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Optimize Fireflies.ai API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Fireflies.ai integrations. Trigger with phrases like "fireflies performance", "optimize fireflies", "fireflies latency", "fireflies caching", "fireflies slow", "fireflies batch".
npx skill4agent add jeremylongshore/claude-code-plugins-plus-skills fireflies-performance-tuning| Operation | P50 | P95 | P99 |
|---|---|---|---|
| Read | 50ms | 150ms | 300ms |
| Write | 100ms | 250ms | 500ms |
| List | 75ms | 200ms | 400ms |
import { LRUCache } from 'lru-cache';
const cache = new LRUCache<string, any>({
max: 1000,
ttl: 60000, // 1 minute
updateAgeOnGet: true,
});
async function cachedFireflies.aiRequest<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;
}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;
}import DataLoader from 'dataloader';
const firefliesLoader = new DataLoader<string, any>(
async (ids) => {
// Batch fetch from Fireflies.ai
const results = await firefliesClient.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([
firefliesLoader.load('id-1'),
firefliesLoader.load('id-2'),
firefliesLoader.load('id-3'),
]);import { Agent } from 'https';
// Keep-alive connection pooling
const agent = new Agent({
keepAlive: true,
maxSockets: 10,
maxFreeSockets: 5,
timeout: 30000,
});
const client = new Fireflies.aiClient({
apiKey: process.env.FIREFLIES_API_KEY!,
httpAgent: agent,
});async function* paginatedFireflies.aiList<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 paginatedFireflies.aiList(cursor =>
firefliesClient.list({ cursor, limit: 100 })
)) {
await process(item);
}async function measuredFireflies.aiCall<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;
}
}| 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 |
const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
measuredFireflies.aiCall(name, () =>
cachedFireflies.aiRequest(`cache:${name}`, fn)
);fireflies-cost-tuning