I’ve been fascinated by AI agents for a while, intrigued by how they can automate tasks that I’m currently doing manually. Recently, I decided to test the waters.
I wanted to create a workflow that takes a given subject, queries an AI (I’m using my OpenAI account) to get a list of items on that subject, and then for each item, get more details from the AI and generate actual Next.js components. It sounded simple in my head: feed a prompt, iterate over the answer, and produce a file.
But as it turns out, choosing the right tool for this multi-step process wasn’t as straightforward as I hoped.
Why Experiment with AI Agents?
The buzz around AI tools and large language models has been growing steadily. Besides the hype, there’s real potential in letting an AI handle part of the development process. Working on my projects part time, and mostly as a hobby, doesn’t scale much. I need more BANG. That was precisely the idea I wanted to explore: a system that I can feed some of my ideas into, and outputs a partially backed Next.js component that I can use.
The payoff is significant. By investing in a repeatable workflow, I could cut down on the tedious parts. Think about it as a programmatic SEO project. Plus, it’s exciting to see AI bridging the gap between idea and deployment.
The Plan: Building a Flow for Next.js
My ultimate goal was to create a pipeline that starts with a subject prompt—let’s say, a “Universities in the state of New York” – and kicks off a series of interactions with OpenAI. The AI would produce a list of universities, provide the relevant details for each one of them, and produce the relevant Next.js components, adding styling or functionality. By the final step, I’d expect a somewhat fleshed-out file ready for use in my project.
With that vision in mind, I went on a quest for a tool or workflow builder that would:
- Allow chained prompts and responses.
- Store or output the final code in a structured format.
First Stop: Trying Zapier AI Agents
I initially used Zapier because I’ve had success with it for straightforward automations. In many ways, it’s a solid and well respected solution – connecting apps to triggers and actions with minimal fuss. However, my particular requirement was a multi-step AI conversation, and I just couldn’t get Zapier’s agent to generate anything useful past the initial query to Open AI.
Diving into Langflow: A Complex but Powerful Tool
Next on my list was Langflow, which promises visually enhanced AI workflows. At first glance, it seemed perfect – each node represented a piece of the flow, and everything connected like a flowchart. It’s highly capable if you need branching logic or intricate sequences.
But that power comes with a learning curve. Even though I managed to piece together a simple flow, adding more prompts and conditions, iterating over the results, etc. quickly ballooned in complexity. Data-handling between nodes became confusing, and I felt like I was constantly backtracking to see which node connected where. For a one-off, targeted pipeline, it felt like overkill. I decided to keep Langflow in mind for future, more complex projects – just not this one.
Experimenting with n8n: Lacking File Exports?
My third attempt was with n8n. Installation was a breeze, and I enjoyed how easy it was to chain multiple nodes. Integrating OpenAI was also straightforward, letting me pass the results from one query into the next without too much hassle. Iterating over the results was a bit more complex, and I had to go with a custom component – never a good sign.
The challenge came when I tried to output the final Next.js component as an actual file. n8n’s strength lies in passing data through various nodes, not necessarily in generating or storing code beyond its internal workflow. Writing files to a local folder was just confusing. While I suspect it’s achievable, I couldn’t do so elegantly within my timeframe.
Moving Forward: Considering Vercel’s AI SDK
After my adventures with these platforms, I’m leaning toward implementing a stripped-down custom solution using Vercel’s AI SDK. It would let me control each prompt and the final output without wrangling an external UI. Although it’s more coding, it means full transparency and the flexibility to write out Next.js files exactly how I like.
Conclusion
Exploring AI agents for generating Next.js components has been an interesting exercise. The old-school developer in me still cringe a bit at what can be achieved these days, but like everyone else, I’m slowly doing more with tools like v0.dev and Bolt.new.