The Problem
Real-time scraping in LangChain agents is brittle, expensive, and hard to cache. Live Selenium pipelines break weekly; per-query costs compound.
The Scavio Solution
Asynchronous DaaS architecture: Scavio dorks discovery → /extract markdown → LLM transformation → SQLite cache → MCP serving for downstream agents. Pattern from r/LangChain.
Before
Live Selenium pipeline failing on Cloudflare and captchas; per-query latency 3-8 seconds; weekly maintenance.
After
Daily 4 AM cron pre-warms cache; downstream agents read in 50ms; weekly maintenance near zero.
Who It Is For
LangChain teams building DaaS agents, CrewAI builders running multi-agent crews, government-data SDRs, compliance research teams.
Key Benefits
- 50ms cache reads via SQLite
- MCP-served typed JSON for downstream agents
- PDF support via /extract
- No live-scraping fragility
- Scales to multiple LangChain crews
Python Example
Python
import os, requests, sqlite3, json, time
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
conn = sqlite3.connect('daas.db')
conn.execute('CREATE TABLE IF NOT EXISTS items(url TEXT PRIMARY KEY, payload TEXT, ts REAL)')
def discover(q):
return requests.post('https://api.scavio.dev/api/v1/search', headers=H, json={'query': q}).json()
def fetch(url):
return requests.post('https://api.scavio.dev/api/v1/extract', headers=H, json={'url': url, 'format': 'markdown'}).json()JavaScript Example
JavaScript
const H = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
async function discover(q) { return fetch('https://api.scavio.dev/api/v1/search', { method:'POST', headers:H, body: JSON.stringify({ query: q }) }).then(r => r.json()); }Platforms Used
Web search with knowledge graph, PAA, and AI overviews