Scavio for n8n LLM Research Assistant

The Problem

An r/n8n thread asked for a search API that integrates search plus content extraction for LLM workflows. Most APIs return raw HTML breaking token limits or stripped snippets losing meaning. A Scavio + n8n workflow returns 5 typed snippets plus 1-2 full markdown extracts in one webhook call.

How Scavio Helps

• One vendor for search + extract
• Drop-in n8n HTTP Request nodes
• Typed JSON fits LLM context
• Webhook-trigger for ad-hoc; Cron for daily digest
• Lower cost than Tavily + Firecrawl combo

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "best mcp server practices 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 n8n users running LLM workflows, RevOps teams building research pipelines, automation engineers

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your n8n llm research assistant 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.