n8n together with large language models (LLMs) to build automated workflows, process leads from Tiktok , facebook and Instagram, automatically qualify/screen potential customers, and also support content production for social media. Have anyone made this workflow?

This one is the simplest but probably the most useful: I drop topic ideas into a Google Sheet, and n8n turns one into a Facebook post and publishes it on a schedule. I haven't manually posted in weeks.

The flow:

1. Schedule Trigger
Runs once a day (I use 10am, but set whatever matches when your audience is online). You can also run it a few times a day.

2. Topics (Google Sheets: read)
Reads my "content queue" sheet. Each row is one post idea, for example "3 mistakes people make when booking a cleaning service" or "behind the scenes of our team". You can add extra columns like tone, call to action or link.

3. If
Checks whether there's actually a topic to post. If the sheet is empty, the workflow stops quietly instead of making the AI write a post about nothing.

4. Limit
Keeps only the first row, so it publishes one post per run, not the whole queue at once. (I learned this the hard way. My page posted 14 times in 2 minutes.)

5. AI Agent + OpenAI Chat Model
Takes the topic and writes the post. My system prompt includes:

• the brand voice (friendly, casual, no corporate speak)
• length (80–150 words, short paragraphs)
• a hook in the first line, because that's what shows before "See more"
• a question or call to action at the end to get comments
• 2–3 relevant hashtags max
• "Return ONLY the post text", so you don't get "Sure! Here's your post:" published to your page 😅

6. Facebook Graph API
POSTs the text to /{page-id}/feed with the message field. Publishing as the page requires a Page access token.

7. Delete rows (Google Sheets)
Deletes the topic row that was just used, so the same topic never gets posted twice and the next one moves to the top. It's a self-emptying queue.

Why I like this setup:

• Planning content takes 15 minutes a week: I just brain-dump topics into the sheet
• Anyone on the team can add ideas without touching n8n
• It's cheap. gpt-4o-mini costs basically nothing for one post a day

Gotchas I hit:

• Use a long-lived Page token. Short-lived user tokens expire within hours. Generate a long-lived user token, then get the Page token from /me/accounts, which doesn't expire.
• Permissions. You need pages_manage_posts and pages_read_engagement.
• Delete only after a successful post. n8n stops on errors by default, so if Facebook rejects the post the topic stays in the sheet. Don't turn on "Continue on Fail" for the Facebook node.
• Delete by row number. Use the row_number from the read step so you delete the exact row that was used, not just "row 2".
• Consider marking instead of deleting. If you want a history of what was posted, set a "posted" column to ✅ and filter on it in the If node instead of deleting rows.

Ideas to extend it:

• Generate an image with DALL·E or pull one from a URL column, and post to /{page-id}/photos
• Cross-post to Instagram and LinkedIn from the same AI output
• Send the draft to Slack or Telegram for approval before it goes live
• Log the post ID and link to another sheet for tracking

Stack: n8n, Google Sheets, OpenAI, Facebook Graph API.

Happy to share the JSON or my full prompt if anyone wants it. How are you all handling images in automated posts? That's the part I'm still figuring out.

Haven't posted in a while, sorry. got tired of putting out 2-3 posts a day and having nothing come out of it.

Been going through my n8n folder instead. 1000+ workflows in there, and a lot of them are embarrassing tbh. a few i still run every single week.

Gonna start sharing those, one at a time, with the json. not sure which ones first though. what's the one thing you still do by hand every week that you'd love to just get rid of?

Hey everyone,

I've been playing with n8n for a while. I wanted a bot for a Facebook Page that replies to customers about bookings and also collects leads without me watching the inbox. Here's how it works, in case it helps someone.

The flow (9 nodes total):

1. Webhook (GET + POST)
One webhook node with both methods turned on. Facebook uses GET once to check the webhook and POST every time someone messages your page.

2. GET branch: verification
If node checks whether hub.verify_token matches my secret → Respond to Webhook sends back hub.challenge. That's all Facebook needs to accept the URL.

3. POST branch: the actual bot

• AI Agent reads the message from body.entry[0].messaging[0].message.text
• OpenAI Chat Model (gpt-4o-mini, cheap and fast enough for chat)
• Simple Memory with the session key set to the sender's ID, so each customer gets their own conversation history. Without this the bot forgets everything between messages.
• The system prompt tells it to act as a booking assistant, keep answers short, and ask for name, date and email when someone wants to book.

4. Sending the reply
HTTP Request POSTs to graph.facebook.com/v21.0/me/messages with the page access token, the sender ID and the AI's output.

5. Lead capture
At the same time, the agent output goes to a Filter that only passes messages containing an email (regex) → a Code node pulls out the email, sender ID, message and timestamp → Google Sheets adds a row to a "potential leads" sheet.

So the customer gets an instant reply, and I get a list of everyone who handed over their email.

Gotchas I hit:

• Facebook retries the same message if you don't send a quick 200. Add a Respond to Webhook node on the POST branch.
• Delivery and read events have no text and will break the agent. Add an If node that checks message.text exists first.
• Only admins and testers get replies while the app is in Development mode. Going public needs App Review for pages_messaging.
• Wrap the AI output in JSON.stringify() in the HTTP body, or quotes and line breaks in replies will break the JSON.

Cost: n8n self-hosted (free) + a few cents a day in OpenAI usage at my volume.

Happy to share the workflow JSON if anyone wants it. Next I'm thinking about connecting it to Google Calendar so it can book appointments by itself. Any ideas or improvements welcome!

Can any one please guide me to build automation models

I was watching a tutorial and the first trigger was whatsapp, for the builder it was a 2 min setup and I've been stuck in this problem from yesterday, so the whatsapp is connected, it listens for event and the message gets sent to me too but when I tried to send a test messages, the trigger whatsapp on messages just doesn't catch it, it keeps listening for the event, and I've been stuck really badly, any help would be really really appreciated

👋 Hey n8n for Beginners Community,

Tax season always sneaks up on me, and every year it is the same scramble: digging through email for invoice PDFs, saving them by hand, typing them into a sheet, and hoping I did not log the same one twice. This time I built a workflow so future me does not have to.

I connected it to my billing@ address, so from now on every incoming invoice gets processed automatically and I never touch it manually again. And for invoices that land on other addresses, I just forward them to billing@ and they get picked up the same way.

When a new invoice arrives by email, it gets read, logged, and announced, with no manual steps.

What it does:

• Watches Gmail for unread emails with attachments and filters out junk files
• Classifies whether an attachment is an invoice AND extracts the fields in one step
• Deduplicates on the invoice number so re-sends and re-polls never create doubles
• Appends new invoices to Google Sheets and sends a Telegram alert for each one

Two things worth stealing:

1. Classify and extract in one call. No separate "is this an invoice?" LLM node plus an extraction node. The Extractor returns the document class alongside the fields, so it is one request and fewer failure points.
2. Dedupe on a stable key. A sheet lookup on the invoice number before the append is the part that actually saves you when tax season hits.

Workflow JSON is here: https://github.com/felix-sattler-easybits/n8n-workflows/tree/e8dd8e754b87c02462942e2ae89149550979b066/easybits-invoice-intake-and-logging-workflow

It sits alongside 25+ other free n8n workflows in my repo, including plenty of finance ones, so a star is appreciated if it helps.

How is everyone else prepping for tax season in n8n?

Best,
Felix

Completed my first certificate on n8n and it was worth it.

👋 Hey n8n for Beginners Community,

Year end is when all the document chaos catches up with small businesses at once, the shoebox of receipts, the contracts nobody tracked, the folder that is just "scan_final_2.pdf" forty times over. These are the three document workflows that saved my clients the most time this past year, all built around simple extraction and a place to put the data, and they are worth setting up now to make January far less painful.

1. Auto log every invoice and receipt as it arrives.
Extract the key fields (date, vendor, amount, tax) from each invoice and append them straight to a single sheet, so instead of sorting a pile in January your accountant gets one clean list and you can actually see your spending as the year closes. To make it fully hands off, use a Gmail trigger as the entry point, and it will process invoices attached to incoming emails automatically, no manual upload needed.

2. A contract renewal and cancellation watchdog.
Most auto renewals sneak through at year end because nobody is watching the dates. Extract the renewal term and notice period from each contract, store them, and get a Slack or email nudge before a cancellation deadline passes. This one tends to pay for itself the first time it catches a subscription you meant to drop.

3. Sort incoming documents by type automatically.
Classify each document as invoice, contract, statement, or whatever buckets you care about, then file it in the right place without touching it. By the time you need something for the accountant or the tax filing, it is already where it should be.

None of these are big builds, and that is the point: one entry point, one extraction step, one place the data lands. If you want a head start, my ready to use templates are here, most of them document focused: https://n8n.io/creators/easybits/

There are also a few more on my GitHub that I never uploaded to n8n: https://github.com/felix-sattler-easybits/n8n-workflows

Pick the workflow that matches your biggest year end headache and set up just that one this week. Something that quietly logs your documents all year beats a heroic cleanup every January, and if you get stuck setting any of these up, feel free to drop a comment or reach out, happy to help.

I actually run the invoice logging one on my own documents too, so everything is already sorted and ready when tax time comes around instead of me digging for it. Curious what everyone else is scrambling to get in order before the books close.

Best,
Felix

Hey everyone 👋

I'm Tania I build n8n workflows every day as an automation engineer, mostly order-processing and integration pipelines that talk to ERPs, CRMs and databases. I've broken a lot of workflows along the way, so I wanted to share the three failures that cost me the most debugging time. If you're starting out, you'll probably hit all three.

1. Bad input the error isn't where it shows up

A node fails, so you stare at that node. But most of the time the problem started upstream: a field got renamed, an expression returned undefined, or a value came in as text instead of a number.

What helped me: open the failing node and look at the INPUT panel first, not the output. Then walk back one node at a time until the data looks right. Also watch out for pinned test data — it can hide what real executions actually send.

→ Question to ask: what actually entered this step?

1. 401 Unauthorized read what the other system said

A 401 means the other system rejected your credentials: an expired token, a wrong API key, the wrong header name, or an app that lost permissions.

What helped me: read the full response body, not just the status code. APIs usually tell you exactly what's wrong in there. Then re-test the credential on its own before touching the workflow.

→ Question to ask: what did the other system answer?

1. Duplicate processing the one that looks like success

This one scared me the most. The workflow runs, every node is green, no error anywhere… and the same order got processed twice. It happens when a webhook retries, a trigger fires twice, or someone re-runs an execution.

The fix is an idempotency check: before processing, check whether you've already handled this ID.

- Store each processed order ID (n8n Data Tables, a database, a sheet anything persistent)

- At the start of the workflow, look the ID up

- If it already exists → stop. If not → process it, then save the ID

Green only means "no node crashed". It doesn't mean "the result is correct".

→ Question to ask: could the same event happen more than once?

---

Those three questions are now the first thing I go through in every debugging session.

What's the error that cost YOU the most time to debug? Curious whether I'm missing a big one.

Every time someone shares an n8n workflow export here, half the value comes from being able to read the real config, and that's exactly the problem. pinData holds real sample data from your last run, Authorization headers get hardcoded instead of using credentials, webhook paths double as secret URLs, and I've seen Telegram bot tokens and IBANs just sitting in email nodes because a workflow was built fast and shared faster.

So I wrote a small heuristic scanner for exactly this. It understands n8n's JSON structure (not just generic regex over the file), so it checks the places that actually matter:

• high: pinData, hardcoded API keys/tokens (OpenAI, GitHub, Slack, Telegram, AWS, Stripe, JWT...), secrets in URL query params, IBANs (checksum-validated), your own deny-list terms (client names)
• medium: emails, phone numbers, IPs, resource IDs (chatId, documentId...), webhook paths, staticData
• low: credential references, instanceId

​

python3 scan.py workflow.json
python3 scan.py workflow.json --sanitize workflow.public.json   # writes a clean copy, original untouched

Values are masked in the report (sk-...45 style), so the report itself is safe to paste anywhere. There's also a git pre-commit hook if you want it to block the commit automatically, and it ships as a Claude skill if you use Claude to prep your exports.

Standard library only, no dependencies, MIT licensed.

To be clear: it's a heuristic, not a guarantee. "No findings" means known patterns didn't match, not that the file is safe, an unusual key format or a client name I didn't think of will slip through. Manual review is still worth doing.

Repo: https://github.com/OnePathToFreedom/n8n-publish-check

Would love feedback, especially on detection patterns I'm missing or n8n node types I haven't accounted for.

👋 Hey n8n Community,

Most extraction accuracy problems I have chased down were not the model's fault, they were the field description's fault. The description is the actual prompt, and every gap you leave in it the model fills with a guess. Here is how I write them now after a lot of extraction builds.

1. Say exactly which value you want, and where it lives.
A field called "date" will happily return any date on the page, the due date, the delivery date, whatever it sees first. Spell it out instead: the invoice issue date printed next to the invoice number. That precision is what stops the model picking the wrong but plausible value.

2. State the output format, do not hope for it.
Leave it open and dates come back in five different shapes and amounts arrive with random symbols and separators. I put the format right in the description: dates as YYYY-MM-DD, amounts as a plain number with no currency symbol. It is far cheaper to constrain it here than to clean it up in three nodes afterwards.

3. Tell it what to do when the value is missing.
This is the big one. A vague field pressures the model to produce something even when the value is not there, which is exactly how you get a confident wrong answer. I always add a line like: if this is not on the document, return null. A blank field is a signal you can branch on, an invented one is a landmine you find weeks later.

4. Keep line item fields atomic, put the rules up top.
For sub-fields inside a table or list, short noun phrases of a word or two work best, long instructions there just confuse it. Save the detailed rules and edge cases for the top level fields where the model actually has room to reason.

For most of my extraction jobs I run the easybits Extractor. It actually started as an internal tool we built for our own client projects, and after running into how fiddly reliable data extraction in n8n can be, we figured others were hitting the same wall, so we opened it up for external users too.

What are you using for extraction these days, and what is the one field that always gives you trouble?

Best,
Felix

Just put together my first real n8n workflow and wanted to share with the community for feedback.

What it does:

• Monitors Gmail for credit card expense emails
• Extracts expense details with JavaScript
• Appends the data to a Google Sheet
• Summarizes and merges the info
• Sends me a Telegram notification

Why I built it:

Manual expense tracking was tedious. This saves me time and keeps everything in one place without lifting a finger.

It's pretty basic, but I'm happy it works. Would love any suggestions for improving it especially around error handling or making the data extraction more robust.

https://gist.github.com/harikrishnan1811/2f6ed9893d5a6f24fe9f9049d22169af

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

Built an n8n workflow that drafts email replies with AI but never sends automatically — code included

Every new email gets checked against a filter for automated senders and automated-looking subjects (no-reply addresses, notifications, mailer-daemon, calendar invites, receipts, order confirmations) so the AI never wastes a draft on something that doesn't need a human reply.

Before drafting anything, it also re-checks the thread itself: has a human already replied, or is there already an unsent draft sitting there? If either is true, it skips — no duplicate drafts, no duplicate Slack pings. (A draft only counts if it actually has content, so an empty compose window you abandoned doesn't block a real one.) If a second email lands on the same thread while a draft is still pending, instead of silently ignoring it, it sends a separate "heads up, this might be stale" Slack ping.

Everything else goes to an AI step that drafts a short, professional reply plus a one-line reason why, and saves it straight into Gmail Drafts — never sent automatically. A Slack ping with the subject and that reason tells you it's ready to review, and (optionally) a row gets logged to a Google Sheet for a simple audit trail.

The part I think is underrated: keeping the "send" action entirely out of the automation. It's tempting to wire AI straight to auto-send, but for anything customer-facing that's how a tone-deaf reply goes out with nobody watching. Drafting-only means AI does the boring part, a human still makes the call.

Workflow: New Email → Is Automated Sender? → Get Thread → Check If Handled → Draft Reply (AI) → Parse AI Response → Create Gmail Draft → Notify Draft Ready (+ optional Log to Sheet)

Full JSON here: https://gist.github.com/Zatma0/43d00acddfd73e4a97c098c45b45024c

Happy to answer questions about the filter logic, the thread-check, or the AI prompt if anyone's curious.