Scavio for Local LLM Search-Grounded

The Problem

Local LLMs are great for privacy but terrible for current facts. Injecting live search results into the prompt bridges the gap without sending user data to an LLM provider.

How Scavio Helps

• Works with any OpenAI-compatible local endpoint
• Only search queries leave the local machine
• No fine-tuning, no RAG infrastructure, no vector store
• Scavio structured JSON is more parseable than raw HTML for local models
• Switchable: Ollama for dev, vLLM for production, same search integration

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "User query → Scavio search (5 results) → inject snippets into system prompt → forward to localhost:11434/api/chat → grounded response with 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 users, privacy-focused developers, enterprise teams with data residency requirements, hobbyists running models on consumer hardware

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