Hermes v0.12.0 Search: Hardware Is Not the Problem

Hermes v0.12.0 search quality is poor and the hardware is not the problem. Users running RTX 3080Ti, 16GB RAM, Ryzen 9 report the same issues as users on modest rigs: searches return irrelevant results, web search silently fails, and the agent confabulates answers instead of admitting it could not find data. The bottleneck is the search backend, not compute.

Why local SearXNG fails for Hermes

Most Hermes setups default to a local SearXNG instance for web search. SearXNG aggregates results from upstream engines -- Google, Bing, DuckDuckGo, Brave. The problem: these upstream engines actively block automated queries from SearXNG instances. Google blocks within hours. Bing rate-limits aggressively. The result is that SearXNG returns partial, stale, or empty results without clear error messages. Hermes receives these empty results and does what any LLM does when it lacks context: it makes something up.

The silent failure pattern

The failure is especially dangerous because it is silent. Hermes does not tell you "search returned no results." It seamlessly blends its parametric knowledge with whatever fragments it received and presents the answer confidently. You get an answer that looks grounded but is not. The only way to catch this is to check the raw search results before they reach the model.

Diagnosing the actual problem

Before blaming Hermes, test the search backend directly. Hit your SearXNG instance with curl and check what comes back. If the results are empty or irrelevant, the problem is upstream of Hermes entirely.

# Test your SearXNG instance directly
curl "http://localhost:8080/search?q=python+requests+library&format=json" | python3 -m json.tool | head -40

# Common failure modes:
# 1. Empty "results" array -> upstream engines blocking your instance
# 2. Only Wikipedia results -> only DuckDuckGo instant answers working
# 3. Timeout errors -> DNS or network issues in Docker container
# 4. Results from 2023-2024 -> stale cache, no fresh results

# Check which engines are actually responding
curl "http://localhost:8080/search?q=test&format=json" | python3 -c "
import sys, json
data = json.load(sys.stdin)
engines = set()
for r in data.get('results', []):
    engines.update(r.get('engines', []))
print('Working engines:', engines or 'NONE')
"

The API fallback fix

Replace the SearXNG backend with a search API call. This is a configuration change, not a Hermes code change. Most Hermes setups allow custom tool definitions. Swap the search tool to call an external API instead of local SearXNG. The model does not care where the results come from -- it just needs structured search data.

import requests, os, json

API = "https://api.scavio.dev/api/v1/search"
H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}

def hermes_search_tool(query: str) -> str:
    """Drop-in replacement for Hermes SearXNG search tool.

    Returns formatted text that Hermes can consume directly.
    Configure this as a custom tool in your Hermes setup.
    """
    try:
        resp = requests.post(API, headers=H, json={
            "query": query,
            "platform": "google",
            "num_results": 8
        }, timeout=10)
        resp.raise_for_status()
        results = resp.json().get("organic_results", [])

        if not results:
            return "No search results found for this query."

        formatted = []
        for i, r in enumerate(results, 1):
            formatted.append(
                f"[{i}] {r.get('title', 'No title')}
"
                f"    URL: {r.get('link', '')}
"
                f"    {r.get('snippet', 'No description')}"
            )
        return "

".join(formatted)

    except requests.RequestException as e:
        return f"Search failed: {str(e)}. Using model knowledge only."

# Test the replacement
results = hermes_search_tool("Hermes v0.12 search configuration")
print(results)

Hardware actually does not matter here

An RTX 3080Ti and Ryzen 9 affect inference speed and context window handling. They do not affect search quality at all. Search is an I/O operation: send a query, get results. Whether you run Hermes on a 3080Ti or a 3060 makes zero difference to whether SearXNG returns good results. The GPU determines how fast Hermes processes the search results, not whether those results exist in the first place.

When to keep SearXNG

SearXNG still makes sense if you need fully air-gapped search (no external API calls), if you have a working proxy rotation setup that keeps upstream engines from blocking you, or if your queries are low-volume enough that rate limits do not trigger. For anything above 50-100 queries/day, a paid search API is more reliable and costs less in maintenance time than keeping SearXNG alive.

How Scavio fits

Scavio replaces the unreliable SearXNG layer with a single API call. The free tier at 250 credits/mo covers 250 Hermes search operations -- enough for a personal setup making 8 searches/day. The $30/mo plan at 7,000 credits handles heavier usage. Each search returns structured JSON including organic results, AI overviews, and People Also Ask data that Hermes can consume directly for better-grounded responses.