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langfuse: AI engineering insights

Get AI metrics and observability with langfuse, an open source platform for startup founders
intermediate30 minutes💵 Free (self-hosted)
34,469 stars3,757 forksTypeScriptGuide quality 9/10Updated 9/11/2026100% free · open source
What it is

Develops and operates large-language-models with tools for evaluation, observability, and metrics.

Real use case example

"A founder uses langfuse to monitor LLM performance and optimize it for better results. First, they install langfuse and set up OpenTelemetry for metrics and observability. Next, they create a prompt management system and configure it to integrate with the LLM. Finally, they run a series of tests to fine-tune the model's performance. With langfuse, they're able to scale their LLM operations efficiently and make data-driven decisions."

When to use it
  • When you need to monitor and optimize the performance of your LLMs
  • When you want to manage and version your prompts and datasets for LLMs
  • When you need to integrate your LLMs with other tools and platforms like OpenTelemetry, Langchain, or OpenAI SDK
Ready-to-paste prompt
Run 'npm run eval -- --model <your-llm-model> --prompt <your-prompt-text>' to evaluate your LLM's performance on a specific prompt
Heads up: Make sure you have a compatible version of Node.js (14 or higher) and TypeScript installed, as Langfuse is built with TypeScript and relies on Node.js for its development server and build process
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/langfuse && curl -fsSL https://workflowstacks.com/api/skills/langfuse/claude-skill -o ~/.claude/skills/langfuse/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How langfuse: AI engineering insights works
Codeflow
Free to inspect

Langfuse: AI engineering insights is a very large TypeScript project (~585k lines across 4317 code files, plus 444 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, Other license, has a test suite.

Size
Very large codebase
~585k lines · 4317 code files · days to read — use, don't read
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Node.js · Docker
No API keys detected
TypeScript 98%JavaScript 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    package.json
    Dependencies and the commands it exposes
What's in each folder
web/Frontend / UI3229 files
packages/Sub-packages / plugins1330 files
worker/Backend / API433 files
fern/Folder46 files
scripts/Helper scripts27 files
ee/Folder10 files
patches/Patches5 files
.agents/Tool / agent settings160 files
READMEHas testsDocumentedCI checksDocker readyOther licenseUpdated this month
Quick Actions
Details
Creator
langfuse
Language
TypeScript
Category
analytics
Published
5/18/2023

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