Scavio for Multi-Agent Web Intelligence

The Problem

Single-agent research hits a ceiling at the complexity of the query. Multi-agent swarms promise higher-quality answers but fail when each agent gets different schemas from different search tools. Teams need a deterministic data layer so agents can be evaluated against each other rather than against search flakiness.

How Scavio Helps

• Deterministic schema across all five platforms
• Parallel-safe call pattern with no per-agent auth
• Works identically in LangGraph, CrewAI, Mastra, Hermes
• Predictable latency enables reliable swarm planning
• Error codes enable clean fallback logic in agent loops

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "swarm research: best vector db for 2026":

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 builders, research platforms, applied AI teams

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your multi-agent web intelligence 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.