Solution

LLM Wiki Research Stack

Building a Karpathy-style LLM Wiki requires pulling from web SERP, Reddit threads, YouTube transcripts, and arxiv. Stitching 4-5 single-purpose vendors creates per-vendor billing,

The Problem

Building a Karpathy-style LLM Wiki requires pulling from web SERP, Reddit threads, YouTube transcripts, and arxiv. Stitching 4-5 single-purpose vendors creates per-vendor billing, per-vendor failure modes, and per-vendor SDK maintenance.

The Scavio Solution

Scavio (search + extract + reddit_search + youtube_search) covers four of the five surfaces under one key. Pair with Qdrant for vector storage and any LLM for citation-grounded answers.

Before

Tavily + Reddit scraper + YouTube Data API + Firecrawl + Qdrant + LLM = 6 vendors, 6 billing systems, 6 failure modes.

After

Scavio + Qdrant + LLM = 3 vendors, one credit pool for ingestion, single MCP attachment.

Who It Is For

RAG-pipeline maintainers, AI wiki builders, knowledge-base product teams, founders shipping research-agent products.

Key Benefits

  • 4 ingestion surfaces under one key
  • Per-credit cost $0.0043 for both search and extract
  • Citation-ready typed JSON
  • Hosted MCP attachable to Claude Code/Cursor
  • Stack cost ~$30 + Qdrant Cloud + LLM tokens

Python Example

Python
import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def ingest(topic):
    return {
        'web': requests.post('https://api.scavio.dev/api/v1/search', headers=H, json={'query': topic}).json(),
        'reddit': requests.post('https://api.scavio.dev/api/v1/reddit/search', headers=H, json={'query': topic}).json(),
        'youtube': requests.post('https://api.scavio.dev/api/v1/youtube/search', headers=H, json={'query': topic}).json(),
    }

JavaScript Example

JavaScript
const H = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
const ingest = async (q) => {
  const opts = (b) => ({ method: 'POST', headers: H, body: JSON.stringify(b) });
  const [web, reddit, youtube] = await Promise.all([
    fetch('https://api.scavio.dev/api/v1/search', opts({ query: q })).then(r => r.json()),
    fetch('https://api.scavio.dev/api/v1/reddit/search', opts({ query: q })).then(r => r.json()),
    fetch('https://api.scavio.dev/api/v1/youtube/search', opts({ query: q })).then(r => r.json()),
  ]);
  return { web, reddit, youtube };
};

Platforms Used

Google

Web search with knowledge graph, PAA, and AI overviews

Reddit

Community, posts & threaded comments from any subreddit

YouTube

Video search with transcripts and metadata

Frequently Asked Questions

Building a Karpathy-style LLM Wiki requires pulling from web SERP, Reddit threads, YouTube transcripts, and arxiv. Stitching 4-5 single-purpose vendors creates per-vendor billing, per-vendor failure modes, and per-vendor SDK maintenance.

Scavio (search + extract + reddit_search + youtube_search) covers four of the five surfaces under one key. Pair with Qdrant for vector storage and any LLM for citation-grounded answers.

RAG-pipeline maintainers, AI wiki builders, knowledge-base product teams, founders shipping research-agent products.

Yes. Scavio's free tier includes 500 credits per month with no credit card required. That is enough to validate this solution in your workflow.

LLM Wiki Research Stack

Scavio (search + extract + reddit_search + youtube_search) covers four of the five surfaces under one key. Pair with Qdrant for vector storage and any LLM for citation-grounded ans