---
name: fireflies-performance-tuning
description: |
  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".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
---

# Fireflies.ai Performance Tuning

## Overview
Optimize Fireflies.ai API performance with caching, batching, and connection pooling.

## Prerequisites
- Fireflies.ai 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
```typescript
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;
}
```

### Redis Caching (Distributed)
```typescript
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

```typescript
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'),
]);
```

## Connection Optimization

```typescript
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,
});
```

## Pagination Optimization

```typescript
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);
}
```

## Performance Monitoring

```typescript
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;
  }
}
```

## Instructions

### Step 1: Establish Baseline
Measure current latency for critical Fireflies.ai 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
```typescript
const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
  measuredFireflies.aiCall(name, () =>
    cachedFireflies.aiRequest(`cache:${name}`, fn)
  );
```

## Resources
- [Fireflies.ai Performance Guide](https://docs.fireflies.com/performance)
- [DataLoader Documentation](https://github.com/graphql/dataloader)
- [LRU Cache Documentation](https://github.com/isaacs/node-lru-cache)

## Next Steps
For cost optimization, see `fireflies-cost-tuning`.