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Langfuse Prod Checklist

DevOps & Security
langfuse-prod-checklist

Langfuse Prod Checklist is an Agent Skill for devops & security published in the Dicklesworthstone/pi_agent_rust repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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".

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  3. Review files and risks before approving any action.
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Inspect the Agent Skill at https://github.com/Dicklesworthstone/pi_agent_rust/blob/HEAD/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langfuse-pack/skills/langfuse-prod-checklist/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/langfuse-prod-checklist, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Langfuse Production Checklist

Overview

Comprehensive checklist for deploying Langfuse observability to production.

Prerequisites

  • Completed development and staging testing
  • Access to production secrets management
  • Production Langfuse project created

Production Readiness Checklist

1. Authentication & Security

ItemStatusCommand/Action
Production API keys[ ]Create in Langfuse dashboard
Keys in secrets manager[ ]AWS/GCP/Vault
Keys NOT in code/git[ ]git log --all -- '*key*'
Environment isolation[ ]Separate dev/staging/prod keys
PII scrubbing enabled[ ]Verify scrub functions

2. SDK Configuration

ItemStatusRecommended Value
flushAt[ ]15-50 (batch size)
flushInterval[ ]5000-10000ms
requestTimeout[ ]10000-30000ms
enabled[ ]true for prod
Singleton pattern[ ]Single client instance

3. Error Handling

ItemStatusImplementation
Try/catch around traces[ ]All trace operations
Span .end() in finally[ ]Prevent hanging spans
Error level traces[ ]Log errors to Langfuse
Graceful shutdown[ ]shutdownAsync() on exit

4. Performance

ItemStatusVerification
Async tracing (non-blocking)[ ]Don't await traces in hot path
Batching configured[ ]flushAt > 1
No memory leaks[ ]Monitor heap usage
Connection pooling[ ]Singleton client

5. Observability

ItemStatusSetup
Trace URL logging[ ]Log trace IDs for debugging
SDK error monitoring[ ]Catch/log SDK errors
Dashboard alerts[ ]High error rate, latency
Cost tracking[ ]Token usage monitoring

Instructions

Step 1: Verify Production Configuration

// config/langfuse.prod.ts
import { Langfuse } from "langfuse";

function validateConfig(config: Record<string, any>): void {
  const required = ["publicKey", "secretKey"];
  const missing = required.filter((key) => !config[key]);

  if (missing.length > 0) {
    throw new Error(`Missing Langfuse config: ${missing.join(", ")}`);
  }

  // Verify production keys (not dev/test)
  if (config.publicKey.includes("test") || config.publicKey.includes("dev")) {
    console.warn("WARNING: Using non-production API keys!");
  }
}

export function createProductionLangfuse() {
  const config = {
    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
    secretKey: process.env.LANGFUSE_SECRET_KEY!,
    baseUrl: process.env.LANGFUSE_HOST || "https://cloud.langfuse.com",
    // Production settings
    flushAt: 25,
    flushInterval: 5000,
    requestTimeout: 15000,
    enabled: process.env.NODE_ENV === "production",
  };

  validateConfig(config);

  return new Langfuse(config);
}

Step 2: Implement Production Error Handling

// lib/langfuse-production.ts
import { Langfuse } from "langfuse";

let langfuseInstance: Langfuse | null = null;

export function getLangfuse(): Langfuse {
  if (!langfuseInstance) {
    langfuseInstance = createProductionLangfuse();

    // Register graceful shutdown
    const shutdown = async () => {
      console.log("Flushing Langfuse traces...");
      await langfuseInstance?.shutdownAsync();
      console.log("Langfuse shutdown complete");
    };

    process.on("SIGTERM", shutdown);
    process.on("SIGINT", shutdown);
    process.on("beforeExit", shutdown);
  }

  return langfuseInstance;
}

// Production-safe trace wrapper
export async function safeTrace<T>(
  name: string,
  operation: (trace: ReturnType<typeof langfuse.trace>) => Promise<T>,
  metadata?: Record<string, any>
): Promise<T> {
  const langfuse = getLangfuse();

  let trace;
  try {
    trace = langfuse.trace({
      name,
      metadata: {
        ...metadata,
        environment: "production",
      },
    });
  } catch (error) {
    console.error("Failed to create Langfuse trace:", error);
    // Continue without tracing - don't fail the operation
    return operation(null as any);
  }

  try {
    const result = await operation(trace);
    trace.update({ output: { success: true } });
    return result;
  } catch (error) {
    trace.update({
      output: { error: String(error) },
      level: "ERROR",
    });
    throw error;
  }
}

Step 3: Run Pre-Deployment Verification

// scripts/verify-langfuse-prod.ts
import { Langfuse } from "langfuse";

async function verifyProduction() {
  console.log("=== Langfuse Production Verification ===\n");

  const checks: Array<{ name: string; check: () => Promise<boolean> }> = [
    {
      name: "Environment variables set",
      check: async () => {
        const required = [
          "LANGFUSE_PUBLIC_KEY",
          "LANGFUSE_SECRET_KEY",
        ];
        const missing = required.filter((key) => !process.env[key]);
        if (missing.length > 0) {
          console.log(`  Missing: ${missing.join(", ")}`);
          return false;
        }
        return true;
      },
    },
    {
      name: "API keys are production keys",
      check: async () => {
        const key = process.env.LANGFUSE_PUBLIC_KEY || "";
        const isProduction =
          !key.includes("test") &&
          !key.includes("dev") &&
          key.startsWith("pk-lf-");
        if (!isProduction) {
          console.log("  Key appears to be non-production");
        }
        return isProduction;
      },
    },
    {
      name: "Can create trace",
      check: async () => {
        const langfuse = new Langfuse();
        const trace = langfuse.trace({
          name: "production-verification",
          metadata: { test: true },
        });
        await langfuse.flushAsync();
        return true;
      },
    },
    {
      name: "Graceful shutdown works",
      check: async () => {
        const langfuse = new Langfuse();
        langfuse.trace({ name: "shutdown-test" });
        await langfuse.shutdownAsync();
        return true;
      },
    },
  ];

  let passed = 0;
  let failed = 0;

  for (const { name, check } of checks) {
    process.stdout.write(`[ ] ${name}...`);
    try {
      const result = await check();
      if (result) {
        console.log("\r[✓]");
        passed++;
      } else {
        console.log("\r[✗]");
        failed++;
      }
    } catch (error) {
      console.log(`\r[✗] Error: ${error}`);
      failed++;
    }
  }

  console.log(`\n=== Results: ${passed} passed, ${failed} failed ===`);

  if (failed > 0) {
    process.exit(1);
  }
}

verifyProduction();

Step 4: Configure Production Monitoring

// Monitor Langfuse health in production
interface LangfuseMetrics {
  tracesCreated: number;
  tracesFailed: number;
  flushLatencyMs: number[];
  lastFlushTime: Date | null;
}

const metrics: LangfuseMetrics = {
  tracesCreated: 0,
  tracesFailed: 0,
  flushLatencyMs: [],
  lastFlushTime: null,
};

// Expose metrics endpoint
app.get("/metrics/langfuse", (req, res) => {
  const avgFlushLatency =
    metrics.flushLatencyMs.length > 0
      ? metrics.flushLatencyMs.reduce((a, b) => a + b) /
        metrics.flushLatencyMs.length
      : 0;

  res.json({
    tracesCreated: metrics.tracesCreated,
    tracesFailed: metrics.tracesFailed,
    avgFlushLatencyMs: avgFlushLatency.toFixed(2),
    lastFlushTime: metrics.lastFlushTime?.toISOString(),
    errorRate: (
      (metrics.tracesFailed / (metrics.tracesCreated || 1)) *
      100
    ).toFixed(2),
  });
});

Output

  • Verified production configuration
  • Error handling implemented
  • Graceful shutdown configured
  • Monitoring in place
  • All checklist items passed

Production Configuration Example

// Final production Langfuse configuration
export const productionLangfuseConfig = {
  // Authentication
  publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
  secretKey: process.env.LANGFUSE_SECRET_KEY!,
  baseUrl: process.env.LANGFUSE_HOST || "https://cloud.langfuse.com",

  // Batching (optimized for production)
  flushAt: 25,          // Batch 25 events
  flushInterval: 5000,  // Flush every 5 seconds

  // Timeouts
  requestTimeout: 15000, // 15 second timeout

  // Enable/disable
  enabled: process.env.NODE_ENV === "production",

  // Debug (disabled in production)
  debug: false,
};

Error Handling

IssueCauseSolution
Missing traces in prodFlush not calledVerify shutdown handler
High latencyLarge batchesReduce flushAt
Memory growthClient recreationUse singleton pattern
Lost traces on deployNo graceful shutdownAdd SIGTERM handler

Resources

Next Steps

For SDK upgrades, see langfuse-upgrade-migration.