firecrawl-performance-tuning
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".
- License 许可
- MIT
- 最近更新
- 2026-03-20
- Stars
- ★ 4
- Forks
- 0
01 前言 Front Matter
- name
- firecrawl-performance-tuning
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- version
- 1.0.0
- 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
- compatible-with
- claude-code, codex, openclaw
03 SKILL.md 正文
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
| Operation | P50 | P95 | P99 |
|---|---|---|---|
| Read | 50ms | 150ms | 300ms |
| Write | 100ms | 250ms | 500ms |
| List | 75ms | 200ms | 400ms |
Caching Strategy
Response Caching
import { LRUCache } from 'lru-cache';
const cache = new LRUCache<string, any>({
max: 1000, # 1000: 1 second in ms
ttl: 60000, // 1 minute # 60000: 1 minute in ms
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, # 30000: 30 seconds in ms
});
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
| Issue | Cause | Solution |
|---|---|---|
| Cache miss storm | TTL expired | Use stale-while-revalidate |
| Batch timeout | Too many items | Reduce batch size |
| Connection exhausted | No pooling | Configure max sockets |
| Memory pressure | Cache too large | Set 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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