Workflow Templates

🎯 Description Automatically generates, designs, stores, and logs complete Instagram carousel posts. It transforms a simple text prompt into a full post with copy, visuals, rendered images, Google Drive storage, and a record in Google Sheets. ⚙️ Use case / What it does This workflow enables creators, educators, or community managers to instantly produce polished, on-brand carousel assets for social media. It integrates OpenAI GPT-4.1, Pixabay, Templated.io, Google Drive, and Google Sheets into one continuous content-production chain. 💡 How it works 1️⃣ Form Trigger – Collects the user prompt via a simple web form. 2️⃣ OpenAI GPT-4.1 – Generates structured carousel JSON: titles, subtitles, topic, description, and visual keywords. 3️⃣ Code (Format content) – Parses the JSON output for downstream use. 4️⃣ Google Drive (Create Folder) – Creates a subfolder for the new carousel inside “RRSS”. 5️⃣ HTTP Request (Pixabay) – Searches for a relevant image using GPT’s visual suggestion. 6️⃣ Code (Get first result) – Extracts the top Pixabay result and image URL. 7️⃣ Templated.io – Fills the design template layers (titles/subtitles/topic/image). 8️⃣ HTTP Request (Download renders) – Downloads the rendered PNGs from Templated.io. 9️⃣ Google Drive (Upload) – Uploads the rendered images into the created folder. 10️⃣ Google Sheets (Save in DB) – Logs metadata (title, topic, folder link, description, timestamp, status). 🔗 Connectors used OpenAI GPT-4.1 (via n8n LangChain node) Templated.io API (design rendering) Pixabay API (stock image search) Google Drive (storage + folder management) Google Sheets (database / logging) Form Trigger (input collection) 🧱 Input / Output Input: User-submitted “Prompt” (text) via form Output: Generated carousel images stored in Google Drive Spreadsheet row in Google Sheets containing title, topic, description, Drive URL, status ⚠️ Requirements / Setup Valid credentials for: OpenAI API (GPT-4.1 access) Templated.io API key Pixabay API key Google Drive + Google Sheets OAuth connections Existing Google Drive folder ID for RRSS storage Spreadsheet with matching column headers (Created At, Title, Topic, Folder URL, Description, Status) Published form URL for user prompts 🌍 Example applications / extensions Educational themes (mental health, fitness, sustainability). Extend to auto-publish to Instagram Business via Meta API. Add Notion logging or automated email notifications. Integrate scheduling (Cron node) to batch-generate weekly carousels.

Overview: Automated Vacancy Launch & AI Marketing This workflow streamlines the entire job opening process by connecting your ATS to your operational and marketing tools. It not only manages deadlines but also automates the promotion of the vacancy. Key Features: Schedule: Creates SLA and Expiration events in Google Calendar based on ATS dates. Track: Creates a central task in ClickUp to manage the selection process. Content Generation: Uses GPT-4o to analyze the job description and write a compelling marketing post. Publish: Automatically posts the job to LinkedIn and logs the action back in the ClickUp task. Setup Instructions Webhook: Configure your Recrutei ATS (or similar) to trigger this workflow. Google Calendar: Select the calendar for deadline tracking. ClickUp: Map the Team, Space, and List where vacancy tasks should be created. OpenAI: Ensure you have a valid API Key. LinkedIn: Connect your profile or company page.

BigQuery RAG with OpenAI Embeddings This workflow demonstrates how to use Retrieval-Augmented Generation (RAG) with BigQuery and OpenAI. By default, you cannot directly use OpenAI Cloud Models within BigQuery. Try it This template comes with access to a *public BigQuery table** that stores part of the n8n documentation (about nodes and triggers), allowing you to try the workflow right away: n8n-docs-rag.n8n_docs.n8n_docs_embeddings* ⚠️ *Important:* BigQuery uses the *requester pays model.* The table is small (~40 MB), and BigQuery provides *1 TB of free processing per month**. Running 3–4 queries for testing should remain within the free tier, unless your project has already consumed its quota. More info here: BigQuery Pricing* Why this workflow? Many organizations already use BigQuery to store enterprise data, and OpenAI for LLM use cases. When it comes to RAG, the common approach is to rely on dedicated vector databases such as Qdrant, Pinecone, Weaviate, or PostgreSQL with pgvector. Those are good choices, but in cases where an organization already uses and is familiar with BigQuery, it can be more efficient to leverage its built-in vector capabilities for RAG. Then comes the question of the LLM. If OpenAI is the chosen provider, teams are often frustrated that it is not directly compatible with BigQuery. This workflow solves that limitation. Prerequisites To use this workflow, you will need: A good understanding of BigQuery and its vector capabilities A BigQuery table containing documents and an embeddings column The embeddings column must be of type FLOAT and mode REPEATED (to store arrays) A data pipeline that generates embeddings with the OpenAI API and stores them in BigQuery This template comes with a public table that stores part of the n8n documentation (about nodes and triggers), so you can try it out: n8n-docs-rag.n8n_docs.n8n_docs_embeddings How it works The system consists of two workflows: Main workflow** → Hosts the AI Agent, which connects to a subworkflow for RAG Subworkflow** → Queries the BigQuery vector table. The retrieved documents are then used by the AI Agent to generate an answer for the user.

How it works This workflow automatically generates and publishes marketing blog posts to WordPress using AI. It begins by checking your PostgreSQL database for unprocessed records, then uses OpenAI to create SEO-friendly, structured blog content. The content is formatted for WordPress, including categories, tags, and meta descriptions, before being published. After publishing, the workflow updates the original database record to track processing status and WordPress post details. Step-by-step Trigger workflow** Schedule Trigger – Runs the workflow at defined intervals. Fetch unprocessed record** PostgreSQL Trigger – Retrieves the latest unprocessed record from the database. Check Record Exists – Confirms the record is valid and ready for processing. Generate AI blog content** OpenAI Chat Model – Processes the record to generate blog content based on the title. Blog Post Agent – Structures AI output into JSON with title, content, excerpt, and meta description. Format and safeguard content** Code Node – Prepares structured data for WordPress, ensuring categories, tags, and error handling. Publish content and update database** WordPress Publisher – Publishes content to WordPress with proper categories, tags, and meta. Update Database – Marks the record as processed and stores WordPress post ID, URL, and processing timestamp. Why use this? Automates end-to-end blog content generation and publishing. Ensures SEO-friendly and marketing-optimized posts. Maintains database integrity by tracking published content. Reduces manual effort and accelerates content workflow. Integrates PostgreSQL, OpenAI, and WordPress seamlessly for scalable marketing automation.

Automatically extract structured information from emails using AI-powered document analysis. This workflow processes emails from specified domains, classifies them by type, and extracts structured data from various attachment formats. Who is this for Operations teams, coordinators, and business professionals who receive proposals or reports from multiple sources via email and need to consolidate the information into a structured database. What this workflow does Monitors Gmail every 30 minutes for emails from specified domains Classifies emails into three categories based on customizable keywords Processes attachments intelligently based on file type and email classification Extracts structured data: dates, times, names, amounts, and other fields Saves to Google Sheets with full metadata and classification Labels processed emails in Gmail for tracking Setup requirements Gmail OAuth2 credentials OpenAI API key (GPT-4 Vision) Google Sheets OAuth2 credentials AWS S3 bucket for temporary image storage ConvertAPI account for PPTX/PDF conversion How to customize Edit the domain list and classification keywords in the code nodes to adapt for your specific use case.

How it works This workflow automates post-event and post-course communications for candidates, while also notifying referring partners at the correct milestones. The workflow is triggered when Airtable updates timestamp fields related to Info Event Outcome or Course Outcome. Airtable controls when the workflow runs, and n8n controls what happens next. This separation avoids race conditions and keeps the system reliable. After triggering, the workflow normalizes record data and determines exactly one action path using a centralized Code node. Based on the outcome, it sends the correct candidate email and SMS, and optionally notifies the referring person. Each message is sent only once using checkbox “sent” flags stored in Airtable. Setup steps Connect Airtable and select the table containing candidate records. Ensure Airtable includes timestamp fields for Info Event Outcome and Course Outcome updates. Ensure checkbox fields exist to track which messages have already been sent. Connect your email provider (Brevo) and SMS provider. Customize message content inside the Email and SMS nodes if needed. Initial setup typically takes 15–20 minutes. When to use this template You need reliable post-event and post-course messaging You want to notify referring partners automatically You must prevent duplicate emails or SMS

This is an official n8n workflow that helps you follow our sticky note and naming guidelines - required for getting your template published on the n8n template library. How it works: Parses the workflow's nodes, connections, and spatial layout. Uses GPT-4o to group nodes into logical clusters and generate descriptive sticky notes. Resolves any overlapping sticky notes through iterative collision detection. Optionally renames all nodes to follow descriptive naming conventions via a second AI pass. Setup steps Add your OpenAI API credentials to the two OpenAI Chat Model nodes. Paste your target workflow JSON into the "Set Workflow Variables" node. Set renameNodes to true or false depending on whether you want node renaming.

Description Automatically score candidate questionnaire responses using Azure OpenAI (GPT-4o-mini), combine them with existing evaluations from Google Sheets, and keep your candidate database up to date—all in near real time. Get consistent, structured scores and key takeaways for faster, fairer decisions. ⚡📊 What This Template Does Monitors new questionnaire submissions in Google Sheets every minute. ⏱️ Evaluates responses with Azure OpenAI and returns structured JSON (score + takeaways). 🤖 Parses model output safely and normalizes fields. 🧩 Retrieves existing candidate data from a central Google Sheet. 📂 Calculates combined final scores and updates/append records by candidate name. ➕ Key Benefits Consistent, objective scoring across all responses. 🎯 Real-time processing from form submission to database update. 🚀 Clear JSON outputs for downstream reporting and analytics. 📈 No-code customization of questions, weights, and fields. 🛠 Scales effortlessly with high submission volumes. 📥 Features Continuous polling of the “BD Questionarie” → “Form Responses 1” sheet. 🔄 AI evaluation with GPT-4o-mini returning score (0–30) and takeaways. 🧠 Resilient JSON parsing (handles code fences and errors). 🧼 Candidate lookup in “Resume store” → “Sheet2” for data fusion. 🔗 Additive scoring model: Final Score = Existing Score + Questionnaire Score. ➕ Append or update records by name while preserving existing data. 📝 Requirements n8n instance (Cloud or self-hosted). 🌐 Google Sheets access: “BD Questionarie” spreadsheet (sheet: “Form Responses 1”) for new responses. “Resume store” spreadsheet (sheet: “Sheet2”) for existing profiles. Credentials configured in n8n (OAuth/Service Account) with read/write where needed. 🔐 Azure OpenAI access with a GPT-4o-mini deployment for evaluation and JSON output. 🤖 Ability to customize evaluation questions and scoring weights within the workflow. ⚙️ Target Audience Teams evaluating candidate questionnaires and consolidating scores. 👥 Operations teams centralizing hiring data in Google Sheets. 🗂️ Organizations seeking real-time, AI-assisted screening. 🧭 No-code/low-code builders standardizing hiring workflows. 🧱 *Step-by-Step Setup Instructions * Connect Google Sheets in n8n Credentials; grant access to “BD Questionarie” and “Resume store.” 🔑 Add Azure OpenAI credentials in n8n; ensure a GPT-4o-mini deployment is available. 🤝 Import the workflow, assign credentials to each node, and set the sheet IDs/ranges. 📋 Confirm name is the matching key, and adjust evaluation weights or questions as needed. ⚖ Run once to validate parsing and score calculation, then enable polling (every minute). ▶️

How it works A user query is received via the Chat Trigger node. The Planning Agent decides whether the question requires a general knowledge answer or a research-oriented response. If a research query is detected, the arXiv Search node queries the arXiv API and retrieves recent relevant papers. JSON Parsers** process the API response and extract metadata such as titles, abstracts, and links. The arXiv-Grounded Agent summarizes each paper and generates a final answer to the user question based strictly on retrieved content. The final response includes summaries and clickable citations from arXiv.

This n8n template demonstrates how to automate stock market technical analysis to detect key trading signals and send real-time alerts to Discord. It's built to monitor for the Golden Cross (a bullish signal) and the Death Cross (a bearish signal) using simple moving averages. Use cases are many: Automate your personal trading strategy, monitor a portfolio for significant trend changes, or provide automated analysis highlights for a trading community or client group. 💡 Good to know This template relies on the Alpha Vantage API, which has a free tier with usage limits (e.g., API calls per minute and per day). Be mindful of these limits, especially if monitoring many tickers. The data provided by free APIs may have a slight delay and is intended for informational and analysis purposes. Disclaimer**: This workflow is an informational tool and does not constitute financial advice. Always do your own research before making any investment decisions. ⚙️ How it works The workflow triggers automatically every weekday at 5 PM, after the typical market close. It fetches a list of user-defined stock tickers from the Set node. For each stock, it gets the latest daily price data from Alpha Vantage via an HTTP Request and stores the new data in a PostgreSQL database to maintain a history. The workflow then queries the database for the last 121 days of data for each stock. A Code node calculates two Simple Moving Averages (SMAs): a short-term (60-day) and a long-term (120-day) average for both today and the previous day. Using If nodes, it compares the SMAs to see if a Golden Cross (short-term crosses above long-term) or a Death Cross (short-term crosses below long-term) has just occurred. Finally, a formatted alert message is sent to a specified Discord channel via a webhook. 🚀 How to use Configure your credentials for PostgreSQL and select them in the two database nodes. Get a free Alpha Vantage API Key and add it to the "Fetch Daily History" node. For best practice, create a Header Auth credential for it. Paste your Discord Webhook URL into the final "HTTP Request" node. Update the list of stock symbols in the "Set - Ticker List" node to monitor the assets you care about. The workflow is set to run on a schedule, but you can press "Test workflow" to trigger it manually at any time. ✅ Requirements An Alpha Vantage account for an API key. A PostgreSQL database to store historical price data. A Discord account and a server where you can create a webhook. 🎨 Customising this workflow Easily change the moving average periods (e.g., from 60/120 to 50/200) by adjusting the SMA_SHORT and SMA_LONG variables in the "Compute 60/120 SMAs" Code node. Modify the alert messages in the "Set - Golden Cross Msg" and "Set - Death Cross Msg" nodes. Swap out Discord for another notification service like Slack or Telegram by replacing the final HTTP Request node.

I wanted a journal but never had the discipline to write one. Most of my day happens in Discord anyway, so I built this to do it for me. Every night, it reads my Discord channel, asks GPT-4 to write a short reflection, generates an image that captures the vibe of the day, and saves everything to Notion. I wake up with a diary entry I didn't have to write. How it works Runs daily at whatever time you set Grabs messages from a Discord channel (last 100) Filters to today's messages only GPT-4 writes a title, summary, mood, and tags DALL-E generates an image based on the day's themes Uploads image to Cloudinary (Notion needs a public URL) Creates a Notion page with everything formatted nicely Setup Discord Bot credentials (read message history permission) OpenAI API key Free Cloudinary account for image hosting Notion integration connected to your database Notion database properties needed Title (title) Date (date) Summary (text) Mood (select): 😊 Great, 😌 Good, 😐 Neutral, 😔 Low, 🔥 Productive Message Count (number) Takes about 15 minutes to set up. I use Gallery view in Notion with the AI image as cover - looks pretty cool after a few weeks.

n8n – Outlook AI Categorization & Labeling (Fully Automated) > Zero manual mapping. The workflow automatically discovers your Outlook folders, understands the context, assigns the correct category, and moves the email into the right folder. It uses the original Microsoft Outlook nodes plus an AI Agent. You can connect OpenAI or any other LLM provider. ✨ Features Self-Discovery:** Scans your Outlook folders automatically – no manual mapping required. AI-Powered Decisions:** Considers sender, subject, content, links, attachments, timing, and business context. Label + Move:** Assigns the right Outlook category and moves the email into the correct folder. Dual Category Logic:** Can apply both a primary and a secondary category (e.g., Action + Project). Error Handling:** Captures errors and continues without breaking the workflow. Flexible AI Backend:** Replace OpenAI with your own LLM if preferred. 🚀 Setup (5 Steps) Connect Outlook In n8n → Credentials → Microsoft Outlook, grant at least Mail.ReadWrite. Connect AI In n8n → Credentials, set up OpenAI (or another model). Works best with GPT-4.x or GPT-4o. Import the Workflow n8n → Workflows → Import from File/Clipboard and paste the provided JSON. Enable Trigger Adjust the Schedule Trigger (e.g., every 5 minutes). Run & Verify Test run and watch emails get categorized and moved automatically. 🧠 How It Works Schedule Trigger pulls new emails Loop Over Items processes them one by one Markdown / varEmail cleans the content Get Many Folders fetches Outlook categories and folders Summarize + Code prepare category IDs AI Agent applies deep categorization logic Update Category applies the Outlook category Move Folder places the email in the right folder Error Handling ensures workflow stability 🧩 System Prompt Example You are an advanced AI email categorization system. Your mission is to intelligently analyze and categorize emails with maximum accuracy and context awareness. INTELLIGENT CATEGORIZATION ENGINE: Parse all available categories: {{ $json.category }} Multi-layer analysis: Sender, Subject, Body, Links, Attachments Prioritize: Security threats, Action Required, Business Context Specialized: SaaS, Hosting, E-commerce, Finance, Support, Corporate Anti-Spam: Pattern detection, spoofing, red-flag subjects Dual Logic: Primary + Secondary categories when applicable OUTPUT FORMAT (JSON only): { "subject": "EXACT_EMAIL_SUBJECT", "category": "PRIMARY_CATEGORY_FROM_AVAILABLE_LIST", "subCategory": "SECONDARY_CATEGORY_IF_APPLICABLE", "analysis": "Reasoning", "confidence": "HIGH/MEDIUM/LOW" } Available Categories: {{ $json.category }} ⚙️ Parameters & Notes Uses only existing Outlook categories (never invents new ones). Works with any LLM that supports Chat Completions. Requires Mail.ReadWrite permissions. Safe fallback: if unsure, it uses the Action category. 🛡️ Security Processes only what is needed for classification. No external logging of email content unless you configure it. AI provider can be swapped for self-hosted LLMs for compliance. 📄 License & Sharing License:** MIT (or your choice). Tags:** n8n, Outlook, Email, AI, Automation, Categorization Import Method:** Copy/paste workflow JSON into n8n. ✅ Summary Connect → Import → Run. No manual mapping. AI-powered categorization that labels and organizes your Outlook mailbox automatically.