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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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