Sending automated congratulations with Google Sheets, Twilio, and n8n
In this post, you’ll learn to automate sending personalized congratulations with n8n and never forget a birthday and anniversary again.
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Google Gemini Chat Model and Twilio integration
Save yourself the work of writing custom integrations for Google Gemini Chat Model and Twilio and use n8n instead. Build adaptable and scalable AI, Langchain, Communication, and Development workflows that work with your technology stack. All within a building experience you will love.
How to connect Google Gemini Chat Model and Twilio
- Step 1: Create a new workflow
- Step 2: Add and configure nodes
- Step 3: Connect
- Step 4: Customize and extend your integration
- Step 5: Test and activate your workflow
Step 1: Create a new workflow and add the first step
In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.
Step 2: Add and configure Google Gemini Chat Model and Twilio nodes
You can find Google Gemini Chat Model and Twilio in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Google Gemini Chat Model and Twilio nodes one by one: input data on the left, parameters in the middle, and output data on the right.
Step 3: Connect Google Gemini Chat Model and Twilio
A connection establishes a link between Google Gemini Chat Model and Twilio (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.
Step 4: Customize and extend your Google Gemini Chat Model and Twilio integration
Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Google Gemini Chat Model and Twilio with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.
Step 5: Test and activate your Google Gemini Chat Model and Twilio workflow
Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Google Gemini Chat Model to Twilio or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.
Popular Google Gemini Chat Model and Twilio workflows
+8
AI WhatsApp Support with Human Handoff using Gemini, Twilio, and Supabase RAG
Quick Overview
What it is: An n8n workflow that enables AI-first WhatsApp support with seamless human handoff.
Why it’s unique: The AI agent answers queries using RAG (Supabase vector store + Gemini). If a human intervenes, the AI steps down. If there’s no human reply within 2 hours, the AI resumes.
Channel constraints: Respects WhatsApp’s 24-hour customer care window and requires approved message templates for out-of-window messages.
How It Works
AI-first: Incoming WhatsApp messages are routed to an AI agent (Gemini) with knowledge grounded by a Supabase vector store.
Human-in-the-loop: When a human responds in the dashboard, AI pauses for 2 hours for that conversation.
Auto-resume: If no human reply within 2 hours, AI automatically resumes.
Compliance: Only responds within 24 hours of the user’s last message, or via approved templates when outside this window.
Architecture (At a Glance)
Transport: Twilio WhatsApp; n8n http node.
RAG: Supabase (Postgres + embeddings) stores knowledgebase.
LLM: Google Gemini (free API key supported).
Handoff: Human dashboard (GitHub project) logs and labels AI vs Human responses, and controls AI pause/resume.
Prerequisites
n8n (self-hosted or cloud) with public webhook access.
Twilio account with a WhatsApp-enabled number.
Supabase project for vector store.
Google Gemini API key.
Human dashboard: https://github.com/shadrack-ago/whatsapp-dashboard.git
Setup Steps (n8n + Integrations)
Import workflow in n8n
Create new workflow → Import from JSON → Paste the provided JSON.
Enable the workflow.
Create credentials
Twilio: Add TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_PHONE_NUMBER. Do NOT paste real secrets publicly.
Gemini: Add GEMINI_API_KEY.
Supabase: Add SUPABASE_URL, SUPABASE_ANON_KEY (or service role where needed), and your table/bucket names.
Connect Twilio WhatsApp
WhatsApp Business setup in Twilio, or Sandbox for testing.
Point Twilio incoming webhook to your n8n webhook URL.
Ensure approved templates for any out-of-window messaging.
Set environment variables (examples)
TWILIO_ACCOUNT_SID=...
TWILIO_AUTH_TOKEN=...
TWILIO_PHONE_NUMBER=+1...
GEMINI_API_KEY=...
SUPABASE_URL=...
SUPABASE_ANON_KEY=...
SUPABASE_TABLE=knowledge_base
EMBEDDING_MODEL
HUMAN_TIMEOUT_MS=7200000 (2 hours)
Human-in-the-loop dashboard
Follow the repo guide: https://github.com/shadrack-ago/whatsapp-dashboard.git
Run the dashboard and connect it to the same conversation store used by the workflow.
Verify that human responses are captured and labeled; confirm AI pause/resume logic.
Supabase for RAG
Create table(s) for documents and embeddings.
Ingest content per this tutorial:Supabase Tutorial
Confirm your n8n nodes query the vector store before calling Gemini.
Gemini setup
Get API key: Gemini API
Set model (e.g., gemini-pro or latest available in your environment).
Test
Send a WhatsApp message to your Twilio number.
Observe AI response.
Trigger a human reply via the dashboard → confirm AI pauses for that thread.
Wait 2 hours or adjust HUMAN_TIMEOUT_MS to test auto-resume.
Customization
Providers: You can swap Twilio for Meta’s WhatsApp Cloud API; keep the 24-hour and template rules.
Tone/Policies: Adjust system prompts and fallback behaviors in the LLM node.
RAG Quality: Tune chunking, TOP_K, and embedding model for better retrieval.
Timeouts: Change HUMAN_TIMEOUT_MS to alter handoff duration.
WhatsApp Policy Notes
24-hour window: Replies must occur within 24 hours of user’s last message; otherwise use an approved template.
Templates: Create and get approval inside Twilio/Meta before sending out-of-window messages.
Security & Reliability
Secrets: Store all keys in n8n credentials or environment variables. Never commit secrets to repos.
Logging: Use the dashboard to audit AI vs Human messages.
Rate limits: Add retry/backoff nodes for Twilio and LLM calls.
Troubleshooting
No replies: Check Twilio webhook URL and n8n workflow is active.
Policy blocks: Ensure template use outside 24-hour window.
Poor answers: Improve RAG data, increase TOP_K, refine prompts.
Handoff not pausing: Verify dashboard is writing the “human active” flag that the workflow reads.
Links
Human dashboard (Full guide): GitHub Repo Link
+2
Reduce Meeting No-Shows with Gemini AI, Email & WhatsApp Reminders for Calendly
How it works:
This n8n workflow automates communication with meeting invitees to decrease no-show rates by sending timely email and WhatsApp reminders, and a clarification request if more information is needed to prepare the meeting.
Step-by-step:
The workflow is triggered by an incoming email notification from Calendly about a newly scheduled meeting.
It uses AI to extract key meeting data from the email content.
It checks if the invitee didn't provide sufficient information, and, if there is a need for more information, sends a clarification request email.
It calculates the waiting time required for the 24-hour and 1-hour reminders.
It uses an If node to determine the correct waiting path based on the meeting time.
It uses Wait nodes for timing the reminders correctly.
Finally, it sends a reminder email and a WhatsApp reminder before the meeting.
Customization Options:
Replace Google Gemini with your preferred LLM model (though Gemini works on the free tier).
Tailor email and WhatsApp messages to speak your brand's language.
Replace Twillio node to WhatsApp node to be a completly free usage flow.
Build your own Google Gemini Chat Model and Twilio integration
Create custom Google Gemini Chat Model and Twilio workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.
Google Gemini Chat Model and Twilio integration details
Other integrations with Google Gemini Chat Model
Other integrations with Twilio
Google Gemini Chat Model and Twilio integration tutorials
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FAQs
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