langfuse.com

scanned 7/25/2026, 10:40:53 PM · cached · refresh in 22h · rubric 2026.07.4

Built with Next.js

Needs work

This is how AI agents (not browsers) experience langfuse.com. The score weights five categories of machine-readability; the ticks on the arc are the 30 individual checks behind it.

✉ Email me this report + alert me when it changes ↓

Top fixes

Checks tagged “emerging” are 2026 agent-protocol standards most of the web hasn’t adopted yet — adopting early is an edge, not a defect.

failCanonical URLDiscoverability

No <link rel="canonical"> tag in <head>. Agents use canonical URLs to dedupe and cite.

Fix · Next.js: Set alternates.canonical in the Metadata API (generateMetadata) for each route.

failJSON-LD presentStructured data

No JSON-LD blocks found. Add a <script type="application/ld+json"> with a schema.org type for this page.

Fix · Next.js: Emit JSON-LD from a Server Component or the Metadata API: a <script type="application/ld+json"> with Organization / Article / Product for the page.

failLink response headersDiscoverability

No HTTP Link: response headers. Emit canonical/alternate/describedby relations so HEAD-only or stream-rendering agents get them without parsing HTML.

Fix: Emit `Link:` headers for canonical, alternate-language, and describedby relations. Agents that fetch HEAD-only or stream-render rely on these.

failText-to-markup ratioReadability

Visible text is 2.0% of HTML weight (10572/528128 bytes). Low. Markup may be crowding out agent-usable text.

Fix: Heavy ad or navigation markup crowds out agent-usable text. Aim for a text-to-DOM ratio above 15%.

warnHeading hierarchyReadability

Heading issues: 2 <h1> tags (expected 1).

Fix: Use a single H1 and avoid skipping heading levels (H1 → H3).

failContent-Signal directivesemergingDiscoverability

No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.

Fix: Add `Content-Signal:` directives to your robots.txt. This is the emerging standard (Cloudflare-driven) for declaring fine-grained AI usage policy beyond a binary allow/disallow.

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Fix it

Generated, ready-to-ship files for the gaps above — copy them or download and drop them into your repo.

Add JSON-LD structured data

Paste inside <head>. Use Article/Product instead of Organization on those page types.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Langfuse",
  "url": "https://langfuse.com/",
  "description": "Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency."
}
</script>

What the agent receives

The agent’s-eye view

Langfuse by ClickHouse 🐐 Hiring in Europe and SF Looking for GOATS! Product Overview LLM Observability Prompt Management Evaluation Metrics langfuse Get Started with Tracing Step-by-step guide to ingesting your first trace using OpenAI, LangChain, or the SDKs. Get Started with Tracing This guide walks you through ingesting your first trace. Read docs Resources Academy Workshop Blog Changelog Roadmap Users Example Project Walkthroughs Support langfuse ClickHouse Langfuse joins ClickHouse Our goal continues to be building the best AI engineering platform Read story Docs Changelog Pricing Launch App L Get Demo G Community Stats GitHub Stars 31.8k Contributors 300+ Community Q&amp;A threads 1.8k Roadmap threads 1.6k Latest OSS release 2 days ago Changelog View All Any table is a chart 2 days ago Keep large observation content inspectable 2 days ago Track and alert on boolean scores 5 days ago Self Hosting Guides Docker Compose Kubernetes (Helm) AWS (Terraform) GCP (Terraform) Azure (Terraform) Used by 21 of Fortune 50 10+ billion observations/month 100,000+ engineers building on Langfuse Used by 21 of Fortune 50 10+ billion observations/month 100,000+ engineers building on Langfuse Used by 21 of Fortune 50 10+ billion observations/month 100,000+ engineers building on Langfuse Open Source Agent Evals &amp; Observability Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency. Start free S Documentation D Read story Read story Read story Read story Gain deep visibility into your traces Launch, observe, improve — repeat. Langfuse connects tracing, monitoring, datasets, experiments, and evaluation in one continuous loop. Use production signals to understand behavior, test improvements, and ship better agents with confidence. The full LLM engineering loop See how observability, prompts, evals, experiments, and human feedback work together. Learn in Academy All the tools, one integrated platform. One integrated platform to trace, manage prompts, evaluate, and experiment from prototype to production scale. Observability Hierarchical traces capture every LLM call, tool invocation, and retrieval step. Filter by user, session, cost, latency, or custom metadata. Evaluation LLM-as-a-judge, heuristic functions, or human review. Run evaluators on production data or during experiments. Prompt Management Separate prompts from code with one-click deployments and rollbacks. Turn improving your production prompts a team sport. Playground Test prompts on real production inputs and compare models side-by-side. Experiments Define test cases and run experiments. Compare results side by side. Human Annotation Collaborative Human-in the-Loop workflows to review traces and create golden datasets. Cost &amp; Latency Monitor cost, latency, and quality with dashboards and automated alerts. Start free S Canva Canva&#x27;s AI team relies on Langfuse to trace and debug their generative design features in production. Works with any stack. Langfuse works with any language and framework supporting OTel instrumentation. Additionally, 100+ integrations make getting started even easier. No framework lock-in. Languages (via OTel) Python (Native SDK) TypeScript (Native SDK) Go Java .NET Ruby PHP Swift Agent frameworks LangChain Vercel AI SDK LiteLLM Pydantic AI Google ADK CrewAI LiveKit and many more… Model providers OpenAI Anthropic Amazon Bedrock Azure OpenAI Mistral AI Google Gemini xAI vLLM Groq and many more… 100+ more integrations Claude Code LiteLLM (Proxy/Gateway) OpenClaw Claude Agent SDK (Python) LangChain DeepAgents OpenWebUI Ollama OpenAI Agents SDK Dify Langflow OpenRouter n8n Spring AI Cursor PostHog Claude Code LiteLLM (Proxy/Gateway) OpenClaw Claude Agent SDK (Python) LangChain DeepAgents OpenWebUI Ollama OpenAI Agents SDK Dify Langflow OpenRouter n8n Spring AI Cursor PostHog DSPy Amazon AgentCore Strands Agents LlamaIndex Agno Agents Temporal ClickHouse Agentic Data Stack Mastra Claude Agent SDK (JS) Promptfoo Microsoft Agent Framework Google Vertex AI Ragas AutoGen RAGflow DSPy Amazon AgentCore Strands Agents LlamaIndex Agno Agents Temporal ClickHouse Agentic Data Stack Mastra Claude Agent SDK (JS) Promptfoo Microsoft Agent Framework Google Vertex AI Ragas AutoGen RAGflow See all integrations Don&#x27;t find your integration? Request it → Open platform. Open source. We are huge fans of open standards and data portability. Langfuse won&#x27;t lock in your data, ever. Self-host at scale Docker Compose Kubernetes (Helm) AWS (Terraform) GCP (Terraform) Azure (Terraform) MIT license All product features MIT licensed Scales to billions of monthly events Fork, modify, contribute APIs &amp; exports REST APIs for everything Query SDK S3 blob storage export Active OSS community 22,000+ GitHub stars 5,000+ Discord members Weekly releases and community hours Made for developers , loved by agents . Work in the app or from your IDE. The Assistant investigates production and takes approved actions; SKILL.md, CLI, and MCP connect coding agents to Langfuse. 00 In-app Langfuse Assistant Automate the AI engineering loop: investigate production data, understand what happened, and turn findings into approved actions without leaving Langfuse. Debug traces Find failed generations and traces with high latency Optimize spend Break down token spend, cost, and latency Build evals Create regression datasets and score configs View documentation → 0 1 Coding agents SKILL.md A ready-made skill for managing prompts, traces, and evals through natural language. Install the skill → 0 2 Terminal Langfuse CLI Full API access from the terminal for agent workflows, scripts, and CI/CD. Configure the CLI → 0 3 IDE agents Platform MCP Server Structured access for IDE agents to manage prompts, query traces, and use Langfuse data. Configure MCP → Enterprise scale and security . Traditional observability handles many small spans. LLM systems run differently. Every step carries rich, verbose I/O that legacy platforms can&#x27;t handle at scale. Langfuse ingests and queries LLM traces reliably at enterprise scale while following strict compliance frameworks. Architecture Clickhouse OLAP database Async ingestion via Redis queue S3/Blob storage for large payloads Edge-cached prompts Reliability at scale 50M+ SDK installs/month 10+ billion observations processed per month 2300+ customers 99.9% uptime Security &amp; compliance SOC 2 Type II ISO 27001 GDPR EU &amp; US Data Regions HIPAA-ready region Start free S Canva Canva&#x27;s AI team relies on Langfuse to trace and debug their generative design features in production. Why use Langfuse? Langfuse is the most widely adopted open-source LLM engineering platform. Developers who value open-source and control over their data build production grade agents and LLM applications with Langfuse. The full cycle Langfuse powers the entire development cycle from prototype to full scale production loads. Unified platform Open source (MIT) OTel native 100+ integrations Built for scale Async by default Loved by agents Production-proven Shipping velocity The full cycle Langfuse powers the entire development cycle from prototype to full scale production loads. Unified platform All components of Langfuse work great standalone but excel when used together. Open source (MIT) Inspect the code. Self-host for free. We are the largest OSS community in our category. OTel native Standard trace format. Works with existing OpenTelemetry instrumentation. 100+ integrations Works with any model, any framework, and stack. Built for scale ClickHouse backend allows to query millions of traces in milliseconds. Async by default Tracing never blocks your application. Background processing, automatic batching. Loved by agents CLI, MCP, accessible docs - coding agents love working with Langfuse. Production-proven Billions of events processed per month. 50M+ SDK installs/month. Fortune 50 de

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Full breakdown

13 pass1 warn13 fail3 skip

Show only failures

Discoverability

pass

robots.txt present

A robots.txt is reachable at the site root.

100/100

pass

robots.txt allows AI agents

All 8 answer-time access agents allowed.

100/100

fail

Content-Signal directivesemerging

No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.

0/100

pass

llms.txt present

Found /llms.txt (10779 bytes), H1 + at least one link present.

100/100

pass

Sitemap present

sitemap.xml reachable and referenced from robots.txt.

100/100

fail

Link response headers

No HTTP Link: response headers. Emit canonical/alternate/describedby relations so HEAD-only or stream-rendering agents get them without parsing HTML.

0/100

fail

Canonical URL

No <link rel="canonical"> tag in <head>. Agents use canonical URLs to dedupe and cite.

0/100

fail

MCP server cardemerging

No valid MCP Server Card at /.well-known/mcp/server-card.json. Publish one so agents can discover your tools without HTML scraping.

0/100

fail

OAuth authorization metadataemerging

No RFC 8414 metadata at /.well-known/oauth-authorization-server. Agents that act on behalf of users need this to discover your authorization endpoints.

0/100

fail

OAuth resource metadataemerging

No RFC 9728 metadata at /.well-known/oauth-protected-resource. Publish it so agents can discover required scopes without hand-coded credentials.

0/100

fail

API catalog / OpenAPIemerging

No /.well-known/api-catalog (RFC 9727) and no /openapi.json|yaml. Publish one so agents that integrate with APIs can discover your endpoints.

0/100

fail

Web Bot Authemerging

No Web Bot Auth JWKS at /.well-known/http-message-signatures-directory.json. Publish one to allow trusted agents while keeping a default-deny posture for the rest.

0/100

Readability

fail

Markdown negotiationemerging

Accept: text/markdown returns HTML, not markdown. Serve a markdown variant of primary content when requested; agents summarize and cite it more reliably.

0/100

fail

Text-to-markup ratio

Visible text is 2.0% of HTML weight (10572/528128 bytes). Low. Markup may be crowding out agent-usable text.

15/100

skip

Content without JavaScript

Browser pass unavailable; cannot measure JS dependency.

—

warn

Heading hierarchy

Heading issues: 2 <h1> tags (expected 1).

75/100

pass

Cookie wall blocks content

No common consent-modal markers detected.

100/100

pass

Page title

A concise <title> is present (8 chars).

100/100

pass

Meta description

A well-sized meta description is present (179 chars).

100/100

Structured data

fail

JSON-LD present

No JSON-LD blocks found. Add a <script type="application/ld+json"> with a schema.org type for this page.

0/100

skip

Structured data validates

No JSON-LD blocks present; nothing to validate (covered by structured_data.jsonld_present).

—

pass

Schema type coverage

Page intent unclear; no specific schema.org type expected.

100/100

Actionability

pass

Paywall / login wall

No paywall or login-wall detected on the landing URL.

100/100

fail

Agent Skills manifestemerging

No Agent Skills manifest at /.well-known/agent-skills.json. Enumerate the tasks agents can perform (search, add-to-cart, contact-support) so they pick the right one without scraping.

0/100

fail

WebMCP actionsemerging

No WebMCP detected. On pages with first-class actions (cart, support, account), embed a WebMCP server so on-page agents invoke tools directly.

0/100

pass

Primary action reachable

Primary offering identified ("LLM observability and agent engineering platform") with price/CTA ("Start free").

100/100

pass

Reachability

HTTP 200.

100/100

Performance

pass

Time to first byte

TTFB 43ms. Healthy.

100/100

skip

Full render time

Full-render time unavailable (browser pass skipped or failed).

—

pass

Page weight

Initial document is a lean 516 KB.

100/100

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