🚀 Building Bridges: Empowering OutSystems with n8n
- Alex Fernandes
- Jul 29
- 8 min read
Introduction
If there’s one thing I love about working in tech, it’s how every new tool feels like a new superpower. Recently, I decided to dive more into automation and AI integration, exploring how we can make our apps even smarter and more efficient. That’s when I stumbled upon n8n — an open-source workflow automation tool that promised to connect everything, automate anything, and make life easier.
As an OutSystems developer, I couldn’t help but wonder: how could n8n fit into my world of low-code applications? Could it help me connect my OutSystems apps to external services, automate more processes around them, and even enhance them with AI capabilities – all without adding complexity to the core app?
In this article, I’ll share my journey learning n8n, the small projects I built to practice, and how I started integrating it with OutSystems. I’ll also share a few lessons learned along the way — the good, the challenging, and the exciting possibilities ahead.
Whether you’re an OutSystems developer, an automation enthusiast, or just someone curious about new ways to power up your projects, I hope this story inspires you to start your own exploration too!
⚡ What is n8n?
Coming from a background in full-stack development, system automation, and API integrations, I’m always on the lookout for tools that can make development smarter, faster, and more flexible.
n8n is an open-source workflow automation tool that allows you to connect APIs, automate tasks, and build complex workflows — all through an intuitive, low-code interface.
What really drew me to n8n was:
Its visual approach, which fits naturally with the way I already structure applications in OutSystems.
Familiarity with tools like Talend DI, which I had worked with in the past — making the learning curve smoother.
Its powerful library of nodes and integrations, covering a wide range of services, APIs, and protocols right out of the box.
Built-in AI integration capabilities, enabling fast experimentation with AI models and intelligent workflows without heavy infrastructure work.
It’s open, self-hostable architecture gives developers the freedom to build unlimited integrations and workflows without being tied to vendor restrictions.
🔍 How I Saw the Opportunity to Connect It with OutSystems
Challenged by one of my seniors, and inspired by the idea of extending capabilities within an existing OutSystems project, I started thinking:
How could I leverage n8n to build something that would work in the real world?
That curiosity led me to dive deeper — exploring n8n’s nodes, testing different integrations, and imagining how these automations could fit alongside an OutSystems application.
The more I experimented, the more it became clear that there was real potential to create smart, scalable solutions by combining the two platforms.
This exploration eventually led to my very first integration use case between OutSystems and n8n — something I’ll walk you through in more detail a little later.
🧱 Learning Through Building
When I first started exploring n8n, I decided to take the simple route. Instead of setting up everything from scratch, I chose to use the cloud version to get up and running quickly — avoiding the installation process and jumping straight into building workflows.
I began by following YouTube tutorials, replicating basic use cases, and slowly personalizing the workflows to fit my own ideas.
At first, everything felt new: navigating the editor, connecting nodes, handling errors — but with each experiment, I became more comfortable.
I especially enjoyed:
Exploring different types of nodes (HTTP, Webhook, AI connectors).
Connecting with AI tools to automate tasks and create smart workflows.
Testing how data flows between services and how to transform it along the way.
Some tools and integrations required a bit of experimentation — but that was part of the fun. It often took a few iterations to get things working just the way I envisioned, but with a curious mindset, I was able to move from idea to execution.
But the feeling when a workflow finally ran successfully was unmatched — and those small wins became a huge source of motivation to keep going.
Looking back, it wasn’t about mastering the tool immediately — it was about staying curious, experimenting without fear, and celebrating small victories that eventually built real confidence.
📚 Use Case 1: Enhancing a Bookshelf App with AI-Generated Content
📘 The Idea
The bookshelf application I worked with was originally developed by one of my colleagues using OutSystems, allowing users to browse and search for books.
Looking at it, I saw an opportunity to take it further — what if, whenever a book was searched, the system could automatically research it, summarize its content, generate a blog post, and even recommend similar titles?
That’s when I realized how n8n could act as the automation engine and provide a more dynamic experience.
⚙️ The Solution
The flow starts in OutSystems. When a user searches for a book, the app sends a request to n8n via a webhook.
From there, n8n uses an AI Agent to:
Research the book.
Generate a blog-style summary.
Recommend 3 related books.
The results are then saved into a Google Sheet. Once the task is done, an Outlook Event is generated to notify the user that the enriched content is ready and needs review.
🔧 Key Tools & Nodes Used
Webhook: entry point from OutSystems.
AI Agent: used to research and generate blog content and suggestions.
Google Sheets: stores the results.
Outlook Event: triggers blog post review notification.
💡 What I Learned
This was my first time working with webhooks in n8n — and it opened the door to seamless communication between OutSystems and external tools.
I also realized how powerful AI Agents can be when paired with workflow automation. Seeing real-time enrichment of app content with minimal code was a breakthrough moment.
Plus, getting it all to work with clear, structured steps helped me appreciate how n8n simplifies multi-step, cross-platform workflows.
📚 Use Case 2: RAG Pipeline and AI-Powered Chatbot (with OutSystems Integration)
📘 The Idea
I wanted to explore Retrieval-Augmented Generation (RAG) — the idea that a chatbot could answer questions not just using a pre-trained model, but by pulling relevant information from user-provided documents.
The challenge: could I automate this process end-to-end and make it accessible inside an OutSystems app?
⚙️ The Solution
It starts when a user uploads a document to a specific Google Drive folder.
n8n detects this upload, downloads the file, and splits it into chunks for processing.
Then it uses the Pinecone node to store the chunks in a Pinecone vector database — making the content searchable by context.
Unlike traditional database or spreadsheets like Google Sheets, witch stores and retrieves exact keyword matches, a vector database like Pinecone enables semantic search – understanding the meaning behind a user’s query. Pinecone transforms and stores document chunks as numerical vectors, allowing the chatbot to find the most relevant context based on similarity, not just exact word. This makes responses far more accurate and intelligent.
The next step is the chatbot itself. I built a chatbot workflow in n8n using an AI agent that queries the vector store and answers user questions based on the uploaded content.
To make the experience seamless, I exposed the chatbot via a public URL, and integrated it into my OutSystems app. This allows users to interact with the AI directly inside the application — typing questions and getting document-aware answers in real time.
At the time, OutSystems workbench was not a reality yet….
🔧 Key Tools & Nodes Used
Google Drive: watches for new document uploads.
Data Loader Node: breaks files into manageable text chunks.
Pinecone Node: indexes content into vector database.
AI Agent: handles user queries with vector-enhanced retrieval.
Public Chatbot URL: exposed the chatbot interface.
OutSystems: embeds the chatbot in the app via HTTP integration.
💡 What I Learned
This project helped me connect all the pieces of a modern RAG pipeline — from unstructured data ingestion to vector indexing and natural language interaction.
But what stood out the most was the power of combining n8n with OutSystems.
With just a simple integration (using a web block), I was able to bring a context-aware AI chatbot directly into my app — without having to build or host a full conversational interface from scratch.
The result? A smart, document-aware assistant seamlessly embedded into an OutSystems UI - powered entirely by a lightweight, automated n8n workflow.
🤝 How n8n and OutSystems can Work Together?
It’s amazing to see these two platforms working together and truly empowering each other. n8n excels at handling external integrations, webhooks, and APIs — especially in cases where OutSystems might not offer a native or flexible connection out of the box.
It also helps offload background automation tasks, like sending notifications, managing workflows, or orchestrating AI services — keeping your OutSystems apps clean, fast, and focused.
What really stands out is how n8n speeds up prototyping and allows you to extend your apps with AI functionalities — without needing to write or maintain heavy backend logic inside OutSystems.
At the same time, OutSystems provides the robust front-end, data models, and user experience that make these automations meaningful to end users.
Together, they form a powerful combination: OutSystems at the core, and n8n expanding its reach.
🧠 Challenges and What I Learned
No learning journey is complete without a few bumps along the way — and my experience with n8n was no exception.
One of the main challenges I faced early on was that some nodes didn’t have detailed documentation, which made it difficult to understand how to configure them properly for my use case.
Rather than giving up or guessing blindly, I turned to the community. I started watching YouTube videos and reading forum threads from other users who had worked with the same node. Seeing real examples and how others approached similar problems gave me the clarity I needed.
More than anything, this experience reinforced the mindset I’ve built over the years — that patience and curiosity are essential in this field. When documentation falls short, curiosity becomes your most valuable tool. By experimenting, digging into examples, and not being afraid to fail a few times, I was able to turn frustration into progress.
Every obstacle became a small breakthrough — and every breakthrough made me more confident in what I could build next.
🔮 What’s Next
This is just the beginning.
I plan to continue exploring n8n’s potential — building more use cases, experimenting with different types of AI Agents, and pushing the boundaries of what I can automate and integrate with OutSystems.
I’m especially interested in using n8n to prototype real business automations, streamline manual processes, and deepen my understanding of orchestration between systems.
If you’re working on something similar or have ideas you’d like to collaborate on, feel free to reach out — I’d love to connect and exchange thoughts!
✨ Final Thoughts
This journey has been incredibly rewarding.
It deepened my understanding of the AI space, taught me how vector databases work and how we can use them effectively, and gave me hands-on experience creating workflows that solve real-world problems.
I also learned how to install and manage n8n locally, and how to structure smart, reusable workflows that integrate seamlessly with platforms like OutSystems.
More than anything, this journey reminded me how much we can accomplish when we stay curious, build small, and experiment boldly.
Whether you’re just getting started or already deep into automation, I encourage you to try something new — even a small idea can grow into something powerful.
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