serpapi-performance-tuning
Optimize SerpApi performance with caching, async searches, and result filtering. Use when reducing latency, minimizing credit consumption, or optimizing search throughput. Trigger: "serpapi performance", "optimize serpapi", "serpapi caching", "serpapi slow".
Best use case
serpapi-performance-tuning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Optimize SerpApi performance with caching, async searches, and result filtering. Use when reducing latency, minimizing credit consumption, or optimizing search throughput. Trigger: "serpapi performance", "optimize serpapi", "serpapi caching", "serpapi slow".
Teams using serpapi-performance-tuning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/serpapi-performance-tuning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How serpapi-performance-tuning Compares
| Feature / Agent | serpapi-performance-tuning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Optimize SerpApi performance with caching, async searches, and result filtering. Use when reducing latency, minimizing credit consumption, or optimizing search throughput. Trigger: "serpapi performance", "optimize serpapi", "serpapi caching", "serpapi slow".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# SerpApi Performance Tuning
## Overview
SerpApi typical latency: 2-5 seconds per search (real-time scraping). Main optimization: aggressive caching since search results change slowly. Secondary: use Google Light API for faster responses, reduce `num` parameter, and parallelize independent searches.
## Instructions
### Step 1: Multi-Layer Caching
```typescript
import { LRUCache } from 'lru-cache';
import { Redis } from 'ioredis';
import { getJson } from 'serpapi';
// L1: In-memory (fastest, per-instance)
const l1 = new LRUCache<string, any>({ max: 1000, ttl: 600_000 }); // 10 min
// L2: Redis (shared across instances)
const redis = new Redis(process.env.REDIS_URL!);
async function cachedSearch(params: Record<string, any>): Promise<any> {
const key = `serpapi:${JSON.stringify(params)}`;
// L1 check
const l1Hit = l1.get(key);
if (l1Hit) return l1Hit;
// L2 check
const l2Hit = await redis.get(key);
if (l2Hit) {
const parsed = JSON.parse(l2Hit);
l1.set(key, parsed);
return parsed;
}
// Cache miss: real API call
const result = await getJson({ ...params, api_key: process.env.SERPAPI_API_KEY });
l1.set(key, result);
await redis.setex(key, 3600, JSON.stringify(result)); // 1 hour in Redis
return result;
}
```
### Step 2: Google Light API (Faster)
```python
# Google Light API: ~1s instead of 2-5s, limited result fields
result = client.search(engine="google_light", q="fast query", num=5)
# Returns: organic_results with title, link, snippet only
# No knowledge_graph, answer_box, or rich snippets
```
### Step 3: Reduce Response Size
```python
# Only get the fields you need
result = client.search(
engine="google", q="query",
num=5, # Fewer results = faster
no_cache=False, # Use SerpApi's server-side cache (default)
)
# Strip metadata to reduce memory/storage
clean = {
"organic_results": result.get("organic_results", []),
"answer_box": result.get("answer_box"),
"search_id": result["search_metadata"]["id"],
}
```
### Step 4: Parallel Search
```typescript
import PQueue from 'p-queue';
const queue = new PQueue({ concurrency: 5, interval: 1000, intervalCap: 5 });
async function batchSearch(queries: string[]): Promise<any[]> {
return Promise.all(
queries.map(q =>
queue.add(() => cachedSearch({ engine: 'google', q, num: 5 }))
)
);
}
// 10 queries, 5 parallel, rate limited: ~4 seconds total
const results = await batchSearch(['query1', 'query2', /* ... */]);
```
## Latency Benchmarks
| Method | Typical Latency | Credits |
|--------|----------------|---------|
| Google Search (uncached) | 2-5s | 1 |
| Google Light | 1-2s | 1 |
| L1 cache hit | < 1ms | 0 |
| Redis cache hit | 1-5ms | 0 |
| Archive retrieval | 500ms | 0 |
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Cache stampede | TTL expiry under load | Stale-while-revalidate |
| High latency | Complex queries | Use Google Light API |
| Memory pressure | Large cache | Limit LRU max entries |
## Resources
- [Google Light API](https://serpapi.com/google-light-api)
- [SerpApi Caching](https://serpapi.com/search-api#api-parameters-serpapi-parameters-no-cache)
## Next Steps
For cost optimization, see `serpapi-cost-tuning`.Related Skills
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