Draco

Other AIs guess. DRACO knows — and proves it.

DRACO answers coding, systems, and security questions using retrieval over a library of 1,855 real technical books (354M characters, 116,558 indexed passages) — and cites the exact book behind every claim. No knowledge cutoff. No hallucination laundering. Receipts attached.

Try it now: https://draco.thetempleofdoom.com — 10 free questions a day, no signup.

Why it's different

Quickstart

Chat in the browser

https://draco.thetempleofdoom.com — streaming answers, visible reasoning, sources bar under every response. 10 free/day, no account.

REST API

# 1. Get a free key (30 credits/month, email only, no card)
curl -s -X POST https://draco.thetempleofdoom.com/api/signup \
  -H 'Content-Type: application/json' \
  -d '{"email":"you@domain.tld"}'
# → {"api_key":"sk-draco-..."}

# 2. Ask — RAG-grounded answer with citations (1 credit)
curl -s -X POST https://draco.thetempleofdoom.com/api/ask \
  -H 'X-API-Key: sk-draco-...' -H 'Content-Type: application/json' \
  -d '{"q":"explain the ret2libc technique with a minimal poc"}'
# → {"answer":"...[1]...[2]","sources":[{"title":"...","category":"..."}]}

# 3. Raw passage search across all 1,855 books (1 credit)
curl -s "https://draco.thetempleofdoom.com/api/search?q=tcp%20syn%20flood&k=5" \
  -H 'X-API-Key: sk-draco-...'

MCP — use it from Claude, GPT, or any agent

{
  "mcpServers": {
    "draco": { "url": "https://draco.thetempleofdoom.com/mcp" }
  }
}

Tools:

• draco_ask(question) → cited, book-grounded answer
• draco_search(query, k) → raw BM25 passages with titles + scores
• draco_status() → live library stats

Officially listed on the MCP registry: com.thetempleofdoom.draco/draco

Manual MCP handshake:

curl -s https://draco.thetempleofdoom.com/mcp \
  -H 'Content-Type: application/json' -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"smoke","version":"1"}}}'

How it works

question ──► BM25 retrieval over 116k book passages
                │
                ▼
        top passages + question ──► local LLM (self-hosted GPU)
                │
                ▼
        answer with inline [n] citations + sources array

1. Ingest (ingest.py) — PDF/CHM/DJVU → clean plain text. Junk scans dropped automatically.
2. Index (build_index.py) — 300-token overlapping chunks → Snowball-stemmed BM25 index.
3. Serve (Flask + gunicorn) — retrieval-augmented generation with SSE streaming, separate reasoning and answer channels, and automatic degenerate-output detection/retry.
4. Expose — REST API, streamable-http MCP server, and a full agent-discovery kit: /llms.txt, /llms-full.txt, /openapi.json, /.well-known/ai-plugin.json, /.well-known/mcp-server.json, /sitemap.xml.

Pricing

Bitcoin only. No subscription traps, no KYC, no card processor.