Over the last few years, I’ve spent a lot of time researching AI and building projects with it. I began by coding projects by hand, sometimes with AI’s assistance, to prototype ideas and understand the ins and outs of working with AI. As an architecture purist, I quickly fell in love with LangChain, even though it was barely usable and manageable in its early days. I encountered countless problems, tried many workarounds, and learned valuable lessons. Now, I feel confident saying: I know what to build with AI and how to do it. But along the way, something changed in me that I never expected.
I’ve always relied on code editors and their powerful plugins to build my solutions. This approach worked well for years. However, when AI entered the picture, I found myself coding brittle solutions too slowly—even with AI as a development tool. Realizing the need to iterate faster, I started exploring no-code platforms that could support me in the long run. As an old-school developer with over 30 years of experience, I didn’t anticipate the transformative value n8n would bring to my workflow.
With n8n, I can rapidly assemble automation workflows that incorporate AI agents with clearly defined and limited scopes of operation. This capability has put me on an entirely new path.
Advantages of n8n
The benefits of using n8n are clear:
- Speed of Development: I can iterate and deploy solutions far more quickly than before.
- Easy Sharing: Developers can build and share solutions effortlessly. Rich workflows are often just a copy-paste away, ready to adapt and integrate into new projects.
- Simplified Understanding: While open-source Git repositories provide reusable code, such code is often scattered across multiple files. This makes understanding and integration much more complex and error-prone. With n8n, workflows are visual, providing a clear view of how they operate throughout their lifecycle. Whether you’re a hardcore developer or a savvy newcomer, the visual nature of workflows simplifies the process.
This shift has brought joy to my daily work. I can effortlessly add new tools to my AI agents in no time. While I still code frontends, having n8n as a backend accelerates my iterations to the point where I’d recommend it to anyone.
Limitations and Feature Requests
Of course, n8n isn’t without its limitations. For instance:
- Streaming Responses: It doesn’t yet support streaming responses.
- Simplified RAG Solutions: The current Retrieval-Augmented Generation (RAG) implementation lacks metadata output, which is frustrating.
I hope these issues will be resolved soon as the n8n team grows and can implement feature requests more quickly. To contribute to this growth, here’s my own list of outstanding requests that I believe would add immense value:
- AOP-Style Hooks: Enable calling HTTP endpoints before and after a tool’s execution, or introduce an n8n event node to create listeners that translate n8n events into custom actions. For example, this could support notification systems.
- Failover LLM: Retry failed operations with another LLM provider to enhance reliability.
- Conditional AI Agent Tools: Allow tools to be bypassed based on conditions. This would reduce noise and streamline the logic for LLMs to act upon.
Final Thoughts
n8n has fundamentally changed the way I approach backend development. Its visual workflows, ease of sharing, and speed of iteration have made it a game-changer in my AI projects. While there are some areas for improvement, the potential of this platform is enormous, and I’m excited to see how it evolves. Let’s support the team and help them implement these critical features faster!
