Fireflies performance tuning

Fireflies.ai Performance Tuning

Overview

Optimize Fireflies.ai GraphQL API performance. The biggest wins: request only needed fields (transcripts with sentences can be very large), cache immutable transcripts, and batch operations within rate limits.

Prerequisites

• FIREFLIES_API_KEY configured
• Understanding of your access pattern (list vs detail, frequency)
• Optional: Redis or LRU cache library

Instructions

Step 1: Field Selection -- The Biggest Win

Transcript responses with sentences can be enormous. Always request the minimum fields needed.

// BAD: Fetching everything when you only need titles
const HEAVY = `{ transcripts(limit: 50) {
  id title date duration sentences { text speaker_name start_time end_time }
  summary { overview action_items keywords outline bullet_gist }
  analytics { speakers { name duration word_count } }
} }`;

// GOOD: Light query for listing
const LIGHT = `{ transcripts(limit: 50) {
  id title date duration organizer_email
} }`;

// GOOD: Full query only when drilling into a specific transcript
const DETAIL = `query($id: String!) { transcript(id: $id) {
  id title
  sentences { speaker_name text start_time end_time }
  summary { overview action_items keywords }
} }`;

Step 2: Cache Transcripts (They Are Immutable)

Once a transcript is processed, its content never changes. Cache aggressively.

import { LRUCache } from "lru-cache";

const transcriptCache = new LRUCache<string, any>({
  max: 500,
  ttl: 1000 * 60 * 60, // 1 hour -- transcripts are immutable
});

async function getCachedTranscript(id: string) {
  const cached = transcriptCache.get(id);
  if (cached) return cached;

  const data = await firefliesQuery(`
    query($id: String!) {
      transcript(id: $id) {
        id title date duration
        speakers { name }
        sentences { speaker_name text start_time end_time }
        summary { overview action_items keywords }
      }
    }
  `, { id });

  transcriptCache.set(id, data.transcript);
  return data.transcript;
}

Step 3: Redis Cache for Multi-Instance Deployments

import Redis from "ioredis";

const redis = new Redis(process.env.REDIS_URL!);
const CACHE_TTL = 3600; // 1 hour in seconds

async function getTranscriptCached(id: string) {
  const cacheKey = `fireflies:transcript:${id}`;

  // Check cache
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  // Fetch from API
  const data = await firefliesQuery(`
    query($id: String!) {
      transcript(id: $id) {
        id title date duration
        sentences { speaker_name text start_time end_time }
        summary { overview action_items keywords }
      }
    }
  `, { id });

  // Cache the result
  await redis.set(cacheKey, JSON.stringify(data.transcript), "EX", CACHE_TTL);
  return data.transcript;
}

Step 4: Batch Processing with Rate Limit Awareness

import PQueue from "p-queue";

// Business plan: 60 req/min. Safe rate: 1 req/sec with headroom.
const queue = new PQueue({
  concurrency: 1,
  interval: 1100,
  intervalCap: 1,
});

async function batchFetchTranscripts(ids: string[]) {
  console.log(`Fetching ${ids.length} transcripts (rate-limited)...`);

  const results = await Promise.all(
    ids.map(id => queue.add(() => getCachedTranscript(id)))
  );

  const cacheHits = ids.filter(id => transcriptCache.has(id)).length;
  console.log(`Done. Cache hits: ${cacheHits}/${ids.length}`);
  return results;
}

Step 5: Warm Cache on Webhook Events

// When a transcript completes, pre-cache it immediately
async function onWebhookEvent(event: { meetingId: string; eventType: string }) {
  if (event.eventType === "Transcription completed") {
    // Pre-warm the cache so future reads are instant
    await getCachedTranscript(event.meetingId);
    console.log(`Pre-cached transcript: ${event.meetingId}`);
  }
}

Step 6: Pagination for Large Result Sets

async function getAllTranscripts(batchSize = 50) {
  const allTranscripts: any[] = [];
  let hasMore = true;
  let offset = 0;

  while (hasMore) {
    const data = await firefliesQuery(`
      query($limit: Int, $skip: Int) {
        transcripts(limit: $limit, skip: $skip) {
          id title date duration
        }
      }
    `, { limit: batchSize, skip: offset });

    allTranscripts.push(...data.transcripts);

    if (data.transcripts.length < batchSize) {
      hasMore = false;
    } else {
      offset += batchSize;
      // Rate limit: wait between pages
      await new Promise(r => setTimeout(r, 1100));
    }
  }

  return allTranscripts;
}

Performance Benchmarks

Optimization	Before	After	Improvement
Field selection (list)	~2s (with sentences)	~200ms (metadata only)	10x
LRU cache (detail view)	~500ms (API call)	<1ms (cache hit)	500x
Batch with queue	Rate limited/errors	Smooth throughput	Reliable
Webhook pre-cache	Cold fetch on user visit	Instant from cache	UX improvement

Error Handling

Issue	Cause	Solution
Slow list queries	Requesting sentences in list	Use light query without sentences
Rate limit 429	Burst requests	Use PQueue with 1.1s interval
Large response OOM	Transcript with 2+ hour meeting	Stream/paginate sentences
Stale cache	(Not a real issue -- transcripts are immutable)	N/A

Output

• Field-optimized GraphQL queries (light list, full detail)
• LRU and Redis caching for immutable transcripts
• Rate-limited batch processor
• Webhook-driven cache warming

Resources

• Fireflies API Docs
• lru-cache
• p-queue

Next Steps

For cost optimization, see fireflies-cost-tuning.