langfuse-incident-runbook

Troubleshoot and respond to Langfuse-related incidents and outages. Use when experiencing Langfuse outages, debugging production issues, or responding to LLM observability incidents. Trigger with phrases like "langfuse incident", "langfuse outage", "langfuse down", "langfuse production issue", "langfuse troubleshoot".

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langfuse-pack Plugin
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langfuse-pack

Claude Code skill pack for Langfuse LLM observability (24 skills)

saas packs v1.0.0
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Installation

This skill is included in the langfuse-pack plugin:

/plugin install langfuse-pack@claude-code-plugins-plus

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Instructions

Langfuse Incident Runbook

Overview

Step-by-step procedures for Langfuse-related incidents, from initial triage (2 min) through resolution and post-incident review. Your application should work without Langfuse -- these procedures focus on restoring observability.

Severity Classification

Severity Description Response Time Example
P1 Application impacted by tracing 15 min SDK throwing unhandled errors, blocking requests
P2 Traces not appearing, no app impact 1 hour Missing observability data
P3 Degraded performance from tracing 4 hours High latency from flush backlog
P4 Minor issues 24 hours Occasional missing traces

Instructions

Step 1: Initial Assessment (2 Minutes)


set -euo pipefail
echo "=== Langfuse Incident Triage ==="
echo "Time: $(date -u)"

# 1. Check Langfuse cloud status
echo -n "Status page: "
curl -s -o /dev/null -w "%{http_code}" https://status.langfuse.com || echo "UNREACHABLE"
echo ""

# 2. Test API connectivity
HOST="${LANGFUSE_BASE_URL:-${LANGFUSE_HOST:-https://cloud.langfuse.com}}"
echo -n "API health: "
curl -s -o /dev/null -w "%{http_code} (%{time_total}s)" "$HOST/api/public/health" || echo "FAILED"
echo ""

# 3. Test auth
if [ -n "${LANGFUSE_PUBLIC_KEY:-}" ] && [ -n "${LANGFUSE_SECRET_KEY:-}" ]; then
  AUTH=$(echo -n "$LANGFUSE_PUBLIC_KEY:$LANGFUSE_SECRET_KEY" | base64)
  echo -n "Auth test: "
  curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Basic $AUTH" "$HOST/api/public/traces?limit=1" || echo "FAILED"
  echo ""
fi

# 4. Check app error logs
echo ""
echo "--- Recent errors ---"
grep -i "langfuse\|trace.*error\|flush.*fail" /var/log/app/*.log 2>/dev/null | tail -10 || echo "No log files found"

Step 2: Determine Incident Type and Response

Symptom Likely Cause Immediate Action
No traces appearing SDK not flushing Check shutdown handlers; set flushAt: 1 temporarily
401 Unauthorized Key rotation or mismatch Verify keys match the correct project
429 Too Many Requests Rate limited Increase batch size, reduce flush frequency
SDK throwing errors Unhandled exception Wrap in try/catch; check SDK version
High request latency Sync flush in hot path Switch to async; increase requestTimeout
Complete Langfuse outage Service-side issue Enable fallback mode

Step 3: Fallback Mode (P1 -- App Impacted)

If Langfuse is causing application issues, disable tracing immediately:


// Emergency disable via environment variable
// Set LANGFUSE_ENABLED=false in your deployment

// In your tracing initialization:
if (process.env.LANGFUSE_ENABLED === "false") {
  console.warn("Langfuse tracing DISABLED (emergency fallback)");
  // Don't initialize SDK -- all observe/startActiveObservation calls
  // will still work but produce no-op spans
}

For v3, use the enabled flag:


const langfuse = new Langfuse({
  enabled: process.env.LANGFUSE_ENABLED !== "false",
});

Step 4: Common Resolution Procedures

Procedure A: Missing Traces


// 1. Verify SDK is initialized
console.log("Langfuse configured:", !!process.env.LANGFUSE_PUBLIC_KEY);

// 2. Check flush is happening
// v4+: Verify NodeSDK is started and shutdown is registered
// v3: Verify flushAsync() or shutdownAsync() is called

// 3. Temporarily set aggressive flush for debugging
const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 1000,
  maxExportBatchSize: 1,
});

Procedure B: Rate Limit (429) Recovery


// Increase batching to reduce API calls
const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 30000, // 30s flush
  maxExportBatchSize: 200,     // Large batches
});

// Or temporarily enable sampling
const EMERGENCY_SAMPLE_RATE = 0.1; // Only trace 10%

Procedure C: Self-Hosted Instance Down


set -euo pipefail
# Check container status
docker ps -a | grep langfuse

# Check logs
docker logs langfuse-langfuse-1 --tail 50

# Check database
docker exec langfuse-postgres-1 pg_isready -U langfuse

# Restart if needed
docker compose restart langfuse

Step 5: Post-Incident Verification


set -euo pipefail
# Verify traces are flowing again
echo "=== Post-Incident Check ==="

HOST="${LANGFUSE_BASE_URL:-https://cloud.langfuse.com}"
AUTH=$(echo -n "$LANGFUSE_PUBLIC_KEY:$LANGFUSE_SECRET_KEY" | base64)

# Check recent trace count
TRACE_COUNT=$(curl -s \
  -H "Authorization: Basic $AUTH" \
  "$HOST/api/public/traces?limit=5" | python3 -c "import sys,json; print(len(json.load(sys.stdin).get('data',[])))" 2>/dev/null || echo "ERROR")

echo "Recent traces: $TRACE_COUNT"

if [ "$TRACE_COUNT" = "0" ] || [ "$TRACE_COUNT" = "ERROR" ]; then
  echo "WARNING: Traces may not be flowing yet"
else
  echo "OK: Traces are appearing"
fi

Step 6: Post-Incident Review (P1/P2)

Document for post-mortem:

  1. Timeline: When detected, when resolved, total duration
  2. Impact: Traces lost, application impact, user impact
  3. Root cause: Why did the incident occur?
  4. Resolution: What fixed it?
  5. Prevention: What changes prevent recurrence?
  6. Action items: Improvements to implement

Escalation Path

Level Who When
L1 On-call engineer All incidents -- run triage
L2 Platform team lead P1/P2 unresolved after 30 min
L3 Langfuse support Confirmed service-side issue

Langfuse support channels:

Error Handling

Issue Immediate Fix Permanent Fix
SDK crashes app Set LANGFUSE_ENABLED=false Wrap all tracing in try/catch
Lost traces Increase batch size Add shutdown handlers
High latency Disable sync flush Use async-only patterns
Auth failures Rotate and redeploy keys Add key validation at startup

Resources

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