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