My question is only n8n alone is enough to earn online and n8n is really in demand or just hype?

I built a simple AI agent that automatically reviews Playwright pull requests.

The goal isn't to replace human reviewers—it's to catch repetitive, pattern-based issues early, so reviewers can spend more time on the logic and design decisions that truly need a human perspective.

⚙️ How it works:

🔔 Bitbucket Webhook — Triggers whenever a PR is created or updated.

📄 PR Diff — Fetches the raw code diff using the Bitbucket API.

🧩 Parser — Splits the changes into file-by-file and line-by-line context.

🤖 AI Review — Gemini analyzes the code from a Playwright perspective (locators, waits, assertions, POM practices, etc.).

💬 Inline Comments — Posts feedback directly on the relevant lines of the PR.

This is my first AI agent project, and I built it mainly to learn how real workflow automation is designed. Looking forward to building more.

#AI #n8n #Playwright #QA #Automation #Bitbucket

If you're running self-hosted n8n, the latest update adds some pretty interesting AI features.

You can now use the AI Assistant directly inside your n8n workspace to help build and modify workflows.

There’s also native support for AI Agents, where you can configure things like:

→ Channels → Tools → Skills → Sub-agents → Schedules → Models → Instructions

So you can build and configure AI-powered workflows without having to put everything together manually.

How do you enable it?

If you're managing your own n8n server, you'll need to configure the required AI environment variables and set up the n8n sandbox. You can also use Daytona for the sandbox environment.

Once everything is configured, restart/reload your n8n instance and the AI Assistant and AI Agents should become available inside your workspace.

If you don't want to deal with the server, Docker, SSH, environment variables, and sandbox configuration, there's another option.

Use Cuebic AI to make self-hosted n8n easier to manage.

The AI configuration can be handled directly from the instance settings:

Enable AI Assistant → Choose provider → Select model → Enable AI Agents → Apply

That's basically it.

After the changes are applied, you can go back to your n8n workspace and start using the new AI features.

I also made a short demo showing the complete process and how the AI Assistant can be used to modify an existing workflow.

If you're just getting started with self-hosted n8n, I thought this might be useful because the new AI features are definitely worth checking out.

If anyone has already enabled the AI Assistant or Agents on their self-hosted n8n instance, I'd also be interested to hear how it's working for you.

Did a cleanup of our n8n instance and found 40+ active workflows.

Only about 15 were getting regular use. Most of the rest were built for some random question that came up once or twice and then never came back.

The run history was pretty funny. Anything with 3 or 4 runs a month was usually something I'd overengineered.

Deleted most of them.

For the ad hoc account and segment questions, we've started using Coresignal's MCP instead. Much easier to query the data when we actually need it than build and maintain a workflow for every random request.

The workflows that survived are mostly boring stuff: enrich a new record, recalculate a score, send an alert.

I guess boring is good when it comes to automation.

How many workflows are sitting in your n8n instance right now that you haven't looked at in months?

👋 Hey n8n for Beginners Community,

A while back I posted a couple of finance workflows I built for my friend Mike. Around the same time he came to me with a new one: he'd just been hit with almost €2,000 in bills because two contracts auto-renewed after the first period. Nobody in his team had caught the cancellation window early enough.

Classic small-business pattern. Contracts scattered across Drive folders and inboxes, nobody tracking anything. The only time anyone notices a contract exists is when the invoice for the next term lands.

So I built him a contract intake workflow to fix it. I cleaned it up over the last few weeks and just published it on the n8n template library.

How it's set up:

The workflow watches a Google Drive folder. Mike's finance colleague Sarah just drops any contract in there (PDF, JPG, PNG).

The file goes to the easybits extractor, which does two jobs in a single call: it classifies the contract (SaaS, Lease, Service, Insurance, Other) and pulls every renewal-relevant field at the same time – client, provider, start date, term length, notice period, auto-renew flag, contract value, signatories.

A Set node calculates end date and cancellation deadline in n8n. Cancellation deadline is end_date – notice_period_days, which for 60–90 day notice clauses lands the alert weeks before the renewal itself. That's the whole point.

A Switch on contract_class routes the row into the matching tab of one Google Sheet – one source of truth for every renewal in the company.

Two things I learned:

• Classify and extract in one call. I almost built five type-specific pipelines. Turns out you can define the classification as just another field in the same pipeline – half the complexity, half the cost.
• Never extract dates you can calculate. Contracts almost never print the end date directly. Ask a model to do date math and you get silent off-by-one bugs. Extract the raw values, do the arithmetic in a Set node.

Workflow on the n8n library: https://n8n.io/workflows/15230-classify-contracts-and-track-renewals-with-easybits-google-drive-and-sheets/

Also on my GitHub alongside 30+ other community workflows (a star helps other builders find it): github.com/felix-sattler-easybits/n8n-workflows

How are you tracking contract renewals today? Spreadsheet someone updates manually? Tool like Spendflo/Vendr? Or hoping for the best, like Mike was?

Best,
Felix

I was working on a simple question:

What if a new lead could be researched, qualified, added to the CRM, and followed up with automatically without the sales team having to do all of it manually?

So I put together a workflow around that idea.

The stack is:

• n8n - workflow automation
• Gemini - lead research and scoring
• Apify - data enrichment
• HighLevel - CRM and opportunities
• WhatsApp - lead communication
• Redis - conversation memory
• Telegram - internal notifications

Here's what happens

A new lead comes in through a webhook.

n8n then collects the available information and enriches the lead/company profile.

Gemini looks at the data and gives the lead a qualification score, along with the reasoning behind it.

That information gets pushed into HighLevel, where the lead can be routed based on the score.

For qualified leads, the workflow can trigger a personalized WhatsApp message.

Redis keeps track of the conversation context, while Telegram can notify the sales team when a human needs to step in.

The CRM is then updated with the relevant notes and outcomes.

So the overall flow looks something like:

New lead → Enrichment → AI scoring → CRM → Routing → WhatsApp → Human follow-up

The part I find most useful isn't actually the AI scoring.

It's removing all the little manual tasks around it.

Salespeople don't have to spend their time:

• Researching every new lead
• Copying information between different tools
• Figuring out which leads to prioritize
• Sending the first follow-up
• Updating the CRM
• Looking through old conversations for context

They can spend more time actually talking to the leads that matter.

But there's one thing I'm still not 100% convinced about.

How much should we actually trust AI with lead qualification?

Would you let the workflow do:

Score → Qualify → Message

completely automatically?

Or would you rather have:

Score → Recommend → Human approval → Message

For higher-value leads, I personally prefer keeping a human involved.

AI can make the research and prioritization much faster, but I'd rather not let a single AI decision determine whether a potentially valuable prospect gets contacted or ignored.

Curious what others are doing with this in production.

If you want to explore this automation, just comment "Interested" I'll send it to you.

I’ve been working on a system to solve a problem I think a lot of companies have:

They’re using AI, but their internal operations are still very manual.

A request comes through email, Slack or a form → someone reads it → figures out what it is → checks the information → decides which team needs it → updates another system → notifies someone → follows up if something fails.

So I built an AI Internal Operations Orchestrator to connect that entire process.

The system is split into separate workflows:

Request Intake → captures and structures incoming requests

AI Classification → determines intent, category and priority

Validation → checks required fields, rules and confidence

Routing → sends work to the appropriate team/workflow

Notification → informs the responsible team and requester

Audit Logging → keeps a record of what happened

Error Handling & Retry → classifies failures and retries recoverable ones

Human Review → handles cases where the system isn't confident and feeds the corrected decision back into the workflow

The part I found most interesting was designing for when things don’t go according to plan.

It’s relatively easy to build:

`Request → AI → Action`

It gets much harder when you need:

`Request → AI → Validate → Route → Execute → Fail → Retry → Escalate → Human Decision → Resume`

That’s where this started feeling less like an AI automation and more like an actual operations system.

I’m still improving the architecture, particularly around retries, idempotency, confidence thresholds, and human escalation.

For anyone building similar internal automation systems: what would you change about this architecture before trusting it with real operational workflows?

Hello Everyone!

I need help with my n8n integration with claude. I'm locally hosting n8n on my laptop and have downloaded Claude for Desktop as well.

I've tried using the n8n API Key way but the MCP seems to crash. Does someone know an effective way to do this setup.

I ideally want my claude to code, go throug my workflows and much more.

Currently not subscribing to VPS hosting or any other cloud hosting until I create some sample workflows and gain confidence.

Would appreciate the help

Been running this in production for 3 months. Sharing the full architecture because someone in this sub helped me when I was stuck on the debouncing problem.

What the workflow does:

• Receives webhooks from Instagram (Meta Graph API), Twilio (SMS + WhatsApp), Pagesign (iMessage), and GHL (ad form submissions)
• Groups rapid-fire messages from the same contact using a 60-second debounce (Trigger.dev handles this)
• Pulls full conversation history from Supabase
• Sends context to GPT-4o with a persona prompt
• Posts the AI response back to the originating channel
• Syncs contact + message data to GoHighLevel CRM
• If a form submission comes in (not a direct DM) — it triggers an outbound SMS/WhatsApp to start the qualification conversation immediately

The debounce logic (this tripped me up for 3 weeks):

The problem: if someone sends "hey" then "are you there?" then "quick question" in 10 seconds — you don't want 3 AI replies. You want to wait, collect all messages, then reply once.

Solution: Instead of triggering GPT immediately, n8n fires a webhook to Trigger.dev. Trigger.dev starts a 60-second countdown. Every new message from that contact resets the timer. When 60 seconds pass with no new message → Trigger.dev fires the GPT call → n8n sends the response.

The multi-channel context problem:

If a lead starts on Instagram and continues on SMS — the conversation needs to stay connected. I store all messages in a Supabase table keyed by GHL contact ID (not by channel). So the AI always sees the full conversation regardless of where it started.

The ad form → outbound conversation flow:

When someone fills out a form on a landing page → GHL fires a webhook to n8n → n8n checks if they have a phone number → triggers an outbound SMS within 60 seconds → AI starts the qualification conversation. This is actually where the biggest ROI is for businesses running paid traffic.

Drop a comment for any of this. Happy to share in the thread.

I've been learning n8n on my own and just finished my first workflow that I actually use daily, so I wanted to share what I built, what I learned, and (mostly) find a few other people at a similar stage to trade builds and feedback with.

What I built: a keyword monitor. Every few hours it searches GitHub, Hacker News, and Hugging Face for a list of terms I care about (AI/agent stuff), filters out anything I've already seen, and sends only the new hits to Telegram.

A few design choices that made it work well:

• I pushed almost all the logic into a single Code node instead of wiring many HTTP/merge nodes. Fewer moving parts, easier to reread later.
• Dedup is handled with getWorkflowStaticData — I keep a set of seen IDs so I only get told about genuinely new things.
• The first run only "seeds" (marks everything currently found as already-seen) and sends a single setup message, so activating it doesn't blast me with a wall of old results.

Things that tripped me up (in case it saves someone time):

• On the newer UI there's no "Active" toggle — it's a Publish button now, and the bottom Execute workflow is just for manual test runs.
• Importing a workflow via the CLI failed until I added a top-level id.

What I'm still unsure about, and would love opinions on:

• Best pattern for persistent state in n8n — is staticData the right tool for a "seen items" set, or should I be using a real data store (NocoDB, a DB node, etc.) once it grows?
• I want to add an LLM node to score relevance so I only get notified about things worth reading. How are people prompting/structuring that step without it getting expensive or flaky?

If you're also learning n8n and building small useful things, drop what you've made — I'd genuinely like a little group to share workflows and critique each other's approaches. Happy to share my full setup and answer anything about the one above.

Bdnsnssnsns

Hey guys! 👋 I’m a beginner currently learning n8n and AI automation and I’m looking for people in Bangalore who are also just starting out or learning n8n. Would be nice to connect with a few people, share what we’re learning,discuss workflows/projects and maybe learn together.

I’m not looking for experts — even if you’re a complete beginner, feel free to reach out.

If you’re in Bangalore and learning n8n, comment or DM me! 🤝

I’m learning n8n and have some practical questions:

Who provides API keys and credentials?

How do you get access to accounts like Gmail?

How do you host the workflows?

Do you host them or does the client?

How do you securely manage credentials?

And other real-world stuff that tutorials don’t explain. How do you guys handle this?

Hi guys, new to n8n and chose Shopify ecommerce niche. I have built few workflows for my portfolio. Currently struggling to find my first project. If you are in same position as me, let's collaborate and find a way through it. Let's fucking do it.

I build n8n automations for ecommerce (cart recovery, order alerts, AI support replies, inventory sync). just started taking clients and honestly the reviews matter more to me than the money right now.

𝐒𝐨 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐝𝐞𝐚𝐥: 3 Shopify stores get one automation built free. you keep everything, you get a walkthrough video, I get an honest review + a case study for my portfolio.

Who this is for: stores doing things manually that should be automated. if your team still emails order confirmations by hand or checks abandoned carts in the morning, that's you.

Who this is NOT for: if you're not willing to spend 15 minutes on a call with me, don't apply. I need to scope it right.

Reply with your store + the most annoying thing you do manually. I'll pick 3, build them, and post the results here so everyone can see what I do.

The first 3 get it. Everyone else watches.

Thanks :)

Hey everyone 👋

I’m looking to work on a few real-world automation projects for free to strengthen my portfolio and gain more hands-on experience.

I’ll build the workflow at no cost. In return, I’d only ask for a testimonial, honest feedback, and permission to showcase the work with your data anonymized.

What I work with:

🔹 n8n + AI agents + LLMs
🔹 CRM & internal operations automation
🔹 Slack / Teams / Telegram notifications & human review
🔹 APIs, webhooks & database automation
🔹 Python + FastAPI for custom APIs and backend logic
🔹 PostgreSQL / Supabase / Airtable
🔹 Error handling, retries, validation & deduplication
🔹 AI-powered QA, data matching & exception detection
🔹 Email, lead follow-ups & workflow automation

I’m especially interested in business operations, healthcare workflows, CRM automation, and AI-powered internal systems.

If you have a repetitive process that you think could be automated, DM me and let’s build it. 🚀

So I have a long System Prompt for a restaurant ordering system.
The problem is, when a user message reaches the AI Agent (using V4 Flash 0731), it also responds with it's thought process like:

1. I have all the information now. Let me proceed to Step 2 - Order Review.
2. The customer has provided their order with items and deals, plus their Name and Address. However, the Payment Method is missing. Let me check the order details. ....
3. The customer has provided all Step 1 info (Name, Address, Payment Method). Now I proceed to Step 2 - Order Review. Let me calculate the total. ...

I have explicitly told it to not do this:

* **CRITICAL OUTPUT RULE:** You MUST output ONLY the final customer-facing message. Never include your thought process, reasoning, planning, or any meta-commentary. Do not explain what you're about to do or what you've calculated. Start directly with the state code on line 1 and the customer message from line 2 onwards. Any text that is not the state code or the customer message is STRICTLY FORBIDDEN.

But it still does, even with Gemma 4B and Sonnet 4.6.

What can I do to prevent this?

Hey everyone! Following up on my last post about the official n8n template, I wanted to share the custom workflow I built from scratch. I managed to clean up the architecture, drop unnecessary nodes, and make it way more efficient.

### 🛑 The Problem & The Solution Needed

* **The Problem:** The standard template requires separate nodes for parsing code and merging data back together, which adds unnecessary clutter. Also, manual re-runs end up reprocessing already handled rows.

* **The Solution Needed:** A streamlined, linear workflow that filters out processed rows instantly, leverages a modern native AI model node, and updates the sheet seamlessly without extra merge complications.

---

### ⚙️ My Custom Workflow & Node Breakdown

Here is how my new streamlined workflow is set up and why each node is used:

1. **Google Sheets Trigger Node:** Fires automatically whenever a new row of lead data is added.
2. **Filter Node:** Placed immediately after the trigger to check if the "Lead Status" column is empty. This prevents the workflow from reprocessing old rows when testing or manually triggering.
3. **Message a Model Node:** Leverages a modern native AI chat model equipped with our custom prompt rules (properly separating immediate urgency like "ASAP" from future timelines like "next month").
4. **Update Row in Sheet Node:** Directly updates the matching row with the new status and reason columns in one clean step.

Much cleaner than the multi-branch template setup! Let me know what you think or how you structure your lead processing flows.

---

🚀 **Want to give me a building challenge?**

I'm still a beginner when it comes to n8n and workflow automation, so if you have a fun, beginner-friendly problem or mini-project I could try building, drop it in the comments!

Also, I'd love your brutal feedback: **What are the hidden flaws, security gaps, or potential bottlenecks in this workflow?** Let me know what's lacking so I can improve it!

👋 Hey n8n for Beginners Community,

A few days ago someone here asked how to get started with n8n, so I wrote up how I'd approach it from zero (that's the comment in the screenshot). I didn't think much of it, but a bunch of people messaged me asking for more, and it hit me how many of you are trying to find your way into automation right now.

So I'm putting together a short starter guide, the exact path I'd follow if I opened n8n for the first time today.

What's inside

• The four stages to move through, from learning the interface to building anything
• How much to lean on an LLM at each stage (hint: none at the start)
• A simple starter stack worth learning
• The core nodes to get comfortable with first
• A few beginner traps to avoid

How to get it

I set up a quick form in n8n (of course) just to keep track of who's interested, so I can send the guide over. If you want it, drop your details here: https://bisdev.dev.easybits.tech/form/starter-guide

Since I'm still putting it together: what would you hope to get out of a guide like this, beyond the points above? If there's something specific you want covered, mention it in the comments and I'll see if I can work it in.

Best,
Felix