lindy-performance-tuning

---
name: lindy-performance-tuning
description: |
  Optimize Lindy AI agent performance and response times.
  Use when improving latency, optimizing throughput,
  or reducing response times.
  Trigger with phrases like "lindy performance", "lindy slow",
  "optimize lindy", "lindy latency".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
---

# Lindy Performance Tuning

## Overview
Optimize Lindy AI agent performance for faster response times and higher throughput.

## Prerequisites
- Production Lindy integration
- Baseline performance metrics
- Access to monitoring tools

## Instructions

### Step 1: Measure Baseline Performance
```typescript
import { Lindy } from '@lindy-ai/sdk';

interface PerformanceMetrics {
  avgLatency: number;
  p95Latency: number;
  p99Latency: number;
  throughput: number;
  errorRate: number;
}

async function measureBaseline(agentId: string, iterations = 100): Promise<PerformanceMetrics> {
  const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
  const latencies: number[] = [];
  let errors = 0;

  const start = Date.now();

  for (let i = 0; i < iterations; i++) {
    const runStart = Date.now();
    try {
      await lindy.agents.run(agentId, { input: 'Benchmark test' });
      latencies.push(Date.now() - runStart);
    } catch (e) {
      errors++;
    }
  }

  const totalTime = (Date.now() - start) / 1000;
  latencies.sort((a, b) => a - b);

  return {
    avgLatency: latencies.reduce((a, b) => a + b, 0) / latencies.length,
    p95Latency: latencies[Math.floor(latencies.length * 0.95)],
    p99Latency: latencies[Math.floor(latencies.length * 0.99)],
    throughput: iterations / totalTime,
    errorRate: errors / iterations,
  };
}
```

### Step 2: Optimize Agent Instructions
```typescript
// BEFORE: Verbose instructions (slow)
const slowAgent = {
  instructions: `
    You are a helpful assistant that should carefully consider
    each request and provide detailed, comprehensive responses.
    Think step by step about each query. Consider all possibilities.
    Provide examples and explanations for everything.
  `,
};

// AFTER: Concise instructions (fast)
const fastAgent = {
  instructions: `
    Be concise. Answer directly. Skip pleasantries.
    Format: [Answer] (1-2 sentences)
  `,
  config: {
    maxTokens: 100, // Limit response length
  },
};
```

### Step 3: Enable Streaming
```typescript
// Non-streaming (waits for full response)
const result = await lindy.agents.run(agentId, { input });
console.log(result.output); // Logs after full response

// Streaming (immediate partial responses)
const stream = await lindy.agents.runStream(agentId, { input });

for await (const chunk of stream) {
  process.stdout.write(chunk.delta); // Real-time output
}
```

### Step 4: Implement Caching
```typescript
import NodeCache from 'node-cache';

const cache = new NodeCache({ stdTTL: 300 }); // 5 minute TTL

async function runWithCache(agentId: string, input: string) {
  const cacheKey = `${agentId}:${crypto.createHash('md5').update(input).digest('hex')}`;

  // Check cache
  const cached = cache.get(cacheKey);
  if (cached) {
    return cached;
  }

  // Run agent
  const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
  const result = await lindy.agents.run(agentId, { input });

  // Cache result
  cache.set(cacheKey, result);

  return result;
}
```

### Step 5: Optimize Concurrency
```typescript
// Poor: Sequential execution
for (const input of inputs) {
  await lindy.agents.run(agentId, { input }); // Slow!
}

// Better: Parallel with controlled concurrency
import pLimit from 'p-limit';

const limit = pLimit(5); // Max 5 concurrent

const results = await Promise.all(
  inputs.map(input =>
    limit(() => lindy.agents.run(agentId, { input }))
  )
);
```

## Performance Checklist
```markdown
[ ] Baseline metrics captured
[ ] Instructions are concise
[ ] Max tokens configured appropriately
[ ] Streaming enabled for long responses
[ ] Caching implemented for repeated queries
[ ] Concurrency optimized
[ ] Connection pooling enabled
[ ] Timeout values tuned
```

## Output
- Baseline performance metrics
- Optimized agent configuration
- Caching implementation
- Concurrency patterns

## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| High latency | Verbose instructions | Simplify prompts |
| Timeouts | Response too long | Add max tokens |
| Throttling | Too concurrent | Limit parallelism |

## Examples

### Complete Performance Client
```typescript
class PerformantLindyClient {
  private lindy: Lindy;
  private cache: NodeCache;
  private limiter: any;

  constructor() {
    this.lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
    this.cache = new NodeCache({ stdTTL: 300 });
    this.limiter = pLimit(5);
  }

  async run(agentId: string, input: string) {
    const cacheKey = `${agentId}:${input}`;
    const cached = this.cache.get(cacheKey);
    if (cached) return cached;

    const result = await this.limiter(() =>
      this.lindy.agents.run(agentId, { input })
    );

    this.cache.set(cacheKey, result);
    return result;
  }
}
```

## Resources
- [Lindy Performance Guide](https://docs.lindy.ai/performance)
- [Best Practices](https://docs.lindy.ai/best-practices)
- [Caching Strategies](https://docs.lindy.ai/performance/caching)

## Next Steps
Proceed to `lindy-cost-tuning` for cost optimization.