I Built an AI Agent Army in n8n That Replaced My Assistant

n8n is one of the best tools for building AI agent workflows without writing a full application. It handles orchestration, scheduling, and integrations out of the box. But n8n agents are only as useful as the data they can access. By connecting Scavio as an HTTP tool, your n8n agents can search Google, look up products on Amazon and Walmart, pull YouTube data, and search Reddit -- all through a single API.

Why n8n for AI Agents

n8n gives you a visual workflow builder with native support for AI agents, LLM nodes, and tool-calling. You can build an agent that receives a Slack message, searches the web, processes the results, and replies -- all without writing deployment code. The built-in cron triggers mean your agents can run on schedule without external infrastructure.

The limitation is data access. n8n's built-in web search options are limited, and scraping nodes are brittle. Scavio fills this gap with a single HTTP request node that returns structured data from five platforms.

Setting Up the Scavio Tool in n8n

Create an HTTP Request node in n8n with the following configuration. This becomes a reusable tool that any agent workflow can call:

{
  "method": "POST",
  "url": "https://api.scavio.dev/api/v1/search",
  "headers": {
    "x-api-key": "{{ $credentials.scavioApiKey }}",
    "Content-Type": "application/json"
  },
  "body": {
    "platform": "{{ $json.platform }}",
    "query": "{{ $json.query }}",
    "type": "search"
  }
}

Store your API key in n8n's credential manager using a generic credential type. Reference it in the header as shown above.

Agent 1: Daily News Briefing

This agent runs every morning via a cron trigger. It searches for news on topics you care about, summarizes the results with an LLM, and sends a digest to Slack or email:

• Cron trigger at 7:00 AM
• HTTP Request to Scavio for each topic in your watchlist
• LLM node to summarize and highlight key developments
• Slack or email node to deliver the briefing

The Scavio query for news looks like this:

{
  "platform": "google",
  "query": "AI agent frameworks news this week",
  "type": "search",
  "mode": "full"
}

Agent 2: Product Price Monitor

This agent monitors product prices across Amazon and Walmart. Set it to run hourly or daily, compare prices against your thresholds, and alert you when a deal drops:

{
  "platform": "amazon",
  "query": "Sony WH-1000XM6",
  "type": "search"
}

// Compare with Walmart
{
  "platform": "walmart",
  "query": "Sony WH-1000XM6",
  "type": "search"
}

The agent compares prices from both platforms, checks against your target price stored in a Google Sheet or database, and sends a notification only when the price drops below your threshold.

Agent 3: Research Assistant

Build a research assistant that responds to Slack messages. When someone asks a question in a designated channel, the agent searches Google and YouTube, synthesizes the results, and replies in-thread:

• Slack trigger on new messages in a research channel
• Scavio Google search for web results
• Scavio YouTube search for video tutorials and explanations
• LLM node to synthesize both sources into a concise answer
• Slack reply with the answer and source links

This is genuinely useful for teams. Instead of everyone doing their own Google searches, the agent centralizes research and keeps answers in-thread for future reference.

Scaling Your Agent Army

Each agent is a separate n8n workflow. They share the same Scavio credentials but operate independently. Start with the agent that solves your most frequent pain point. Once it works, duplicate the workflow pattern for new use cases. The common pattern is always the same: trigger, search via Scavio, process with an LLM, deliver the result. The only thing that changes between agents is the query, the platform, and the output destination.