The Problem
Research agents that run in the terminal are hard to share with non-technical stakeholders. Product managers, analysts, and executives want to trigger research queries and see results in a browser, not in a terminal window. Building a full web app for an internal research tool is overkill. The team needs something between a terminal script and a production web application.
The Scavio Solution
Build a Streamlit app that wraps your research agent's search functionality with a web interface. Users enter a research question, the app queries Scavio across multiple platforms, displays structured results with expandable sections, and offers CSV export. Streamlit handles the UI. Scavio handles the data. The entire app is under 100 lines of Python.
Before
Before the Streamlit UI, the research agent ran in a terminal. Only the developer who built it could use it. Research requests went through a bottleneck: someone asked the developer, the developer ran the script, then shared screenshots of the output.
After
After deploying the Streamlit app, anyone on the team can run research queries directly. The bottleneck disappeared. Usage went from 5 queries/week (developer-mediated) to 30 queries/week (self-service). Stakeholders export results as CSV for their own analysis.
Who It Is For
Developers who need to share research agent functionality with non-technical teammates. Teams building internal research tools that need a quick web UI without building a full application.
Key Benefits
- Full research agent UI in under 100 lines of Streamlit code
- Self-service research for non-technical stakeholders
- CSV export for downstream analysis in spreadsheets
- Multi-platform search (Google, Reddit, Amazon) in one interface
- Deployable on Streamlit Cloud for team-wide access
Python Example
import requests
import json
API_KEY = "your_scavio_api_key"
# Scavio has one endpoint per product: there is no dispatcher URL and no
# "platform" request parameter.
ENDPOINTS = {
"google": "https://api.scavio.dev/api/v2/google",
"reddit": "https://api.scavio.dev/api/v1/reddit/search",
}
def results_of(payload):
"""Google v2 returns its payload at the top level; every other
product wraps it in "data"."""
body = payload.get("data") if isinstance(payload.get("data"), dict) else payload
for key in ("organic_results", "results", "products", "search_item_list", "timeline", "users"):
if body.get(key):
return body[key]
return []
def link_of(item):
"""Google organic results use "link"; product and video results use "url"."""
return item.get("link") or item.get("url") or ""
def research_query(query: str, platforms: list[str] = None) -> dict:
"""Multi-platform research query for Streamlit UI."""
if platforms is None:
platforms = ["google", "reddit"]
all_results = {}
for platform in platforms:
res = requests.post(
ENDPOINTS[platform],
headers={"Authorization": f"Bearer {API_KEY}"},
json={"query": query, "ai_overview": True if platform == "google" else False},
timeout=15,
)
res.raise_for_status()
data = res.json()
all_results[platform] = {
"organic": [{"title": r.get("title", ""), "link": link_of(r), "snippet": r.get("snippet", "")} for r in results_of(data)[:5]],
"ai_overview": data.get("ai_overview", {}).get("text", "") if platform == "google" else "",
}
return {"query": query, "platforms": all_results}
# Streamlit usage:
# import streamlit as st
# query = st.text_input("Research question")
# if query:
# results = research_query(query, ["google", "reddit"])
# for platform, data in results["platforms"].items():
# st.subheader(platform)
# for r in results_of(data):
# st.write(f"**{r["title"]}**: {r["snippet"]}")
results = research_query("best search api for research agents 2026")
for platform, data in results["platforms"].items():
print(f"\n{platform.upper()}:")
for r in results_of(data):
print(f" {r["title"]}: {r["snippet"][:80]}")JavaScript Example
const API_KEY = "your_scavio_api_key";
// Scavio has one endpoint per product: there is no dispatcher URL and no
// "platform" request parameter.
const ENDPOINTS = {
google: "https://api.scavio.dev/api/v2/google",
reddit: "https://api.scavio.dev/api/v1/reddit/search",
};
// Google v2 returns its payload at the top level; every other product
// wraps it in `data`.
function resultsOf(payload) {
const body = payload?.data ?? payload;
for (const key of ["organic_results", "results", "products", "search_item_list", "timeline", "users"]) {
if (body?.[key]) return body[key];
}
return [];
}
// Google organic results use `link`; product and video results use `url`.
const linkOf = (item) => item?.link ?? item?.url ?? "";
async function researchQuery(query, platforms = ["google", "reddit"]) {
const results = {};
for (const p of platforms) {
const res = await fetch(ENDPOINTS[p], {
method: "POST",
headers: { Authorization: `Bearer ${API_KEY}`, "content-type": "application/json" },
body: JSON.stringify({ query, ai_overview: p === "google" }),
});
const data = await res.json();
results[p] = { organic: (resultsOf(data) ?? []).slice(0, 5).map((r) => ({ title: r.title ?? "", link: linkOf(r) ?? "", snippet: r.snippet ?? "" })), aiOverview: data.ai_overview?.text ?? "" };
}
return results;
}
const results = await researchQuery("best search api for research agents 2026");
for (const [p, data] of Object.entries(results)) {
console.log(`\n${p.toUpperCase()}:`);
resultsOf(data).forEach((r) => console.log(` ${r.title}: ${r.snippet.slice(0, 80)}`));
}Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
Amazon
Product search with prices, ratings, and reviews