https://preview.redd.it/r0n0fpc8mb2h1.png?width=664&format=png&auto=webp&s=58f063d28b845b35445d345ccf8f37fb4ec10878

I am constantly getting this issue, how do I fix this issue. I want to show my client the workflow works, but im not able to connect to my my own gmail/sheets

I recently started automation and jumped directly to n&n and made some automations with some help of claude. but now i am very confused what NEXT??

Please help me if anyone in same path as me.

Please share your experience if u can.

Share your thoughts

Workflow Code:
https://gist.github.com/iamvaar-dev/6544545160d058f716b121e7e38e52bc

Pre-requesties:
- Apaleo Client id and secret.

- Apaleo basic authentication key which we recieve just after creation of the dev app.

- seam.co Api key as we need it to generate smart lock pin.

- Twillio API

Workflow 1: Smart Lock PIN Generation & Notifications

This is the primary workflow that runs whenever a room is assigned to a guest.

1. When Reservation Unit Assigned (Apaleo Trigger)
   • What it does: This is the starting point. It listens for a specific webhook event from Apaleo (reservation/unit-assigned). As soon as a staff member assigns a physical room to a booking, this node triggers the automation.
2. Fetch Reservation Details (Apaleo Node)
   • What it does: It takes the ID from the trigger node and makes a request (BookingReservationsByIdGet) to Apaleo to grab the full details of the reservation, specifically the arrival and departure times needed for the smart lock.
3. Map Room ID to Lock ID (Code Node)
   • What it does: This runs custom JavaScript to match the Apaleo room string (e.g., "BER-VWD 1.001") against a hardcoded database of your physical locks. It finds the corresponding Seam locker_id and prepares a clean JSON payload containing the device ID, reservation ID, and the exact start/end times for the lock access.
4. Post Access Code to Seam (HTTP Request Node)
   • What it does: It sends a POST request to Seam's API (/access_codes/create) using the data prepared in the previous step. Seam generates a unique PIN that will automatically activate at check-in time and expire at check-out time.
5. Append PIN to Reservation Note (Apaleo Node)
   • What it does: Uses a PATCH request to update the Apaleo reservation. It takes the newly generated PIN from Seam and saves it directly into the reservation's comments/notes so hotel staff have a record of it.
6. Fetch Customer Contact Data (HTTP Request Node)
   • What it does: Makes another call to Apaleo's API using a Bearer token to pull expanded customer details, specifically the primaryGuest and booker profiles. This is required to get the guest's email address and phone number.
7. Send Email via Gmail (Gmail Node) & Send PIN via Twilio (Twilio Node)
   • What they do: The workflow splits into two parallel actions here.
     • The Gmail node takes the customer's email and sends a beautifully formatted, custom HTML email containing their secure PIN and instructions.
     • The Twilio node simultaneously takes the customer's phone number and sends a concise SMS text message with the PIN for easy access on the go.

Workflow 2: Automated API Token Rotation

Because the Apaleo Bearer token (used in the "Fetch Customer Contact Data" node) likely expires every 60 minutes, this background workflow runs independently to refresh it.

1. Every 58 Minutes Trigger (Schedule Trigger Node)
   • What it does: Acts as a cron job, firing off this secondary sequence exactly every 58 minutes.
2. Fetch Access Token (HTTP Request Node)
   • What it does: Sends a POST request to Apaleo's identity server (identity.apaleo.com/connect/token) using a static Basic Auth key to request a brand new, short-lived access token.
3. Patch API Credential (HTTP Request Node)
   • What it does: This is a clever use of the n8n API itself. It takes the fresh token fetched in the previous step and makes a PATCH request to your n8n instance. It dynamically overwrites the existing "Apaleo Bearer Auth" credential saved in n8n. This ensures that Workflow 1 always has a valid token when it needs to fetch customer data.

Hi there, I’m looking to learn AI Automation from scratch as a non-tech person coming from HR. Who can be of help?

One thing I wish beginners heard earlier:

If your n8n workflow works in a test run, that doesn’t mean it’s ready.

The real questions are:

• what if the API fails?
• what if the same item arrives twice?
• what if one field is missing?
• what if the token expires?
• what if it runs 100 times instead of once?

The first time I understood automation, it was when I stopped asking “did it run?” and started asking “will it keep running?”

What was the first reliability lesson you learned in n8n?

I am new to n8n and built a workflow. Typeform submission creates a ClickUp task and notifies a manager in Slack for approval. It works for one approver.

Now my boss wants multi step approvals. If amount is under 500, manager only. If over 500, manager then finance. If over 5000, add the CFO. My n8n graph is already a mess and the If nodes are hard to maintain. Plus if someone is on PTO I have to edit the workflow. How do beginners handle dynamic approval chains without rebuilding the whole thing each time?

https://preview.redd.it/iv5wvknrew1h1.png?width=1442&format=png&auto=webp&s=9a3f8b71d61ef38c698e09699661e370d5d0edff

Built one n8n workflow with four ways to wire a reasoning harness into an agent. Single chat trigger; prefix selects the branch.

The harness in the example is Ejentum, a reasoning API that returns a structured scaffold per call (failure patterns to avoid, target patterns, amplify/suppress signals) which the agent absorbs into its prompt before answering.

- `/inject /reasoning` (or `/code` `/memory` `/anti-deception`) — locked routing, harness always applied as a system prompt injection. You pick the mode.

- `/reasoning` — single tool, model decides when to call it.

- `/full` — four tools, model decides which to call and when.

- `/ejentum-mcp` — same as `/full` but one MCP Client node instead of four HTTP tools.

The tradeoff axis is how much routing discretion you hand to the model. Determinism on the left, flexibility on the right.

The four wiring patterns are generic. Drop in any HTTP tool or MCP server in the same slot and they still apply.

This workflow beyond the tool usage is an example of harness that activates branches with command like "slash" calls. That makes the workflow modular and ready to modify to its builder use case.
I am not doing self promotion, i am just showing the possibility to consider a middleware of cognitive frameworks that increases performance that may be for u crucial but for the agent less relevant and apply a reasoning structure that demands verification and a clear execution logic to apply. Each reasoning ability the agent receives is a tested self contained cognitive operation that is designed to give procedural steps intead of theatrical content. I appreciate the attention poured into the post, here u can find more links about ejentum project. cheers

the ejentum node is installable inside community nodes as " n8n-nodes-ejentum " :
https://www.npmjs.com/package/n8n-nodes-ejentum

Template + README:

https://github.com/ejentum/agent-teams/tree/main/n8n-harness-integration-patterns
ejentum.com
github.com/ejentum

Free tier on the API is 100 calls, no card.

Hey everyone,

I’m currently in the early stages of learning n8n and building my understanding of automation, workflows, APIs and AI integrations.

Right now I’m looking to connect with people who are also learning or already working with these tools, with the idea of creating a small collaborative space where we can:

• Discuss real client situations and use cases
• Break down problems and design possible workflow solutions
• Share ideas, templates, resources and learning material
• Build a consistent rhythm of learning (weekly or daily discussions)

My intention is not just to “consume content”, but to actively grow through practice, discussion and shared problem-solving — and ideally, help each other level up in the process.

If this resonates with you, feel free to comment or DM me. Would be great to build something small but consistent with like-minded people. :)

I am building WhatsApp ai agent for restaurant..

Been self-hosting Immich for a while and wanted a "just us" album that stays updated automatically — no manual curation, no group shots sneaking in, no solo photos. So I built it with n8n.

It's two workflows:

1. Full Library Backfill — run once, scans your entire library (fully paginated, no 1000 photo cap), populates the album from day one

2. Daily Incremental — runs every night at 3:30am, only checks the last 2 days, sends you a Telegram notification with the results

The filter is strict: `people.length === 2` and both must be your configured people. Solo shots and group photos are automatically excluded.

Telegram message looks like this every morning:

📸 Immich Couple Album Update

✅ 3 new photo(s) added

👫 Person1 & Person2

🔁 12 already in album

🔍 15 couple photos found / 47 scanned

📅 Since: 2026-05-13

A few things I ran into building this that might save someone else time:

- fetch and $http don't work in n8n Code nodes with the task runner — helpers.httpRequest` is the one that works

- The Merge node in newer n8n throws a "Fields to Match" error in multiplex mode — I do the second person lookup inside a Code node instead, no Merge node needed

- Error workflows need an **Error Trigger** node as the first node or they won't show up in the dropdown

I'm not a developer — this is me learning n8n by building something I actually wanted. There's definitely room for improvement and I'd love feedback from people who know what they're doing.

GitHub: https://github.com/dhaval9911/n8n-immich-couple-album-auto

Note: It will need to have both the person's face ids are associated to the asset.

Full README with step-by-step setup is in the repo. Would love to hear if anyone adapts it for a family album or friend group — the filter is easy to tweak for more than 2 people.

Happy to answer questions! 🙏

Tired of your agents forgetting everything after one session, duplicating work, or fighting each other?

I just launched a premium prompt pack with:

- 10 battle-tested memory prompts

- 5 multi-agent handoff templates

- 3 plug-and-play workflows with real LangGraph + CrewAI code examples

- Troubleshooting guide + advanced tips

It turns chaotic single agents into a reliable team that actually remembers and compounds knowledge.

Instant download. Works with LangGraph, CrewAI, OpenAI Agents, LangChain, etc.

→ https://[YOUR GUMROAD LINK HERE]

Would love any feedback from people building multi-agent systems!

Over the last few days, I’ve been building an automation workflow that:

• Accepts a resume + job description
• Extracts resume content automatically
• Performs ATS analysis using AI
• Identifies missing keywords and weak areas
• Rewrites the resume dynamically based on the JD
• Generates a clean ATS-friendly PDF resume
• Optimizes formatting to stay within 2 pages
• Returns recruiter-optimized output automatically

Tech stack used:

• n8n forms
• Azure Open AI
• PDFBolt for HTML → PDF generation
• Prompt Engineering

One of the most interesting parts was solving:

• ATS keyword alignment
• PDF layout compression
• recruiter readability
• hallucination prevention
• dynamic JD adaptation
• compact HTML rendering

The workflow now behaves almost like an “AI Resume Architect” instead of just another ATS checker.

Current flow:

Upload Resume
→ Extract Text
→ ATS Analysis
→ Dynamic Keyword Extraction
→ AI Resume Rewrite
→ HTML Optimization
→ PDF Generation
→ Email Delivery

What surprised me most:
Prompt engineering mattered more than the AI model itself.

The biggest improvement came from:

• structured prompts
• layout optimization
• dynamic ATS target extraction
• recruiter readability constraints

This started as a simple automation experiment using n8n, but it’s slowly evolving into a real micro-SaaS idea.

Curious to know:
What features would YOU expect from an AI resume optimization platform?

https://preview.redd.it/04ivficovo1h1.png?width=2136&format=png&auto=webp&s=f274180a807fa9f8e4d51a8453c70350587f9c25

https://preview.redd.it/fqg7z91svo1h1.png?width=1712&format=png&auto=webp&s=7a79721fc736e070c59b2c6414a25a2f3fba56f4

https://preview.redd.it/ao2hamewvo1h1.png?width=2868&format=png&auto=webp&s=5250163ebbcee8d7b4c87c0514610ff57c5fba1d

Problem I Solved

I wanted a system that could:

✅ Collect jobs from multiple LinkedIn searches
✅ Merge all job feeds into one pipeline
✅ Detect duplicate jobs automatically
✅ Identify whether a job was already processed earlier
✅ Notify only for NEW job postings
✅ Maintain a persistent tracking database
✅ Prevent duplicate emails every day

What Makes This Useful

The workflow now behaves like a lightweight job aggregation engine.

Example:

• 20 jobs fetched today
• 18 already exist
• Only 2 new jobs detected
• Email sent ONLY for those 2 jobs

This completely eliminates notification spam and duplicate tracking.

Tech Stack

• n8n
• Rapid API
• HTTP Request Nodes
• n8n Data Tables
• JavaScript Code Nodes
• Gmail Integration

Key Learnings

Some interesting things I learned while building this:

• Data table operations in n8n are item-based
• Deduplication architecture matters a lot
• Loop handling can easily create duplicate executions
• Large LinkedIn job IDs should be stored as strings
• Notification systems need state management (Notified=True/False)

https://preview.redd.it/b7y1t3za1j1h1.png?width=2388&format=png&auto=webp&s=e8710ce5d40be90670d6e80ac287331a9cb6ffab

https://preview.redd.it/fhxa3srm1j1h1.png?width=2164&format=png&auto=webp&s=276cff53bcb66bead1e312a72a5d99d3d9281e3e

https://n8n.arulvelu21.cloud/workflow/jBFQs7AgTLiBkegB

Hello, I am 19 years old and I decided to start the AI automation business a week ago. I started with Make and created my first simple automation, which sends a personalized email to a prospect and a reminder 24 hours later. I would like to try to sell this first simple automation to get an idea of the market, but I am facing a problem: I do not know how to find my first customer. If you have already started in AI automation, could you give me some advice, please?

Hello Good day everyone I'm planning to tackle and learn N8N automation I've heard and research its more technical. I've already made projects from Make(Integromat) and Zappier.

Now I have 2 questions

First is: I'm planning to subscribe to Claude Pro and is there any tips or Idea to to leverage Claude pro aside from using Claude Code to automatically create workflow from me.

Second: Should I subscribe to N8n directly or use Hostinger to self host? Is there any difference from the two?

Hey everyone

I’m looking for serious people who want to work together on AI automation projects and build tools/SaaS products together

Mainly looking for people with skills in:

• n8n automation

• AI agents

• WhatsApp automation

• OpenAI or Gemini integrations

• API integrations

• SaaS building

• Workflow automation

• Lead gen systems

• AI chatbots

Goal is not just freelance client work

I want to build long term automation products and sell systems together

If you already work with automation or want to build something big together DM me with:

• What you do

• Your skills

• What kind of projects you worked on

• Discord or Telegram

Only serious people who actually want to build and grow together 🚀

absolute beginner in the tech-osphere here and i need some assistance innerstanding why the node wont accept my credentials and will not fulfill my get requests

Hello everyone

I am trying to build the whatsapp automation in N8N for one month.

I almost made other workflows, but connecting my whatsapp business through meta became too complex

I have created the app, and added my number, still it is showing the test number

After, that I added the receipt number and generated the access token, still it is showing an error in loading the number

I don't know what to do, if anyone did it, can you please help me to clear my doubts

What if we can take a third party tool and take whatsapp api and connect it to the N8N and create it

And also please help me with the limitations, i didn't get the 1000 free messages

Please help me🥺🥺

Hi, I am Vaar an automation dev just like most of you here 😄

Workflow Link: https://gist.github.com/iamvaar-dev/4a94ecac1296325d0484df2d581314f6

Here is a node-by-node explanation:
This workflow operates as an AI-powered customer service assistant for an HVAC company, communicating with customers via WhatsApp and managing contacts and appointments in GoHighLevel (GHL).

1. Core Execution Flow (The Main Pathway)

These nodes form the step-by-step sequence of actions triggered when a user sends a message.

• WhatsApp Trigger
• Purpose: This is the entry point of the workflow. It constantly listens for incoming WhatsApp messages.
• Function: When a user sends a message, this node captures the payload, including the user's phone number and the text message they sent.
• If Valid Sender Exists
• Purpose: Acts as a safety check/filter.
• Function: It checks the incoming data from the WhatsApp Trigger to ensure the sender's phone number ($json.messages[0].from) is not empty. If valid, the workflow proceeds to the "True" branch.
• Fetch GHL Contacts
• Purpose: Database lookup.
• Function: It takes the sender's phone number from the trigger and searches GoHighLevel (GHL) to see if a contact profile already exists for this user. It always outputs data, passing either the found contact details or an empty result to the next node.
• Customer Service AI Agent1
• Purpose: The "brain" of the operation. This is a LangChain Agent node.
• Function: It receives the user's message, the current date/time, and the contact information fetched from GHL. Following an extensive system prompt, it adopts the persona of "Alex" to understand the customer's HVAC issue, ask for missing contact details (if the GHL lookup failed), and determine the next best action (using tools or chatting). Once it formulates a final response, it passes it down the line.
• Send WhatsApp Response
• Purpose: The final action in the standard flow.
• Function: It takes the final text output generated by the AI Agent and sends it back to the user's phone number via WhatsApp.

2. AI Agent Inputs (The "Brain's" Resources)

These nodes are connected directly to the AI Agent to provide it with intelligence, memory, and the ability to interact with external systems.

• Gemini Chat Model
• Purpose: The Language Model.
• Function: Powered by Google Gemini, this node processes the natural language, understands the user's intent, and generates conversational text based on the Agent's instructions.
• Redis Chat History Memory
• Purpose: Conversational memory.
• Function: It stores the back-and-forth chat history using Redis. It uses the user's WhatsApp phone number as a unique session key. This ensures that if the user sends multiple messages, the AI remembers what was said earlier in the conversation (up to 15 messages).

3. AI Tools (The Agent's Actions)

These are specialized HighLevel tool nodes that the AI Agent can choose to "trigger" autonomously during the conversation to fulfill its instructions.

• Create or update a contact in HighLevel
• Purpose: Lead capture.
• Function: If the initial "Fetch GHL Contacts" node found no record, the AI is instructed to ask for the user's Name and Email. Once provided, the AI uses this tool to automatically create a new contact in GHL using those details plus their WhatsApp number.
• Save user issue in notes
• Purpose: Record keeping.
• Function: When the user describes their HVAC problem (e.g., "My AC is blowing warm air"), the AI immediately triggers this tool to write a summary of the issue directly into the user's GHL contact notes.
• Fetch Available Calendar Slots
• Purpose: Checking availability.
• Function: Before offering appointment times, the AI uses this tool to check the GHL calendar. It inputs a start and end date (in Unix timestamps), and the tool returns a list of free 30-minute slots.
• Book Calendar Appointment
• Purpose: Finalizing the service.
• Function: Once the user agrees on a specific time, the AI uses this tool to officially book the appointment in the GHL calendar using the Contact ID, Calendar ID, and the agreed-upon Start Time (in ISO 8601 format).

Summary of the Workflow Interaction:

1. A user texts via WhatsApp.
2. The system checks if they are an existing GHL Contact.
3. The AI Agent (Gemini) reads the message, recalling past context from Redis.
4. The AI chats with the user, autonomously using Tools to create their profile, save their AC/Heating issues, check the calendar, and book a timeslot.
5. The AI sends the conversational reply back via WhatsApp.

Let me know if you had any questions regarding this workflow. I would love to explain you this.

Thanks,
Vaar