emerge and existing ones change their behavior.

Resources You'll Need

See also, see Best AI Visibility Tools for AI Bot/Crawler Analytics (2026).

Common Issues & How to Fix Them

Can't Access Server Logs

Likely cause: Shared hosting providers often restrict direct log access or don't provide detailed logs by default to save resources.

Fix: Contact your hosting provider's support team directly to request access or ask if they can enable it in your control panel. If they cannot provide logs, consider upgrading to a VPS or dedicated server for full control.

Too Many Unknown User Agents

Likely cause: New AI bots emerge constantly, and some malicious or poorly configured bots don't clearly identify themselves in their user agent strings.

Fix: Use behavioral analysis to identify bot-like patterns (e.g., rapid, sequential requests from a single IP with no image or CSS requests). Cross-reference the IP addresses with known AI company ranges and public IP reputation databases. Maintain an updated list of emerging AI bot user agents from industry sources.

Overwhelming Log File Size

Likely cause: High-traffic sites can generate gigabytes of log data daily, making manual processing with tools like `grep` slow and impractical.

Fix: Use log rotation to work with smaller, time-based chunks (daily or hourly logs). Implement automated analysis scripts to process files during off-peak hours. For very large volumes, use specialized tools like Splunk or the ELK Stack, which are designed for processing massive log datasets.

Bot Traffic Consuming Excessive Bandwidth

Likely cause: AI training bots are crawling your site more aggressively than necessary, downloading and caching large amounts of content and media files.

Fix: Implement rate limiting using robots.txt `crawl-delay` directives as a first step. For guaranteed enforcement, use server-level rate limiting (e.g., Nginx `limit_req`) or CDN bot management features. Consider completely blocking the most aggressive training bots if they provide no clear value to your AI visibility.

Conclusion

Key Takeaways

• Server logs are essential: Traditional analytics miss AI bot activity entirely, making log analysis the only reliable method to see which AI bots are crawling your site.
• Verification prevents spoofing: Always verify high-volume bot traffic through reverse DNS lookups and IP range checking to distinguish legitimate bots from malicious actors.
• Strategic bot management: Balance AI visibility goals with resource management by creating targeted policies for different types of AI crawlers based on their purpose and behavior.

FAQ

How do I see which AI bots are crawling my site?

The most reliable method for how to see which AI bots are crawling your site is to analyze your server's raw access logs, as these capture 100% of bot requests that are invisible to standard analytics. Use command-line tools like `grep` to search for known AI bot user agents such as 'GPTBot', 'ClaudeBot', or 'PerplexityBot' within your log files (e.g., grep "GPTBot" access.log). For a more automated approach, you can use specialized AI visibility platforms like Indexly to monitor bot activity or tools like CrawlerCheck to verify bot access permissions.

Why don't AI bots show up in Google Analytics?

AI crawlers do not appear in Google Analytics because they are server-side programs that deliberately avoid executing the client-side JavaScript tracking code that analytics platforms rely on to record visits. These bots focus on accessing the raw HTML content for training data or answering user queries, completely bypassing the mechanisms that track user sessions, pageviews, and events. This creates a significant blind spot where you may be serving substantial bot traffic without any visibility in your standard analytics dashboards.

How can I tell if an AI bot is legitimate or fake?

To verify a bot's authenticity, you must perform a reverse DNS lookup on its IP address using a command like nslookup [IP_ADDRESS]. Legitimate bots will resolve to domains owned by their parent companies (e.g., an IP used by GPTBot will resolve to a hostname ending in `.openai.com`). Cross-reference these findings against the officially published IP ranges from AI companies. Since about 5.7% of AI crawler requests use spoofed user agents, this IP-based verification is essential for security.

Should I block AI bots from my website?

This depends on your goals and server resources. Blocking all AI bots eliminates your visibility in AI-generated answers, potentially missing out on AI search traffic—user visits originating from citations or links within AI-generated answers—that can convert at 4.4 times the rate of traditional organic search. A better strategy is to allow valuable search bots (like ChatGPT-User, PerplexityBot) for AI visibility while potentially blocking or rate-limiting resource-heavy training bots (like GPTBot, ClaudeBot) if bandwidth is a concern. Use tools like Indexly to track your AI Visibility and make informed decisions.

What's the difference between training bots and search bots?

Training bots like GPTBot and ClaudeBot collect vast amounts of content to build and improve AI models; they typically crawl extensively but do not refer traffic back to your site. In contrast, search bots like ChatGPT-User and PerplexityBot fetch specific pages in real-time to answer a user's question, which can drive awareness and traffic through citations in the AI's response. Search bots are generally more valuable for immediate visibility, while training bots consume more resources for long-term model improvement.

How often should I check for new AI bots?

For high-traffic sites, you should review your logs weekly to catch new bots early, while a monthly review is sufficient for smaller sites to establish patterns. The AI bot landscape changes rapidly, with new crawlers emerging regularly. The best practice is to set up automated monitoring to alert you whenever a new, high-volume user agent appears, and maintain an updated list of known AI bots. Tools like Indexly automatically track new AI crawlers and provide ongoing monitoring.

Can AI bots crawl content behind paywalls or login pages?

Most legitimate AI bots respect standard access controls and will not attempt to crawl content behind authentication barriers or paywalls. However, some aggressive or poorly configured bots may attempt to access restricted content. You should monitor your logs for bots repeatedly hitting login pages or protected directories and implement server-level IP blocking if necessary. Ensure your robots.txt file clearly disallows these areas and that protected content returns the proper HTTP status codes (401/403) rather than a 200 response with a login form.

What should I do if I detect suspicious AI bot activity?

First, verify the IP address against official ranges from the claimed AI company. If the IP does not match legitimate ranges, it is likely spoofed and should be blocked immediately at the firewall level. If the IP is legitimate but the behavior is abnormal (e.g., an excessive request rate far above its baseline), implement server-level rate limiting or a temporary block while you investigate further. Always maintain detailed logs for forensic analysis and use specialized bot detection tools to help automate threat identification and response.

This guide is based on analysis of server log data, AI bot behavior patterns observed across multiple platforms in 2026, and testing with various detection tools. Bot behavior and identification methods may evolve as AI platforms update their crawling strategies. Always verify current bot documentation and update your detection methods accordingly.