Build a Smart AI Chat Assistant with GPT-4o Multimodal

Why this template is worth your time

Imagine having a chat assistant that does not just understand text, but can also look at images, read PDFs, and keep track of what you talked about earlier. That is exactly what this n8n workflow template helps you build, using OpenAI’s GPT-4o multimodal model.

Whether you want a customer support bot, a personal AI helper, or a chat widget embedded in your app, this template gives you a ready-made foundation that you can tweak to fit your own use case.

What this n8n workflow actually does

At a high level, the workflow connects a chat interface with OpenAI’s GPT-4o model and a set of memory nodes inside n8n. It can:

• Receive user messages and file uploads like images or PDFs
• Analyze those files with GPT-4o’s multimodal capabilities
• Store and reuse conversation context with memory nodes
• Generate smart, context-aware responses through an AI Agent node

The result is a multimodal AI chat assistant that feels more like a helpful human than a simple Q&A bot.

How the workflow starts: the chat trigger

Everything begins with a chat trigger node. This is where your users type their messages or upload files. From here, the workflow decides what to do next based on whether the user sent plain text, attached a file, or did both.

Once a message comes in, the workflow checks: is there a file attached that needs special handling, or is this a regular text-only interaction?

Smart file handling with GPT-4o multimodal

One of the coolest parts of this template is how it deals with uploaded files. If your users share an image or a PDF, the workflow does not just store it, it actually analyzes it.

Step 1 – Detecting uploads with the If node

The first decision point is an If node. Its job is simple but important:

• It checks if the incoming message includes a file, such as an image or a PDF.
• If no file is present, the workflow can continue as a normal text-only conversation.
• If a file is present, the workflow branches into a more advanced analysis path.

Step 2 – Analyzing images and PDFs with GPT-4o

When a file is detected, it is handed off to an OpenAI node configured with the GPT-4o multimodal model. This is where the magic happens:

• Images can be interpreted, described, or inspected for specific details.
• PDFs can be read and summarized, or used as a source of information for later questions.

Instead of you manually parsing content, GPT-4o does the heavy lifting and returns a structured understanding of the file that the rest of the workflow can use.

Step 3 – Saving file insights to chat memory

After GPT-4o analyzes the file, the resulting content is stored in a memory node called chatmem. This is a dedicated memory store that keeps track of what was extracted from the uploaded file, so the assistant can refer back to it later in the conversation.

That way, if the user asks something like “What did that PDF say about pricing again?” the assistant can answer without having to reprocess the file.

Step 4 – Extra processing with a Basic LLM Chain

Before moving on, the analyzed content goes through a Basic LLM Chain using the OpenAI chat model. This step is useful when you want to:

• Summarize or clean up the extracted content
• Transform it into a more useful format for your use case
• Run task-specific logic, such as classification or extraction

The Basic LLM Chain acts like a mini processing pipeline that prepares the content so the final AI response is more focused and helpful.

Keeping the conversation alive with memory

A good AI assistant should not feel like it forgets everything after each message. This template solves that with several memory nodes that track the state of the conversation and any analyzed files.

Simple Memory nodes for session context

The workflow uses multiple Simple Memory buffer nodes that store information based on the user’s session ID. These nodes help with:

• Remembering previous messages in the same conversation
• Maintaining context across multiple steps or branches
• Handling different users without mixing up their data

This setup lets your assistant respond in a way that feels continuous and context-aware, instead of treating each message like a brand new interaction.

Retrieving earlier content with chatmem1

Once the file handling and any initial processing are complete, another memory node named chatmem1 comes into play. Its role is to:

• Pull in content from earlier in the conversation
• Include past file analyses and relevant context
• Feed that combined history into the main AI Agent

In other words, chatmem1 helps the assistant “remember” what has already happened so it can respond naturally.

The AI Agent – your main conversational brain

At the center of the whole workflow is the AI Agent node. This node uses OpenAI’s GPT-4o chat model and takes into account:

• The latest user input
• Conversation history from the memory nodes
• File analysis results and any LLM chain processing

With all of that context, the AI Agent generates a response that feels tailored to the user and their current situation, not just a generic answer.

When to use this n8n template

This workflow is a great fit if you want to build:

• Customer support bots that can read attached screenshots or PDFs and help users faster
• Personal AI assistants that remember what you upload and reference it later
• Knowledge base helpers that can understand documents and answer detailed questions about them
• Embedded chat widgets for your product that feel smart, interactive, and context-aware

If your users share files or need deeper, more continuous conversations, this template gives you a strong starting point.

How to customize and expand the template

The template comes with helpful sticky notes that highlight where you will probably want to make changes. Here is how you can adapt it to your own project.

1. Tailor the AI Agent prompt

The first thing most people customize is the prompt used by the AI Agent. This is where you define the assistant’s personality, tone, and role. For example, you can make it:

• A friendly customer support bot that focuses on troubleshooting and FAQs
• A proactive personal assistant that helps with planning, reminders, and summaries
• A precise knowledge base helper that sticks closely to documentation and uploaded files

By tweaking the prompt, you can keep the same technical workflow but completely change how the assistant behaves.

2. Fine-tune memory for your conversation length

Next, look at the Simple Memory buffer nodes. You can adjust them to better match your app’s needs, for example:

• Increase memory limits for longer conversations
• Control how much history is passed to the AI Agent
• Refine how session data is stored and retrieved

This helps you balance performance, cost, and conversational quality, especially if users tend to have long, detailed chats.

3. Extend file type and media handling

Out of the box, the workflow focuses on images and PDFs, but you are not limited to that. You can expand the file handling part to:

• Support more document types
• Add richer media analysis flows
• Branch logic based on file type and user intent

If your users regularly upload different formats, this is a great place to customize and grow the template.

Why this template makes your life easier

Instead of wiring everything from scratch, this n8n workflow template gives you:

• A prebuilt structure for chat triggers, memory, and AI responses
• Working examples of GPT-4o multimodal analysis for images and PDFs
• A clear path for customization so you can focus on your use case, not low-level wiring

You get a solid, production-ready starting point that you can adapt quickly, which means faster experiments and less time reinventing the wheel.

Try the GPT-4o multimodal assistant in your own stack

If you are ready to add smarter conversations to your app or workflow, this template gives you everything you need to get going. You can:

• Spin up a multimodal AI assistant that understands text, images, and PDFs
• Customize prompts, memory, and file handling to match your product
• Iterate quickly as you learn how your users interact with the assistant

Explore the template, make it your own, and deploy a powerful AI chat experience that feels natural and genuinely helpful.