Scavio for Multi-Agent Web Research

The Problem

Multi-agent architectures need tools that return structured, reliable data. Most web scraping solutions are brittle, rate-limited, and return unstructured HTML. Agents need clean JSON from multiple platforms to reason effectively.

How Scavio Helps

• Single API endpoint for five major platforms
• Structured JSON output designed for LLM consumption
• MCP integration for Claude, Cursor, and Windsurf agents
• LangChain tool integration for orchestrated agent workflows

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "best standing desk 2026 reviews":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for AI engineers, agent framework developers, LLM app builders

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your multi-agent web research solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.