Overall AI readiness score
A combined result across all audit signals.
Excellent
AI readiness audit: 7/21/2026Public technical data
Overall AI readiness assessment
How ready helicone.ai is for AI systems
Helicone was independently audited by llmsmap. helicone.ai currently scores 75/100 for AI readiness. The result combines AI-specific files, crawler policy, sitemap discovery, structured homepage data, and—when available—the mobile Google Lighthouse technical profile.
Audit context
llms.txt is accessible and contains 621 tokens. No accessible llms-full.txt was detected, so deeper context still has to be assembled from regular pages. No separate ai.txt policy was detected; it is optional, but can clarify training, retrieval, and attribution preferences.
robots.txt is available. 11 of 11 tracked AI bots are not blocked. Declared sitemaps: 2. No Schema.org type was detected on the homepage. 7 OpenGraph tags were found and markup completeness is 60%, leaving more entity interpretation to crawlers.
The mobile Lighthouse profile adds Performance 45/100, Accessibility 90/100, Best Practices 92/100, SEO 92/100, and experimental Agentic Browsing 67/100. These signals have a limited weight: they complement rather than replace llms.txt, robots.txt, and structured-data checks.
Confirmed strengths
- Accessible llms.txt
- 2 sitemap declarations
Priority improvements
- 1Add llms-full.txt for richer first-party context.
- 2Add JSON-LD for the organisation, website, and core entities.
- 3Complete OpenGraph and canonical homepage metadata.
- 4Reduce mobile rendering delay and main-thread work.
llms.txt tokens621
llms-full.txt tokens—
ai.txt
sitemap.xml
Google Lighthouse technical profile
A mobile Lighthouse measurement. Google’s experimental Agentic Browsing category is explained separately and does not replace the broader llmsmap AI-readiness score.
45
Performance
90
Accessibility
92
Best Practices
92
Technical SEO
67
Agentic Browsing
What these results mean
Mobile performance is 45/100, with the largest visible content block appearing in 13.1 s and the browser main thread blocked for 880 ms. Layout shift was 0. The main thread is where the browser runs JavaScript, calculates layout, and paints the page; long work there delays both user input and browser-agent actions.
Accessibility scored 90/100, Best Practices 92/100, and technical SEO 92/100. The experimental Agentic Browsing category scored 67/100. It measures signals Google currently tests for software agents and is shown separately from the llmsmap AI-readiness score.
1
Reduce code that loads without helping the page
Remove unused CSS and JavaScript, load heavy widgets on demand, and limit third-party scripts. Less code means less parsing and background work on the device.
2
Optimise images and their loading order
Serve correctly sized modern formats, prioritise the primary visual, and lazy-load content below the first viewport.
3
Reduce network delay
Improve server response time, remove unnecessary redirects and repeat downloads, and use compression, caching, and selective preconnect hints.
4
Clarify search and canonical signals
Verify unique titles and descriptions, crawlability, canonicals, descriptive link text, and language relationships. This reduces ambiguity for search engines and AI systems selecting a page.
FCP3.9 s
First content
LCP13.1 s
Main content
CLS0
Layout stability
TBT880 ms
Blocking time
SI4.3 s
Visual speed
Metric glossary
- FCP · First content
- When the first text or image appeared on screen.
- LCP · Main content
- When the largest visible element in the first viewport rendered.
- CLS · Layout stability
- How much content shifted unexpectedly while loading; lower is better.
- TBT · Blocking time
- How long the browser main thread could not respond quickly to input.
- SI · Visual speed
- How quickly the visible viewport filled with content.
7/21/2026Lighthouse 13.4.0Mobile profile
AI readiness checks
Machine-readable files, crawler policy, discovery, and homepage markup.
llms-full.txt
Full version was not found
ai.txt
ai.txt file was not found
Sitemap in robots.txt2
2 sitemaps found
Schema.org (JSON-LD)
Schema.org markup was not found on the homepage
OpenGraph60%
7 OG tags found
AI bot access
Based on robots.txt analysis
GPTBotNot mentioned
OAI-SearchBotNot mentioned
ChatGPT-UserNot mentioned
Google-ExtendedNot mentioned
ClaudeBotNot mentioned
Claude-SearchBotNot mentioned
Claude-UserNot mentioned
BytespiderNot mentioned
CCBotNot mentioned
PerplexityBotNot mentioned
Perplexity-UserNot mentioned
Sitemaps
Declared discovery routes for crawlers and agents.
OpenGraph tags
Social preview metadata found on the homepage.
og:titleHeliconeog:descriptionAI Gateway & LLM Observabilityog:urlhttps://www.helicone.aiog:site_nameHelicone.aiog:localeen_USog:typewebsite# Helicone > Helicone is an open-source observability platform for LLM users. It helps companies monitor usage, latency, and costs for AI models like GPT-3, enabling optimization of AI applications and reduction of OpenAI bills. Helicone provides key insights into spend, performance, and usage patterns. Helicone offers tools for developers to monitor, analyze, and optimize their use of large language models (LLMs). Key features include: - Usage and cost tracking across multiple AI models - Latency monitoring and performance optimization - Request caching and model-swapping capabilities - Custom property tracking for detailed analytics - Integration with popular AI frameworks and platforms Helicone is designed to be easily integrated into existing AI workflows, with support for self-hosted deployments and cloud-based solutions. ## Docs - [Quick Start Guide](https://docs.helicone.ai/getting-started/quick-start): Introduction to setting up and using Helicone - [Gateway Integration](https://docs.helicone.ai/getting-started/integration-method/gateway): Guide to integrating Helicone as a proxy for AI model requests - [Custom Properties](https://docs.helicone.ai/features/advanced-usage/custom-properties): Advanced usage for tracking custom metrics and properties - [Self-Hosted Deployment](https://docs.helicone.ai/getting-started/self-deploy): Instructions for deploying Helicone on your own infrastructure - [Authentication](https://docs.helicone.ai/helicone-headers/helicone-auth): Details on authenticating requests with Helicone ## Examples - [First AI App with Helicone](https://www.helicone.ai/blog/first-ai-app-with-helicone): Tutorial on building an AI app with Helicone integration - [Product Hunt Launch Automation](https://www.helicone.ai/blog/product-hunt-automate): Case study on using Helicone for automating a product launch - [Helicone vs Competitors](https://www.helicone.ai/blog/portkey-vs-helicone): Comparison of Helicone with other LLM observability tools ## Optional - [GitHub Repository](https://github.com/Helicone/helicone): Source code and open-source contributions - [Y Combinator Profile](https://www.ycombinator.com/companies/helicone): Information about Helicone's participation in Y Combinator - [Company Blog](https://www.helicone.ai/blog): Latest updates, tutorials, and insights from the Helicone team - [API Reference](https://docs.helicone.ai/getting-started/quick-start): Detailed API documentation for advanced integrations
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Added 7/19/2026