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
• 250 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 (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.