One Chat App… ALL the AI Models 🤯 (n8n + Lovable)

Key Concepts:

• AI Model Selection: Automatically choosing the best AI model based on the user's question.
• Lovable: A no-code front-end platform for building web applications.
• Naden (N8N): A no-code back-end platform for building AI agents and workflows.
• Webhooks: A way for applications to communicate with each other in real-time.
• AI Agent: An AI that can perform tasks and make decisions.
• LLM Chain: A sequence of language models that are used to perform a task.
• Output Parser: A tool that is used to control the output of a language model.
• Model Selector: A feature in Naden that allows you to select different AI models for different purposes.
• Memory: A feature that allows an AI agent to remember previous conversations.

1. Building the Front-End with Lovable:

• The video demonstrates building a chat-like interface using Lovable, a no-code platform.
• A detailed prompt is used to instruct Lovable to create a modern, elegant web app with a clean UI, inspired by Coach Hello and Chat GPT.
• The prompt includes instructions for sending user messages to a Naden webhook via POST JSON and rendering the reply in a chat interface.
• The Naden webhook URL is embedded in the prompt to ensure communication between the front-end and back-end.
• Example: The prompt specifies the desired UI elements, such as buttons and chat windows.

2. Setting up the Naden Back-End:

• The video explains how to create a Naden workflow to handle incoming messages from the Lovable front-end.
• A webhook trigger is added to the workflow to listen for POST requests from Lovable.
• The webhook path is set to "/chat" for easy identification.
• The "Respond to Webhook" mode is initially set to "Respond Immediately" for testing purposes.
• Example: The webhook URL is copied from Naden and pasted into the Lovable prompt.

3. Categorizing Requests with a Basic LLM Chain:

• A Basic LLM Chain node is added to the Naden workflow to categorize incoming requests based on their intent.
• An OpenAI chat model (GPT-4.1 mini) is used for this purpose.
• A structured output parser is used to control the output format of the LLM chain.
• The system prompt instructs the LLM to classify requests into one of the following categories: general, reasoning, coding, or search.
• The output format is specified as a single lowercase category with no additional text.
• Example: The system prompt includes detailed descriptions of each category to guide the LLM.

4. Implementing the AI Agent with Model Selector:

• An AI Agent node is added to the Naden workflow to process the categorized requests.
• The Model Selector feature is used to dynamically select the appropriate AI model based on the request type.
• Four different AI models are used:
  • Claude Sonnet 4 (via Entropic) for coding-related requests.
  • Gemini 2.5 Flash (via Open Router) for reasoning-related requests.
  • GPT-4.1 mini (via OpenAI) for general questions.
  • Perplexity (via Open Router) for search-related requests.
• Rules are defined to map each request type to the corresponding AI model.
• A memory node is added to the AI Agent to maintain conversation history.
• Example: The rule for Claude Sonnet 4 specifies that it should be used when the request type is "coding."

5. Responding to the Front-End:

• A "Respond to Webhook" node is added to the Naden workflow to send the AI agent's response back to the Lovable front-end.
• The "Respond to Webhook" mode in the webhook trigger is changed to "Using Respond to Webhook mode."
• This ensures that the response is sent only after the AI agent has finished processing the request.

6. Testing and Troubleshooting:

• The video demonstrates how to test the workflow by sending different types of requests from the Lovable front-end.
• The Model Selector is verified to be working correctly by observing which AI model is used for each request.
• Troubleshooting steps are shown for fixing issues with the Lovable front-end and the Naden workflow.
• Example: The video shows how to use Lovable's built-in chat feature to report issues and receive assistance.

7. Monetization and Community:

• The video briefly mentions the potential for monetizing the AI assistant by building SAS apps or offering consulting services.
• The video encourages viewers to join the Naden community for support and resources.
• The community offers access to blueprints, Q&A sessions, and courses on AI and Naden.

Notable Quotes:

• "That's exactly what I built here using lovable as a front end. and then Naden AI agent as a backend that has the ability to automatically route and choose different AI models based on the question that's coming in from our front end here."
• "This is a complete no code solution and I'm going to show you step by step how to build both the front end using Lovable and the back end using NAN."
• "As you can see, this is super powerful. I mean, it's like I said, Chacht essentially on steroids."

Technical Terms:

• SAS: Software as a Service
• Web Hook: A mechanism for applications to send real-time information to each other.
• JSON: JavaScript Object Notation, a standard format for data interchange.
• LLM: Large Language Model
• API: Application Programming Interface

Logical Connections:

• The video starts by introducing the concept of an AI assistant that can automatically select the best AI model for a given task.
• It then explains how to build the front-end of the AI assistant using Lovable.
• Next, it describes how to set up the back-end of the AI assistant using Naden.
• The video then shows how to categorize requests, implement the AI agent with Model Selector, and respond to the front-end.
• Finally, it demonstrates how to test and troubleshoot the AI assistant.

Conclusion:

The video provides a comprehensive guide to building an AI assistant that can automatically select the best AI model for a given task. It uses Lovable for the front-end and Naden for the back-end, and it leverages the Model Selector feature in Naden to dynamically choose the appropriate AI model. The video also covers testing, troubleshooting, and monetization, making it a valuable resource for anyone interested in building AI-powered applications.