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 (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.