Scavio for Local LLM Fact-Checked Research Agent

The Problem

An r/LocalLLaMA post showed Qwen 9B-35B hallucinating on web-search-grounded answers when fed raw HTML. Tight context windows compress signal proportionally more than cloud LLMs.

How Scavio Helps

• 10x reduction in hallucination on grounded queries
• Token-efficient JSON (~1.5K vs 25-40K HTML)
• AI Overview cross-check as ground-truth signal
• Works on any Ollama-compatible model
• Stack cost ~$30 (Scavio) + $0 (local)

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Qwen 27B answers research question grounded in Scavio's top-10 typed JSON results with [N] citations":

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 Local LLM enthusiasts, privacy-first agent builders, on-prem/air-gap-curious teams, Ollama/LM Studio users

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