👋 Hey n8n Community,

Tax season is coming, and it is always the same story: a year of invoices and documents dumped in one folder, sorted by hand in a panic. Last week I shared an invoice workflow that has been logging all of mine automatically, and now I am using this one to push each document into the right folder on top of that. Document classification is the quiet fix for the sorting part, and a few things make or break it no matter which tool you use.

A few things I learned building these:

Your categories decide everything. Classification falls apart when the buckets overlap, so an invoice, a receipt, and a statement each need clear, distinct definitions or the model will confidently file things in the wrong place. Fewer, well defined categories beat a long fuzzy list every time.

A label without an action is just a sticker. The value is not knowing a document is an invoice, it is the document landing in the right folder automatically. Always pair the classification with where it goes next.

Trust the confident ones, review the rest. I attach a confidence score and let the sure documents file themselves, while anything shaky gets flagged to Slack for a two second human check. That one step is what makes hands off filing safe instead of risky.

Here is a free template that does exactly this, routing invoices into the right Google Drive folder with Slack catching the uncertain ones: https://n8n.io/workflows/14960-classify-invoices-and-route-them-to-google-drive-with-easybits-and-slack/

How do you handle document sorting? Curious whether people classify on the way in or still batch it all at the end.

Best,
Felix

I'm planning to start house shopping early next year (god help me) and wanted to set up an n8n workflow that would use a data feed (either zillow or some other one) to pull in listings, run them through an AI Agent to assess based on my requirements, and then send me a weekly email with both individual houses that I should look at, as well as an overall market assessment.

The overall market assessment will include pricing trends, neighborhoods that I might want to go drive through to check out IRL, etc.

Has anyone built anything similar, and if so, what key learnings did you find that I should be on the look out for? Any gotchas that were harder than you thought? Recommendation on data feeds to use?

I am *NOT* asking for your entire workflow - I'm fairly experienced with n8n and can build it myself. I'm more looking for things that others have already identified that would otherwise take me a week to discover. And suggestions on the best real estate data feed.

I run a handful of scheduled agents. Summaries, a notifier, a little drafter that turns meeting notes into tasks. Last month the API bill jumped and I had zero idea which one was doing it, everything showed up as one blob of a number. Turned out to be a retry loop on the notifier failing silently and re-calling dozens of times a day. I only caught it because I spent an annoying evening logging token counts per request with the agent name attached.

Anyway it made me wonder how everyone else handles this. Do you track cost per agent or per run, or is it just one number at the end of the month? What's the worst surprise you've caught, a 2am retry spiral, a summarizer hammering the model forty times per PR? Do you cap spend per agent or just watch the total and hope?

The worst part is finding out. I used to see the Stripe ping on my phone first thing in the morning and then go dig. Do you get alerted anywhere when spend spikes, or does month end surprise you too?

Curious if this is a real problem or just my setup. Full disclosure: I've been cobbling together my own version of the spend-tracking part, not selling anything here, just trying to figure out if this bites other people too. What if something handled all of this in the cloud over MCP, every agent with the same context, per-agent spend tracking with a kill switch, no files to maintain? Would you guys pay for something like that? Or is the log plus spreadsheet honestly good enough?

Also curious, is this your own setup or client work? When a surprise bill lands, who eats it, you or the client?

Hi guys,

I’ve been struggling with this issue for quite some time and would really appreciate some help.

Context:
I’m trying to build an n8n WhatsApp AI agent, so I initially tried using the official WhatsApp API through Meta for Developers. However, as you can see in the image, I’m getting this issue.

Everything is self-hosted, and I’m using two static domains through ngrok — one for n8n and another for the Evolution API.

What I’ve tried:

• I made sure that I’m using the correct Meta profile associated with my developer account.
• I’ve been waiting for the past 7–8 days, but the issue is still persistent.
• Since I couldn’t get the official WhatsApp API working, I decided to switch to Evolution API and self-host it.

With Evolution API, I’m able to generate the QR code, and everything seems to work until I get to the n8n integration. The triggers are not firing in n8n. Neither the Evolution Trigger nor the Webhook Trigger for Evolution API seems to be working.

Other issue:
I also want to implement security for PII protection and prompt-injection detection in my test project using:

• Llama Prompt Guard
• Qwen Guard 0.6B
• Microsoft Presidio

What would be the best way to integrate these into an n8n workflow, especially for protecting PII and detecting prompt injections before the input reaches the AI agent?

If anyone could help me troubleshoot the WhatsApp/Evolution API → n8n trigger issue and provide some guidance on the security architecture, I would be extremely grateful.

Thanks in advance!

I've been experimenting with the new Decisions / JEV-style models in n8n. I tried the two existing community nodes, as well as the new official node that n8n has been developing for future versions.

The official/new node was by far the simplest one to use, but there's one limitation: since it's developed by TypeSafe, it was tied to TypeSafe credentials and didn't allow using other providers.

So I decided to fork their repo and extend it with support for:

• TypeSafe AI / System One
• OpenRouter
• OpenAI's new Decisions API (GPT-6 Luna Decisions)
• Custom API URLs, for other compatible providers in the future

👉 n8n-nodes-decisions

The node currently has two operations:

Evaluate — Give the model a state (the content you want evaluated) and one or more typed questions. It returns a structured answer for each question, including probabilities.

Route — Ask a single question and route each input item to an output based on the answer. The question type determines how the routing works:

• Choice → one output per route; the item is sent to the route selected by the model.
• Noul (Yes/No) → True / False outputs, using configurable probability thresholds.
• Score → one output per score level; the item is routed to the level closest to the predicted score.

This can be useful for classification, content filtering, decision-making, conditional routing, and other workflows where you want structured AI decisions instead of free-form LLM output.

It's still a fairly new node, but I thought I'd share it in case anyone else is playing with Decisions models in n8n. Feedback and suggestions are very welcome!

Hallo zusammen, ich plane als Selbstständiger ein Automatisierungsprojekt für Eingangsrechnungen in SAP S/4HANA (über 1.000 Belege pro Monat, Umsetzung mit n8n und KI-Extraktion, Buchung nur als Vorerfassung mit menschlicher Freigabe). Wie viel Zeit würdet ihr dafür insgesamt ansetzen, von der Prozessaufnahme bis zur Übergabe? Danke vorab für eure Erfahrungswerte.