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Learn how to integrate Runpod Serverless with n8n, a workflow automation tool. By the end of this tutorial, you’ll have a vLLM endpoint running on Runpod that you can use within your n8n workflows.
For a faster start, you can point your n8n workflow to an OpenAI-compatible Public Endpoint instead of deploying a vLLM worker. To do this, skip to step 2 to create your workflow, then in step 3, set the base URL to the Public Endpoint URL for Qwen3 32B AWQ:
https://api.runpod.ai/v2/qwen3-32b-awq/openai/v1

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What you’ll learn

In this tutorial, you’ll learn how to:
  • Deploy a vLLM worker serving the Qwen/qwen3-32b-awq model.
  • Configure your environment variables for n8n compatibility.
  • Create a simple n8n workflow to test your integration.
  • Connect your workflow to your Runpod endpoint.

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Requirements

Before you begin, you’ll need:

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Step 1: Deploy a vLLM worker on Runpod

First, you’ll deploy a vLLM worker to serve the Qwen/qwen3-32b-awq model.
1

Create a new vLLM endpoint

Open the Runpod console and navigate to the Serverless page.Click New Endpoint and select vLLM under Ready-to-Deploy Repos.
2

Configure your endpoint

For more details on vLLM deployment options, see Deploy a vLLM worker.
In the deployment modal:
  • In the Model field, enter Qwen/qwen3-32b-awq.
  • Expand the Advanced section to configure your vLLM environment variables:
    • Set Max Model Length to 8192.
    • Near the bottom of the page, check Enable Auto Tool Choice.
    • Set Tool Call Parser to Hermes.
    • Set Reasoning Parser to Qwen3.
  • Click Next.
  • Click Create Endpoint.
When using a different model, you may need to adjust your vLLM environment variables to ensure your model returns responses in the format that n8n expects.
Your endpoint will now begin initializing. This may take several minutes while Runpod provisions resources and downloads your model. Wait until the status shows as Running.
3

Copy your endpoint ID

Once deployed, you’ll be taken to the detail page for your endpoint in the Runpod console. You can find your endpoint ID in the Overview tab:
You can also find your endpoint ID in the URL of the endpoint detail page. For example, if the URL for your endpoint is https://console.runpod.io/serverless/user/endpoint/isapbl1e254mbj, the endpoint ID is isapbl1e254mbj.Copy your endpoint ID to your clipboard. You’ll need it to configure your n8n workflow.

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Step 2: Create an n8n workflow

Next, you’ll create a simple n8n workflow to test your integration.
1

Create a new workflow

Open n8n and navigate to your workspace, then click Create Workflow.
2

Add a chat message trigger

Click Add first step and select On chat message. Click Test chat to confirm.
3

Add AI Agent node

Click the + button and search for AI Agent and select it. Click Execute step to confirm.
4

Add OpenAI Chat Model node

Click the + button labeled Chat Model. Search for OpenAI Chat Model and select it.
5

Create a new credential

Click the dropdown under Credential to connect with and select Create new credential.

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Step 3: Configure the OpenAI Chat Model node

Now you’ll configure the n8n OpenAI Chat Model node to use the model running on your Runpod endpoint.
1

Add your Runpod API key

Under API Key, add your Runpod API Key. You can create an API key on the settings page of the Runpod console.
2

Configure the base URL

Under Base URL, replace the default OpenAI URL with your Runpod endpoint URL:
https://api.runpod.ai/v2/ENDPOINT_ID/openai/v1
Replace ENDPOINT_ID with your vLLM endpoint ID from Step 1.
3

Test the connection

Click Save, and n8n will automatically test your endpoint connection.It may take a few minutes for your endpoint to scale up a worker to process the request. You can monitor the request using the Workers and Requests tabs for your vLLM endpoint in the Runpod console.If you see the message “Connection tested successfully,” that means your endpoint is reachable, but it doesn’t gaurantee that it’s fully compatible with n8n—we’ll do that in the next step.
4

Select the Qwen3 model

Press escape to return to the OpenAI Chat Model configuration modal.Under Model, select qwen/qwen3-32b-awq, then press escape to return to the workflow canvas.
5

Type a test message

Type a test message into the chat box like “Hello, how are you?” and press enter.If everything is working correctly, you should see each of the nodes in your workflow go green to indicate successful execution, and a response from the model in the chat box.
Make sure to Save your workflow before closing it, as n8n may not save changes to your model node configuration automatically.

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Next steps

Congratulations! You’ve successfully used Runpod to power an AI agent on n8n. Now that you’ve integrated with n8n, you can: