AI Search Engine Visibility Monitoring Toolkit: Track How Your Content Is Cited in ChatGPT, Claude, and Perplexity

With the rise of generative AI search, traditional SEO can no longer fully measure content visibility. This article introduces the open-source Python toolkit ai-visibility-monitor, which helps website operators monitor how their content is cited in AI engines like ChatGPT, Claude, and Perplexity—no SaaS subscription needed. It provides a zero-cost GEO (Generative Engine Optimization) solution for small and medium-sized enterprises and independent operators.

Traditional SEO focuses on Google search rankings, but AI search directly generates answers with citations. If a website isn’t cited by AI, it might remain invisible even if it ranks high in traditional searches. Existing commercial GEO tools start at $300-$500 per month, which is too costly. This toolkit aims to provide an open-source, self-hosted, low-cost alternative.

The toolkit includes 4 Python scripts:

1. prereqs_sweep.py: Checks robots.txt, llms.txt, sitemap.xml, and AI crawler access permissions—no API key required.
2. citation_check.py (core): Simulates user queries via the Anthropic API, analyzes domains cited by Claude, and directly measures actual AI behavior.
3. gsc_pull.py: Pulls Google Search Console data (top queries, traffic distribution, etc.) and correlates traditional search with AI citation rates.
4. ga4_pull.py: Analyzes traffic from AI platforms (e.g., chatgpt.com) in GA4 to measure the effectiveness of AI citations converting into visits.

Deployment steps:

1. Clone the repository and install dependencies;
2. Edit sites.json to list monitored domains;
3. Write 5-10 target customer queries in queries.md;
4. Set up API credentials (Anthropic API key, Google ADC);
5. Run the scripts to output data in JSON format. For Google APIs, using Application Default Credentials is recommended to avoid key storage risks.

Script running frequency:

• Monthly: prereqs_sweep.py (technical check), citation_check.py (citation data accumulation trends);
• Weekly: gsc_pull.py, ga4_pull.py (weekly traffic changes);
• Ad-hoc: After deploying changes that affect crawler access (e.g., robots.txt modifications). Automation via crontab is possible.

Target Users: Independent consultants/small agencies (multi-client monitoring), in-house marketing teams (self-hosted solution), technical operators (integrate JSON into BI tools). Non-technical users may need a visual interface. Cost: Tool is free (MIT license); Anthropic API costs $1-$3 per citation check; Google APIs are free for normal use; no server costs for local running.

Current limitations: Lack of Core Web Vitals monitoring, Bing Webmaster Tools support, database output options, and multi-crawler user agent testing. Future plans include implementing these features via community contributions (already listed in issues).

ai-visibility-monitor focuses on solving AI visibility monitoring issues for small and medium-sized operators. Action recommendations:

1. Deploy prereqs_sweep.py to check technical foundations;
2. Define 10-15 core queries and track citations with citation_check.py;
3. Establish baseline data;
4. Review trends monthly;
5. Analyze the conversion funnel of AI-recommended traffic. AI search is reshaping how information is discovered—monitoring capabilities are the first step to staying competitive.