Search API Built for Autonomous Agents

The Problem

Autonomous agents need to query the web as part of multi-step reasoning chains, but most search APIs were designed for human-driven dashboards. The response payloads are too large for context windows, latency is unpredictable, and error handling assumes a human will read a status page and retry manually. When an agent hits a timeout or a malformed response in the middle of a ten-step plan, the entire chain derails. Agents running in production loops need a search backend that is fast, predictable, and returns data shaped for machine consumption, not for rendering in a browser.

The Scavio Solution

Scavio was designed from the ground up for machine consumers. Responses are compact structured JSON that fits inside tool-call token budgets. Latency is consistently under two seconds so agent loops do not stall. Error responses use standard HTTP codes with machine-readable error bodies so the agent can retry or fallback without parsing HTML error pages. The endpoint works identically whether the caller is a LangChain agent, a CrewAI crew, an OpenAI Agents SDK runner, or a custom ReAct loop. Your agent gets reliable web access without any adapter layer.

Who It Is For

Agent developers building autonomous systems with LangChain, LangGraph, CrewAI, or OpenAI Agents SDK. If your agent needs to search the web as part of a multi-step plan and you need that step to never be the one that breaks, this is for you.

Key Benefits

• Compact JSON responses sized for LLM context windows
• Sub-two-second latency keeps agent loops responsive
• Machine-readable error codes for automated retry logic
• Works with LangChain, CrewAI, OpenAI Agents SDK, and custom loops
• No adapter or response-reshaping layer required

Python Example

import requests
import json

API_KEY = "your_scavio_api_key"

def agent_search(query: str, platform: str = "google") -> str:
    """Tool for autonomous agents. Returns compact JSON."""
    r = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": platform, "query": query},
        timeout=10,
    )
    r.raise_for_status()
    data = r.json()
    compact = [
        {"title": o["title"], "snippet": o["snippet"], "url": o["link"]}
        for o in data.get("organic", [])[:5]
    ]
    return json.dumps(compact)

# Agent calls this tool as part of a multi-step plan
print(agent_search("latest funding rounds AI startups 2026"))

JavaScript Example

const API_KEY = "your_scavio_api_key";

async function agentSearch(query, platform = "google") {
  const r = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "x-api-key": API_KEY,
      "content-type": "application/json",
    },
    body: JSON.stringify({ platform, query }),
  });
  if (!r.ok) throw new Error(`search failed: ${r.status}`);
  const data = await r.json();
  return (data.organic ?? []).slice(0, 5).map((o) => ({
    title: o.title,
    snippet: o.snippet,
    url: o.link,
  }));
}

// Agent invokes this as a tool step
console.log(await agentSearch("latest funding rounds AI startups 2026"));

Platforms Used