What Are People Actually Building in n8n?

I run n8nlearninghub.com, a little corner of the internet where over 200 automation enthusiasts hang out, share workflows, and help each other solve problems. Every week, I publish tutorials on n8n workflows, and honestly, it’s one of the most rewarding things I do.

But there was this question that kept popping up in the Reddit community (r/n8n): “What are other people actually building with n8n?”

It seemed simple enough. The n8n marketplace is public — thousands of workflows right there for anyone to browse. But here’s the thing: browsing through them one by one? That’s like trying to understand a library by reading every book. What we really needed was a bird’s-eye view.

So I did what any curious engineer would do: I decided to scrape the entire marketplace and find out.

This is the story of how I went from asking a simple question to analyzing over 6,000 workflows, discovering fascinating patterns, and creating a production tool accessible to everyone.

Spoiler alert: HTTP Request is king, AI is taking over, and most workflows are surprisingly simple.

The Itch I Couldn’t Scratch

It started innocently enough. I was working on a tutorial about AI automation in n8n, and I wanted to know: “What’s actually trending in the n8n community?”

I opened the n8n marketplace and started clicking around. Cool, there’s a ChatGPT workflow. Oh, here’s one for Telegram. Wait, that’s interesting…

Two hours later, I’d looked at maybe 50 workflows and had a vague sense that “people like AI stuff.” Not exactly groundbreaking research.

I’m based in Morocco, navigating the job market by day and building automation tools by night. When you’re pursuing opportunities while also creating your own, you develop a deep appreciation for working smart. There had to be a better way.

That’s when the engineer brain kicked in: If this data is public, why am I clicking through it like it’s 1999?

Finding the API (Or: How I Learned to Stop Clicking and Love the Network Tab)

I opened up Chrome DevTools and started poking around the n8n marketplace. You know that feeling when you’re investigating something and you hit gold? That’s what happened when I saw this beautiful request in the Network tab:

https://n8n.io/api/product-api/workflows/search?rows=100&page=1

I stared at it for a second. Then I clicked on it. Then I did a little fist pump at my desk.

The API was simple, elegant, and exactly what I needed:

Paginated results (100 workflows per page)
Clean JSON responses
No authentication required
Full workflow data including nodes, categories, creators, everything

Sometimes the universe just hands you a gift.

Building the Scraper: The Fun Part

I fired up antigravity and started writing Python. The core logic was straightforward:

def scrape_workflows(max_count=1000):    workflows = []    page = 1        while len(workflows) < max_count:        # Fetch a page        params = {'rows': 100, 'page': page}        response = requests.get(base_url, params=params)        data = response.json()                # Add workflows to our collection        workflows.extend(data.get('workflows', []))                # Be respectful - don't hammer their servers        time.sleep(1.5)                page += 1        return workflows

I started with a goal of 1,000 workflows. “That should be enough for some insights,” I thought.

Then I checked how many workflows were actually available.

Over 6,000.

Challenge accepted.

I let the script run while I made tea (Moroccan mint tea, obviously — some traditions you don’t mess with). About 3 hours later, I had 6,837 workflows sitting in a JSON file on my hard drive.

The data was beautiful. Each workflow had:

Complete node structures
Creator information
View counts
Categories
Pricing
Creation dates
Everything

I felt like I’d just been handed the keys to the n8n kingdom.

The Analysis: What 6,000 Workflows Taught Me

Here’s where it got interesting. I wrote some analysis scripts and started crunching numbers. What I found surprised me.

Finding #1: HTTP Request Rules Everything

73% of workflows use the HTTP Request node.

Think about that for a second. Nearly three out of every four workflows are making API calls to external services.

This makes total sense when you think about it — n8n is all about connecting things, and HTTP Request is the universal connector. Can’t find a pre-built node? Just use HTTP Request and hit the API directly.

But 73%? That’s not just popular, that’s dominant.

Finding #2: AI Has Taken Over (And I’m Here for It)

Remember when I said I was writing an AI automation tutorial? Turns out I was onto something.

32% of workflows use OpenAI nodes. And that’s just OpenAI — add in Claude, LangChain, and other AI tools, and AI-related workflows dominate the marketplace.

The n8n community isn’t just experimenting with AI. They’re building entire automated systems around it:

AI customer support bots
Content generation pipelines
Email assistants
Data analysis tools
ChatGPT-powered everything

We’re watching automation meet artificial intelligence in real-time, and it’s happening in people’s n8n instances.

Finding #3: Simple Beats Complex

Here’s something that surprised me: 40% of workflows have 5 nodes or fewer.

I expected to find complex, intricate workflows with dozens of nodes doing sophisticated orchestration. And sure, those exist — 7% of workflows have over 20 nodes.

But the majority? Dead simple!

The average workflow has just 8.5 nodes. People aren’t building Rube Goldberg machines. They’re solving specific problems with focused solutions:

“Get an email, parse it, send to Slack”
“New form submission? Add to Google Sheets and notify me”
“Schedule a tweet, post it, track engagement”

You know how Python developers have import this—The Zen of Python? One of its core principles is "Simple is better than complex." The n8n community seems to have internalized this wisdom. They're not over-engineering solutions. They're building what works, keeping it clean, and moving on.

There’s a lesson here: The best automation isn’t the most impressive one — it’s the one that solves your problem without making you maintain a monster!

Finding #4: The Community is Incredibly Generous

This one made me smile: 86% of workflows on the marketplace are completely free!

Only 14% are paid, and even those average just $12.50. The n8n community isn’t trying to get rich off their workflows — they’re sharing knowledge, helping each other out, building in public.

It reminded me why I started n8nlearninghub.com in the first place. This is a community that lifts each other up.

The Challenges (Because Nothing is Ever Smooth)

Of course, it wasn’t all smooth sailing. Here are some fun problems I ran into:

Problem #1: The Data Was Nested

I naively thought: “I’ll just scrape the workflows and count the nodes.”

Then I looked at the actual data structure:

{  "workflow": {    "nodes": [      {        "name": "some-node",        "parameters": {          "nested": {            "deeply": {              "way": {                "too": {                  "much": "data"                }              }            }          }        }      }    ]  }}

Each workflow was a complex nested structure. Node parameters had their own nested objects. Categories were arrays of objects. Everything was… a lot.

I spent a solid day just flattening and normalizing data structures.

Problem #2: Categorizing Nodes

Nodes in n8n have IDs like n8n-nodes-base.httpRequest or @n8n/n8n-nodes-langchain.lmChatOpenAi.

Useful for computers, terrible for humans.

I had to write logic to parse these, extract the actual node names, categorize them (core nodes vs LangChain vs community), and make them readable:

n8n-nodes-base.httpRequest → "HTTP Request"
@n8n/n8n-nodes-langchain.lmChatOpenAi → "OpenAI Chat Model"

It was tedious, but necessary if I wanted the analysis to make sense.

Problem #3: What to Do With All This Data?

I had this treasure trove of information. Now what?

I could write a blog post with some charts. That would be cool.

Or… I could build something people could actually use.

That’s when I decided to turn this into a proper tool.

From Analysis to Product: Building the Apify Actor

I’ve been learning about the Apify platform — it’s basically a marketplace for automation tools. Developers build “Actors” (containerized apps), publish them, and users can run them on-demand.

Perfect.

I decided to package my scraper as an Apify Actor that anyone could use. But I didn’t want just to build another basic scraper (there are already a few on Apify). I wanted to build something with 10x more value.

So I added:

1. Comprehensive Analytics

Not just raw data, but actual insights:

Top 30 most-used nodes (with percentages!)
Top 15 categories
Pricing breakdown (free vs. paid, average prices)
Complexity analysis (simple, medium, complex, very complex)
Top 20 creators by workflow count
Most viewed workflows

2. ML Training Data Generation

Here’s where it gets fun. I’m planning to fine-tune an LLM to generate n8n workflows from natural language descriptions (that’s coming in Part 2 of this series!).

Get Mustaphaliaichi’s stories in your inbox

Join Medium for free to get updates from this writer.

So I built in functionality to automatically generate training datasets in two formats:

Alpaca format (for Llama, Mistral models)
OpenAI format (for GPT fine-tuning)

The tool analyzes each workflow and creates instruction-output pairs like:

json

{  "instruction": "Create an n8n workflow for: AI Email Assistant",  "input": "",  "output": {    "nodes": [      {"type": "Gmail Trigger"},      {"type": "OpenAI Chat Model"},      {"type": "Gmail"}    ]  }}

From 6,000 workflows, I generated over 4,000 training examples.

3. Professional Output Structure

Everything is organized cleanly:

Dataset: All the raw workflow data
Key-Value Store: Your analysis results
ML Training Data: Ready-to-use training files

You get everything in JSON, CSV, or Excel — whatever format you need.

The Technical Stack

For those curious about the implementation:

Scraping Layer:

Python 3.11
Requests library
Async operations (because waiting is boring)
Rate limiting (1.5s between requests — be nice to APIs!)

Analysis Engine:

Collections.Counter for frequency analysis
Custom categorization logic
Statistical calculations
Pattern recognition

Deployment:

Apify SDK
Docker containerization
Input/output schema definitions
Cloud-based execution

The whole thing is about 400 lines of Python. Clean, modular, and production-ready.

What I Learned (Beyond the Data)

Technical Lessons

1. Always Check for an API First

Before you start parsing HTML with BeautifulSoup, check the Network tab. You might find a beautiful API just waiting to be used.

2. Rate Limiting is Your Friend

That 1.5-second delay between requests? That’s not just being polite — it’s being smart. Nobody likes getting their IP blocked.

3. Data Cleaning Takes 80% of the Time

Everyone talks about the exciting parts — building ML models, creating visualizations. Nobody talks about the hours spent normalizing node names and flattening JSON structures.

But that’s where the real work is.

Product Lessons

4. Basic Scraping ≠ Value

Anyone can scrape data. The value is in what you do with it. Analysis, insights, ML training data — that’s where you differentiate.

Anyone can scrape data. The value is in what you do with it. Analysis, insights, ML training data — that’s where you differentiate.

5. Build What the Community Needs

I didn’t build this in a vacuum. I built it because my community kept asking questions about marketplace trends. Listen to your users.

6. Series > Single Article

Instead of cramming everything into one massive article, I’m breaking this into a series:

Part 1: Scraping and analysis (you’re here!)
Part 2: Fine-tuning an LLM on the data
Part 3: Building a workflow generator

Each part stands alone, but together they tell a complete story.

The Results: Real Insights, Real Tool

The analyzer is now live on Apify Store, and here’s what it can do:

For Content Creators: Find out what’s trending. See which workflow types get the most views. Discover gaps in the marketplace where you could create tutorials.

For ML Engineers: Get ready-to-use training data for fine-tuning LLMs on workflow generation. No need to scrape and prepare data yourself.

For Market Researchers: Understand automation trends. Track which integrations are growing. Analyze the n8n ecosystem.

For Curious Developers: Just explore! See what 6,000+ creative people have built. Get inspired. Steal ideas (it’s open source, we encourage it).

Currently, it’s essentially free to try while I collect feedback and build the user base. Because just like those 86% of free workflows on the marketplace, I believe in the power of community sharing.

The Fun Statistics (Because Numbers are Cool)

Let me hit you with some more fascinating findings:

Try It Yourself

The analyzer is live on Apify Store: n8n-marketplace-analyzer

You can:

Scrape 1 to 10,000 workflows
Get comprehensive analytics
Generate ML training data
Export everything in multiple formats

It’s currently priced at basically free (about $0.01 per 1,000 workflows) while I collect feedback. I’m also joining Apify’s $1M Challenge, so every user helps!

If you try it, I’d love to hear what you discover. Drop a comment or find me on Reddit here.

What’s Next: The Fine-Tuning Journey

This is just Part 1 of the journey. In Part 2, I’m going to share something even more exciting: how I used this data to fine-tune Llama 3 8B to generate n8n workflows from natural language.

Spoiler: My first attempt with Mistral 7B failed spectacularly. The model overfit so badly that by step 50, the loss hit 0.0001. Turns out a loss of zero is not a good thing — it means the model memorized the training data instead of learning from it.

The debugging process, switching to Llama 3, adjusting hyperparameters, and finally getting a working model that can actually generate sensible workflows… well, that’s a story with more plot twists than I expected.

But I’ll save that for Part 2.\

Join the Journey

If you’re into n8n, automation, or just enjoy watching someone build stuff in public:

Website: n8nlearninghub.com (free tutorials)
Reddit: r/n8n (join 141K automation enthusiasts)
GitHub: MuLIAICHI(all my projects are open source)

I publish new n8n tutorials every week, and I’m documenting the whole fine-tuning journey as I go.

The beautiful thing about this project is that it started with a simple question from the community and turned into something that benefits everyone. That’s the power of building in public and sharing what you learn.

The beautiful thing about this project is that it started with a simple question from the community and turned into something that benefits everyone. That’s the power of building in public and sharing what you learn.

The Real Lesson

Here’s what I want you to take away from this:

Curiosity + Time + Willingness to Share = Magic

I didn’t start this project with a master plan. I didn’t know I’d end up analyzing 6,000 workflows or building an Apify actor or fine-tuning an LLM.

I just had a question and decided to answer it.

Then I decided to share what I found.

Then I decided to build a tool so others could answer their own questions.

That’s how most good things get built — not from grand visions, but from curiosity and a willingness to help others.

So if you have a question that bugs you, if you’re curious about something in your field, if you think “someone should really analyze this”…

Be that someone.

Build the tool. Share the insights. Write the article.

The community will thank you for it.

Part 2 coming soon: “I Fine-Tuned Llama 3 on 6,000 n8n Workflows — Here’s What Happened”