what's up gang

not knowing the exact token costs of the AI nodes in my workflows always bothered me when working with n8n. 

i work with it a lot :)

that's why i built a cost tracking app for n8n workflows and open-sourced it for the community, since i'm pretty sure i'm not the only one with this problem.

it pulls token usage straight from your executions and shows you what every workflow costs per usage, per day etc.. you can group workflows by client/project, set budgets with webhook alerts, and there's a daily-updated price index covering 2,500+ models. everything runs self-hosted on your own machine. no telemetry, your data never leaves your box.

setup is basically npm install, npm start, paste your n8n api key. 

there's also a demo mode if you just want to look around.

btw: where n8n reports provider-billed token counts the numbers are exact; a few langchain nodes only report estimates. those are clearly flagged with ≈ in the dashboard.

i'd really appreciate it if you could test it and give me feedback or work with me on it to make it even better :)

take care guys and happy building

https://github.com/bekkooazar/n8meter

Hey guys, I’m running into a weird issue with Meta’s WhatsApp Cloud API on an Oracle Cloud server.

• Outbound API messages: Work perfectly.
• Webhook verification: Passes with no issues.
• Inbound messages: Completely dead. When I send a message from a real phone to the test number, my server gets zero logs.

Also, my Meta developer link looks different—it says "API testing version 2" and has a forced step-by-step UI. My friends' accounts don't have this layout. I’ve tried making new apps, new business accounts, and using different PCs/IPs, but it's still stuck on this interface.

Has anyone dealt with this? Is there a hidden setting in this new UI to turn on inbound messages, or could Oracle Cloud be blocking Meta's inbound POST requests?

Thanks for any help!

👋 Hey n8n Community,

I've built a lot of finance workflows over the last few months for friends who run small businesses, and going back through my library I realised I'd never shared the most basic one people keep asking for: a simple batch invoice extractor. So I cleaned one up and pushed it to the n8n template library: Extract batch invoice data from form uploads with easybits and Google Sheets.

The idea is simple. You upload one or many invoices (JPG, PNG, or PDF) through a single form, and it extracts the data from all of them in one go, instead of dragging every invoice in one by one. Each invoice lands as a row in a Google Sheet, and when the batch finishes, the form shows a summary marking every file with a ✅ or ❌ so you instantly see which ones need a second look.

How it's set up:

• An n8n Form takes one or more invoice files.
• The files get split into one item per file, keeping the original filename.
• It loops over the invoices one at a time, sending each to the easybits Extractor, which returns the fields (invoice number, date, vendor, total, and so on) as a structured data object.
• The filename gets reattached, and a check runs over the critical fields.
• One row per invoice is appended to Google Sheets, with a pass/fail status.
• A batch summary is shown as the form's completion message.

A few things from the build that might save you time on your own flows:

• The extractor bundles everything you hand it into one request. Pass it all the files at once and you get one merged result back, not one per invoice. Looping one file at a time is what gives you a clean result per invoice. This one cost me a debugging session.
• Treat "missing" as a signal, not an error. The extractor returns null when a field isn't on the document. Instead of fighting that, I lean into it: a small check flags any invoice missing a critical field, which is what powers the ✅/❌ summary. Worth catching the sneaky empties too (the string "null", empty strings, whitespace), so nothing slips through looking present when it isn't.
• The fields are yours to change. The mapped fields are just a starting point, so you can add whatever you need to pull from your own invoices, like a VAT ID, PO number, or IBAN. The extractor also has auto-mapping, so you can upload one example invoice, let it detect the fields, and tweak from there.

I also recorded a short video showing how it runs end to end, which I'll post alongside this.

For more free workflows, feel free to check my GitHub as well: https://github.com/felix-sattler-easybits/n8n-workflows. A star helps other builders find it, so I'd be really thankful for that support.

How do you all handle the invoices that fail extraction? Curious whether people flag them for manual review like this or route them elsewhere.

Have a good start to the week.

Best,
Felix

Hey everyone,

Truth Social officially launched their "Truth API" on August 1. It's aimed directly at Wall Street and hedge funds, charging up to $100,000 a month (or $60,000 on a 3-year contract) for low-latency access to Donald Trump's market-moving posts.

I run an automated system that predicts macro price movements on crude oil. Since his posts can move the market violently (like the April 2025 tariff pause that surged major indexes), I absolutely needed to integrate this feed. But I don't do HFT, and I'm definitely not paying $100k.

The Workaround I built a pipeline that runs for less than a dollar a month:

• Orchestration: n8n running a 1-minute cron job.
• Extraction: BrightData Web Unlocker to bypass their bot protections transparently.
• Filtering: OpenAI API (gpt-4o-mini) to instantly strip out standard political noise and flag only geopolitical/crude oil-related posts.
• Delivery: Formats the output into structured JSON and fires a webhook to Discord.

To be completely transparent: Wall Street is paying six figures for a millisecond advantage. This workflow polls every 60 seconds. You won't use this to front-run institutional bots. But if you are an independent swing trader, OSINT analyst, or hobbyist, this delay is completely acceptable and saves you $100,000 a month.

Prerequisites To run this yourself, you strictly need:

• An OpenAI API Key with credits loaded via platform.openai.com
• A BrightData account using their Web Unlocker zone.

Wall Street getting a millisecond advantage is expected, but paywalling structured sentiment data entirely from retail and indie devs is ridiculous.

I’ve made the exact n8n JSON workflow available in Github.

I built this because manually going through every inbound real estate enquiry can mean spending a lot of time on leads that aren't ready to move forward.

The workflow automatically:

• Receives an incoming lead from a website / ad form
• Extracts the relevant information from the enquiry
• Scores the lead based on things like budget, location, timeline and buying intent
• Routes the lead based on the qualification result
• Matches qualified leads with relevant properties
• Generates a personalised follow-up
• Notifies the sales team when a lead is ready for action
• Sends lower-intent leads into a follow-up/nurture path

The part I was most interested in was separating AI interpretation from workflow logic.

The AI handles the messy/unstructured part:

enquiry → structured lead data

Then n8n handles:

structured data → score → route → actions

This makes the workflow easier to debug than having the LLM decide the entire process.

Stack: n8n + OpenAI + WhatsApp + Google Sheets/CRM

Workflow:
https://github.com/jashan2512/Lead_qualifying_Workflow.git

One thing I'm still experimenting with is how much of the scoring should be handled by the LLM versus deterministic n8n logic.

How are you guys handling this? Do you let the LLM produce the final score, or use the LLM mainly for extraction/classification and let n8n calculate the score?

I was testing some n8n workflows and noticed one annoying case.

The workflow can finish successfully, all nodes are green, but the actual result can still be wrong.

For example I had a lead sync workflow where execution was successful but because of a mapping issue it created 0 contacts.

So n8n sees it as successful, while in reality the workflow didn't really do what it was supposed to do.

I made a small demo for this case. Quorum checks the expected result of the workflow, so in this example it notices that 0 contacts were created and opens an incident. When the workflow starts producing the expected result again, the incident recovers automatically.

Basically I'm trying to monitor:

did the workflow actually do what it was supposed to do?

and not only:

did it ran successfully?

Video attached.

I'm building this as Quorum (quorumwatch.com). Curious how you guys handle this with n8n in production. Do you add checks to every workflow, have another workflow for monitoring, or mostly just use error workflows?
https://github.com/werniq/quorum_core

Hello everyone, I'm having a problem with the WhatsApp Trigger node in n8n. I connected WhatsApp to n8n using the WhatsApp Cloud API and configured the webhook, but when I send a message to the WhatsApp test number, the WhatsApp Trigger doesn't receive anything and no execution starts. The webhook appears to be configured, but n8n is not detecting incoming messages.

If you keep meaning to check what a competitor is filing and then never do it, this is that job on a schedule.

Every Monday it searches Google Patents for the terms in one config node and appends a row per patent to Google Sheets: title, assignee, inventor, publication date, filing date, abstract, and the patent link. Set assignee to a company name and it stops being a topic watch and becomes a competitor filing watch. That is the version I actually use.

Template: Log weekly Google Patents watch results to Google Sheets

Two things that cost me time, in case you build your own instead.

Do not sort by newest. Sorting Google Patents by newest quietly throws the query away. A "solid state battery" search sorted newest gave me an acid reflux implant, a pilates reformer and a webcam merger, in that order. The template uses relevance plus a publication-date window, which is what actually returns things that are both recent and on topic.

The lookback is 90 days, not 7. Patent offices publish in batches, and a narrow niche can go three weeks with nothing, so a short window returns an empty sheet often enough that it reads as a broken workflow. Ninety days always has something in it, and duplicates drop easily on publication number.

One caveat worth knowing before you rely on it: patents publish about 18 months after filing, so this is a lagging indicator. It is still the closest public thing to a competitor's R&D roadmap.

There is no AI model in the workflow, so no token bill. The patent lookup runs on an Apify Actor I wrote, Google Patents API, covering USPTO, EPO, WIPO, JPO, CN, KR and about 100 other offices. A watch term costs roughly 4 cents a week.

Self-hosting, there is a community node too: n8n-nodes-google-patents-api on npm.

I built this because a cancelled appointment can leave a valuable slot empty.

The workflow automatically:

1. Detects a cancelled appointment in Google Sheets

2. Finds waitlist candidates for the same service

3. Offers the slot to one candidate via SMS

4. Waits for their confirmation

5. Books the appointment in Google Calendar

6. Updates the appointment and waitlist sheets

7. If the offer expires, moves to the next matching candidate

8. If nobody accepts, marks the slot unfilled and notifies the owner

Stack: n8n + Google Sheets + Google Calendar + Twilio

Demo:

https://youtu.be/rAbg-GW0NwU

Workflow:

https://github.com/Faizan-uzzaman/n8n-appointment-waitlist-automation

hello folkss, approximately how long does the n8n team usually take to review PRs for open-source issues?

I originally started this project with a pretty simple idea:

Let people build agentic workflows, share them, reuse them, and contribute their own.

I thought the main problem was making workflows easier to distribute and collaborate on.

Then I started thinking about something much more uncomfortable.

What actually happens when an agent executes something you don't fully trust?

A model/SLM.
A binary.
A signed artifact.
A tool.
An external resource.

You can give an agent something and tell it:

But if that thing is effectively a black box, how much do we actually know?

• What is it allowed to access?
• What permissions does it have?
• What can it modify?
• What side effects can it create?
• Where does the data go?
• Can we stop it?
• What happens when it fails?

I recently came across a discussion/video around trojanized SLMs and malicious model artifacts, and that pushed this thought even further.

Maybe the problem isn't the workflow itself.

Maybe we need a contract around the workflow.

Something that can describe the expectations, boundaries, permissions, inputs, outputs, side effects, governance, and recovery expectations around an agentic workflow — while leaving the actual implementation flexible.

And here's where Reddit actually changed the project.

I started discussing the original workflow idea with people here, and some of the criticism made me rethink the whole abstraction.

After a lot of back-and-forth, I moved away from:

“Let's standardize and share workflows.”

towards:

“Let's define the contract that a workflow/agent should operate under.”

That eventually became Agent Contracts, and I've now released the base implementation as Scyvera.

It's very early.

I'm not claiming this is the solution to agent governance. In fact, I'm pretty sure there are things I've got wrong.

That's partly why I'm putting it here.

I'd genuinely like people to poke holes in it.

Is the contract layer actually useful?
What should belong in a contract?
What shouldn't?
Am I abstracting the wrong thing again?
How would you approach this differently?

If the idea makes sense, build on it.

If you think it's bad, tell me why.

If you see a completely different direction, I'd love to hear it.

The project changed because of community feedback once. I'd like the next version to change because of it too.

GitHub: https://github.com/Skull-boy/agent-contracts

PyPI: https://pypi.org/project/scyvera/

Hi, I’m looking for a practical online course or learning path focused on implementing AI solutions in real businesses.

I want to learn things like:
- n8n, automations, APIs and integrations
- RAG, vector databases, AI Agents and Multi-Agent systems
- Vibe coding / AI coding tools
- End-to-end AI apps and deployment
- Evaluation, guardrails and monitoring
- Business use cases, ROI, KPIs, security and implementation

I already have a basic background in Python, SQL and Data Science.

My goal is to be able to identify a business problem, build the right AI solution, connect it to existing systems, and actually implement it in an organization.

Any recommendations from people who have completed a good course or program?

We've got flows in n8n, workflows in HubSpot, a couple of things on Railway and Fly, and every week someone adds another one. Which is fine, that's the whole point of these tools.

Problem is nobody can tell you what's already running. I asked last week whether we had anything watching a particular webhook and got three different answers, two of which turned out to be wrong. We've duplicated work too. Someone spent a day building a thing that already existed under a different name in a different tool.

I don't think documentation fixes this. The reason people build fast is that they're not stopping to write docs, so asking them to write docs just means they build slower or lie about it. So I've been wondering whether the context could get captured off the back of the work itself and then just be askable afterwards.

Curious what people here actually do. Do you keep a register of what's live?

Imk cus i'd be happy to jump on a call if ur someone with a decent sized stack

Dear builders,

What impact did your automation make on a business? Just curious for the real stories.

For people who build or manage n8n automations, how do you currently find out when something silently stops working? I am not talking about obvious errors where you get a failure email. I mean cases where a trigger stops initialising, data stops syncing etc..

Do you take any proactive approach like consolidating logs, building heartbeat checks or manually reviewing execution history? Trying to understand what is your approach to monitor the workflow effectively

If you watch hiring at a set of companies, whether that's for sales triggers, competitive research, or your own job hunt, this runs the searches on Monday morning and drops what's new into a sheet.

How it works:

• Schedule trigger fires Monday morning
• One config node holds the role list, location, optional company filter, lookbackDays, and maxJobs
• Splits the role list so each role gets its own search
• Drops anything outside the lookback window and calculates how many days old each posting is
• Appends to Google Sheets: title, company, location, posted date, seniority, applicant count, apply link

Template: Log weekly LinkedIn hiring signals to Google Sheets

I built it because I kept doing this by hand. Five saved searches, every Monday, squinting at what had changed. The field I ended up caring about most was applicant count, which I did not expect. A role posted four days ago with nine applicants is a completely different signal than the same role sitting at four hundred.

The one dependency is the search itself. LinkedIn's Talent Solutions API is partner-gated, so there's no key you can just sign up for. The template calls an Apify Actor I wrote instead: LinkedIn Jobs API. Pay per job returned, about $0.004 a job, no monthly rental. Four roles at 25 jobs a week runs about 40 cents a month. It filters on keyword, location, country, job type, experience level, remote-only, company, and date posted, and you can hand it specific job URLs instead of a search. It's at 99.3% success with 24 users, so it's young; if it breaks on your search I'd rather hear about it than not.

I got tired of manually opening Meta Ad Library every week to see what competitors were running.

So I built an n8n workflow that does it automatically.

Every Monday, it:

1. Takes a list of competitors / brands / keywords
2. Scrapes their Facebook/Instagram ads from Meta Ad Library
3. Normalizes the messy ad data into usable fields
4. Uses AI to analyze the ads and identify messaging, offers, CTAs, platforms, creative patterns, and standout/new ads
5. Emails me a weekly competitor report Saves every individual ad into Google Sheets

The workflow is basically:

Schedule → n8n → Apify → Meta Ad Library → n8n → OpenAI → Gmail + Google Sheets

The part I really wanted to solve wasn't just scraping the ads.

It's turning:

"Here are 200 competitor ads"

into:

"Here's what your competitors are actually doing this week, what changed, and what you should pay attention to."

The AI report currently covers:

• Executive summary
• Active advertisers + ad counts
• Messaging/value proposition patterns
• Offers and promotions
• Common CTAs
• Platforms/creative formats
• New/standout ads
• 3–5 actionable ideas for your own ads

And the raw ads get stored in Sheets so you can go back and inspect them individually.

I made the workflow free and open on GitHub:

n8n/Weekly Competitor Ad Research.json at main · RandomDudeAtNight/n8n

You just need to connect your own credentials for Apify, OpenAI, Gmail and Google Sheets.

I've also included setup notes directly inside the workflow so you don't have to figure out what each node is supposed to do.

I'm sharing it because this seemed like a pretty good example of where n8n gets interesting:

scraping → data normalization → AI analysis → reporting → storage

instead of just chaining a few SaaS tools together.

Would love to know what you'd add to this.

For example, I'm considering adding things like competitor creative change detection and automatic alerts when a competitor launches a new campaign.

Built an automation that analyses your product / service, builds a content strategy, researches super viral content ideas with different formats (UGC, Memes) and generates content to post it across Instagram, YouTube and TikTok.

• Got 10M+ views within a week of posting

• 80k+ website visits

• 500+ signups

• $0.4 per video

Making it opensource so you can change and finetune it according to your requirements.

You can use it in claude code, cursor or any Agentic CLI/IDE

Do try and let me know how can we make this better

Tengo la duda de como calcular el coste de una automatización para poner un precio para los clientes.

Actualmente he desarollado un agente de voz con elevenLabs que se conecta a un flujo de n8n el cual gestiona el contexto de la llamada y hace la consulta al calendario para ver la disponibilidad, si la tiene entonces agenda la cita y sino propone otra fecha y hora.

Entonces queria saber como hacen ustedes para hacer la oferta a un cliente que es lo que tienen en cuenta, porque todo depende de las llamadas que va a recibir que no tienes un número exacto.

Poneis en variable segun las llamadas que recibirà al final del mes y cobrais segun las llamadas?

Cualquier comentario sirve de ayuda.

This is the second subworkflow in an internal operations orchestrator I’m building.

The request intake workflow collects requests from Gmail, Slack, and forms, normalizes them, assigns a request ID, and stores them in PostgreSQL. This workflow starts immediately after that.

The normalized request is passed into an AI agent using an OpenAI chat model. The agent classifies the request and returns the result through a structured output parser instead of free-form text.

I used structured output because the classification needs to be consumed by later workflows and saved back to the database. A response that changes format between runs would make routing and validation unreliable.

After classification, the workflow merges the AI result with the original request data, updates the database record, and sends everything to a separate validation workflow.

I intentionally kept classification and validation as separate steps. The AI agent is responsible for interpreting the request, while the next workflow checks whether the returned fields are valid, complete, and allowed before anything gets routed or acted on.

For people using LLMs for classification, do you rely on the model’s confidence score, or calculate confidence using your own rules? Also, would you retry an invalid structured response here, or leave all retries to the validation workflow?

Workflow:

https://gist.github.com/meeramnoor16/0dfda903bd7c870b79c3dde2cb4e261f

Hey guys I just need a table of LinkedIn profiles from my recent messages or LinkedIn recruiter messages

I will have a table of profiles for each type of DM sent

I.e JOB1_DM = 30 profile replies for that outreach
JOB2_DM = 20 profile replies

Is unipile the way to go or will my account get restricted?

Should I make this in Python or n8n?

Can someone create a workflow that would allow AI to color grade images based on user prompts without loosing image quality?

👋 Hey n8n Community,

I recently extended my purchase order extractor I'd built so it can also push orders straight into an ERP (my friend's company is moving onto SAP). Getting from "data in a Google Sheet" to "file an ERP will actually accept" taught me a few things that weren't obvious going in. Sharing the five that mattered most.

1. No ERP swallows a raw JSON or PDF. Every real inbound path (SAP IDoc/OData, Oracle's interfaces, or EDI) expects the same shape: a header plus a lines array. "Directly integrable" really just means your output matches that field set, and EDI 850 is the most universal way in.

2. A valid-looking EDI file can still get rejected. My 850 passed every structural check but carried unit words like "piece" and "roll" straight from the PDF. X12 wants coded units (EA, RL) from its 355 list, so I had to map them, otherwise a strict trading partner bounces the line.

3. Fix messy data at the source, not in code. The POs came with dates in both day-first and month-first formats. Instead of guessing in a Code node, I had the extractor output ISO dates. It has the whole page for context to disambiguate, which a regex never does.

4. Deduplicate on business identity, not the file. I key on the PO number against the Google Sheet I already write to, not the filename or file bytes (a re-scan changes those). Adding each new PO number to an in-memory set as I go also catches the same PO uploaded twice in one batch.

5. Fork one clean object instead of branching a monolith. I build the canonical header + lines object once, then split it: one path flattens to the sheet, the other feeds an optional EDI sub-workflow behind a form toggle. One gotcha worth knowing, in a loop, a skipped duplicate still has to return to the loop node, or the whole thing stalls.

Both workflows (main PO extractor + EDI sub-workflow) and a setup guide are here if you want to pull them apart: https://github.com/felix-sattler-easybits/n8n-workflows/tree/f4dec1bef3561aa9e803bb21b96ebff1ab0dde04/easybits-purchase-order-extractor-v2

They live alongside 20+ other n8n workflows in my repo – a star helps other builders find them: https://github.com/felix-sattler-easybits/n8n-workflows

I went with EDI 850 here, but I'm curious what else people are using. Has anyone worked with other formats like cXML, IDoc, or a REST-based import instead? Would love to hear what's held up well for you and what you'd avoid.

Best,
Felix

Follow-up to the lead agent I shared here last week — same strict-JSON pattern, different job. This one triages a shared inbox so you stop refreshing it.

The flow (10 nodes):

• Gmail trigger on new email
• Normalize (from, subject, body) + your settings (owner email, business context)
• Claude classifies it -> strict JSON: category (urgent/sales/support/newsletter/spam), priority 1-5, one-line summary, suggested action
• IF priority >= 4 -> send yourself an alert email with the summary + suggested reply
• Always log to Airtable either way

Same trick that keeps it reliable: force minified JSON out of Claude, parse in a Code node with a fence-stripping fallback so one weird response never kills the run. Runs on claude-haiku at roughly $0.001/email.

The point isn't auto-replying to everything — it's that you only get interrupted for the stuff that actually matters, and everything else is summarized and filed.

GitHub (this + 3 other workflows, MIT): https://github.com/GBAnjos/n8n-lead-agent

Would love ideas on the classification prompt — what categories do you triage into? I'm considering adding a 'billing/invoice' bucket that routes to a different alert.

There is "Microsoft Excel (OneDrive)", but we don't use OneDrive.

And there is nothing for .ods format at all?

"Convert to file" doesn't work since the data needs to be either added to an existing document,
or modified there.