> For the complete documentation index, see [llms.txt](https://docs.readyidc.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.readyidc.com/gpu-as-a-service/linux/docker/how-to-using-n8n-automation-guide.md).

# How to using n8n Automation guide

> Build automated workflows that use your self-hosted vLLM endpoint. Examples: document summarization, chatbots, scheduled AI tasks, webhook-triggered processing. **Prerequisite:** A running vLLM endpoint (see the vLLM Docker Deployment Guide).

## 1. What is n8n?

[n8n](https://n8n.io/) is a workflow automation tool (like Zapier/Make, but self-hosted). It connects apps, APIs, and AI models into automated pipelines via a visual editor.

**Common LLM use cases:**

* Summarize incoming emails/documents automatically
* Build a chatbot triggered by webhook
* Scheduled content generation
* Extract structured data from text
* Multi-step AI pipelines (classify → route → respond)

## 2. Deployment

### Standalone `docker-compose.yml`

```yaml
services:
  n8n:
    image: n8nio/n8n:latest
    container_name: n8n
    restart: unless-stopped
    ports:
      - "5678:5678"
    environment:
      - N8N_HOST=0.0.0.0
      - N8N_PORT=5678
      - N8N_PROTOCOL=http
      - NODE_ENV=production
      - GENERIC_TIMEZONE=Asia/Bangkok
      # Basic auth (recommended)
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=change-me-strong-password
    volumes:
      - ./n8n-data:/home/node/.n8n

volumes:
  n8n-data:
```

### Add to Existing vLLM Stack

If you want n8n in the **same compose file** as vLLM (so they share a Docker network), add the `n8n` service block above into your existing `docker-compose.yml`. This lets n8n reach vLLM via the service name `http://vllm:8000`.

### Launch

```bash
docker compose up -d
docker compose logs -f n8n
```

Access: `http://<your-server-ip>:5678`

On first launch, create your owner account.

## 3. Connecting n8n to vLLM

n8n has a built-in **OpenAI node** that works with any OpenAI-compatible API — including vLLM.

### Create OpenAI Credentials in n8n

1. In n8n: **Credentials → New → OpenAI API**
2. Set:
   * **API Key:** your vLLM `--api-key` value (e.g. `sk-llm-key`)
   * **Base URL:** your vLLM endpoint
     * Same compose stack: `http://vllm:8000/v1`
     * Separate host: `http://<server-ip>:8000/v1`
3. Save

### Network Note

| n8n location              | Base URL                          |
| ------------------------- | --------------------------------- |
| Same compose file as vLLM | `http://vllm:8000/v1`             |
| Different host/server     | `http://<vllm-server-ip>:8000/v1` |
| Behind reverse proxy      | `https://api.yourdomain.com/v1`   |

## 4. Example Workflows

### Example A: Webhook Chatbot

Receives a message via webhook, sends to vLLM, returns the answer.

**Nodes:** `Webhook` → `OpenAI` → `Respond to Webhook`

1. **Webhook node**
   * Method: POST
   * Path: `/chat`
2. **OpenAI node**
   * Resource: Chat
   * Model: `Qwen/Qwen2.5-7B-Instruct-AWQ`
   * Messages: `={{ $json.body.message }}`
3. **Respond to Webhook node**
   * Body: `={{ $json.choices[0].message.content }}`

Test:

```bash
curl -X POST http://<server-ip>:5678/webhook/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What is Kubernetes?"}'
```

### Example B: Scheduled Document Summarizer

Runs daily, fetches documents, summarizes with the LLM.

**Nodes:** `Schedule Trigger` → `HTTP Request (fetch docs)` → `OpenAI (summarize)` → `Send Email / Save`

1. **Schedule Trigger** — daily at 09:00
2. **HTTP Request** — fetch your data source
3. **OpenAI node**
   * System prompt: "Summarize the following in 3 bullet points"
   * User: `={{ $json.content }}`
4. **Output** — email, Slack, database, etc.

### Example C: Text Classification & Routing

Classify incoming text, then route to different actions.

**Nodes:** `Webhook` → `OpenAI (classify)` → `Switch` → (multiple branches)

1. **OpenAI node**
   * Prompt: "Classify this message as: support / sales / spam. Reply with one word only."
2. **Switch node** — route based on classification
3. Branch actions per category

### Tips for LLM Nodes

* Set **temperature** low (0.1-0.3) for consistent/structured output
* Use **system prompts** to control format
* For JSON output, instruct the model explicitly and parse with a **Code node**
* Set reasonable **max tokens** to control response length

## 5. Troubleshooting

#### n8n can't reach vLLM

* Same compose file → use `http://vllm:8000/v1` (service name, not localhost)
* Different host → use the server IP and ensure port 8000 is reachable
* Test from n8n container:

  ```bash
  docker exec -it n8n wget -qO- http://vllm:8000/v1/models
  ```

#### "Unauthorized" / 401

* API key in n8n credentials must match vLLM `--api-key`
* If vLLM has no `--api-key`, you can put any placeholder in n8n

#### Model not found

* Use the exact model name from `curl http://<vllm>:8000/v1/models`
* Must match the `--model` value in your vLLM config

#### Workflow times out on long generations

* Increase n8n node timeout in workflow settings
* Reduce max tokens, or use a smaller/faster model

#### Data not persisting after restart

* Ensure the `./n8n-data:/home/node/.n8n` volume is mounted
* Without it, workflows are lost on container recreation


---

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