Non-Engineer AI Agent with n8n and LLaMA

You do not need to write code to build a functional AI agent in 2026. n8n provides a visual workflow builder, LLaMA runs locally via Ollama, and search APIs plug in via HTTP nodes. Total cost: $0-30/month depending on your search volume.

What you need

• n8n: open source workflow automation (self-hosted free, cloud from $20/mo)
• Ollama: runs LLaMA locally on your machine (free, needs 8GB+ RAM)
• Search API: web search grounding (Scavio: 250 free/mo, $30/mo for 7K)
• Total setup time: 30-60 minutes with no coding experience

Step 1: Install Ollama and LLaMA

# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.com/install.sh | sh

# Pull a LLaMA model (7B fits in 8GB RAM)
ollama pull llama3.2

# Verify it works
ollama run llama3.2 "What is web scraping?"

# Ollama exposes an API at http://localhost:11434

Step 2: Set up n8n

# Option A: Docker (recommended)
docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  n8nio/n8n

# Open http://localhost:5678 in your browser
# No coding required from here -- everything is visual

Step 3: Build the agent workflow in n8n

In n8n, create a new workflow with these nodes connected in sequence:

1. Webhook trigger (receives your question via HTTP)
2. HTTP Request node (calls search API for grounding data)
3. HTTP Request node (sends question + search context to Ollama)
4. Respond to Webhook node (returns the answer)

Step 4: Configure the search node

Add an HTTP Request node with these settings:

{
  "method": "POST",
  "url": "https://api.scavio.dev/api/v1/search",
  "headers": {
    "x-api-key": "YOUR_SCAVIO_API_KEY",
    "Content-Type": "application/json"
  },
  "body": {
    "query": "={{ $json.query }}",
    "num_results": 5
  }
}

Step 5: Configure the LLM node

Add another HTTP Request node pointing to your local Ollama:

{
  "method": "POST",
  "url": "http://host.docker.internal:11434/api/generate",
  "headers": {
    "Content-Type": "application/json"
  },
  "body": {
    "model": "llama3.2",
    "prompt": "Based on these search results:\n{{ $json.organic_results }}\n\nAnswer this question: {{ $('Webhook').item.json.query }}",
    "stream": false
  }
}

What this gives you

• A personal AI agent that answers questions with real web data
• No API keys for OpenAI/Anthropic needed (LLaMA runs locally)
• Search grounding prevents hallucinations on current topics
• Visual workflow you can modify without touching code
• Runs entirely on your machine (except search API calls)

Extending the agent

Once the basic workflow works, add more capabilities visually:

• Schedule node: run research automatically every morning
• Email node: send results to your inbox
• Slack node: post summaries to a channel
• Google Sheets node: log results for tracking
• IF node: route different question types to different search platforms

Limitations to know

• LLaMA 7B is less capable than GPT-4o or Claude for complex reasoning
• Local inference is slower than cloud APIs (10-30 seconds vs 1-3 seconds)
• 8GB RAM minimum, 16GB recommended for smooth operation
• n8n workflows can get complex -- start simple and add nodes gradually