name: langchain-prod-checklist description: 'Production readiness checklist for LangChain applications.

Use when preparing for launch, validating deployment readiness,

or auditing existing production LangChain systems.

Trigger: "langchain production", "langchain prod ready",

"deploy langchain", "langchain launch checklist", "go-live langchain".

' allowed-tools: Read, Write, Edit, Bash(node:), Bash(npm:) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• langchain
• deployment
• audit compatibility: Designed for Claude Code, also compatible with Codex and OpenClaw

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.exit(1), 10000); // force after 10s
});

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 === "true" ? "PASS" : "WARN: disabled";

  // 5. Health endpoint
  try {
    const res = await fetch("http://localhost:8000/health");
    results["Health"] = res.ok ? "PASS" : "FAIL";
  } catch { results["Health"] = "FAIL: not reachable"; }

  console.table(results);
  const allPass = Object.values(results).every((v) => v === "PASS");
  console.log(allPass ? "READY FOR PRODUCTION" : "ISSUES FOUND - FIX BEFORE LAUNCH");
  return allPass;
}

Error Handling

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.