Scavio for Qwen Local Agentic Search

The Problem

Local LLMs hallucinate on current facts. Connecting Qwen to Scavio via a simple HTTP call gives it real-time search results without sending user queries to an LLM cloud provider.

How Scavio Helps

• Private inference: user queries stay on local hardware
• Only the search query hits the cloud — not the full conversation
• Qwen 2.5 7B fits in 8GB VRAM with 4-bit quantization
• Scavio returns structured JSON that local models parse reliably
• Works with Ollama, vLLM, or llama.cpp as the local runtime

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "User asks local Qwen about today's news → agent calls Scavio /api/v1/search → injects top-5 snippets into context → Qwen answers with citations → all inference local":

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 Privacy-conscious developers, local LLM enthusiasts with consumer GPUs, researchers running Qwen/Llama on 3090/4090

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