Scavio for LangGraph Multi-Source Research Agent

The Problem

Research agents that use a single search source produce biased or incomplete results. Google provides authoritative sources but misses community sentiment. Reddit captures real-world experience but lacks official sources. A multi-source agent provides balanced research.

How Scavio Helps

• Cross-reference 3 source types per research query
• Scavio as a single LangGraph tool covering multiple platforms
• Structured JSON output works directly with LangGraph state
• Citation-backed answers from Google, Reddit, and YouTube
• 3 searches per query at $0.015 for comprehensive research

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "A LangGraph agent receives 'What is the best vector database for production RAG in 2026?' The agent tool-calls Scavio three times: Google for benchmarks and documentation, Reddit for deployment experiences, YouTube for tutorial coverage. Each source adds a different perspective. The agent synthesizes a citation-backed recommendation. 3 queries = $0.015.":

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, AI agent builders, teams building research assistants

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