Local LLM Web Search: Beyond SearXNG in 2026

Most local LLM web search setups default to SearXNG, but it gets blocked under sustained use and returns inconsistent JSON. In 2026, better options exist: MCP-connected search APIs that work with Ollama, LM Studio, oMLX, and Pi without self-hosting a search engine. You get structured results, multi-platform coverage, and no proxy maintenance.

The SearXNG problem for local LLMs

Google blocks SearXNG IP after a few hundred queries
Public instances share bans across all users
JSON output format varies between SearXNG versions
Requires Docker, config files, and ongoing maintenance
No multi-platform search (Amazon, YouTube, TikTok not available)

Option 1: MCP search with Ollama

Python

# ollama_with_search.py
# Give any Ollama model web search via Scavio API

import os
import requests
import ollama

def web_search(query: str) -> str:
    """Web search tool for Ollama models."""
    resp = requests.post(
        "https://api.scavio.dev/api/v2/google",
        headers={"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"},
        json={"query": query},
        timeout=90,
    )
    data = resp.json()
    results = []
    for r in data.get("organic_results", [])[:5]:
        results.append(f"- {r['title']}: {r.get('snippet', '')}")
    return "\n".join(results)

# Use with function calling
tools = [{
    "type": "function",
    "function": {
        "name": "web_search",
        "description": "Search the web for current information",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    }
}]

response = ollama.chat(
    model="qwen3:8b",
    messages=[{"role": "user", "content": "What are the best AI frameworks in 2026?"}],
    tools=tools
)

Option 2: MCP config for oMLX and Pi

JSON

{
  "mcpServers": {
    "scavio": {
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "x-api-key": "YOUR_KEY"
      }
    }
  }
}

Option 3: Direct API call from any local LLM

JavaScript

// Universal search function for any local LLM setup
async function searchWeb(query) {
  const resp = await fetch("https://api.scavio.dev/api/v2/google", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.SCAVIO_API_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({ query })
  });

  const data = await resp.json();
  return (data.organic_results || []).slice(0, 5).map(r => ({
    title: r.title,
    url: r.link,
    snippet: r.snippet || ""
  }));
}

// Works with LM Studio, Ollama, or any local model
// that supports tool/function calling

Comparison: SearXNG vs API for local LLMs

Text

Factor         | SearXNG          | MCP Search API
Setup          | Docker + config  | API key + JSON config
Maintenance    | Ongoing          | None
Reliability    | Degrades         | Managed
Multi-platform | No               | Yes (Google, YT, Amazon...)
Cost           | Free (hosting)   | 50 free on signup, then $0.01/search
Privacy        | Full local       | Queries sent to API
JSON quality   | Inconsistent     | Typed schema

Privacy tradeoff

SearXNG's main advantage is privacy -- queries never leave your network. If that matters (medical research, legal queries), keep SearXNG. For coding assistance, documentation lookup, and general research, the API approach is more practical. The query goes to the API provider, but the LLM processing stays local.

Recommendation

Start with Scavio MCP (50 free credits on signup). If you need more than 50 searches, the $30/month plan covers 7,000 queries. Keep SearXNG as a fallback for privacy-sensitive queries only.

Option 1: MCP search with Ollama

Python

# ollama_with_search.py
# Give any Ollama model web search via Scavio API

import os
import requests
import ollama

def web_search(query: str) -> str:
    """Web search tool for Ollama models."""
    resp = requests.post(
        "https://api.scavio.dev/api/v2/google",
        headers={"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"},
        json={"query": query},
        timeout=90,
    )
    data = resp.json()
    results = []
    for r in data.get("organic_results", [])[:5]:
        results.append(f"- {r['title']}: {r.get('snippet', '')}")
    return "\n".join(results)

# Use with function calling
tools = [{
    "type": "function",
    "function": {
        "name": "web_search",
        "description": "Search the web for current information",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    }
}]

response = ollama.chat(
    model="qwen3:8b",
    messages=[{"role": "user", "content": "What are the best AI frameworks in 2026?"}],
    tools=tools
)

Option 3: Direct API call from any local LLM

JavaScript

// Universal search function for any local LLM setup
async function searchWeb(query) {
  const resp = await fetch("https://api.scavio.dev/api/v2/google", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.SCAVIO_API_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({ query })
  });

  const data = await resp.json();
  return (data.organic_results || []).slice(0, 5).map(r => ({
    title: r.title,
    url: r.link,
    snippet: r.snippet || ""
  }));
}

// Works with LM Studio, Ollama, or any local model
// that supports tool/function calling

Comparison: SearXNG vs API for local LLMs

Text

Factor         | SearXNG          | MCP Search API
Setup          | Docker + config  | API key + JSON config
Maintenance    | Ongoing          | None
Reliability    | Degrades         | Managed
Multi-platform | No               | Yes (Google, YT, Amazon...)
Cost           | Free (hosting)   | 50 free on signup, then $0.01/search
Privacy        | Full local       | Queries sent to API
JSON quality   | Inconsistent     | Typed schema

Privacy tradeoff