Scavio for Streamlit Research Agent Interface

The Problem

Research agents running in terminals are unusable by non-technical stakeholders, creating a bottleneck where developers manually run queries on behalf of product managers and executives.

How Scavio Helps

• Full research agent UI in under 100 lines of Streamlit code
• Self-service research for non-technical team members
• CSV export for downstream analysis in spreadsheets
• Multi-platform search in one interface
• Deployable on Streamlit Cloud for team access

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Streamlit research agent search API web UI Python 2026":

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 Developers building internal research tools, teams with non-technical stakeholders who need self-service research, and Streamlit enthusiasts

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