jeremylongshore

firecrawl-performance-tuning

@jeremylongshore/firecrawl-performance-tuning
jeremylongshore
1,004
123 forks
Updated 1/18/2026
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Optimize FireCrawl API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for FireCrawl integrations. Trigger with phrases like "firecrawl performance", "optimize firecrawl", "firecrawl latency", "firecrawl caching", "firecrawl slow", "firecrawl batch".

Installation

$skills install @jeremylongshore/firecrawl-performance-tuning
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Details

Pathplugins/saas-packs/firecrawl-pack/skills/firecrawl-performance-tuning/SKILL.md
Branchmain
Scoped Name@jeremylongshore/firecrawl-performance-tuning

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: firecrawl-performance-tuning description: | Optimize FireCrawl API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for FireCrawl integrations. Trigger with phrases like "firecrawl performance", "optimize firecrawl", "firecrawl latency", "firecrawl caching", "firecrawl slow", "firecrawl batch". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

FireCrawl Performance Tuning

Overview

Optimize FireCrawl API performance with caching, batching, and connection pooling.

Prerequisites

  • FireCrawl SDK installed
  • Understanding of async patterns
  • Redis or in-memory cache available (optional)
  • Performance monitoring in place

Latency Benchmarks

OperationP50P95P99
Read50ms150ms300ms
Write100ms250ms500ms
List75ms200ms400ms

Caching Strategy

Response Caching

import { LRUCache } from 'lru-cache';

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 60000, // 1 minute
  updateAgeOnGet: true,
});

async function cachedFireCrawlRequest<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttl?: number
): Promise<T> {
  const cached = cache.get(key);
  if (cached) return cached as T;

  const result = await fetcher();
  cache.set(key, result, { ttl });
  return result;
}

Redis Caching (Distributed)

import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);

async function cachedWithRedis<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSeconds = 60
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);

  const result = await fetcher();
  await redis.setex(key, ttlSeconds, JSON.stringify(result));
  return result;
}

Request Batching

import DataLoader from 'dataloader';

const firecrawlLoader = new DataLoader<string, any>(
  async (ids) => {
    // Batch fetch from FireCrawl
    const results = await firecrawlClient.batchGet(ids);
    return ids.map(id => results.find(r => r.id === id) || null);
  },
  {
    maxBatchSize: 100,
    batchScheduleFn: callback => setTimeout(callback, 10),
  }
);

// Usage - automatically batched
const [item1, item2, item3] = await Promise.all([
  firecrawlLoader.load('id-1'),
  firecrawlLoader.load('id-2'),
  firecrawlLoader.load('id-3'),
]);

Connection Optimization

import { Agent } from 'https';

// Keep-alive connection pooling
const agent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 30000,
});

const client = new FireCrawlClient({
  apiKey: process.env.FIRECRAWL_API_KEY!,
  httpAgent: agent,
});

Pagination Optimization

async function* paginatedFireCrawlList<T>(
  fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator<T> {
  let cursor: string | undefined;

  do {
    const { data, nextCursor } = await fetcher(cursor);
    for (const item of data) {
      yield item;
    }
    cursor = nextCursor;
  } while (cursor);
}

// Usage
for await (const item of paginatedFireCrawlList(cursor =>
  firecrawlClient.list({ cursor, limit: 100 })
)) {
  await process(item);
}

Performance Monitoring

async function measuredFireCrawlCall<T>(
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  const start = performance.now();
  try {
    const result = await fn();
    const duration = performance.now() - start;
    console.log({ operation, duration, status: 'success' });
    return result;
  } catch (error) {
    const duration = performance.now() - start;
    console.error({ operation, duration, status: 'error', error });
    throw error;
  }
}

Instructions

Step 1: Establish Baseline

Measure current latency for critical FireCrawl operations.

Step 2: Implement Caching

Add response caching for frequently accessed data.

Step 3: Enable Batching

Use DataLoader or similar for automatic request batching.

Step 4: Optimize Connections

Configure connection pooling with keep-alive.

Output

  • Reduced API latency
  • Caching layer implemented
  • Request batching enabled
  • Connection pooling configured

Error Handling

IssueCauseSolution
Cache miss stormTTL expiredUse stale-while-revalidate
Batch timeoutToo many itemsReduce batch size
Connection exhaustedNo poolingConfigure max sockets
Memory pressureCache too largeSet max cache entries

Examples

Quick Performance Wrapper

const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
  measuredFireCrawlCall(name, () =>
    cachedFireCrawlRequest(`cache:${name}`, fn)
  );

Resources

Next Steps

For cost optimization, see firecrawl-cost-tuning.

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