langfuse-hello-world

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Create a minimal working Langfuse trace example. Use when starting a new Langfuse integration, testing your setup, or learning basic Langfuse tracing patterns. Trigger with phrases like "langfuse hello world", "langfuse example", "langfuse quick start", "first langfuse trace", "simple langfuse code".

AI & Automation 2,266 stars 315 forks Updated today MIT

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Skill Content

# Langfuse Hello World ## Overview Create your first Langfuse trace with real SDK calls. Demonstrates the trace/span/generation hierarchy, the `observe` wrapper, and the OpenAI drop-in integration. ## Prerequisites - Completed `langfuse-install-auth` setup - Valid API credentials in environment variables - OpenAI API key (for the OpenAI integration example) ## Instructions ### Step 1: Hello World with v4+ Modular SDK ```typescript // hello-langfuse.ts import { startActiveObservation, observe, updateActiveObservation } from "@langfuse/tracing"; import { LangfuseSpanProcessor } from "@langfuse/otel"; import { NodeSDK } from "@opentelemetry/sdk-node"; // Register OpenTelemetry processor (once at startup) const sdk = new NodeSDK({ spanProcessors: [new LangfuseSpanProcessor()], }); sdk.start(); async function main() { // Create a top-level trace with startActiveObservation await startActiveObservation("hello-world", async (span) => { span.update({ input: { message: "Hello, Langfuse!" }, metadata: { source: "hello-world-example" }, }); // Nested span -- automatically linked to parent await startActiveObservation("process-input", async (child) => { child.update({ input: { text: "processing..." } }); await new Promise((r) => setTimeout(r, 100)); child.update({ output: { result: "done" } }); }); // Nested generation (LLM call tracking) await startActiveObservation( { name: "llm-response", asType: "generatio...

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Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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