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Apu Chakraborty
Apu Chakraborty

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The Ultimate Guide to Running n8n with Ollama LLM Locally Using Docker

#n8n #llama3 #aiagent #docker

Would you like to automate tasks using AI locally—without relying on the cloud, incurring API costs, or risking data leakage?

This guide will demonstrate how to operate n8n, a robust open-source workflow automation tool, in conjunction with Ollama, a high-speed local LLM runtime (similar to LLaMA, Mistral, or others)—all facilitated through Docker on your personal computer.

Indeed, it is possible to establish a completely local AI automation system at no cost whatsoever.
Let us explore this further.

📁 1. Create a folder structure 

mkdir n8n-ollama cd n8n-ollama touch docker-compose.yml
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🧾 2. Create docker-compose.yml

Create a file named docker-compose.yml with:

 services: ollama: image: ollama/ollama ports: - "11434:11434" container_name: ollama networks: - n8n-network volumes: - ollama_data:/root/.ollama n8n: image: n8nio/n8n container_name: n8n ports: - "5678:5678" networks: - n8n-network environment: - N8N_HOST=localhost - N8N_PORT=5678 - N8N_EDITOR_BASE_URL=http://localhost:5678 - WEBHOOK_URL=http://localhost:5678 - NODE_FUNCTION_ALLOW_EXTERNAL=* volumes: - n8n_data:/home/node/.n8n networks: n8n-network: volumes: ollama_data: n8n_data:
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This puts both containers in the same Docker network so n8n can reach ollama using the hostname ollama.

▶️ 3. Start both services 

docker-compose up -d
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you will get response like this -

[+] Running 2/2 ✔ Container ollama Started 0.6s ✔ Container n8n Started
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✅ Verify container 

#verify containers docker ps
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 0d99d7a06ff9 n8nio/n8n "tini -- /docker-ent…" 3 days ago Up 2 minutes 0.0.0.0:5678->5678/tcp, :::5678->5678/tcp n8n c5eabfa39b70 ollama/ollama "/bin/ollama serve" 3 days ago Up 2 minutes 0.0.0.0:11434->11434/tcp, :::11434->11434/tcp ollama
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You should see both ollama and n8n containers running.
n8n - http://localhost:5678
ollama - http://localhost:11434

🎉 Great! That means n8n successfully connected to Ollama, and now the only issue is:

⛓️ Pull the correct model inside the Ollama container

Open a terminal inside the Ollama container: 

docker exec -it ollama bash
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You're now inside the container.

Pull a valid model (e.g., llama3): 

ollama pull llama3 # --- ollama pull llama3.2 # --- ollama pull deepseek-r1:1.5b
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root@c5eabfa39b70:/# ollama list NAME ID SIZE MODIFIED deepseek-r1:1.5b e0979632db5a 1.1 GB 3 days ago llama3.2:latest a80c4f17acd5 2.0 GB 3 days ago llama3:latest 365c0bd3c000 4.7 GB 3 days ago
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⭐ This will download the official llama3 model.

Exit the container: 

exit
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In n8n, update your model name:

When setting up the Ollama node in n8n, use:

llama3
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✌🏻 This matches the model you pulled.

curl http://localhost:11434/api/generate \ -X POST \ -H "Content-Type: application/json" \ -d '{ "model": "llama3.2", "prompt": "5+5 ?", "stream": false }'
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From outside, run:

docker exec -it ollama ollama list
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step1

Image step2

n8n-ollama connection setup

Image last

🛑 stop the container: 

docker-compose down
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You Did It! Now Build AI Agents Locally—Fast & Free.

With Ollama + n8n, you can:

  • Run AI like Llama offline (no APIs, no costs)
  • Automate content, support, or data tasks in minutes
  • Own your AI workflow (no limits, no middlemen)

Your turn—launch a workflow and see the magic happen. 🚀

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