langfuse-install-auth
Install and configure Langfuse SDK authentication for LLM observability. Use when setting up a new Langfuse integration, configuring API keys, or initializing Langfuse tracing in your project. Trigger with phrases like "install langfuse", "setup langfuse", "langfuse auth", "configure langfuse API key", "langfuse tracing setup".
Install
mkdir -p .claude/skills/langfuse-install-auth && curl -L -o skill.zip "https://mcp.directory/api/skills/download/8278" && unzip -o skill.zip -d .claude/skills/langfuse-install-auth && rm skill.zipInstalls to .claude/skills/langfuse-install-auth
About this skill
Langfuse Install & Auth
Overview
Install the Langfuse SDK and configure authentication for LLM observability. Covers both the legacy langfuse package (v3) and the modern modular SDK (v4+/v5) built on OpenTelemetry.
Prerequisites
- Node.js 18+ or Python 3.9+
- Package manager (npm, pnpm, or pip)
- Langfuse account (cloud at https://cloud.langfuse.com or self-hosted)
- Public Key (
pk-lf-...) and Secret Key (sk-lf-...) from project settings
Instructions
Step 1: Install SDK
TypeScript/JavaScript (v4+ modular SDK -- recommended):
set -euo pipefail
# Core client for prompt management, datasets, scores
npm install @langfuse/client
# Tracing (observe, startActiveObservation)
npm install @langfuse/tracing @langfuse/otel @opentelemetry/sdk-node
# OpenAI integration (drop-in wrapper)
npm install @langfuse/openai
# LangChain integration
npm install @langfuse/langchain
TypeScript/JavaScript (v3 legacy -- single package):
npm install langfuse
Python:
pip install langfuse
Step 2: Get API Keys
- Open Langfuse dashboard (https://cloud.langfuse.com or your self-hosted URL)
- Go to Settings > API Keys
- Click Create new API key pair
- Copy both keys:
- Public Key:
pk-lf-...(identifies your project) - Secret Key:
sk-lf-...(grants write access -- keep secret)
- Public Key:
- Note the host URL (cloud default:
https://cloud.langfuse.com)
Step 3: Configure Environment Variables
# Set environment variables
export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_SECRET_KEY="sk-lf-..."
export LANGFUSE_BASE_URL="https://cloud.langfuse.com"
# Or create .env file
cat >> .env << 'EOF'
LANGFUSE_PUBLIC_KEY=pk-lf-your-public-key
LANGFUSE_SECRET_KEY=sk-lf-your-secret-key
LANGFUSE_BASE_URL=https://cloud.langfuse.com
EOF
Note: v4+ uses
LANGFUSE_BASE_URL. Legacy v3 usesLANGFUSE_HOSTorLANGFUSE_BASEURL.
Step 4: Initialize and Verify (v4+ Modular SDK)
// src/lib/langfuse.ts
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation } from "@langfuse/tracing";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
// 1. Register the OpenTelemetry span processor (once at app startup)
const sdk = new NodeSDK({
spanProcessors: [new LangfuseSpanProcessor()],
});
sdk.start();
// 2. Create the Langfuse client for prompt/dataset/score operations
export const langfuse = new LangfuseClient({
publicKey: process.env.LANGFUSE_PUBLIC_KEY,
secretKey: process.env.LANGFUSE_SECRET_KEY,
baseUrl: process.env.LANGFUSE_BASE_URL,
});
// 3. Verify connection
async function verify() {
await startActiveObservation("connection-test", async (span) => {
span.update({ input: { test: true } });
span.update({ output: { status: "connected" } });
});
console.log("Langfuse connection verified. Check dashboard for trace.");
}
verify();
Step 5: Initialize and Verify (v3 Legacy SDK)
import { Langfuse } from "langfuse";
const langfuse = new Langfuse({
publicKey: process.env.LANGFUSE_PUBLIC_KEY,
secretKey: process.env.LANGFUSE_SECRET_KEY,
baseUrl: process.env.LANGFUSE_HOST,
});
// Verify with a test trace
const trace = langfuse.trace({
name: "connection-test",
metadata: { test: true },
});
await langfuse.flushAsync();
console.log("Connected. Trace URL:", trace.getTraceUrl());
// Clean shutdown
process.on("beforeExit", async () => {
await langfuse.shutdownAsync();
});
Step 6: Python Verification
from langfuse import Langfuse
import os
langfuse = Langfuse(
public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
secret_key=os.environ["LANGFUSE_SECRET_KEY"],
host=os.environ.get("LANGFUSE_HOST", "https://cloud.langfuse.com"),
)
# Test trace
trace = langfuse.trace(name="connection-test", metadata={"test": True})
langfuse.flush()
print(f"Connected. Trace: {trace.get_trace_url()}")
SDK Version Comparison
| Feature | v3 (langfuse) | v4+ (@langfuse/*) |
|---|---|---|
| Package | Single langfuse | Modular: @langfuse/client, @langfuse/tracing, @langfuse/otel |
| Base URL env var | LANGFUSE_HOST | LANGFUSE_BASE_URL |
| Tracing | langfuse.trace() | startActiveObservation() / observe() |
| Client class | Langfuse | LangfuseClient |
| OpenAI wrapper | observeOpenAI() from langfuse | observeOpenAI() from @langfuse/openai |
| Foundation | Custom | OpenTelemetry |
Error Handling
| Error | Cause | Solution |
|---|---|---|
401 Unauthorized | Invalid or expired API key | Re-check keys in Langfuse dashboard Settings > API Keys |
ECONNREFUSED | Wrong host URL or server down | Verify LANGFUSE_BASE_URL / LANGFUSE_HOST |
Missing required configuration | Env vars not loaded | Ensure dotenv/config imported at entry point |
Module not found | Package not installed | Run npm install or pip install again |
| Using pk- key as secret | Keys swapped | Public key starts pk-lf-, secret starts sk-lf- |
Resources
Next Steps
After auth is working, proceed to langfuse-hello-world for your first traced LLM call.
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