Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/langfuse/langfuse-docsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/langfuse/langfuse-docs/integration-page)<a href="https://agentmods.dev/rules/langfuse/langfuse-docs/integration-page"><img src="https://agentmods.dev/badge/rules/langfuse/langfuse-docs/integration-page/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/langfuse/langfuse-docs/integration-page"><img src="https://agentmods.dev/badge/rules/langfuse/langfuse-docs/integration-page.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01497 |
| Opus 5.5 | $0.00000 | $0.00599 |
| Sonnet 5.5 | $0.00000 | $0.00299 |
| Haiku 4.5 | $0.00000 | $0.00150 |
Grade A, and why
integration-page scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integration Pages Guidelines
Overview
Integration pages document how to integrate Langfuse with various frameworks, tools, and platforms. They are written as Jupyter notebooks to enable users to easily run and test the integrations themselves. These pages serve as practical, executable guides for users looking to implement Langfuse in their existing tech stack.
File Placement
- Place all integration pages in the
/cookbookdirectory - Use a clear naming convention (e.g.,
integration_[framework_name].ipynb)
Frontmatter Configuration Options
Each integration page should define the following frontmatter fields:
-
title (string, required): The title of the integration, displayed prominently in both the header and index.
-
description (string, required): A concise, SEO optimized summary of the integration covered in the guide.
Content Structure
Each integration page should follow this specific structure:
H1 Title
- A short, clear title for the integration
- Example: "Integrate Langfuse with LlamaIndex Workflows"
Introduction
- 1-2 sentences explaining what this cookbook is about
- Example: "This guide shows you how to trace LlamaIndex Workflows with Langfuse to monitor and evaluate your AI application."
Framework/Tool Introduction
- Brief explanation of the framework/tool being integrated
- Include a link to the official documentation
- Example:
> **What are LlamaIndex Workflows?** [LlamaIndex Workflows](mdc:https:/docs.llamaindex.ai/en/stable/module_guides/workflow) is a flexible, event-driven framework designed to build robust AI agents. In LlamaIndex, workflows are created by chaining together multiple steps—each defined and validated using the `@step` decorator. Every step processes specific event types, allowing you to orchestrate complex processes such as AI agent collaboration, RAG flows, data extraction, and more.
Langfuse Introduction
- Brief explanation of Langfuse and its benefits
- Example:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 147 lines · 0 tokens per session scan A 1d02110acd9d
integration-page is a cursor rule published in the GitHub repository langfuse/langfuse-docs (246 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,497 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other cursor rules, from other repositories
js-sdk-genai-framework-instrumentation
Guide for implementing JS SDK agent and framework instrumentations, such as LangChain, LangGraph, OpenAI Agents, Claude Agent SDK, Google ADK, and Vercel AI.
coding-agents-hook
Authoring and testing coding-agent hook adapters, including payload probing, session state, capture modes, and plugin manifests. Consult the telemetry convention when changing canonical attributes.
coding-agents-convention
Canonical coding-agent telemetry schema, inbound mappings, and data contracts. Applies to semantic conventions, emission, processing, and readers—not hook implementation mechanics.
controller-design
Design principles and invariants for the OpenLIT controller (Go agent that discovers and instruments LLM workloads) and the Agents-page server layer that consumes its data.
frontend
UI, messaging, project hierarchy, and CE/enterprise-boundary guidance for OpenLIT client presentation code.
security-testing
Authorization, input validation, CE boundary, secrets, and test guidance for OpenLIT server code.