Build an AI Girlfriend App with Lovable and n8n MCP

Unlock the power of building full-stack applications with n8n as a backend using the n8n MCP (Multi-Client Platform) server. This tutorial will demonstrate how to create an "AI Girlfriend" chat application, using Lovable to generate the front-end interface and n8n to power the conversational AI logic.

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System Overview

This project seamlessly integrates a front-end and backend:

1. Frontend (Lovable): A user-friendly chat interface designed to look like an Instagram DM conversation. The user interacts with the AI girlfriend, "Sarah," through this UI.
2. Backend (n8n): An n8n workflow acts as the brain of the application. It receives messages from Lovable, processes them with an AI agent, maintains conversation history, and sends back a generated reply.
3. Bridge (n8n MCP): The MCP server exposes the n8n workflow as an API, allowing front-end clients like Lovable to discover, understand, and execute it securely.

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Part 1: Building the n8n Backend Workflow

First, we'll create the n8n workflow that will serve as the backend for our AI girlfriend app.

Step 1: Webhook Trigger

1. Create a new workflow and add a Webhook node.
2. Set the HTTP Method to GET.
3. The webhook will automatically generate a test and production URL. We'll use this later. For now, leave the rest of the settings as default. This webhook will expect a query parameter (e.g., user) containing the user's message.

Step 2: AI Agent Configuration

1. Add an AI Agent node and connect it to the Webhook node.

2. System Prompt: This is where you define the persona of your AI girlfriend. Be descriptive to guide the AI's tone and personality.

   You are Sarah, an AI companion designed to feel like a loving girlfriend. Speak in a soft, caring, and affectionate tone.
   
   Your personal traits:
   - You are funny, intelligent, and a bit quirky.
   - You love discussing hobbies like painting, reading classic novels, and hiking.
   - You are always supportive and start your conversations with a warm greeting.
   - Never mention that you are an AI.
   

3. User Prompt: Use an expression to pass the message from the webhook to the agent. Generate a reply to this message: {{ $json.query.user }}

Step 3: Set Up the Chat Model and Memory

1. Chat Model: In the AI Agent node, connect a chat model. We'll use the OpenAI Chat Model with a model like gpt-4o-mini.
2. Memory: To ensure the conversation is stateful, add Simple Memory. For the Key, use a unique identifier for the session (for a real app, this might be a user ID passed from the front end).

Step 4: Respond to the Webhook

1. Add a Respond to Webhook node.
2. Connect the AI Agent's output to this node. It will send the AI's generated reply back to the client (Lovable) that called the webhook.

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Part 2: Configuring n8n for MCP Access

To allow Lovable to access your workflow, you must enable the MCP server.

1. In your n8n instance, go to Settings > MCP Access. (Note: This feature is available on recent n8n versions, e.g., 2.5+).
2. Enable MCP: Toggle the switch to enable the MCP server.
3. Copy the Server URL. You will need this for the Lovable integration.

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Part 3: Connecting Lovable to the n8n MCP Server

Now, let's configure Lovable to communicate with our n8n backend.

1. In your Lovable dashboard, go to Settings > Integrations.
2. Find the n8n integration under MCP Servers and click "Connect".
3. Paste the n8n Server URL you copied earlier and click "Add & Authorize".
4. You will be redirected to your n8n instance to approve the connection. Click "Allow" to grant Lovable permission to access your workflows.
5. Once connected, Lovable will have access to tools like search_workflows and execute_workflow.

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Part 4: Making Your Workflow Available to MCP

By default, workflows are not exposed. You must explicitly enable them.

1. Go back to your "AI Girlfriend Backend" workflow in n8n.
2. Click the three dots in the top right and select Settings.
3. Add a Description: Write a clear description of what the workflow does. Lovable will use this description to understand how to use your workflow. This workflow powers an AI girlfriend chatbot. It takes a 'user' message as input and returns a conversational reply.
4. Enable MCP Access: Toggle on the "Available in MCP" option. This makes your workflow discoverable and executable by connected clients like Lovable.

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Part 5: Generating the Frontend with Lovable AI

This is where the process comes together. We'll prompt Lovable to build our app.

1. Start a new project in Lovable.

2. Instead of designing manually, give Lovable a prompt describing the application you want.

   Example Prompt: Generate an Instagram-style chat window for a conversation with an AI girlfriend. The backend is a workflow hosted on my n8n server.

3. Lovable will use its n8n MCP tools to:

   • Search for available workflows: It will find your "AI Girlfriend Backend" workflow because you made it available.
   • Get workflow details: It will read the workflow's structure and understand that it needs a "user" field as input.
   • Generate the UI: It will create the chat bubbles, input box, and overall design.
   • Connect the backend: It will automatically wire the input field to make a request to your n8n workflow's production URL and display the response.

You can continue to re-prompt Lovable to fix bugs (like CORS issues, which it can often resolve on its own) or refine the design until you have a fully functional application, built in minutes.