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 (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.