How I Built a Working n8n Workflow Using DeepWiki

In the ever-expanding landscape of AI-assisted development, new tools emerge almost every week. Every once in a while, something really shifts the way we work (that’s why I don’t focus on developing new features since they do better than me). Recently, I came across one of those tools: DeepWiki by Devin AI.

Built with over $300,000 in R&D investment, DeepWiki is not just another AI chatbot for GitHub repositories. It deeply understands project structure, dependencies, and code relationships — and the results are surprisingly usable.

For researchers, automation builders, and developers, DeepWiki provides a way to extract meaningful insight from complex repositories. Not to mention for no-code developers like me, who spend hours experimenting with n8n workflows, it offers a new level of accessibility.

If you’re wondering how to use DeepWiki or looking for real-world use cases of DeepWiki, this article walks you through a practical example of using it to build a fully functioning n8n workflow.

Use Case: Writing a Booking Workflow in n8n with DeepWiki

To use it, simply go to GitHub page of n8n, and replace the “”github” in the URL with “deepwiki”, then you will be guided to DeepWiki page for n8n.

My goal is to create an n8n workflow that:

• Triggers when I receive an email about a meeting
• Checks my Google Calendar for availability
• Sends a “yes” response if I’m free, or “no” if not, along with alternate times

In the past, I would search for templates from the n8n workflow, drag nodes manually, and troubleshoot node-by-node. It could easily take an hour or two. Instead, this time I opened DeepWiki and typed:

I want to build a booking workflow that: 1. triggered by email if it is about meeting invitation, 2. check my Google calendar and look for available time, 3. if available, then reply with ‘yes’, else reply with ‘no’ and suggested time.

DeepWiki instantly analyzed the n8n repo and produced a 201-line JSON that I could paste directly into the workflow editor.

Not only did it work, but it also:

• Suggested relevant nodes like Gmail Trigger, Google Calendar, and Send Email
• Structured conditional branches for checking availability
• Built in placeholder logic that I could easily modify

This saved me hours of trial and error.

Why DeepWiki Works So Well and Unique

Unlike generic LLMs that generate JSON based on guesswork, DeepWiki is designed to understand the exact context of the codebase it’s paired with. Here’s what I found powerful:

• Contextual Awareness: DeepWiki scanned n8n’s node definitions, canvas structure, and package files to provide correct field names and flow logic
• Accurate Output: The generated workflow had fewer syntax errors compared to similar output from ChatGPT
• Multi-file Querying: It can navigate across a codebase — asking questions like “Where is the calendar node defined?” or “What props are needed for the Send Email node?”
• No Hallucination: So far, I’ve encountered significantly less “LLM hallucination” with DeepWiki

Therefore, if you’re still asking, “Is there any good example showing DeepWiki in action?”, this booking workflow test is one clear answer.

Other Advanced Use Cases for DeepWiki

Beyond just automation workflows, I see a few key areas where DeepWiki can be a game-changer:

• Reverse Engineering Legacy Code — Instead of reading hundreds of lines, ask DeepWiki to summarize modules and relationships
• Code Documentation — Great for explaining function responsibilities, API calls, and file hierarchies
• Security Reviews — Developers can query permission logic or look for sensitive data flows
• Prompt Engineering Support — Integrate DeepWiki answers into larger prompt chains for automated code generation

This is not just another AI interface — it’s a domain-specific cognitive layer on top of your codebase.

n8nChat vs DeepWiki: Why I Switched

Previously, I had explored n8nChat (see my article here), a Chrome extension that lets you build workflows in a chat interface by talking to ChatGPT.

When I first tried n8nChat, I was genuinely impressed. The idea of having a natural language interface built right into n8n felt refreshing. I could simply type something like “build me a tweet scheduler,” and it would immediately start assembling the relevant nodes. However, as I spent more time with it, the limitations became clear. The generated workflows often required heavy editing before they worked properly. Responses were sometimes too vague or generic to be useful. Most importantly, n8nChat can only help create new workflows — it doesn’t understand or edit existing ones, which significantly limits its practical value.

In contrast, DeepWiki doesn’t just generate — it explains, debugs, and iterates. It’s like having a technical teammate who already read your entire repo. That said, I still see a future where n8nChat matures into a better real-time assistant — but for now, DeepWiki gives me more control, more accuracy, and way better results.

Final Thoughts: AI That Truly Understands Code

As someone transitioning from biology to automation and data science, I’m constantly looking for tools that help me bridge the gap between ideas and execution. DeepWiki is easily the best AI-assisted repo tool I’ve used this year.

The real takeaway? AI tools won’t replace thinking — they reward it. The better you can describe a problem or process, the better output you’ll get. With something like DeepWiki, even non-developers can start creating complex automations. If you’re building with n8n, testing new workflows, or trying to learn by doing — I’d highly recommend giving DeepWiki a try.

Got questions? Drop them in the comments — or let me know what workflow you’d like me to build next!