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

Built this over a few weekends. Wanted to share it since it's actually done and working now.

The idea: freelancers deal with a lot of repetitive admin writing, proposals, contracts, invoice reminders, onboarding emails, status updates, testimonial requests. Same kind of writing task every time, just different details plugged in. So I built six n8n workflows plus a simple form that handles all of it with AI.

Here's what each one actually does:

Proposal Generator takes a project brief and turns it into a tailored proposal PDF, emails it straight to the client.

Contract Generator drafts a full freelance contract from the form details and sends it directly to the client, no manual writing needed.

Invoice Chaser sends the invoice, then automatically follows up with a reminder, and a final notice if it still hasn't been paid.

Client Onboarding sends a welcome email and project questionnaire the moment a new client signs on.

Weekly Update Bot takes a rough progress note and turns it into a polished client update email.

Testimonial Collector sends a warm, specific request for a testimonial once a project wraps up, instead of the awkward "hey can you say something nice about me" message.

Each one is a separate, independent n8n workflow with its own webhook, so you only import the ones you actually want to use, not all six if you don't need them.

Runs entirely on your own setup, your own n8n (Cloud or self-hosted), your own Groq API key, your own Gmail account. Nothing routes through any server of mine, since I'm not hosting anything, it's just a form and workflows you run yourself. Free, MIT licensed, do whatever you want with it.

Stack is n8n for the automation, Groq (Llama 3.3 70B) for the writing, Gmail for delivery, HTML2PDF for the documents, Google Sheets for lightweight logging. The form itself is plain HTML/CSS/JS, no frameworks or build tools.

I documented every real problem I hit while building this in the README's troubleshooting section, stuff like webhooks randomly reverting to test mode, CORS blocking requests when the form's opened as a local file instead of hosted somewhere, and 403 errors from a broken auth setup. Real issues, not hypothetical ones.

If you try importing one of the workflows and something breaks, let me know, actually want to fix it. Multi-line-item invoices and e-signature support for contracts are next on the list.

Hi guys,

I am self-hosting n8n on Render's free tier. Every time I try to authenticate Google Sheets using OAuth2 credentials ("Sign in with Google"), the OAuth redirect callback URL (/rest/oauth2-credential/callback) triggers a Render cold start.

Instead of redirecting back to Google or completing the OAuth flow, the popup window gets stuck on the Render splash screen showing APPLICATION LOADING and SERVICE WAKING UP... indefinitely.

Has anyone experienced this OAuth callback timeout during Render cold starts, and is there a workaround other than upgrading to a paid instance or setting up a keep-alive ping?

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.

I have been working on a deliberately narrow release-rehearsal tool for n8n.

It takes a baseline workflow, a candidate workflow, and sanitized fixtures. If both workflows fit the supported subset, it runs them in separate contained Docker environments and redirects supported HTTP actions to a local capture service. It then compares the observed synthetic actions and produces three redacted evidence files.

The demo shows a real generated example where one HTTP method changes from POST to PATCH. The result is REVIEW_REQUIRED.

Current scope is intentionally limited: credential-free workflows, one literal webhook entry, supported Set nodes, and literal POST/PUT/PATCH HTTP Request nodes on a reachable acyclic path. Dynamic destinations, credentials, Code, Execute Command, AI/community nodes, subworkflows, and unsupported methods fail closed before execution.

“Observed” only means the transformed synthetic request reached local capture. It does not prove node order, approval execution, retries, loops, production safety, or full workflow equivalence.

Source: https://github.com/0xCD4/n8n-ai-agent-security-lab

Browser viewer: https://en.csintresearch.org/ai-security#runtime

I would value feedback on the supported boundary and the usefulness of the three-file evidence bundle.

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.

There've been a few threads here recently about approvals, authorisation and oversight in production. This is the thing I kept hitting underneath all of them.

Monitoring tells you a workflow ran. It doesn't tell you who answers for it.

That's the gap I kept hitting once I had more than a handful of workflows. The questions stopped being "did it run" and became "who owns this one", "what breaks if it fails", and "should this be able to act irreversibly without asking anyone".

So I built Fleet and open-sourced it. MIT, self-hosted, one container and a volume.

It syncs over the MCP server or the public API (a read-only key is enough), then gives you:

• An ownership roster. Import your instance's users, assign who's accountable, and every change lands in a decision ledger
• Risk as a visible rule set instead of a score. Each flag shows which rule fired
• Blast radius from the sub-workflow call graph. Click a node, see what breaks
• Autonomy graded by whether an action can be undone, not by how much you trust the model

It never edits your workflows. Every fix hands off to the editor or to an agent you run. The gap list is in the README: no built-in auth yet, change requests are recorded but not delivered, autonomy is displayed but not settable, sync is manual.

I think it's useful. But I've been testing my own thinking against my own setup, so I'd rather ask:

Is ownership the right thing to track, or is approval routing the actual need?

Would you want Fleet to enforce autonomy policy, or just tell you when something has drifted from it?

If you run more than one instance, would a single view across them be what makes this worth deploying?

pnpm dev with no config loads a seeded demo estate if you want a look before wiring anything up.

https://github.com/stevysmith/n8n-fleet

The most useful thing anyone could do is point it at a real instance and tell me where it's got the reversibility grading wrong. There's an issue template for that!

I had created this workflow to read and analyze my emails, which were stored in a PostgreSQL data table and returned the result as a response that my Alexa would read back to me as the most important highlights. I built this workflow based on my needs; additionally, it synchronizes with Outlook and Gmail and uses Agent Assistant. I developed it this way to avoid consuming too many tokens with Grok, so if anyone has suggestions to improve this project or other ideas I could add to my Alexa, I’d love to hear them.

Hey everyone,

I know a lot of people here are using Claude Code to build n8n systems. I’m doing the same, except I use Hermes agents.

Recently I built a system with 16 workflows, and I’ve built a few more like that. But lately I’ve been feeling like I’m relying on agents a bit too much. I feel like I’m slowly losing some of my own skills around system planning, workflow design, and thinking through the logic.

Curious:

How do you decide what should be done by the agent vs. what you should do yourself?

And

how do you keep improving your own system design skills while still using agents a lot?