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