langchain-prod-checklist

name	langchain-prod-checklist
description	Production readiness checklist for LangChain applications. Use when this capability is needed.
metadata	{"author":"flight505"}

LangChain Production Checklist

Overview

Comprehensive go-live checklist for deploying LangChain applications to production. Covers configuration, resilience, observability, performance, security, testing, deployment, and cost management.

1. Configuration & Secrets

• All API keys in secrets manager (not .env in production)
• Environment-specific configs (dev/staging/prod) validated with Zod
• Startup validation fails fast on missing config
• .env files in .gitignore

// Startup validation
import { z } from "zod";

const ProdConfig = z.object({
  OPENAI_API_KEY: z.string().startsWith("sk-"),
  LANGSMITH_API_KEY: z.string().startsWith("lsv2_"),
  NODE_ENV: z.literal("production"),
});

try {
  ProdConfig.parse(process.env);
} catch (e) {
  console.error("Invalid production config:", e);
  process.exit(1);
}

2. Error Handling & Resilience

• maxRetries configured on all models (3-5)
• timeout set on all models (30-60s)
• Fallback models configured with .withFallbacks()
• Error responses return safe messages (no stack traces to users)

const model = new ChatOpenAI({
  model: "gpt-4o-mini",
  maxRetries: 5,
  timeout: 30000,
}).withFallbacks({
  fallbacks: [new ChatAnthropic({ model: "claude-sonnet-4-20250514" })],
});

3. Observability

• LangSmith tracing enabled (LANGSMITH_TRACING=true)
• LANGCHAIN_CALLBACKS_BACKGROUND=true (non-serverless only)
• Structured logging on all LLM/tool calls
• Prometheus metrics exported (requests, latency, tokens, errors)
• Alerting rules configured (error rate >5%, P95 latency >5s)

4. Performance

• Caching enabled for repeated queries (Redis or SQLite)
• maxConcurrency set on batch operations
• Streaming enabled for user-facing responses
• Connection pooling configured
• Prompt length optimized (no unnecessary verbosity)

5. Security

• User input isolated in human messages (never in system prompts)
• Input length limits enforced
• Prompt injection patterns logged/flagged
• Tools restricted to allowlisted operations
• LLM output validated before display (no PII/key leakage)
• Audit logging on all LLM and tool calls
• Rate limiting per user/IP

6. Testing

• Unit tests for all chains (using FakeListChatModel, no API calls)
• Integration tests with real LLMs (gated behind CI secrets)
• RAG pipeline validation (retrieval relevance + no hallucination)
• Tool unit tests (valid input, invalid input, error cases)
• Load testing completed (concurrent users, batch operations)

7. Deployment

• Health check endpoint returns LLM connectivity status
• Graceful shutdown handles in-flight requests
• Rolling deployment (zero downtime)
• Rollback procedure documented and tested
• Container resource limits set (memory, CPU)

// Health check endpoint
app.get("/health", async (_req, res) => {
  const checks: Record<string, string> = { server: "ok" };

  try {
    await model.invoke("ping");
    checks.llm = "ok";
  } catch (e: any) {
    checks.llm = `error: ${e.message.slice(0, 100)}`;
  }

  const healthy = Object.values(checks).every((v) => v === "ok");
  res.status(healthy ? 200 : 503).json({ status: healthy ? "healthy" : "degraded", checks });
});

// Graceful shutdown
process.on("SIGTERM", async () => {
  console.log("Shutting down gracefully...");
  server.close(() => process.exit(0));
  setTimeout(() => process.(), ); 
});

8. Cost Management

• Token usage tracking callback attached
• Daily/monthly budget limits enforced
• Model tiering: cheap model for simple tasks, powerful for complex
• Cost alerts configured (Slack/email on threshold)
• Cost per user/tenant tracked

Pre-Launch Validation Script

async function validateProduction() {
  const results: Record<string, string> = {};

  // 1. Config
  try {
    ProdConfig.parse(process.env);
    results["Config"] = "PASS";
  } catch { results["Config"] = "FAIL: missing env vars"; }

  // 2. LLM connectivity
  try {
    await model.invoke("ping");
    results["LLM"] = "PASS";
  } catch (e: any) { results["LLM"] = `FAIL: ${e.message.slice(0, 50)}`; }

  // 3. Fallback
  try {
    const fallbackModel = model.withFallbacks({ fallbacks: [fallback] });
    await fallbackModel.invoke("ping");
    results["Fallback"] = "PASS";
  } catch { results["Fallback"] = "FAIL"; }

  // 4. LangSmith
  results["LangSmith"] = process.env.LANGSMITH_TRACING ===  ?  : ;

  
   {
     res =  ();
    results[] = res. ?  : ;
  }  { results[] = ; }

  .(results);
   allPass = .(results).( v === );
  .(allPass ?  : );
   allPass;
}

Error Handling

Issue	Cause	Fix
API key missing at startup	Secrets not mounted	Check deployment config
No fallback on outage	.withFallbacks() not configured	Add fallback model
LangSmith trace gaps	Background callbacks in serverless	Set LANGCHAIN_CALLBACKS_BACKGROUND=false
Cache miss storm	Redis down	Implement graceful degradation

Resources

• LangChain Production Guide
• LangSmith Production Tracing
• Twelve-Factor App

Next Steps

After launch, use langchain-observability for monitoring and langchain-incident-runbook for incident response.

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Source: flight505/skill-forge — distributed by TomeVault.