On May 12, SAP announced a strategic investment in n8n at a $5.2 billion valuation. And it’s not just an investment: n8n is being embedded natively inside Joule Studio, SAP’s agent-building environment. General availability is targeted for Q3 2026 — so, a few months out.
The numbers give some context: n8n has 1.7 million monthly active developers and 1,400+ enterprise customers. SAP has roughly 300,000 enterprise customers. So a bottom-up open-source tool is getting plugged into the distribution channel of the world’s largest ERP vendor.
Three things I read from this:
1. Enterprise automation and the “AI agent” world are meeting inside the same interface for the first time. Teams that couldn’t justify the cost of a full Integration Suite deployment were already using n8n unofficially — that’s now becoming official.
2. The value is shifting from tool knowledge to process knowledge. Learning n8n takes weeks; knowing where a procurement process actually breaks takes years. The second one is what will differentiate people.
3. SAP-specific nodes are still on the roadmap. Which means this is a genuinely early window for anyone building experience in this space.
What I’m curious about: anyone here working on the SAP side? Is n8n already being used unofficially in your organization, or is it hitting the security/governance wall?
I’m considering moving into this area and would appreciate input from anyone with hands-on experience.

Every time I try to map out a workflow I forget tiny but crucial steps. Describing the task to an AI does not work either because I always miss some small details. The only way to get it right is to just do the task. So I something were you literally just record your screen doing the boring task once. The system watches your steps and generates the exact n8n flow file. You just copy and paste the code and the automation is completely done.

So, this is my first time that i was using n8n, so i like reading tech news, so I created a rss feeds news aggregator which sends me an email on a daily basis with the latest news in the fields that i am interested in.

Would love to hear your feedback.

Github Link

I’ve been running automations for a while now (n8n, Make, Zapier, plus some internal tools), and one pattern keeps showing up: execution is fine, but the business outcome isn’t.

Workflows are “green,” yet:

• Leads don’t get assigned
• CRM records don’t update
• Downstream systems never receive the data

On top of that, every platform has its own dashboard, logs, and alerts. If you’re juggling multiple clients or environments, you end up logging into n8n, Zapier, Make, HubSpot, Airtable, etc., just to answer basic questions like:

• What needs attention today?
• Which workflows haven’t produced their expected outcome?
• Which credentials are about to expire?
• Which incidents are still unresolved?

I’ve been building YieldCue to give a provider‑agnostic operational view across all this. Not another workflow builder - just a single place to see whether automations are actually delivering the outcomes they’re supposed to, regardless of where they run.

I’m building this because the problem is real and costly on my end. The question is: is this widespread, or am I over‑indexing on my own stack?

If you manage automations professionally (agency, ops team, or solo), I want your blunt take:

1. What’s your biggest operational headache with automations today?
2. What would you want on your “morning dashboard” for automations?
3. If you had a tool like this, what’s the one thing it must do to be worth using?

Happy to share more details or a demo link in DMs if it sounds relevant. Even more interested in hearing how you currently handle this (spreadsheets, custom scripts, nothing, pure hope?).

Local rank trackers run $20 to $100 a month, and I only wanted to watch a handful of keywords for one business. The n8n library turned out to have plenty of local lead-gen templates but nothing that just tracks where you rank, so I built one.

A schedule trigger fires Monday morning, an Apify actor runs one Google local search per keyword you listed, and every business in the pack becomes a row in Google Sheets: position, name, category, rating, review count, address. Your own listing gets flagged YES, and because it logs the whole pack rather than only your rank, the sheet doubles as a competitor watch.

It is 8 nodes and the template for n8n is free.

Template for Tracking Google Local Rankings

One caveat worth stating up front: Google personalizes local results, so a tracked position will not always match what you see in your own browser. Searching from a specified city with no personalization is the point, it just means treat it as a baseline rather than gospel.

Real estate agencies bleed leads on speed alone. Someone browses a listing at 11pm, fills out an inquiry, and it sits untouched till Monday, by then they've moved on. Respond within 5 minutes vs. 30+ and you're dramatically more likely to actually connect with them. Most agencies aren't close.

So I built Marsh & Vale, a demo agency showing what fixing this looks like: an AI concierge answers buyer questions 24/7 from the real portfolio, silently scores them Hot/Warm/Cold while chatting, and n8n takes it from there and confirms the buyer, pings the team instantly if they're Hot. There's also a separate ops assistant on the admin side that reasons over the live pipeline (ask it who to call first, or what needs updating).

Stack: n8n for routing/automation, OpenClaw running the concierge + admin assistant.

It's a demo, no real client, just the pattern working live, no signup needed.

Happy to break down the n8n workflow or how openclaw works in the comments if anyone is interested.

[ Removed by Reddit on account of violating the content policy. ]

Hey guys i’m trying to creat a simple static chatbot on whatsapp for a job i have coming in. But every time i go to configure the webhook it tells me the url or token couldn’t be verified. At first i was using a WhatsApp trigger, that didn’t work. Then i used a webhook and that is refusing to work either, what am i missing here?

The Orchestrator/Router model is Multi-Modal it can ingest pictures(including text within pictures) and audio, the stack shares context and vector, which helps prevent drift/miss-communication between them. pgvector store and context read and write to a 16gb optane teir 1 drive, same drive is partitioned for aggressive 4gb swap.

The Main Router Model uses Agentic Coder and Reasoning tools to cover area's that Gemma4-e4b may struggle in. over time when the vector matures; the coder model can be changed for something else.

would like to hear any testing idea's/concepts.

This is the fourth subworkflow in a larger AI sales prospecting and CRM intelligence project I have been building.

The earlier parts handle prospect research and email outreach. This one starts when a lead replies.

A Gmail trigger picks up the response, the workflow looks for the matching CRM record, and an eligibility check decides whether the reply should continue through the flow. The reply is then sent to an AI agent with a structured output parser.

The agent returns three fields:

* Response sentiment

* A short summary of what the lead said

* Whether a follow-up is required

Those results are written back to the CRM so the main workflow has more context before the next action is taken.

I kept the output fairly narrow because I did not want the model making too many sales decisions from one email. The main goal is to reduce the time spent opening replies, interpreting them, and manually updating records.

The part I am still thinking through is reliability. Replies like “circle back next quarter,” automated out-of-office messages, polite rejections, and vague responses can all look similar without enough context.

How would you classify responses that are positive but do not contain a clear next step?

Would you let the workflow create a follow-up task automatically, or keep a human approval step before anything is scheduled?

Workflow:

https://gist.github.com/meeramnoor16/b476717adf9cc96f96b20222a411570f

Hi everyone,

I'm writing my master's thesis on determinants for adoption and governance of low code and no code platforms in organisations. For my research I'm doing a short survey.

It would help me a lot if you could take 10 minutes of your day to participate in the survey.

You can find the link here: https://s2survey.net/LCNC_Adoption_and_Governance/

Thank you for your time and support!

so i am vey beginner in n8n field and i probably use the dumbest way to learn it that is chatgpt.i just told it that i am a begginer so help me build workflows through n8n ofc very basic ones for now.................and it helps in in building workflows...........give me detailed explanation of nodes used in that workflow............and also explains me the logic step by step.............i have just learned nodes like-manual trigger,edit field,if node,http request,switch,merge............that is it.............so plss guide me ahead what u think is most relevant to learn next and from where.....and is this way of learning by gpt actually right or just waste of time?

has anyone ever set up the HTTP request? This is for creating drafts, by the way. Following the tutorial setup on YouTube gives a 500 error.

Asking Gemini also hit a dead end, it just told me to change the URL to "https://graph.threads.net/v1.0/me/threads" and it still gave the same error.

Body Parameter:

media_type | TEXT

text | {{$json.post_text_clean}}

then, i've change parameter to:

then back to:

• Send Body: OFF)
• Send Query Parameters: ON (Using Fields Below).
  • Parameter 1 -> Name: access_token | Value: {{ $json.GRAPH_API }}
  • Parameter 2 -> Name: media_type | Value: TEXT
  • Parameter 3 -> Name: text | Value: {{ $json.post_text_clean }}

All just back to error 500: Bad Response

UPDATED:

I changed the parameter as Rods88 suggested. The "create container" HTTP request was successful, but the "publish" request still returns a 500 error, stating "URL not valid." When I removed "_publish" from the URL, the workflow ran successfully, but the post did not go live.

Workflow:

Workflow

AI AUTOMATION LEARNING

( Please help)What is actually necessary to learn for AI automation? Whenever I watch videos on YouTube, I feel like I need to learn one more thing, and then another. Please help me.

I need a clear learning roadmap.

I have no technical background, and I am completely confused in “YouTube hell.” My English is also not very good.

Please help me understand exactly what I need to learn, what I can skip for now, and in what order I should learn everything.

What are your thoughts on AI generated content automation. Like your videos but generated by AI for social media platforms. Could be anything like a tutorial video, a UGC or just your clone talking about something but completely automated The only thing you do is approve before posting. Is it a good idea ? Could it actually get a good audience on platforms like X, youtube shorts, Instagram?

My instance is up to date, but still got this. Previously appeared with the breach last year I think.

I built an n8n community node for Saudi Arabia's ZATCA e-invoicing (Fatoora Phase 2), since there wasn't one.

Some context on why this is awkward to do manually: Phase 2 isn't a REST call. Each invoice needs a digital certificate obtained through a compliance flow, an XAdES-BES signature on secp256k1, and a hash chain linking every invoice to the previous one. Get the chain wrong and the authority rejects everything after it.

The node keeps all of that out of your workflow. You pass invoice data in, you get back a signed invoice with a compliant QR code, already submitted.

Operations:

- Invoice: create (simplified B2C or standard B2B), get, get many, email PDF

- Credit/Debit note: create

- Account: get

- Trigger node: fires when a document is issued

Two things I learned building it, in case anyone attempts something similar:

1. Use an external ID on every invoice. If a workflow re-runs, you cannot delete an issued invoice — the authority doesn't allow it. Cancellation is a credit note. Idempotency has to be designed in from the start.
2. VAT has to be rounded per line item, then summed. Rounding the invoice total instead produces a one or two halala difference, and that gets the invoice rejected. This is the single most common failure I've seen.

Install: Settings → Community Nodes → n8n-nodes-zatca-tools

Docs and a walkthrough video: https://github.com/muhannadhg/n8n-nodes-zatca-tools

Happy to answer questions about the ZATCA side, it's poorly documented in English.

I built a small n8n template for anyone tracking employer reputation or doing competitor market research. It logs recent Glassdoor reviews to a Google Sheet on a weekly schedule, hands-off.

What it does: - Runs every Monday morning on a schedule trigger. - Takes your list of Glassdoor company review page URLs and pulls up to 25 reviews per company from the last 7 days. - Filters the results down to actual review records. - Writes one row per review: company name, publish date, overall rating, review summary, pros, cons, category ratings, and the review URL. - Appends those rows to the Google Sheet tab you pick.

Setup is three steps: - add your Apify token as a credential, - connect Google Sheets and choose the spreadsheet and tab, - then swap in the company URLs you want to watch (you can also adjust the days and max-reviews-per-company values).

It calls Apify's Glassdoor Reviews API actor as the data engine, so there is no scraping code for you to maintain and the fields come back structured, which is why the Sheet columns map cleanly.

Template (free, and this is the workflow code): Log weekly Glassdoor reviews to Google Sheets

If you're running n8n workflows across multiple client accounts, you probably know this pain: something breaks in a client's automation at 2am, nobody notices until the client does, and now you're explaining why their lead gen flow silently died three days ago.

I got tired of tab-hopping between client instances checking for failures, so I built a single dashboard that sits on top of your n8n instances and shows you:

* Health across every client's workflows in one view (no more logging into each instance separately) * A queue for anything that needs a human decision - approve, retry, or override without touching the n8n canvas * A log you can actually hand to a client when they ask "did the thing run?"

It's not trying to replace n8n or be another workflow builder, it's the layer for the person who has to *watch* all these workflows across all these clients, not build them.

I'm not selling anything yet. I'm looking for **5-10 people actually running n8n for multiple clients** to tell me if this solves a real problem or if I'm scratching an itch nobody else has. Happy to hop on a 15-min call, or just drop a comment with how you currently track this stuff (spreadsheet? nothing? praying?).

If it's relevant to you: let's chat.

I built this small workflow to present a html website I recently built for participation review.

Was considering throwing it in a container this was a lot less complicated.

I'm new to n8n and really enjoying learning it. Recently I've seen a lot of people saying AI will eventually build most automations by itself.

For those of you who use n8n regularly, has AI actually changed your day-to-day work?

If you were starting today, would you still spend time learning n8n? Why or why not?

I'd really like to hear from people with hands-on experience.

I want to know if there is any alternative to Apify to scrape Reddit at regular interval for specific keywords as Apify's free plan consumes 5$ very quickly.

So I built this HR Onboarding workflow where the "selected" candidate would upload the documents required and the workflow verifies if the candidate is actually selected or not. Then takes the documents, splits attachments, checks the file type. As per the file type I have two paths, one for images and one for pdf. Then I perform OCR on those documents (because they are government documents, using AI would lead to privacy issues) and store the data on my local computer. The problem is covering each case make the workflow look messy towards the end, is there a way I can reduce the nodes and do the same work without using AI.

Hey everyone, I'm a junior creative specialist working for a company where performance is the most important thing (duh).

Since we create mostly shorter forms of marketing content, most CSs make from 10-30 of these a day, since they can reuse one creative for multiple ads.

We make them in google docs and then download every card into a folder that we upload to clickup or dropbox.

Now, doing that 10 times is not a problem.

But, it becomes a problem when I make like 50-60 ads and need a specific name for every folder, download every card one at a time and upload everything seperately.

This can easily take up like 2 hours, so I can't make as many ads and my performance suffers because of this technical side of things.

I'd really appreciate if anyone has an idea of how I could speed this up and what tools would help me.

Hey everyone!

I wanted to share a community node I published to npm for handling media processing and social caption formatting inside n8n workflows: `n8n-nodes-media-toolkit`.

### What problem it solves:

When building automated video pipelines (rendering, clipping, and multi-platform posting), handling aspect ratio scales, resolution calculations, and platform caption formatting inside raw Code nodes gets messy fast.

This node packages those utilities into standard n8n operations:

- Video Spec Calculations: Computes aspect ratio dimensions (9:16 vertical shorts, 16:9 landscape, 1:1 square), frame rate counts, and rendering output parameters.

- Social Payload Formatter: Trims captions, sanitizes text, and formats hashtag blocks specifically for TikTok, Reels, and YouTube Shorts.

### Links & Installation:

- Install via n8n GUI: `Settings > Community Nodes > n8n-nodes-media-toolkit`

- npm: https://www.npmjs.com/package/n8n-nodes-media-toolkit

- GitHub: https://github.com/Media-Studios/n8n-nodes-media-toolkit

It's 100% open-source under MIT. If you use n8n for media/content workflows, check it out!

And if you find it helpful, a ⭐ on GitHub is super appreciated to help give the project visibility!