langfuse-prod-checklist

v2026.09.24

Langfuse production readiness checklist and verification. Use when preparing to deploy Langfuse to production, validating production configuration, or auditing existing setup. Trigger with phrases like "langfuse production", "langfuse prod ready", "deploy langfuse", "langfuse checklist", "langfuse go live".

GitHub
安装命令
npx skhub add jeremylongshore/langfuse-prod-checklist
Markdown
SKILL.md

Langfuse Production Checklist

Overview

Comprehensive checklist for deploying Langfuse observability to production with verified configuration, error handling, graceful shutdown, monitoring, and a pre-deployment verification script.

Prerequisites

  • Development and staging testing completed
  • Production Langfuse project created with separate API keys
  • Secret management solution in place

Production Configuration

Recommended SDK Settings

// v4+ Production Config
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 5000,  // Flush every 5s
  maxExportBatchSize: 50,      // Batch size
  maxQueueSize: 2048,          // Buffer limit
});

const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();

// Graceful shutdown on all signals
for (const signal of ["SIGTERM", "SIGINT", "SIGUSR2"]) {
  process.on(signal, async () => {
    await sdk.shutdown();
    process.exit(0);
  });
}
// v3 Legacy Production Config
import { Langfuse } from "langfuse";

const langfuse = new Langfuse({
  flushAt: 25,            // Balance between latency and efficiency
  flushInterval: 5000,    // 5 second flush interval
  requestTimeout: 15000,  // 15s timeout
  enabled: true,          // Explicitly enable
});

process.on("beforeExit", () => langfuse.shutdownAsync());
process.on("SIGTERM", () => langfuse.shutdownAsync().then(() => process.exit(0)));

Production Error Handling

import { observe, updateActiveObservation, startActiveObservation } from "@langfuse/tracing";

// Wrap all traced operations with error safety
const tracedEndpoint = observe({ name: "api-endpoint" }, async (req: Request) => {
  try {
    updateActiveObservation({
      input: { path: req.url, method: req.method },
      metadata: { userId: req.userId },
    });

    const result = await processRequest(req);

    updateActiveObservation({ output: { status: 200 } });
    return result;
  } catch (error) {
    // Log error to trace -- don't let tracing error mask app error
    try {
      updateActiveObservation({
        output: { error: String(error) },
        metadata: { level: "ERROR" },
      });
    } catch {
      // Tracing failure must never break the app
    }
    throw error;
  }
});

Pre-Deployment Verification Script

// scripts/verify-langfuse-prod.ts
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";

async function verify() {
  const checks: Array<{ name: string; pass: boolean; detail: string }> = [];

  // 1. Environment variables
  const requiredVars = ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"];
  for (const v of requiredVars) {
    checks.push({
      name: `Env: ${v}`,
      pass: !!process.env[v],
      detail: process.env[v] ? `SET (${process.env[v]!.slice(0, 10)}...)` : "MISSING",
    });
  }

  // 2. Key validation
  const pk = process.env.LANGFUSE_PUBLIC_KEY || "";
  const sk = process.env.LANGFUSE_SECRET_KEY || "";
  checks.push({
    name: "Key format",
    pass: pk.startsWith("pk-lf-") && sk.startsWith("sk-lf-"),
    detail: `Public: ${pk.startsWith("pk-lf-")}, Secret: ${sk.startsWith("sk-lf-")}`,
  });

  // 3. API connectivity
  try {
    const langfuse = new LangfuseClient();
    // Try fetching prompts as a connectivity test
    await langfuse.prompt.get("__health-check__").catch(() => {});
    checks.push({ name: "API connectivity", pass: true, detail: "Connected" });
  } catch (error) {
    checks.push({ name: "API connectivity", pass: false, detail: String(error) });
  }

  // 4. Trace creation
  try {
    await startActiveObservation("prod-verify", async () => {
      updateActiveObservation({
        input: { test: true },
        output: { verified: true },
        metadata: { verification: "pre-deploy" },
      });
    });
    checks.push({ name: "Trace creation", pass: true, detail: "Trace created" });
  } catch (error) {
    checks.push({ name: "Trace creation", pass: false, detail: String(error) });
  }

  // Report
  console.log("\n=== Langfuse Production Verification ===\n");
  let allPassed = true;
  for (const check of checks) {
    const icon = check.pass ? "PASS" : "FAIL";
    console.log(`  [${icon}] ${check.name}: ${check.detail}`);
    if (!check.pass) allPassed = false;
  }

  console.log(`\n${allPassed ? "All checks passed." : "SOME CHECKS FAILED."}\n`);
  if (!allPassed) process.exit(1);
}

verify();

Production Checklist

Authentication & Security

  • Production API keys created (separate from dev/staging)
  • Keys stored in secret manager (not env files or code)
  • Key prefix validated at startup (pk-lf- / sk-lf-)
  • PII scrubbing enabled on trace inputs/outputs
  • Secret scanning in CI/CD pipeline

SDK Configuration

  • Singleton client pattern (no per-request instantiation)
  • Batch size tuned (flushAt: 25-50)
  • Flush interval set (flushInterval: 5000)
  • Request timeout configured (requestTimeout: 15000)

Reliability

  • Graceful shutdown on SIGTERM/SIGINT
  • All spans end in try/finally (v3) or use observe/startActiveObservation (v4+)
  • Tracing errors caught -- never crash the app
  • Circuit breaker for sustained failures

Monitoring

  • Trace creation success/failure logged
  • Flush latency tracked
  • Rate limit errors monitored
  • Dashboard alerts for quality score regression

Operations

  • Runbook documented for Langfuse outages
  • Fallback behavior defined (app works without Langfuse)
  • Data retention policy configured
  • Log rotation includes redaction of API keys

Instructions

Run the checklist against one named environment and attach the verification script output to the deployment record. Resolve every failed required item or record an explicit time-bounded exception with an owner and rollback plan. Run the check again after changing SDK configuration, secrets, or deployment topology.

Output

Produce a production-readiness receipt with environment, deployment revision, check timestamp, pass/fail results, exception owners, and one trace-health observation. Reference secret names only; never print API keys or trace payloads.

Examples

Before a production release, run the verifier with production secret references available, confirm graceful shutdown in a disposable rollout, and submit one synthetic trace. If the trace is delayed, record telemetry as degraded while keeping the application's health result separate.

Error Handling

IssueCauseSolution
Missing traces in prodNo flush on exitAdd shutdown handler for SIGTERM
Memory growthClient created per requestUse singleton pattern
High latencySmall batchesIncrease flushAt to 25-50
Lost traces on deployNo graceful shutdownAdd SIGTERM handler with sdk.shutdown()

Resources

发现
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版本
最新版本元数据

版本

v2026.09.24

发布时间

2026年9月24日

分类

未分类

许可证

MIT

源路径

skills/.curated/langfuse-prod-checklist

默认分支

main

最新提交

e5a6c3b

Tree SHA

c2dc8e8