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