Scavio for Production Agent Memory + Routing Architecture

The Problem

Production LangChain agents lose context between turns ('amnesia') and pick wrong tools when 8+ tools are attached. Without dedicated memory + routing, agent loops degrade past 3-5 tools.

How Scavio Helps

• Cross-turn memory via checkpointer
• Unambiguous tool routing via semantic MCP names
• One MCP attachment vs 5+ wired tools
• Stack cost ~$35-45/mo
• Documented benchmark: 48% -> 94% task success in r/LangChain post

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "5-step research agent that recalls prior tool calls and picks correctly across 6 named MCP tools":

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 Production agent maintainers, LangChain platform teams, devs shipping multi-turn research agents

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