firecrawl-advanced-troubleshooting

jeremylongshore's avatarfrom jeremylongshore

Apply FireCrawl advanced debugging techniques for hard-to-diagnose issues.Use when standard troubleshooting fails, investigating complex race conditions,or preparing evidence bundles for FireCrawl support escalation.Trigger with phrases like "firecrawl hard bug", "firecrawl mystery error","firecrawl impossible to debug", "difficult firecrawl issue", "firecrawl deep debug".

⭐920stars🔀111forks📁View on GitHub🕐Updated Jan 9, 2026

When & Why to Use This Skill

The FireCrawl Advanced Troubleshooting skill is a specialized toolkit designed for developers and SREs to resolve complex, hard-to-diagnose issues within FireCrawl environments. It automates deep-dive debugging processes, including multi-layer network isolation, race condition detection, and the generation of comprehensive evidence bundles using tools like kubectl and tcpdump to accelerate root cause analysis and support escalation.

Use Cases

  • Investigating intermittent 'mystery errors' and complex race conditions that standard troubleshooting fails to capture.
  • Generating automated evidence bundles containing logs, metrics, and network captures for FireCrawl support escalation.
  • Performing systematic layer-by-layer testing (DNS, TLS, Auth, API) to isolate connectivity issues in distributed environments.
  • Detecting memory leaks and resource exhaustion in long-running crawling processes through automated heap and timing analysis.
  • Creating minimal reproduction cases to verify bug fixes or validate configuration changes in production-like settings.
namefirecrawl-advanced-troubleshooting
description|
allowed-toolsRead, Grep, Bash(kubectl:*), Bash(curl:*), Bash(tcpdump:*)
version1.0.0
licenseMIT
authorJeremy Longshore <jeremy@intentsolutions.io>

FireCrawl Advanced Troubleshooting

Overview

Deep debugging techniques for complex FireCrawl issues that resist standard troubleshooting.

Prerequisites

  • Access to production logs and metrics
  • kubectl access to clusters
  • Network capture tools available
  • Understanding of distributed tracing

Evidence Collection Framework

Comprehensive Debug Bundle

#!/bin/bash
# advanced-firecrawl-debug.sh

BUNDLE="firecrawl-advanced-debug-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$BUNDLE"/{logs,metrics,network,config,traces}

# 1. Extended logs (1 hour window)
kubectl logs -l app=firecrawl-integration --since=1h > "$BUNDLE/logs/pods.log"
journalctl -u firecrawl-service --since "1 hour ago" > "$BUNDLE/logs/system.log"

# 2. Metrics dump
curl -s localhost:9090/api/v1/query?query=firecrawl_requests_total > "$BUNDLE/metrics/requests.json"
curl -s localhost:9090/api/v1/query?query=firecrawl_errors_total > "$BUNDLE/metrics/errors.json"

# 3. Network capture (30 seconds)
timeout 30 tcpdump -i any port 443 -w "$BUNDLE/network/capture.pcap" &

# 4. Distributed traces
curl -s localhost:16686/api/traces?service=firecrawl > "$BUNDLE/traces/jaeger.json"

# 5. Configuration state
kubectl get cm firecrawl-config -o yaml > "$BUNDLE/config/configmap.yaml"
kubectl get secret firecrawl-secrets -o yaml > "$BUNDLE/config/secrets-redacted.yaml"

tar -czf "$BUNDLE.tar.gz" "$BUNDLE"
echo "Advanced debug bundle: $BUNDLE.tar.gz"

Systematic Isolation

Layer-by-Layer Testing

// Test each layer independently
async function diagnoseFireCrawlIssue(): Promise<DiagnosisReport> {
  const results: DiagnosisResult[] = [];

  // Layer 1: Network connectivity
  results.push(await testNetworkConnectivity());

  // Layer 2: DNS resolution
  results.push(await testDNSResolution('api.firecrawl.com'));

  // Layer 3: TLS handshake
  results.push(await testTLSHandshake('api.firecrawl.com'));

  // Layer 4: Authentication
  results.push(await testAuthentication());

  // Layer 5: API response
  results.push(await testAPIResponse());

  // Layer 6: Response parsing
  results.push(await testResponseParsing());

  return { results, firstFailure: results.find(r => !r.success) };
}

Minimal Reproduction

// Strip down to absolute minimum
async function minimalRepro(): Promise<void> {
  // 1. Fresh client, no customization
  const client = new FireCrawlClient({
    apiKey: process.env.FIRECRAWL_API_KEY!,
  });

  // 2. Simplest possible call
  try {
    const result = await client.ping();
    console.log('Ping successful:', result);
  } catch (error) {
    console.error('Ping failed:', {
      message: error.message,
      code: error.code,
      stack: error.stack,
    });
  }
}

Timing Analysis

class TimingAnalyzer {
  private timings: Map<string, number[]> = new Map();

  async measure<T>(label: string, fn: () => Promise<T>): Promise<T> {
    const start = performance.now();
    try {
      return await fn();
    } finally {
      const duration = performance.now() - start;
      const existing = this.timings.get(label) || [];
      existing.push(duration);
      this.timings.set(label, existing);
    }
  }

  report(): TimingReport {
    const report: TimingReport = {};
    for (const [label, times] of this.timings) {
      report[label] = {
        count: times.length,
        min: Math.min(...times),
        max: Math.max(...times),
        avg: times.reduce((a, b) => a + b, 0) / times.length,
        p95: this.percentile(times, 95),
      };
    }
    return report;
  }
}

Memory and Resource Analysis

// Detect memory leaks in FireCrawl client usage
const heapUsed: number[] = [];

setInterval(() => {
  const usage = process.memoryUsage();
  heapUsed.push(usage.heapUsed);

  // Alert on sustained growth
  if (heapUsed.length > 60) { // 1 hour at 1/min
    const trend = heapUsed[59] - heapUsed[0];
    if (trend > 100 * 1024 * 1024) { // 100MB growth
      console.warn('Potential memory leak in firecrawl integration');
    }
  }
}, 60000);

Race Condition Detection

// Detect concurrent access issues
class FireCrawlConcurrencyChecker {
  private inProgress: Set<string> = new Set();

  async execute<T>(key: string, fn: () => Promise<T>): Promise<T> {
    if (this.inProgress.has(key)) {
      console.warn(`Concurrent access detected for ${key}`);
    }

    this.inProgress.add(key);
    try {
      return await fn();
    } finally {
      this.inProgress.delete(key);
    }
  }
}

Support Escalation Template

## FireCrawl Support Escalation

**Severity:** P[1-4]
**Request ID:** [from error response]
**Timestamp:** [ISO 8601]

### Issue Summary
[One paragraph description]

### Steps to Reproduce
1. [Step 1]
2. [Step 2]

### Expected vs Actual
- Expected: [behavior]
- Actual: [behavior]

### Evidence Attached
- [ ] Debug bundle (firecrawl-advanced-debug-*.tar.gz)
- [ ] Minimal reproduction code
- [ ] Timing analysis
- [ ] Network capture (if relevant)

### Workarounds Attempted
1. [Workaround 1] - Result: [outcome]
2. [Workaround 2] - Result: [outcome]

Instructions

Step 1: Collect Evidence Bundle

Run the comprehensive debug script to gather all relevant data.

Step 2: Systematic Isolation

Test each layer independently to identify the failure point.

Step 3: Create Minimal Reproduction

Strip down to the simplest failing case.

Step 4: Escalate with Evidence

Use the support template with all collected evidence.

Output

  • Comprehensive debug bundle collected
  • Failure layer identified
  • Minimal reproduction created
  • Support escalation submitted

Error Handling

Issue Cause Solution
Can't reproduce Race condition Add timing analysis
Intermittent failure Timing-dependent Increase sample size
No useful logs Missing instrumentation Add debug logging
Memory growth Resource leak Use heap profiling

Examples

Quick Layer Test

# Test each layer in sequence
curl -v https://api.firecrawl.com/health 2>&1 | grep -E "(Connected|TLS|HTTP)"

Resources

Next Steps

For load testing, see firecrawl-load-scale.