AI Observability and Visibility Tools
This group covers two kinds of visibility. The first is seeing inside your own AI features: the tracing setup generator writes OpenTelemetry code that records each model call, tool call and agent step with the GenAI semantic conventions, and the audit log designer produces a record format, database table and tamper-evident hash chain for decisions and actions that need an audit trail.
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All AI Observability & Visibility (5)
LLM Tracing Setup Generator
OpenTelemetry tracing code for LLM and agent appsAI Audit Log Designer
Design audit records for AI decisions and agent actionsllms.txt Generator
Create an llms.txt file so AI assistants can read your siteAI Crawler robots.txt Generator
Allow or block GPTBot, ClaudeBot, PerplexityBot and other AI crawlersAI Visibility Checker
Check what AI search and assistants can read on a page
Which tool do I need?
See what an LLM feature is doing in production
Generate tracing setup for your language, SDK and backend, with content capture on or off.
Keep a record of AI decisions for audits
Choose the fields, get a schema, SQL table and hash-chaining code.
Allow AI search but not AI training on your site
Pick per-bot rules and merge them into your existing robots.txt.
Help AI assistants understand your site
Write an llms.txt and check key pages for content that AI crawlers can read.
About AI Observability & Visibility
The second is being visible to AI from the outside. The llms.txt generator builds the proposed llms.txt index of your site's key pages, the AI crawler robots.txt generator sets which training, search and user-triggered bots may fetch your pages, and the visibility checker reviews a page's HTML for the things AI search can read: server-rendered text, headings, structured data and clear answers.
Guides
- Tracing LLM and Agent Apps with OpenTelemetry, Plus Audit LogsGenAI semantic conventions, instrumentation libraries, what to capture and what to keep out of traces, agent span trees, and how audit records and EU AI Act logging differ from traces.
- How to Make Your Website Visible to AI Assistants and AI SearchHow AI crawlers and fetchers find and read pages, robots.txt for AI bots, server-rendered content, structured data, llms.txt, MCP, and what is known versus guesswork.
Frequently asked questions
What is the difference between an LLM trace and an audit log?
A trace is operational data for debugging and performance, often sampled and kept for days or weeks. An audit log is a complete, tamper-evident record of decisions and actions kept for as long as rules or contracts require, usually with less raw content.
Should traces include prompts and responses?
Only if you need them and can protect them. Content helps debugging but may contain personal or confidential data, so many teams record it only in development, redact it, or keep it in a store with stricter access.
Does blocking GPTBot remove my site from ChatGPT?
Not by itself. OpenAI documents separate user agents for model training (GPTBot), for search (OAI-SearchBot) and for fetches a user asks for (ChatGPT-User), each with its own robots.txt entry, so blocking GPTBot stops training crawls but not the others.
Is llms.txt an official standard?
It is a community proposal published at llmstxt.org, not a web standard, and support among AI products varies. It is cheap to add and gives assistants a clean map of your key pages, but it does not replace crawlable HTML.