Scavio for LangGraph Agent with Memory and Search

The Problem

LangGraph agents without memory repeat research from scratch every session. Agents without search ground responses in stale training data. The combination solves both problems.

How Scavio Helps

• Persistent memory across research sessions
• Live web search fills knowledge gaps
• Graph structure captures entity relationships
• Search only for identified gaps, not everything
• Session cost: $0.05-0.25 depending on gap count

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "LangGraph v0.3 state management patterns":

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 LangGraph developers building persistent research and analysis agents

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your langgraph agent with memory and search solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (250 credits/month, no credit card required) and scale to paid plans when you need higher volume.