LangChain Incident Runbook Skill

LangChain Incident Runbook

Overview

Standard operating procedures for LangChain production incidents: provider outages, error rate spikes, latency degradation, memory issues, and cost overruns.

Severity Classification

| Level | Description | Response Time | Example | |-------|-------------|---------------|---------| | SEV1 | Complete outage | 15 min | All LLM calls failing | | SEV2 | Major degradation | 30 min | >50% error rate, >10s latency | | SEV3 | Minor degradation | 2 hours | <10% errors, slow responses | | SEV4 | Low impact | 24 hours | Intermittent issues, warnings |

Runbook 1: LLM Provider Outage

Detect

# Check provider status pages
curl -s https://status.openai.com/api/v2/status.json | jq '.status'
curl -s https://status.anthropic.com/api/v2/status.json | jq '.status'

Diagnose

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

  try {
    const openai = new ChatOpenAI({ model: "gpt-4o-mini", timeout: 10000 });
    await openai.invoke("ping");
    results.openai = "OK";
  } catch (e: any) {
    results.openai = `FAIL: ${e.message.slice(0, 100)}`;
  }

  try {
    const anthropic = new ChatAnthropic({ model: "claude-sonnet-4-20250514" });
    await anthropic.invoke("ping");
    results.anthropic = "OK";
  } catch (e: any) {
    results.anthropic = `FAIL: ${e.message.slice(0, 100)}`;
  }

  console.table(results);
  return results;
}

Mitigate

// Enable fallback — switch to healthy provider
const primary = new ChatOpenAI({
  model: "gpt-4o-mini",
  maxRetries: 1,
  timeout: 5000,
});

const fallback = new ChatAnthropic({
  model: "claude-sonnet-4-20250514",
  maxRetries: 1,
});

const resilientModel = primary.withFallbacks({
  fallbacks: [fallback],
});

// All chains using resilientModel auto-failover

Recover

Monitor provider status page for resolution
Verify primary provider works: await diagnoseProviders()
Remove fallback config (or keep it for resilience)
Document incident timeline for post-mortem

Runbook 2: High Error Rate

Detect

# Check LangSmith for error spike
# https://smith.langchain.com/o/YOUR_ORG/projects/YOUR_PROJECT/runs?filter=error:true

# Check application logs
grep -c "Error\|error\|ERROR" /var/log/app/langchain.log | tail -5

Diagnose

// Common error patterns
const ERROR_CAUSES: Record<string, string> = {
  "RateLimitError":     "API quota exceeded -> reduce concurrency",
  "AuthenticationError": "API key invalid -> check secrets",
  "Timeout":            "Provider slow -> increase timeout",
  "OutputParserException": "LLM output format changed -> check prompts",
  "ValidationError":    "Schema mismatch -> update Zod schemas",
  "ContextLengthExceeded": "Input too long -> truncate or chunk",
};

Mitigate

// 1. Reduce load
// Lower maxConcurrency on batch operations

// 2. Enable caching for repeated queries
const cache = new Map();
async function withCache(chain: any, input: any) {
  const key = JSON.stringify(input);
  if (cache.has(key)) return cache.get(key);
  const result = await chain.invoke(input);
  cache.set(key, result);
  return result;
}

// 3. Enable fallback model
const model = primary.withFallbacks({ fallbacks: [fallback] });

Runbook 3: Latency Spike

Detect

# Prometheus query
histogram_quantile(0.95, rate(langchain_llm_latency_seconds_bucket[5m])) > 5

Diagnose

// Measure per-component latency
const tracer = new MetricsCallback();
await chain.invoke({ input: "test" }, { callbacks: [tracer] });
console.table(tracer.getReport());
// Check: is it the LLM, retriever, or tool that's slow?

Mitigate

Switch to faster model: gpt-4o-mini (200ms TTFT) vs gpt-4o (400ms)
Enable streaming to reduce perceived latency
Enable caching for repeated queries
Reduce context length (shorter prompts)

Runbook 4: Cost Overrun

Detect

# Check OpenAI usage dashboard
# https://platform.openai.com/usage

Mitigate

// 1. Emergency model downgrade
// gpt-4o ($2.50/1M) -> gpt-4o-mini ($0.15/1M) = 17x cheaper

// 2. Enable budget enforcement
const budget = new BudgetEnforcer(50.0); // $50 daily limit
const model = new ChatOpenAI({
  model: "gpt-4o-mini",
  callbacks: [budget],
});

// 3. Enable aggressive caching
// (see langchain-cost-tuning skill)

Runbook 5: Memory/OOM Issues

Detect

# Check process memory
ps aux --sort=-%mem | head -5

# Node.js heap stats
node -e "console.log(process.memoryUsage())"

Mitigate

Clear caches: reset in-memory caches
Reduce batch sizes: lower maxConcurrency
Use streaming instead of accumulating full responses
Restart pods: kubectl rollout restart deployment/langchain-api

Incident Response Checklist

During Incident

[ ] Acknowledge in incident channel
[ ] Classify severity (SEV1-4)
[ ] Check provider status pages
[ ] Run diagnostic script
[ ] Apply mitigation (fallback/cache/throttle)
[ ] Communicate status to stakeholders
[ ] Document timeline

Post-Incident

[ ] Verify full recovery
[ ] Schedule post-mortem (within 48h)
[ ] Write incident report
[ ] Create follow-up tickets
[ ] Update monitoring/alerting rules
[ ] Update this runbook if needed

Resources

Next Steps

Use langchain-debug-bundle for detailed evidence collection during incidents.

Agent Skills: LangChain Incident Runbook

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