Scavio for Hermes Agent Research Pipeline

The Problem

Research agents need current data to be useful. Hermes Agent's skill system means research patterns can be learned and refined over time, but only if the agent has access to live search data. Without search tools, Hermes relies solely on its training data, which limits research to what the model already knows.

How Scavio Helps

• Hermes learns research patterns as reusable skills
• Multi-platform search covers web, discussions, and video content
• Self-improvement loop refines research quality over time
• Profile segmentation keeps research context focused
• 500 free credits/month covers substantial research volume

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Hermes Agent research profile with Scavio MCP. Task: 'Research the current state of RAG frameworks in 2026.' Agent searches Google for recent articles, Reddit for developer discussions, YouTube for tutorial recency. Creates a skill for 'technology landscape research' that it reuses for future similar requests.":

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 Hermes Agent users, researchers building autonomous research pipelines, teams using Hermes for competitive intelligence

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

Start with the free tier (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.