Scavio for LLM Wiki Multi-Source Ingestion

The Problem

LLM wiki projects (inspired by Karpathy's concept) need multi-source data: Google for facts, YouTube for tutorials, Reddit for opinions, Amazon for product data. Integrating 4 separate APIs means 4 auth flows, 4 rate limits, and 4 response schemas. A single multi-platform API simplifies ingestion to one integration.

How Scavio Helps

• Single API covers Google, YouTube, Reddit, and Amazon
• Consistent response schema across all platforms
• One authentication flow and one rate limit to manage
• Timestamped ingestion enables staleness detection
• 500 free credits/month for prototyping wiki ingestion

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Wiki topic: 'Tavily API'. Ingest from Google (official docs, pricing page), YouTube (tutorial videos), Reddit (user opinions and complaints), Amazon (not applicable). One Scavio API key, 4 platform queries. Store each fact with source URL and ingestion timestamp.":

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 researchers building knowledge bases, teams implementing RAG systems, developers building LLM-powered wikis, Karpathy-inspired wiki builders

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