Scavio for Personal Knowledge Base with Local LLM and Search

The Problem

Researchers maintain knowledge bases manually, copying snippets from web searches into notes. This is tedious and falls behind within days. Automated search plus local LLM summarization keeps the KB current without manual effort.

How Scavio Helps

• Daily automated search for tracked topics via Scavio API
• Local LLM (Ollama) summarizes new findings -- no data leaves your machine
• Deduplication prevents the same article from being processed twice
• Structured markdown output with source URLs and retrieval dates
• Cost: under $1/month for 20 topics searched daily

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "ai agent framework updates may 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 Researchers, analysts, and knowledge workers who maintain personal knowledge bases on specific topics

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