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retellai-performance-tuning

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

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Provided by Plugin

retellai-pack

Claude Code skill pack for Retell AI (30 skills)

saas packs v1.0.0
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Installation

This skill is included in the retellai-pack plugin:

/plugin install retellai-pack@claude-code-plugins-plus

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Instructions

# Retell AI Performance Tuning ## Overview Optimize Retell AI API performance with caching, batching, and connection pooling. ## Prerequisites - Retell 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({ max: 1000, ttl: 60000, // 1 minute updateAgeOnGet: true, }); async function cachedRetell AIRequest( key: string, fetcher: () => Promise, ttl?: number ): Promise { 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( key: string, fetcher: () => Promise, ttlSeconds = 60 ): Promise { 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 retellaiLoader = new DataLoader( async (ids) => { // Batch fetch from Retell AI const results = await retellaiClient.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([ retellaiLoader.load('id-1'), retellaiLoader.load('id-2'), retellaiLoader.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 RetellAIClient({ apiKey: process.env.RETELLAI_API_KEY!, httpAgent: agent, }); ``` ## Pagination Optimization ```typescript async function* paginatedRetell AIList( fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }> ): AsyncGenerator { 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 paginatedRetell AIList(cursor => retellaiClient.list({ cursor, limit: 100 }) )) { await process(item); } ``` ## Performance Monitoring ```typescript async function measuredRetell AICall( operation: string, fn: () => Promise ): Promise { 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 Retell 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 = (name: string, fn: () => Promise) => measuredRetell AICall(name, () => cachedRetell AIRequest(`cache:${name}`, fn) ); ``` ## Resources - [Retell AI Performance Guide](https://docs.retellai.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 `retellai-cost-tuning`.

Skill file: plugins/saas-packs/retellai-pack/skills/retellai-performance-tuning/SKILL.md