Workflow Templates

This workflow enriches your WordPress articles by automatically adding an AI-generated heading and a short concluding paragraph. It ensures each post ends with valuable, engaging content to improve user satisfaction, branding, and SEO. How It Works Fetches published articles from your WordPress site via the REST API. Cleans and formats the article text for processing. Sends the content to OpenAI with a structured prompt. AI generates a new heading + 3-line conclusion tailored to the article. Appends the generated text to the original content. Updates the article back in WordPress automatically. Requirements n8n version: 1.49.0 or later (recommended). Active OpenAI API key. WordPress REST API enabled. WordPress API credentials (username + application password). Setup Instructions Import this workflow into n8n. Go to Credentials and configure: OpenAI API (API key). WordPress API (username + application password). Replace https://example.com with your site’s URL. Run manually or schedule it to enhance content automatically. Categories AI & Machine Learning WordPress Content Marketing SEO Tags ai, openai, wordpress, seo, content enhancement, automation, n8n

📌 Short Overview Automatically sync files from Google Drive into a searchable AI knowledge base with Pinecone, and answer user queries using GPT-4o with conversational memory. ⸻ 🛠️ Workflow Usage Steps 1. Watch Google Drive for file changes Trigger the workflow when a new file is uploaded or an existing file is updated in a specific Google Drive folder. 2. Download and process the file Retrieve the file, split it into smaller text chunks with a Recursive Character Text Splitter, and generate vector embeddings using OpenAI. 3. Store embeddings in Pinecone Save the embeddings in a Pinecone vector database to keep your knowledge base continuously updated and searchable. 4. Search context for chat queries When a user asks a question, query Pinecone for relevant context, combine results with conversational memory, and process them with GPT-4o. 5. Respond with AI-powered answers Provide a concise response (100–200 words) that blends knowledge from your documents with the conversation history. ⸻ ✅ Use Cases • Keep a live, AI-ready knowledge base from your Google Drive files. • Enable team members to query company documents instantly. • Build a personal assistant that stays up to date with your latest uploads. ⚙️ Setup Steps Google Drive • Create a Google Cloud project. • Enable the Google Drive API. • Generate OAuth credentials and connect them in n8n. OpenAI • Sign up at OpenAI. • Copy your API key from the dashboard. • Add it to n8n under Credentials → OpenAI API. Pinecone • Create an account at Pinecone. • Create a new index (e.g., docs-embeddings). • Copy your API key and environment, then add them to n8n under Credentials → Pinecone API. Workflow Configuration • Import this workflow into your n8n instance. • Select the Google Drive folder you want to monitor. • Set the Pinecone index name in the workflow. • Adjust chunk size / overlap in the text splitter if needed. Test the Workflow • Upload a new document to your Google Drive folder. • Run the workflow to confirm embeddings are created and stored in Pinecone. • Ask a sample query and verify the AI returns a context-aware answer.

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). ▶️

PROBLEM Thousands of MCP Servers exist and many are updated daily, making server selection difficult for LLMs. Current approaches require manually downloading and configuring servers, limiting flexibility. When multiple servers are pre-configured, LLMs get overwhelmed and confused about which server to use for specific tasks. This template enables dynamic server selection from a live PulseMCP directory of 5000+ servers. How it works A user query goes to an LLM that decides whether to use MCP servers to fulfill a given query and provides reasoning for its decision. Next, we fetch MCP Servers from Pulse MCP API and format them as documents for reranking Now, we use Contextual AI's Reranker to score and rank all MCP Servers based on our query and instructions How to set up Sign up for a free trial of Contextual AI here to find CONTEXTUALAI_API_KEY. Click on variables option in left panel and add a new environment variable CONTEXTUALAI_API_KEY. For the baseline model, we have used GPT 4.1 mini, you can find your OpenAI API key here How to customize the workflow We use chat trigger to initate the workflow. Feel free to replace it with a webhook or other trigger as required. We use OpenAI's GPT 4.1 mini as the baseline model and reranker prompt generator. You can swap out this section to use the LLM of your choice. We fetch 5000 MCP Servers from the PulseMCP directory as a baseline number, feel free to adjust this parameter as required. We are using Contextual AI's ctxl-rerank-v2-instruct-multilingual reranker model, which can be swapped with any one of the following rerankers: 1) ctxl-rerank-v2-instruct-multilingual 2) ctxl-rerank-v2-instruct-multilingual-mini 3) ctxl-rerank-v1-instruct You can checkout this blog for more information about rerankers to learn more about them. Good to know: Contextual AI Reranker (with full MCP docs): ~$0.035/query Includes 0.035 for reranking + ~$0.0001 for OpenAI instruction generation. OpenAI Baseline: ~$0.017/query

Transform meeting notes into organized tasks automatically This workflow uses AI to extract action items, decisions, and key details from any meeting notes format—then creates tasks in Asana and sends a formatted summary to Slack. Perfect for sales teams, project managers, and anyone who wants to stop manually tracking action items from meetings. What gets extracted Action items with assignees and due dates Key decisions made Pain points or challenges mentioned Budget discussions Next meeting dates How it works The workflow uses a simple form where you paste meeting notes (from AI notetakers like Otter.ai, manual notes, or any text). GPT-4o analyzes the content and extracts structured data. Each action item becomes an Asana task with the assignee name, due date, and full meeting context in the notes. All tasks are then aggregated into a formatted Slack message with clickable links, key decisions, pain points, and budget info. Your team gets a complete meeting summary without reading through pages of notes. Setup requirements OpenAI API key for GPT-4o Asana workspace with OAuth2 connection Slack workspace with OAuth2 connection Customization ideas Replace the form trigger with an email trigger to auto-process notes sent to a specific inbox Modify the AI prompt to extract additional fields like risks, dependencies, or next steps Add conditional logic to route different meeting types to different Asana projects or Slack channels Connect to other project management tools like ClickUp, Monday.com, or Jira instead of Asana Add Google Calendar integration to automatically schedule next meetings Good to know GPT-4o costs approximately $0.01-0.03 per meeting analysis The form can be shared with your team for easy submission All meeting context is preserved in Asana task notes for reference Slack messages include clickable task links for quick access

🚀 Turn your random ideas into concrete automation specs This workflow acts as your interactive "n8n Consultant." Simply write down a rough automation idea in Google Tasks (e.g., "Send weather updates to Telegram"), and the AI will research, design, and send a detailed n8n implementation plan to your Slack. ✨ Why is this workflow special? Unlike simple notification workflows, this features a Human-in-the-Loop review process. You don't just get a message; you get control. Regenerate:** Not satisfied with the AI's plan? Click a button in Slack to have the AI rewrite it instantly. Archive:* Happy with the plan? Click "Approve" to automatically save the detailed specs to *Google Sheets** and mark the task as complete. How it works Fetch: The workflow periodically checks a specific Google Tasks list for new ideas. AI Design: The AI (OpenAI) analyzes your idea and generates a structured plan, including node configuration and potential pitfalls. Human Review: It sends the plan to Slack with interactive "Approve" and "Regenerate" buttons. The workflow waits for your input. If Regenerate: The AI re-analyzes the idea and creates a new variation. If Approve: The workflow proceeds to the next step. Archive: The approved plan (Title, Nodes, Challenges) is saved to a Google Sheet for future development. Close: The original Google Task is updated with a "Processed" flag. How to set up Google Tasks: Create a new list named "n8n Ideas". Google Sheets: Create a new sheet with the following headers in the first row (A to H): Date Added Idea Title Status Recommended Nodes Key Challenges Improvement Ideas Alternatives Source Task ID Credentials: Configure credentials for Google Tasks, Google Sheets, OpenAI, and Slack. Configure Nodes: [Step 1] Fetch New Ideas: Select your Task list. [Step 4] Slack — Review & Approve: Select your target channel. [Action] Archive to Sheets: Select your Spreadsheet and Sheet. [Close] Mark Task Done: Select your Task list again. Requirements Google Tasks account Google Sheets account OpenAI API Key Slack account

Who’s it for This workflow is for: People who want to quickly launch simple landing pages without paying monthly fees to landing page creators. It’s ideal for rapid prototyping, generation of large amounts of landing pages, testing campaign ideas, or generating quick web mockups with AI. People launching products that compete in some way with the complete landing page solutions, and want to get an understanding of the basic building blocks of landing page creators How it works / What it does Retrieves or creates session data from n8n Tables Generates a vivid scene description for the hero image using GPT Creates a custom AI-generated hero image (using Gemini Palm or your preferred model) Builds a responsive landing page layout with GPT-4o-mini Saves the generated HTML to an n8n data table Deploys the landing page to Vercel automatically Returns the public live URL of the generated site This workflow combines OpenAI, Google Gemini,Cloudinary, Vercel, and n8n Tables to create, store, and publish your webpage seamlessly from a single prompt. How to set up Create an n8n Table with the following columns: sessionID (text) html (long text) Add your credentials: OpenAI (for text and image generation) Geminoogle Gemini (PaLM) - through the Google Cloud Platform (for text and image generation) Cloudinary (for image upload) Vercel (for live deployment) Update the placeholders as noted inside the workflow: Cloudinary cloud name and upload preset OpenAI model and API key n8n table name and column mapping (sessionID, html) Vercel Header Auth token Run the workflow. After configuration, it will generate, upload, deploy, and return the live landing page URL automatically. Inline notes are included throughout the workflow indicating where you must update values such as credentials, table names, or API keys to make the flow work end to end. Requirements OpenAI API key Google Gemini API key Cloudinary account Vercel account n8n Table with sessionID and html columns How to customize the workflow Modify the OpenAI model or prompt to change the tone, layout, or visual style of the generated landing page. Replace Vercel deployment with your preferred hosting platform (e.g., Netlify or GitHub Pages) if desired. Add extra input fields (e.g., title, CTA, description) to collect richer context before generating the page. Add ability to integreat with databases to turn into a full loveable/Base44 competitor Result After setup, this workflow automatically converts any idea into a fully designed and live landing page within seconds. It generates the hero image, builds the HTML layout, deploys it to Vercel, and provides the final shareable URL instantly. Optional Cleanup Subflow An additional utility subflow is included to help keep your Vercel project clean by deleting older deployments. It preserves the two most recent deployments and deletes the rest. Use with caution — only run it if you want to remove previous test pages and free up space in your Vercel account.

What this template does Collects the latest crypto news from multiple RSS feeds, filters and deduplicates them, uses OpenAI GPT-4 to analyze and select the top stories, translates and formats them into Russian, and posts a digest to a Telegram channel or group. The workflow runs automatically on a schedule and ensures all messages fit Telegram’s 4096-character limit. How it works (high level) RSS Sources: Reads fresh items from CoinDesk, Cointelegraph, Decrypt, Cryptobriefing, and Nulltx. Filter & Deduplicate: Keeps only unique items from the last 24 hours. AI Analysis (Crypto Analyst): An OpenAI agent identifies the most important events and selects the best article for each. AI Formatting (SMM Editor): Another OpenAI agent writes a styled digest in Russian with Telegram-compatible HTML formatting. Message Preparation: Long texts are split into safe chunks ≤ 4096 characters. Telegram Post: The digest is posted automatically to your configured Telegram channel or group. Prerequisites n8n Cloud or n8n >= 1.107.4 Credentials: OpenAI (gpt-4o-mini or gpt-4.1-mini) Telegram Bot with rights to post in your target chat Setup (5 minutes) Import this workflow into n8n. Open the Telegram node “Post to Group” and set your chatId (e.g., @your_channel or numeric ID). Connect your OpenAI and Telegram credentials. (Optional) Adjust the Scheduler interval (default: every 3 hours). Run once manually to test, then activate. Customization Add or replace RSS sources. Modify the prompts in Crypto Analyst and SMM Editor to adapt tone, style, or language. Swap out the Telegram node to publish on other platforms (Slack, Discord, etc.).

🎯 Self-Learning X Content Engine (Creator RAG Booster) Learn your voice. Generate posts that sound like you — not AI. 🧩 Overview This n8n workflow builds a personal RAG (Retrieval-Augmented Generation) system for creators. It learns from your own past posts and generates new tweets, replies, and image prompts in your tone. ⚙️ How it works Step 1 — Ingest Use the “Add to KB” Form to upload your past posts or notes. Text + metadata (topic, style) are stored in Supabase as vectors. Step 2 — Generate Use the “Generate Posts” Form to create new post ideas. The Agent fetches the most relevant style snippets (via Supabase VectorStore) Output includes: 📝 post 💬 quote 💭 reply 🎨 image_prompt 🔧 Setup (3–5 min) Connect Supabase (URL + Key) Make sure the table name is documents Enable vector extension (pgvector) Connect OpenAI API Key Activate both Forms and open the URLs to test. Optionally replace Forms with Webhooks. 💡 Tip: RLS enabled? Ensure your API key allows insert/select for documents. 🧠 Tech Stack n8n (self-hosted) Supabase (Vector Store) OpenAI (gpt-4.1-mini) HTML-based completion form 🪄 Credits Built by Yusuke | @yskautomation License: MITView on GitHub

🚀 Automated LinkedIn Post Generator from Article Links (Telegram → AI → Google Sheets → LinkedIn) This workflow lets you collect article links through a Telegram bot, automatically analyze and summarize them with AI, store everything neatly in Google Sheets, and generate polished LinkedIn posts on demand whenever the user types “generate”. Perfect for creators, marketers, and founders who want to post consistently without spending hours analyzing articles or writing drafts. 🧠 How It Works 1️⃣ User Sends Articles via Telegram Your Telegram bot is the main input point. Whenever the user drops a link, the workflow: Detects the URL Fetches the content Sends it to AI for analysis This keeps the process simple. 2️⃣ AI Analyzes & Summarizes the Article The workflow uses your LLM (OpenAI, Anthropic, etc.) to: Summarize the article Extract key insights Identify main arguments Capture tone and context It produces a clean, structured dataset for each link. 3️⃣ Everything is Saved into Google Sheets Each article becomes a new row in your Google Sheet. The sheet serves as your content library with fields like: Date Title Link Summary Insights Commentary You can save dozens of articles and generate posts from any of them later. 4️⃣ User Requests a Post with “generate” When the user types “generate”, the workflow will: Pull the latest article(s) from Google Sheets (or any selection logic you choose) Build a LinkedIn-ready post using AI Apply the requested tone/style Format it as a clean, professional post The final post is sent right back to Telegram — ready to copy/paste into LinkedIn. 🛠️ Setup Steps 🔧 1. Create a Telegram Bot Go to @BotFather on Telegram Create a new bot Copy the API token Paste the token into the Telegram Trigger node in n8n 🔧 2. Add Your AI Credentials Go to Credentials → OpenAI (or your provider) Add your API key Select this credential in all AI nodes You can switch to GPT-4o, GPT-4o-mini, or any model you prefer. 🔧 3. Connect Google Sheets Go to Credentials → Google Authenticate with your Google account Make sure the sheet contains the required columns: Date Title Link Summary Insights Commentary You can customize or add additional columns as needed. 🔧 4. Adjust Workflow Logic (Optional) You can modify: How the AI summarizes The LinkedIn post style How posts are selected (latest, random, specific tone, etc.) Whether you store more metadata Multi-language support Everything is modular. 🔧 5. Test the Flow Send yourself a link via the Telegram bot Check that it appears in Google Sheets Type “generate” Receive your LinkedIn post instantly 🎉 You’re Ready! This workflow helps you build a personal content pipeline that: Collects links Saves ideas Summarizes insights Generates LinkedIn posts on demand All directly from your phone, inside Telegram. If you remix or extend this template, I’d love to see what you build!

Who is this for? Businesses and developers who want to automate customer support or engagement on Facebook Messenger using AI-powered responses. What does it do? Creates an intelligent Facebook Messenger chatbot that: Responds to messages using OpenAI (gpt-4o-mini) Batches rapid-fire messages into a single AI request Maintains conversation history (50 messages per user) Shows professional UX feedback (seen indicators, typing bubbles) How it works Webhook Verification - Handles Facebook's GET verification request Message Reception - Receives incoming messages via POST webhook Message Batching - Waits 3 seconds to collect multiple quick messages AI Processing - Sends combined message to OpenAI with conversation context Response Delivery - Formats and sends the AI response back to Messenger Setup Configure Facebook Graph API credential with your Page Access Token Configure OpenAI API credential with your API key Set your verify token in the "Is Token Valid?" node Register the webhook URL in Facebook Developer Console Key Features Message Batching: Combines "Hey" + "Can you help" + "with my order?" into one request Conversation Memory: Remembers context from previous messages Echo Filtering: Prevents responding to your own messages Response Formatting: Cleans markdown for Messenger's 2000-char limit

About This Template Turn every sales meeting into a coaching opportunity. This workflow automatically analyzes tldv meeting recordings using OpenAI (GPT-4) to provide instant, actionable feedback to your sales team. It acts as a virtual sales coach, evaluating key performance metrics like listening skills, question quality, and customer engagement without requiring a manager to listen to every call. How It Works Trigger: The workflow starts automatically when a meeting transcript is ready in tldv (via Webhook). Data Retrieval: It fetches the full meeting details and transcript from the tldv API. AI Analysis: GPT-4 analyzes the conversation to score the sales rep's performance (e.g., Speaking vs. Listening balance, Clarity, Next Steps). Delivery: Slack: Sends a summary notification and a detailed markdown report to the team channel. Google Sheets: Archives the scores and meeting data for long-term tracking. Who It’s For Sales Managers:** To monitor team performance and identify coaching needs at scale. Account Executives:** To get immediate feedback on their calls and self-correct. Sales Enablement:** To track KPI trends over time. Requirements n8n** (Cloud or Self-hosted) tldv (Business Plan)** for API/Webhook access OpenAI API Key** (GPT-4 access recommended) Slack** Workspace Google Sheets** Setup Steps Credentials: Configure "Header Auth" for tldv (x-api-key) and OpenAI (Authorization). Connect OAuth for Slack and Google Sheets. Webhook: Copy the Production URL from the first node (Webhook) and add it to your tldv Settings > Integrations > Webhooks (select Event: TranscriptReady). Google Sheets: Create a sheet (e.g., named Sales Feedback) with columns for Meeting Name, Score, Summary, etc. Note: Be sure to update the Google Sheets node in the workflow to match your specific Sheet Name and Column headers.