Solution

Agent Swarm Web Intelligence

A single agent exploring the web hits sequential bottlenecks: one query at a time, one perspective. Complex research questions (competitive landscape, market entry analysis, due di

The Problem

A single agent exploring the web hits sequential bottlenecks: one query at a time, one perspective. Complex research questions (competitive landscape, market entry analysis, due diligence) need parallel exploration across dozens of angles, which requires a swarm of specialized agents.

The Scavio Solution

Scavio handles hundreds of concurrent queries per API key. Spin up 10-50 specialized agents (each focused on a single angle: pricing, reviews, team, funding, complaints), have them fan out against Scavio in parallel, then merge results. Scavio's flat per-search pricing makes agent swarms economical.

Before

One agent, sequential queries, 45-minute research cycles, shallow coverage.

After

20-agent swarm, parallel queries, 3-minute research cycles, deep multi-angle coverage.

Who It Is For

Teams building multi-agent research systems who need high-concurrency web access.

Key Benefits

  • Scales to hundreds of concurrent queries per API key
  • Flat per-search pricing makes swarms economical
  • 5 platforms keep the swarm's coverage broad
  • Normalized schema simplifies result merging
  • Works with LangGraph, AutoGen, CrewAI, and Claude Code

Python Example

Python
import asyncio, os, aiohttp
SCAVIO = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO, 'content-type': 'application/json'}

async def agent(session, angle, company):
    q = f'{company} {angle}'
    async with session.post('https://api.scavio.dev/api/v1/search',
        headers=H, json={'query': q}) as r:
        return angle, (await r.json()).get('organic_results', [])

async def swarm(company):
    angles = ['pricing', 'reviews', 'funding', 'team', 'complaints']
    async with aiohttp.ClientSession() as s:
        return dict(await asyncio.gather(*[agent(s, a, company) for a in angles]))

print(asyncio.run(swarm('acme.com')))

JavaScript Example

JavaScript
const H = { 'x-api-key': process.env.SCAVIO_API_KEY, 'content-type': 'application/json' };

async function agent(angle, company) {
  const r = await fetch('https://api.scavio.dev/api/v1/search', {
    method: 'POST', headers: H,
    body: JSON.stringify({ query: company + ' ' + angle })
  }).then(r => r.json());
  return [angle, r.organic_results || []];
}

async function swarm(company) {
  const angles = ['pricing', 'reviews', 'funding', 'team', 'complaints'];
  return Object.fromEntries(await Promise.all(angles.map(a => agent(a, company))));
}

console.log(await swarm('acme.com'));

Platforms Used

Google

Web search with knowledge graph, PAA, and AI overviews

Reddit

Community, posts & threaded comments from any subreddit

YouTube

Video search with transcripts and metadata

Amazon

Product search with prices, ratings, and reviews

Walmart

Product search with pricing and fulfillment data

Frequently Asked Questions

A single agent exploring the web hits sequential bottlenecks: one query at a time, one perspective. Complex research questions (competitive landscape, market entry analysis, due diligence) need parallel exploration across dozens of angles, which requires a swarm of specialized agents.

Scavio handles hundreds of concurrent queries per API key. Spin up 10-50 specialized agents (each focused on a single angle: pricing, reviews, team, funding, complaints), have them fan out against Scavio in parallel, then merge results. Scavio's flat per-search pricing makes agent swarms economical.

Teams building multi-agent research systems who need high-concurrency web access.

Yes. Scavio's free tier includes 500 credits per month with no credit card required. That is enough to validate this solution in your workflow.

Agent Swarm Web Intelligence

Scavio handles hundreds of concurrent queries per API key. Spin up 10-50 specialized agents (each focused on a single angle: pricing, reviews, team, funding, complaints), have them