Scavio for Agent Memory Wiki Pattern

The Problem

AI agents accumulate facts that go stale. A 'Tavily costs $30/mo' fact from January might be wrong by June. Periodic re-verification via search keeps the agent's knowledge current.

How Scavio Helps

• Facts are timestamped with last_verified date
• Weekly cron re-searches stale entities via Scavio
• LLM diff detects whether stored facts have changed
• Auto-patch updates the knowledge base without human review
• Changelog tracks what changed, when, and from which source

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Agent memory: { entity: 'Tavily', fact: 'Starts at $30/mo', last_verified: '2026-04-01' } → stale (> 7 days) → Scavio search 'Tavily pricing 2026' → LLM: 'Changed? Yes, now $40/mo' → patch + log":

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 agent builders, RAG system maintainers, teams with long-running agents that make factual claims

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your agent memory wiki pattern 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.