Scavio for LLM Citation Mining

The Problem

Answer engines cite different sources for the same query depending on the day, the region, and the model version. Brands have no tooling to capture the full set of cited URLs per engine per query and no historical trend to prove citation wins or losses. Manual checks across five engines do not scale past a dozen queries.

How Scavio Helps

• Full citation list per engine per query on a daily cadence
• Diff today versus yesterday to catch engine updates
• Cross-engine overlap scoring identifies universally cited pages
• Historical trend powers quarterly AEO reports
• Works with LangGraph, Mastra, CrewAI, Hermes Agent pipelines

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "best serp api for ai agents 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 AEO specialists, SEO leads, content strategists, brand monitoring teams

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