Scavio for Karpathy LLM Wiki-Style RAG Agent

The Problem

An r/AI_Agents post asked for tools to build a Karpathy-style LLM Wiki. The data layer needs 4-5 surfaces stitched together; most builders accumulate vendor sprawl before shipping the actual ranking product.

How Scavio Helps

• 4-surface ingestion under one Scavio key
• Per-credit cost $0.0043 for both search and extract
• Citation-ready typed JSON
• Stack cost ~$30 + Qdrant Cloud + LLM tokens
• Ships in a weekend, not a quarter

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "ingest 100 sources/day across web/reddit/youtube for an LLM wiki on AI agent topics":

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 AI wiki builders, RAG-product teams, knowledge-base SaaS founders, research-agent makers

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