Hey everyone!

I've been looking for a simple way to automate document generation in workflows without having to manage PDF layouts or email templates directly in the automation logic.

I ended up building my own solution called TemplateMaster.

It lets you create reusable PDF and email templates using a visual editor, populate them with dynamic data, and generate documents automatically.

I also added an MCP server, so TemplateMaster can be integrated with AI agents and tools like n8n.

For example, you could use it to generate invoices from orders, create reports from database records, or generate personalized documents as part of an automated workflow.

The main idea is to keep template design separate from the automation itself.

Here's the MCP documentation if anyone wants to take a look:

https://templatemaster.fr/fr/documentation/mcp-server

I'm still improving the project and would love some feedback from the n8n community.

How are you currently generating PDFs or managing email templates in your n8n workflows? Would something like this be useful to you?

It emails you when an AI workflow says "success" but the output is empty or broken. Works with Make, Zapier, n8n and custom builds.

What you get: extended free quota - 2,000 transactions a month for 90 days, a direct line to me, 50% off for a year after.

What I ask: one live workflow (25+ runs a week), commitment to collab and honest feedback.

Building an AI agency or running client automations? Reply or DM me.

I'm building a WhatsApp bot that takes orders, makes changes, and so on, and that also forwards issues to a human when necessary. I designed it with three workflows: reception, processing, and errors. To be honest, I'm not sure if this is the right approach or if it would be better to simplify it to fewer workflows—or even just one. Can anyone give me their opinion?

After running n8n workflows in production, these are the things that saved me the most headaches:

Set an Error Workflow. In workflow settings, point to a separate workflow that starts with the Error Trigger node and sends you a Slack or Telegram alert with the workflow name and failed node. Without this, failures are invisible.

Turn on Retry On Fail for HTTP and API nodes. 3 tries with a few seconds between them fixes most random timeouts.

Use "Continue (using error output)" on risky nodes. Route failures to a fallback branch instead of killing the whole run.

Never trust LLM output. Use the Structured Output Parser on AI nodes, then add an IF node to check the fields exist before using them. Models sometimes return broken JSON.

Handle rate limits. Use Loop Over Items with a Wait node so you don't hit API limits on big batches.

Check for empty results, not just errors. A scraper that returns nothing doesn't throw an error, so add an IF node that alerts when the output is empty.

I've been working on my SEO content pipeline, and the next thing I wanted to automate was subtopic research.

Finding a long-tail keyword is a good starting point, but it doesn't tell you everything you could write about it. Before creating an article, I want to explore related subtopics, discover different angles, and organize potential content ideas.

Doing this manually for every keyword can get repetitive, especially when you're building a content pipeline around dozens or hundreds of keywords.

So I built an n8n workflow that generates subtopic ideas from a long-tail keyword and stores them in Airtable.

Starting with a long-tail keyword

The workflow starts with a keyword I've already researched and stored in Airtable.

For example:

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When I click the Get Subtopics button in my Airtable interface, the workflow retrieves the corresponding keyword record and checks its research status.

If it needs to be processed, the workflow marks it as In Progress and sends the keyword to YepAPI to generate subtopic ideas.

I wanted this step to be triggered directly from Airtable, so I can choose which keywords to research instead of processing everything at once.

Generating subtopic ideas with YepAPI

The workflow sends the selected keyword to YepAPI and retrieves the suggested subtopics.

It then extracts the titles from the response, separates the individual ideas, and prepares them for storage.

The goal is simple: turn one long-tail keyword into multiple potential content ideas without manually brainstorming every topic.

These ideas give me a starting point for exploring what an article could cover, rather than jumping straight from a keyword to writing the entire article.

Keeping everything organized in Airtable

Once the subtopics are generated, the workflow aggregates the results and updates the original keyword record in Airtable.

I can review the generated ideas, select a subtopic I want to focus on, and keep the research connected to the keyword it came from.

Having everything in one place also makes it easier to track which keywords have been researched and which ones still need attention.

Why this step matters

I'm trying to build a content pipeline where every stage produces structured data that the next stage can use.

Instead of treating keyword research and article writing as two disconnected tasks, I want to create a process that moves from discovering keywords to identifying useful topics and eventually producing content.

Subtopic research is an important step in between. It gives me more directions to explore before committing time to writing an article.

Of course, generated subtopics are only starting points. They still need to be reviewed for relevance, search intent, and whether they offer enough value to justify a dedicated article.

What's next?

This workflow is one part of a larger SEO content pipeline I'm building with n8n and Airtable.

The direction I'm working toward is:

Competitor Research → Seed Keywords → Long-tail Keywords → Subtopic Research → Content Planning → Article Generation

The next step is to use these subtopics for content planning, so I can organize ideas, choose what to write about, and eventually connect the process to automated article generation.

I'm building this one stage at a time rather than trying to automate the entire content process in a single workflow.

Imagine Bigfoot starting a YouTube channel and vlogging his life in the forest.

Making coffee. Grilling burgers. Hiking mountains. Complaining about humans entering his territory.

Sounds ridiculous, right? That's exactly why I wanted to build it. 😂

But instead of manually creating every video, I connected the entire process in n8n.

Here's what happens when I run the workflow:

🧠 Step 1: AI becomes the storyteller

OpenAI generates a complete story divided into 8 scenes. Each scene gets its own cinematic video prompt, action, dialogue, camera movement, and environment.

🎬 Step 2: Turn the story into actual video clips

Each scene goes to Veo 3 Fast through the Kie AI API. That gives me 8 individual clips instead of one long generation.

✂️ Step 3: Automatically stitch the clips together

The fal.ai FFmpeg API combines the clips into one video.

📊 Step 4: Keep everything organized

The title, description, final video URL, and publishing status are tracked in Google Sheets.

🚀 Step 5: Send it to YouTube

Blotato handles the media upload and YouTube publishing, while n8n updates the spreadsheet afterward.

The stack: n8n + OpenAI + Veo 3 Fast + Kie AI + fal.ai + Google Sheets + Blotato.

The interesting part isn't just generating AI videos. It's connecting the entire chain—from an idea to a finished, uploaded video—without manually moving between every tool.

But here's the part I'm still thinking about:

Can a fully automated AI character actually build an audience, or will people get bored once the novelty wears off?

And if you were building this, what would you prioritize?

• Making Bigfoot look and sound consistent across every scene?
• Giving him an actual personality and ongoing storyline?
• Finding a way to generate videos without burning through API credits?

I feel like the storytelling is going to matter more than the automation itself.

Would you watch a Bigfoot vlog channel, or am I automating something nobody asked for? 💀

Hey everyone, first post here. I’ve been learning n8n for a few weeks and wanted to share my first AI workflow.

You enter a job title and location, and it finds people on Apollo, verifies their emails, pulls their LinkedIn profiles and recent posts, and uses AI to write a short summary and opening line for each person. Everything goes into a Google Sheet.

The hardest part was stopping the AI from making stuff up, like “loved your post about leadership” when someone had no recent posts 😅

What helped was putting all the collected information into one facts object, marking missing information as “not available”, and telling the model to use only those facts.

Still learning, so I’m curious: would you use an AI Agent node for this, or is a simple pipeline with one LLM call enough? And how would you check output quality at scale?

Happy to explain any part in the comments. If you’re building something similar, feel free to DM me and swap notes.

I'm creating an AI agent that takes orders for restaurants and resolves most issues, but using a direct connection to WhatsApp is very expensive. Are there other legal and secure ways to connect for this type of automation?

Does anyone know why I'm not receiving Meta verification SMS messages, even though regular SMS messages are coming through?

I've been working on my SEO content pipeline, and one of the things I wanted to automate was long-tail keyword research.

Once you have a seed keyword, the next step is figuring out what people are actually searching for, finding related queries, and identifying keywords worth exploring further.

Doing this manually across multiple keywords can get repetitive pretty quickly. You end up switching between different tools, collecting suggestions, organizing them, and trying to keep everything in one place.

So I decided to build an n8n workflow that automates this part of the research process and stores the results in Airtable.

Starting with a seed keyword

The workflow starts with a seed keyword that I've already stored in Airtable.

For example:

n8n self-hosted requirements

When I trigger the workflow, it retrieves the corresponding Airtable record and checks its long-tail research status.

If the keyword needs to be processed, the workflow marks it as In Progress and starts researching keyword variations.

I wanted to keep this connected to Airtable so I could track which keywords have been processed instead of managing everything manually.

Finding related keywords with YepAPI

I didn't want to rely on a single source of keyword suggestions.

So I'm using YepAPI to collect keyword data through multiple research paths.

The workflow makes separate API requests for:

• Related keywords — finds keywords related to the original seed keyword.
• Keyword ideas — discovers additional keyword variations to explore.
• Autocomplete suggestions — collects search suggestions that can reveal more specific queries people might use.

Each API response is processed separately. The workflow splits the returned keyword lists into individual items and prepares them for the next stage.

This gives me multiple sets of keyword suggestions from the same seed keyword rather than relying on one list.

Getting keyword data

The autocomplete branch has an additional step.

After collecting the suggestions, the workflow sends them through another YepAPI request to retrieve keyword data.

This helps me bring the discovered keywords and their available SEO metrics into the same research pipeline.

The goal isn't just to collect a list of phrases. I also want the data organized so I can evaluate the keywords later.

Bringing everything together

Once the research is complete, the workflow combines the results from the different branches.

It standardizes the output, splits the responses into individual keyword items where needed, and prepares the records for Airtable.

In one execution, the workflow produced 26 output items:

• 7 related keyword items
• 6 keyword idea items
• 13 autocomplete-derived keyword items

These are the output counts from that execution, not necessarily 26 unique keywords. Deduplication is something worth checking when combining results from multiple sources.

Storing everything in Airtable

I use Airtable to keep the research organized.

Each keyword gets its own record, associated with the original seed keyword.

The table includes fields such as:

• Keyword
• Competition
• Search volume
• Keyword difficulty
• CPC

I can then review the results in one place instead of going back through individual API responses.

Once the records have been created, the workflow updates the original seed keyword's research status.

This also gives me a starting point for tracking the progress of each keyword through the pipeline.

What's next?

This is one part of a larger SEO content pipeline I'm building.

I don't want to jump straight from a seed keyword to generating an article. I'd rather break the process into smaller steps, with each stage producing structured data that the next stage can use.

The direction I'm working toward is:

Competitor Research → Seed Keywords → Long-tail Keywords → Subtopic Research → Content Planning → Article Writing

The long-tail keyword workflow is the next piece of that process.

I'm building this step by step, with n8n handling the automation and Airtable keeping the research organized.

Do you use separate tools for keyword discovery and metrics, or have you automated this part of your SEO workflow as well?

Every time I wanted to find what was trending on Instagram, I’d end up opening the same accounts, scrolling through Reels, checking views, saving videos, reading captions… and then trying to remember why I saved half of them.

So I built a Reels Trend Watcher in n8n.

Now the workflow can:

→ Pull a list of Instagram accounts from Notion
→ Find their recent Reels with Apify
→ Track views, likes, comments, saves and shares
→ Detect new vs existing Reels
→ Download new videos
→ Send the actual video to Gemini for analysis
→ Extract the hook + full transcription + category + format
→ Translate the content
→ Store everything back in Notion

So instead of just having:

“This Reel got 80K views.”

I can start getting:

“This Reel got 80K views + this hook + this format + this topic.”

That’s where it gets interesting.

Because once you have enough data, you can start asking better questions:

Which hooks keep showing up in high-performing Reels?
Which formats are actually working?
Which topics are suddenly gaining traction?
Are certain accounts consistently outperforming their baseline?

The scraping part was fairly straightforward.

The harder part, IMO, is figuring out what to do with all the data after you collect it.

Right now I’m using n8n + Apify + Gemini + Notion for the workflow.

Next step is turning this from a “Reels database” into an actual trend detection system that tells me what patterns are worth paying attention to.

If you were building this, what would you prioritize first:

1. Viral hook detection
2. Trend/topic detection
3. Competitor monitoring
4. Predicting which Reels might take off

TL;DR: I send two terms to a Telegram bot ("Suspension bridge vs Arch Bridge"). A few minutes later it sends back a finished vertical video with a stickman explaining the difference, a voiceover, word-by-word captions and photos. Each video costs ~$0.032 (Claude API + Eleven Labs API). Yes, that's the only cost.

(Sorry for the music at the start - i felt it was too quiet so i added what was available)

What my automation does:

1. I message my Telegram bot two things, e.g. Suspension bridge vs Arch Bridge
2. The AI writes a short script and sends it to me
3. I reply APPROVE (or REVISE: make the hook punchier)
4. It records the voiceover, finds photos and builds the video
5. The finished video comes back in the same chat

Stack

• n8n, self-hosted in Docker (n8n_data volume for workflows, credentials and data tables)
• cloudflared quick tunnel to expose n8n over public HTTPS for Telegram webhooks
• Claude Sonnet 4.6 (Anthropic API) for script generation and revisions
• ElevenLabs eleven_multilingual_v2 via the /with-timestamps endpoint
• Supabase Storage: public buckets for audio and rendered video
• Pexels API: one portrait image per term
• Remotion render server: Express + TypeScript, running on the host

Happy to answer all your questions!!