Workflow Templates

Automate video transcription and Q&A with async VLM processing that scales from short clips to long recordings. What this workflow does Monitors Google Drive for new files in a specific folder and grabs the file ID on create Automatically downloads the binary to hand off for processing Sends the video to VLM Run for async transcription with a callback URL that posts results back to n8n Receives the transcript JSON via Webhook and appends a row in Google Sheets with the video identifier and transcript data Enables chat Q&A through the Chat Trigger + AI Agent. The agent fetches relevant rows from Sheets and answers only from those segments using the connected chat model Setup Prerequisites: Google Drive and Google Sheets accounts, VLM Run API credentials, OpenAI (or another supported) chat model credentials, n8n instance. Install the verified VLM Run node by searching for VLM Run in the nodes list, then click Install. You can also confirm on npm if needed. After install, it integrates directly for robust async transcription. Quick Setup: Google Drive folder watch Add Google Drive Trigger and choose Specific folder. Set polling to every minute, event to File Created. Connect Drive OAuth2. Download the new file Add Google Drive node with Download. Map {{$json.id}} and save the binary as data. Async transcription with VLM Run Add VLM Run node. Operation: video. Domain: video.transcription. Enable Process Asynchronously and set Callback URL to your Webhook path (for example /transcript-video). Add your VLM Run API key. Webhook to receive results Add Webhook node with method POST and path /transcript-video. This is the endpoint VLM Run calls when the job completes. Use When Last Node Finishes or respond via a Respond node if you prefer. Append to Google Sheets Add Google Sheets node with Append. Point to your spreadsheet and sheet. Map: Video Name → the video identifier from the webhook payload Data → the transcript text or JSON from the webhook payload Connect Google Sheets OAuth2. Chat entry point and Agent Add Chat Trigger to receive user questions. Add AI Agent and connect: a Chat Model (for example OpenAI Chat Model) the Google Sheets Tool to read relevant rows In the Agent system message, instruct: Use the Sheets tool to fetch transcript rows matching the question Answer only from those rows Cite or reference row context as needed Test and activate Upload a sample video to the watched Drive folder. Wait for the callback to populate your sheet. Ask a question through the Chat Trigger and confirm the agent quotes only from the retrieved rows. Activate your template and let it automate the task. How to take this further Team memory:** Ask “What did we decide on pricing last week?” and get the exact clip and answer. Study helper:** Drop classes in, then ask for key points or formulas by topic. Customer FAQ builder:** Turn real support calls into answers your team can reuse. Podcast highlights:** Find quotes, tips, and standout moments from each episode. Meeting catch-up:** Get decisions and action items from any recording, fast. Marketing snippets:** Pull short, social-ready lines from long demos or webinars. Team learning hub:** Grow a searchable video brain that remembers everything. This workflow uses the VLM Run node for scalable, async video transcription and the AI Agent for grounded Q&A from Sheets, giving you a durable pipeline from upload to searchable answers with minimal upkeep.

Geekhack Discord Updater How It Works This n8n workflow automatically monitors GeekHack forum RSS feeds every hour for new keyboard posts in Interest Checks and Group Buys sections. When it finds a new thread (not replies), it: Monitors RSS Feeds: Checks two GeekHack RSS feeds for new posts (50 items each) Filters New Threads: Removes reply posts by checking for "Re:" prefix in titles Prevents Duplicates: Queries PostgreSQL database to skip already-processed threads Scrapes Content: Fetches the full thread page and extracts the original post Extracts Images: Uses regex to find all images in the post content Creates Discord Embed: Formats the post data into a rich Discord embed with up to 4 images Sends to Multiple Webhooks: Retrieves all webhook URLs from database and sends to each one Logs Processing: Records the thread as processed to prevent duplicates The workflow includes a webhook management system with a web form to add/remove Discord webhooks dynamically, allowing you to send notifications to multiple Discord servers or channels. Steps to Set Up Prerequisites n8n instance running PostgreSQL database Discord webhook URL(s) 1. Database Setup Create PostgreSQL tables: Processed threads table: CREATE TABLE processed_threads ( topic_id VARCHAR PRIMARY KEY, title TEXT, processed_at TIMESTAMP DEFAULT NOW() ); Webhooks table: CREATE TABLE webhooks ( id SERIAL PRIMARY KEY, url TEXT NOT NULL, created_at TIMESTAMP DEFAULT NOW() ); 2. n8n Configuration Import Workflow Copy the workflow JSON Go to n8n → Workflows → Import from JSON Paste the JSON and import Configure Credentials PostgreSQL: Create new PostgreSQL credential with your database connection details All PostgreSQL nodes should use the same credential 3. Node Configuration Schedule Trigger Already configured for 1-hour intervals Modify if different timing needed PostgreSQL Nodes Ensure all PostgreSQL nodes use your PostgreSQL credential: "Check if Processed" "Update entry" "Insert rows in a table" "Select rows from a table" Database schema should be "public" Table names: "processed_threads" and "webhooks" RSS Feed Limits Both RSS feeds are set to limit=50 items Adjust if you need more/fewer items per check 4. Webhook Management Adding Webhooks via Web Form The workflow creates a form trigger for adding webhooks Access the form URL from the "On form submission" node Submit Discord webhook URLs through the form Webhooks are automatically stored in the database Manual Webhook Addition Alternatively, insert webhooks directly into the database: INSERT INTO webhooks (url) VALUES ('https://discord.com/api/webhooks/YOUR_WEBHOOK_URL'); 5. Testing Test the Main Workflow Ensure you have at least one webhook in the database Activate the workflow Use "Execute Workflow" to test manually Check Discord channels for test messages Test Webhook Form Get the form URL from "On form submission" node Submit a test webhook URL Verify it appears in the webhooks table 6. Monitoring Check execution history for errors Monitor both database tables for entries Verify all registered webhooks receive notifications Adjust schedule timing if needed 7. Managing Webhooks Use the web form to add new webhook URLs Remove webhooks by deleting from the database: DELETE FROM webhooks WHERE url = 'webhook_url_to_remove'; The workflow will now automatically post new GeekHack threads to all registered Discord webhooks every hour, with the ability to dynamically manage webhook destinations through the web form interface.

Quick overview This workflow receives WhatsApp messages via a Meta webhook, uses Anthropic Claude to detect booking intents and extract appointment details, writes updates to Google Sheets, and sends a confirmation or error reply back to the customer on WhatsApp. How it works Receives incoming WhatsApp messages through a Meta webhook endpoint and normalizes key fields like sender phone number, display name, message text, and timestamps. Filters out empty messages and sends the text to Anthropic Claude to classify the intent (book, reschedule, confirm, or unknown) and extract structured appointment details. Validates the request based on intent, including required fields and simple rules like weekday-only appointments and business hours. Appends a new row to the Google Sheets Appointments tab for valid bookings, or updates the appointment status/date/time in Google Sheets for reschedules and confirmations. Builds a personalized WhatsApp reply message describing the result (success, required missing details, or supported actions). Sends the reply back to the customer via the WhatsApp Cloud API and appends the interaction details to the Google Sheets ActivityLog tab. Setup Create a Meta WhatsApp Business (Cloud API) app, register the workflow webhook URL as the WhatsApp callback URL, and complete webhook verification. Add an Anthropic API credential in n8n and ensure the Anthropic Claude model node is connected to your account. Add a Google Sheets OAuth2 credential in n8n and create a spreadsheet with Appointments and ActivityLog tabs and the expected header columns. Replace YOUR_SHEET_ID in all Google Sheets HTTP request URLs with your spreadsheet ID. Replace YOUR_PHONE_NUMBER_ID in the WhatsApp API URL and set the WhatsApp Authorization bearer token (preferably via an n8n credential) in the request headers.

Build an AI HR Assistant to Screen Resumes and Send Telegram Alerts A step-by-step guide to creating a fully automated recruitment pipeline that screens candidates, generates interview questions, and notifies your team. This template provides a complete, step-by-step guide to building an AI-powered HR assistant from scratch in n8n. You will learn how to connect a web form to an intelligent screening agent that reads resumes, evaluates candidates against your job criteria, and prepares unique interview questions for the most promising applicants. | Services Used | Features | | :---------------------------------------------- | :----------------------------------------------------------------------------- | | 🤖 OpenAI / LangChain | Uses AI Agents to screen, score, and analyze candidates. | | 📄 Google Drive & Google Sheets | Stores resumes and manages a database of open positions and applicants. | | 📥 n8n Form Trigger | Provides a public-facing web form to capture applications. | | 💬 Telegram | Sends real-time alerts to the hiring team for qualified candidates. | How It Works ⚙️ 📥 Application Submitted: The workflow starts when a candidate fills out the n8n Form Trigger with their details and uploads their CV. 📂 File Processing: The CV is automatically uploaded to a specific Google Drive folder for record-keeping, and the Extract from File node reads its text content. 🧠 AI Screening Agent: A LangChain Agent analyzes the resume text. It uses the Google Sheets Tool to look up the requirements for the applied role, then scores the candidate and decides if they should be shortlisted. 📊 Log Results: The agent's decision (name, score, shortlisted status) is logged in your master "Applications" Google Sheet. ✅ Qualification Check: An IF node checks if the candidate was shortlisted. ❓ AI Question Generator: If shortlisted, a second LangChain Agent generates three unique, relevant interview questions based on the candidate's resume and the job description. ✍️ Update Sheet: The generated questions are added to the candidate's row in the Google Sheet. 🔔 Notify Team: A final alert is sent via Telegram to notify the HR team that a new candidate has been qualified and is ready for review. 🛠️ How to Build This Workflow Follow these steps to build the recruitment assistant from a blank canvas. Step 1: Set Up the Application Intake Add a Form Trigger node. Configure it with fields for Name, Email, Phone Number, a File Upload for the CV, and a Dropdown for the "Job Role". Connect a Google Drive node. Set the Operation to Upload and connect your credentials. Set it to upload the CV file from the Form Trigger into a specific folder. Add an Extract from File node. Set it to extract text from the PDF CV file provided by the trigger. Step 2: Build the AI Screening Agent Add a Langchain Agent node. This will be your main screening agent. In its prompt, instruct the AI to act as a resume screener. Tell it to use the input text from the Extract from File node and the tools you will provide to score and shortlist candidates. Add an OpenAI Chat Model node and connect it to the Agent's Language Model input. Add a Google Sheets Tool node. Point it to a sheet with your open positions and their requirements. Connect this to the Agent's Tool input. Add a Structured Output Parser node and define the JSON structure you want the agent to return (e.g., candidate_name, score, shortlisted). Connect this to the Agent's Output Parser input. Step 3: Log Results & Check for a Match Connect a Google Sheets node after the Agent. Set its operation to Append or Update. Use it to add the structured output from the agent into your main "Applications" sheet. Add an IF node. Set the condition to continue only if the shortlisted field equals "yes". Step 4: Generate Interview Questions On the 'true' path of the IF node, add a second Langchain Agent node. Write a prompt telling this agent to generate 3 interview questions based on the candidate's resume and the job requirements. Connect the same OpenAI Model and Google Sheets Tool to this agent. Add another Google Sheets node. Set it to Update the existing row for the candidate, adding the newly generated questions. 💬 Need Help or Want to Learn More? Join my Skool community for n8n + AI automation tutorials, live Q&A sessions, and exclusive workflows: 👉 https://www.skool.com/n8n-ai-automation-champions Template Author: Sandeep Patharkar Category: Website Chatbots / AI Automation Difficulty: Beginner Estimated Setup Time: ⏱️ 15 minutes

Quick overview This workflow accepts an article brief via webhook, routes it through a three-stage LLM pipeline to research, draft, and review HTML content, optionally generates a featured image with Google Imagen, then creates a WordPress draft post with tags and Rank Math SEO metadata and sends an optional Slack notification. How it works Receives a POST webhook request with a required prompt (and optional category, tone, and style overrides) and optionally validates a Bearer token. A researcher agent calls the LLM sub-workflow to produce a structured JSON outline, including title, H2 sections, tags, focus keyword, and image subject. Optionally starts the generation of a 16:9 featured image in parallel using Imagen 4 and the outline's image subject. The default option for whether this is enabled (set in CONFIG), can be overridden per run in the request prompt. Optionally fetches recent published WordPress posts and selects up to five relevant URLs to use as internal links. A writer agent drafts the full HTML article body based on the outline (and internal links), tone guide, and style guide from CONFIG. A reviewer agent edits the draft for voice, accuracy, and structure, then returns the revised HTML and SEO metadata as a combined response. The workflow upserts WordPress tags, uploads the featured image if one was generated, uploads it to WordPress Media and sets the image alt text. It then creates a new WordPress post in draft status with the content, excerpt, category, author, featured media, and tag IDs, and optionally sets Rank Math SEO fields via the REST API. A response is sent to the webhook with the draft URL (or returns 400/401/500 on errors). Optionally a Slack notification is sent with the post edit link, focus keyword, token usage, and estimated LLM cost. If any LLM stage fails, the error is caught, a Slack alert is optionally sent to the configured channel, and a 500 response is returned to the webhook. Setup Import and activate the referenced LLM routing sub-workflow Route AI prompts to Anthropic, Google Gemini, Mistral, or OpenAI. Open the CALL RESEARCHER, CALL WRITER, and CALL REVIEWER nodes and update to point to your imported copy of the sub-workflow. Open the CONFIG node and set WORDPRESS_URL, AUTHOR_ID, and DEFAULT_CATEGORY_ID for your site. Set AGENT_RESEARCHER_PROVIDER, AGENT_WRITER_PROVIDER, and AGENT_REVIEWER_PROVIDER in CONFIG to your preferred LLM providers (anthropic, google, mistral, or openai). Set SLACK_CHANNEL to your channel ID for notifications, or leave blank to disable. if needed set WEBHOOK_SECRET in CONFIG and configure your client to send Authorization: Bearer . Link credentials: Google AI (PaLM) API to GENERATE IMAGE; WordPress API to FETCH RECENT POSTS, UPLOAD IMAGE TO WORDPRESS, SET IMAGE ALT TEXT, UPSERT TAG, CREATE WORDPRESS POST, and SET RANK MATH SEO; If you enable image generation, add a Google Gemini (PaLM) / Generative Language API credential with Imagen access and confirm the Imagen model name in IMAGEN_MODEL. If set channel ID in CONFIG and want Slack notifications (including failure alerts), add a Slack OAuth2 credential to SLACK NOTIFICATION and SLACK PIPELINE ERROR. If you want Rank Math SEO fields set, install the Rank Math plugin on your WordPress site and expose rank_math_* meta fields to the WordPress REST API (see the sticky in the workflow for an example using a mu-plugin) Activate the workflow and POST to /webhook/article-publisher with a JSON body containing a prompt field. Requirements n8n version 1.70 or later Anthropic, Google Gemini, Mistral, or OpenAI account (any combination; at least one provider required) Google AI (PaLM) API credential for Imagen 4 image generation WordPress site with Application Password authentication enabled Rank Math SEO plugin with REST API mu-plugin (optional — only needed for SEO field population; see canvas sticky note for plugin code) Slack workspace with OAuth2 app (optional — only needed for notifications) Customization Set ENABLE_IMAGE_GENERATION to false in CONFIG to skip Imagen 4 and post without a featured image. Set ENABLE_INTERNAL_LINKS to false in CONFIG to skip fetching recent posts for internal link suggestions. Change IMAGE_STYLE_PROMPT in CONFIG to match your site's visual style for featured image generation. Replace TONE_GUIDE in CONFIG with your own writing persona to change the voice across all three agents. Replace STYLE_GUIDE in CONFIG with your own formatting rules to control article structure and length. Set AGENT_RESEARCHER_MODEL, AGENT_WRITER_MODEL, and AGENT_REVIEWER_MODEL independently to use different models per stage, or set all three to the same model for single-model mode. Pass category_id, tone_guide, or style_guide in the webhook request body to override CONFIG values on a per-request basis. Additional info Background For more information on how the system works and how to use this WordPress publishing automation workflow, see Refactoring the WordPress Publishing Pipeline: From Single Prompt to Three-Agent Newsroom Who's it for Content teams and solo bloggers who want to automate first-draft production for WordPress without giving up editorial control — the workflow produces a draft for review, not a live post. Assumes basic familiarity with n8n and a working WordPress installation.

Try It Out! Use n8n to extract medical test data from diagnostic reports uploaded to Google Drive, automatically detect abnormal values, and generate personalized health advice. How it works Upload a medical report (PDF or image) to a monitored Google Drive folder Mistral AI extracts text using OCR while preserving document structure GPT-4 parses the extracted text into structured JSON (patient info, test names, results, units, reference ranges) All test results are saved to the "All Values" sheet in Google Sheets JavaScript code compares each result against its reference range to detect abnormalities For out-of-range values, GPT-4 generates personalized dietary, lifestyle, and exercise advice based on patient age and gender Abnormal results with recommendations are saved to the "Out of Range Values" sheet How to use Set up Google Drive folder monitoring and Google Sheets with two tabs: "All Values" and "Out of Range Values" Configure API credentials for Google Drive, Mistral AI, and OpenAI (GPT-4) Upload medical reports to your monitored folder Review extracted data and personalized health advice in Google Sheets Requirements Google Drive and Sheets with OAuth2 authentication Mistral AI API key for OCR OpenAI API key (GPT-4 access required) for intelligent extraction and advice generation Need Help? See the detailed Read Me file at https://drive.google.com/file/d/1Wv7dfcBLsHZlPcy1QWPYk6XSyrS3H534/view?usp=sharing Join the n8n community forum for support

Quick Overview This workflow simulates business decision scenarios using Google Sheets data and AI analysis, generating what-if reports and recommendations for pricing, campaign, or hiring moves, and logs results to Google Sheets and Slack. How it works Listens for incoming scenario requests via webhook or runs weekly on a schedule. Prepares scenario details and validates the scenario type. Fetches historical business data from Google Sheets. Parses and summarizes historical data for scenario context. Constructs an AI-ready prompt with scenario and historical context. Uses an AI model (OpenAI or compatible) to simulate optimistic, realistic, and pessimistic outcomes, generating recommendations. Formats the results into a structured simulation report. Logs the simulation summary into a Google Sheets log and sends a notification to Slack. Returns the full simulation report and recommendation to the original requester via the webhook. Setup Add your Google Sheets API key and enter your historical and log sheet IDs in the relevant HTTP Request nodes. Provide your OpenAI (or compatible AI) API credentials in the workflow setup. Update the Slack webhook URL in the notification node for simulation alerts. Populate your Google Sheet with relevant historical business data using the required structure. If using the webhook, copy its URL and configure the scenario source to send requests to that endpoint.

This template is a fully automated AI invoice processing workflow for n8n. It watches a Google Drive folder for new invoice PDFs, extracts all key information using an AI Agent, assigns the correct booking account, saves the renamed invoice in the right Drive folder, and updates your Google Sheets booking list. A perfect starter template if you want to build your own AI-powered accounting automation. What this workflow does Monitors a Google Drive folder for new invoice PDFs. Downloads and extracts invoice text from the uploaded PDF. Uses an AI Agent (OpenAI + Structured Output Parser) to extract: invoice date vendor currency total amount invoice number booking text booking account matching Google Drive folder ID Automatically renames the PDF to a clean, consistent format (e.g. 250912 Vendor.pdf). Saves the invoice into the correct accounting folder in Google Drive. Updates your booking list in Google Sheets with all extracted fields. Moves the processed invoice to an output folder to avoid duplicates. Everything runs hands-free after setup. Key features 🧠 AI Invoice Reading using OpenAI + LangChain 📑 Structured Output Parser guarantees clean, validated fields 📁 Automated Google Drive File Routing 📊 Google Sheets logging for accounting records 🔄 File movement logic to keep input/output folders organized ⚙️ Chart of Accounts integration from your Google Sheet 🟦 Works out of the box with Invoice Agent – Folder Structure Setup (recommended) Typical use cases Automated accounting workflows Pre-processing invoices before importing into ERP or sevDesk AI-powered invoice extraction for small businesses or freelancers Structured archiving of invoices for tax and audit requirements Fully automated Google Drive invoice inbox How to use this template Connect your Google Drive & Sheets credentials in all relevant nodes. Select your: • Input folder (where invoices are uploaded) • Output folder (where processed invoices go) • Folder structure sheet + booking accounts sheet Upload any invoice PDF into the input folder. The workflow starts automatically and processes the invoice end-to-end. ⸻ Requirements • Google Drive OAuth2 • Google Sheets OAuth2 • OpenAI API key • A Google Sheet containing your chart of accounts • A prepared folder structure (use the “Google Drive Structure Setup” template)

How it works Provide your S3 bucket containing documents such as PDFs and MS Word in the "Get Files from S3" node. You will need to provide AWS credentials that will allow the node to access the bucket and download the files in the specified location. Choose document processing options in the Aryn node. The main options are for text and table extraction. You can also provide a JSON schema for property extraction. You can refer to https://docs.aryn.ai/docparse/processing_options for details on these options. You will also need an Aryn API key which you can obtain by going to https://aryn.ai/signup. Please note that use of vision models for OCR and table extraction is restricted to paid tiers. The resulting content of parsing and extraction is then chunked and ingested into Pinecone. Once at least one document has been ingested into a Pinecone index, you can start asking questions about anything that may be found in ingested documents in the chat box. Setup steps For data retrieval, you will need a "folder" in a bucket on AWS S3 as well as valid AWS credentials with permission to fetch those files. For document parsing, you will need to obtain an Aryn API key. You can sign up for free at https://aryn.ai/signup. For the Pinecone vector database, head over to https://pinecone.io and create an account and create a sample index for free. You will also need to generate an API key. For the AI agent and RAG, you will also need an OpenAI API key. Please go to https://openai.com and get a free API key.

Auto-Call Leads from Google Sheets with VAPI → Log Results + Book Calendar This workflow calls new leads from a Google Sheet using VAPI, saves the call results, and (if there’s a booking request) creates a Google Calendar event automatically. Benefits Auto-call each new lead from your call list Save full call outcomes back to Google Sheets Parse “today/tomorrow + time” into a real datetime (IST) Auto-create calendar events for bookings/deliveries Batch-friendly to avoid rate limits How It Works Trigger: New row in Google Sheets (call_list). Prepare: Normalize phone (adds +), then process in batches. Call: Send number to VAPI (/call) with your assistantId + phoneNumberId. Receive: VAPI posts results to your Webhook. Store: Append/Update Google Sheet with: name, role, company, phone, email, interest level, objections, next step, notes, etc. Parse Time: Convert today/tomorrow + HH:MM AM/PM to start/end in IST (+1 hour). Book: Create Google Calendar event with the parsed times. Respond: Send response back to VAPI to complete the cycle. Who Is This For Real estate / local service teams running outbound calls Agencies doing voice outreach and appointment setting Ops teams that want call logs + auto-booking in one place Setup Google Sheets Trigger:** select your spreadsheet Vapi_real-estate and tab call_list. VAPI Call:** set assistantId, phoneNumberId, and add Bearer token. Webhook:** copy the n8n webhook URL into VAPI so results post back. Google Calendar:** set the calendar ID (e.g., you@domain.com). Timezone:* the booking parser formats times to *Asia/Kolkata (IST)**. Batching:** adjust SplitInBatches size to control pace. ROI & Monetization Save 2–4 hours/week on manual dialing + data entry Faster follow-ups with instant booking creation Package as an “AI Caller + Auto-Booking” service ($1k–$3k/month) Strategy Insights In the full walkthrough, I show how to: Map VAPI tool call JSON safely into Sheets fields Handle missing/invalid times and default to safe slots Add no-answer / retry logic and opt-out handling Extend to send Slack/email alerts for hot leads Check Out My Channel For more voice automation workflows that turn leads into booked calls, check out my YouTube channel where I share the exact setups I use to win clients and scale to $20k+ monthly revenue.

This workflow automatically ingests real-time user behavior events, detects drop-off points across the customer journey, predicts churn risk using AI, and triggers targeted retention actions while logging everything for analysis. Who’s it for • Product teams managing high-churn SaaS products • E-commerce businesses with cart abandonment issues • Subscription services tracking user engagement How it works / What it does Captures new user behavior events (webhook or scheduled poll) Analyzes session events, actions, and engagement metrics for drop-off signals Loads user profile, history, and preferences AI predicts real-time drop-off risk and generates personalized retention actions Sends automated re-engagement messages or campaign triggers Logs predictions, risk scores, and actions in Google Sheets How to set up Import this workflow Set up credentials (Webhook events, Google Sheets, OpenAI/Anthropic) Update user profile defaults and retention endpoints Activate workflow Requirements • Event webhook (Segment, Mixpanel, custom analytics) • Google Sheets • OpenAI / Anthropic / Grok API • User behavior event schema How to customize the workflow • Change AI tone and action templates in the AI node • Modify Python detection logic • Update Google Sheet columns • Adjust retention messaging or campaign endpoints Want a advance level workflow for your business? Our experts can craft it quickly Contact our team

🐱 MemeCoin Art Generator - using Gemini Flash NanoBanana & upload to Twitter Automatically generates memecoin art and posts it to Twitter (X) powered by Google Gemini, NanoBanana image generation, and n8n automation. 🧩 Overview This workflow creates viral style memecoin images (like Popcat) and posts them directly to Twitter with a witty, Gen Z style tweet. It combines text to image AI, scheduled triggers, and social publishing, all in one seamless flow. Workflow flow: Define your memecoin mascot (name, description, and base image URL). Generate an AI image prompt and a meme tweet. Feed the base mascot image into Gemini Image Generation API. Render a futuristic memecoin artwork using NanoBanana. Upload the final image and tweet automatically to Twitter. 🧠 Workflow Diagram ⚙️ Key Components | Node | Function | |------|-----------| | Schedule Trigger | Runs automatically at chosen intervals to start meme generation. | | Define Memecoin | Defines mascot name, description, and base image URL. | | AI Agent | Generates tweet text and creative image prompt using Google Gemini. | | Google Gemini Chat Model | Provides trending topic context and meme phrasing. | | Get Source Image | Fetches the original mascot image (e.g., Popcat). | | Convert Source Image to Base64 | Prepares image for AI based remixing. | | Generate Image using NanoBanana | Sends the prompt and base image to Gemini Image API for art generation. | | Convert Base64 to PNG | Converts the AI output to an image file. | | Upload to Twitter | Uploads generated image to Twitter via media upload API. | | Create Tweet | Publishes the tweet with attached image. | 🪄 How It Works 1️⃣ Schedule Trigger - starts the automation (e.g., hourly or daily). 2️⃣ Define Memecoin - stores your mascot metadata: memecoin_name: popcat mascot_description: cat with open mouth mascot_image: https://i.pinimg.com/736x/9d/05/6b/9d056b5b97c0513a4fc9d9cd93304a05.jpg 3️⃣ AI Agent - prompts Gemini to: Write a short 100 character tweet in Gen Z slang. Create an image generation prompt inspired by current meme trends. 4️⃣ NanoBanana API - applies your base image + AI prompt to create art. 5️⃣ Upload & Tweet - final image gets uploaded and posted automatically. 🧠 Example Output Base Source Image: Generated Image (AI remix): Published Tweet: Example tweet text: > Popcat's about to go absolutely wild, gonna moon harder than my last test score! 🚀📈 We up! #Popcat #Memecoin 🧩 Setup Tutorial 1️⃣ Prerequisites | Tool | Purpose | |------|----------| | n8n (Cloud or Self hosted) | Workflow automation platform | | Google Gemini API Key | For generating tweet and image prompts | | Twitter (X) API OAuth1 + OAuth2 | For uploading and posting tweets | 2️⃣ Import the Workflow Download memecoin art generator.json. In n8n, click Import Workflow → From File. Set up and connect credentials: Google Gemini API Twitter OAuth (Optional) Adjust Schedule Trigger frequency to your desired posting interval. 3️⃣ Customize Your MemeCoin In the Define Memecoin node, edit these fields to change your meme theme: memecoin_name: "doggo" mascot_description: "shiba inu in astronaut suit" mascot_image: "https://example.com/shiba.jpg" That’s it - next cycle will generate your new meme and post it. 4️⃣ API Notes Gemini Image Generation API Docs:** https://ai.google.dev/gemini-api/docs/image-generation#gemini-image-editing API Key Portal:** https://aistudio.google.com/api-keys