Give Agents Web Access Without Rate Limits

The Problem

Rate limits are the silent killer of agent reliability. An agent that works perfectly in testing hits a rate-limit wall the moment it runs in production with real user concurrency. Most search API providers enforce per-second or per-minute caps that are fine for a dashboard but catastrophic for an agent swarm. When ten agents each fire three search calls per reasoning step, the rate limiter starts returning 429 errors, the agent's retry logic adds latency, and the user sees a spinner that never resolves. Worse, some providers do not return clean 429 responses but instead serve degraded results or silent failures that the agent treats as real answers.

The Scavio Solution

Scavio's rate limits are designed for agent-scale concurrency, not dashboard-scale polling. The per-key limits are high enough that a production agent fleet can run without coordinating request timing across instances. When you do approach a limit, the response is a clean 429 with a Retry-After header that any HTTP client handles automatically. There are no silent degradations, no partial results, and no mystery failures. You can also request higher limits by upgrading your plan, with no sales call required. The result is an agent fleet that searches the web at production concurrency without a rate-limit coordination layer in your architecture.

Who It Is For

Agent infrastructure teams running concurrent agent fleets in production. If your agents hit 429 errors during peak traffic and you have built custom rate-limit coordination to work around it, this eliminates that entire layer.

Key Benefits

• Rate limits designed for agent concurrency, not dashboard polling
• Clean 429 responses with Retry-After headers for automatic handling
• No silent degradation or partial results under load
• Plan upgrades increase limits without a sales call
• No rate-limit coordination layer needed across agent instances

Python Example

import requests
import time
from concurrent.futures import ThreadPoolExecutor

API_KEY = "your_scavio_api_key"
URL = "https://api.scavio.dev/api/v1/search"

def resilient_search(query: str, platform: str = "google"):
    for attempt in range(3):
        r = requests.post(
            URL,
            headers={"x-api-key": API_KEY},
            json={"platform": platform, "query": query},
            timeout=15,
        )
        if r.status_code == 429:
            wait = int(r.headers.get("Retry-After", 2))
            time.sleep(wait)
            continue
        r.raise_for_status()
        return r.json()
    raise RuntimeError("rate limit exceeded after retries")

queries = [f"topic {i} latest news" for i in range(20)]
with ThreadPoolExecutor(max_workers=10) as pool:
    results = list(pool.map(resilient_search, queries))
    print(f"Completed {len(results)} concurrent searches")

JavaScript Example

const API_KEY = "your_scavio_api_key";
const URL = "https://api.scavio.dev/api/v1/search";

async function resilientSearch(query, platform = "google") {
  for (let attempt = 0; attempt < 3; attempt++) {
    const r = await fetch(URL, {
      method: "POST",
      headers: {
        "x-api-key": API_KEY,
        "content-type": "application/json",
      },
      body: JSON.stringify({ platform, query }),
    });
    if (r.status === 429) {
      const wait = parseInt(r.headers.get("Retry-After") ?? "2", 10);
      await new Promise((res) => setTimeout(res, wait * 1000));
      continue;
    }
    if (!r.ok) throw new Error(`search failed: ${r.status}`);
    return r.json();
  }
  throw new Error("rate limit exceeded after retries");
}

const queries = Array.from({ length: 20 }, (_, i) => `topic ${i} latest news`);
const results = await Promise.all(queries.map((q) => resilientSearch(q)));
console.log(`Completed ${results.length} concurrent searches`);

Platforms Used