Browse 12,955 Workflows

By Tushar Mishra

This n8n workflow automatically fetches the latest CVE data at scheduled intervals, extracts relevant security details, and creates a corresponding Security Incident in ServiceNow for each new vulnerability. Schedule Trigger – Runs at predefined intervals. Jina Fetch – Retrieves the latest CVE feed. Information Extractor (OpenAI Chat Model) – Processes and extracts key details from the CVE data. Split Out – Separates each CVE entry for individual processing. Create Incident – Generates a ServiceNow Security Incident with the extracted CVE details. Ideal for security teams to ensure timely tracking and remediation of new vulnerabilities without manual monitoring. An n8n automation workflow template by Tushar Mishra.

By Max aka Mosheh

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works • Publishes content to 9 social platforms (Instagram, YouTube, TikTok, Facebook, LinkedIn, Threads, Twitter/X, Bluesky, Pinterest) from a single Airtable base • Automatically uploads media to Blotato, handles platform-specific requirements (YouTube titles, Pinterest boards), and tracks success/failure for each post • Includes smart features like GPT-powered YouTube title optimization, Pinterest Board ID finder tool, and random delays to avoid rate limits Set up steps • Takes ~20–35 minutes to configure all 9 platforms (or less if you only need specific ones) • Requires Airtable personal access token, Blotato API key, and connecting your social accounts in Blotato dashboard • Workflow includes comprehensive sticky notes with step-by-step Airtable base setup, credential configuration, platform ID locations, and quick debugging links for each social network Pro tip: The workflow is modular - you can disable any platforms you don't use by deactivating their respective nodes, making it flexible for any social media strategy from single-platform to full omnichannel publishing. An n8n automation workflow template by Max aka Mosheh.

By Robert Breen

n8n Workflow: OpenAI DALL·E 2 Image Generation & Google Drive Upload Description This n8n workflow automates the process of generating multiple AI-created images from a single prompt using OpenAI's DALL·E 2, then uploads the results directly to a Google Drive folder. It includes a loop to produce several image variations for the same prompt, making it ideal for creative projects, marketing materials, or content experimentation. Step-by-Step Setup Instructions 1. Prepare Your API Keys OpenAI API Key** Sign up or log in at https://platform.openai.com/ Go to API Keys and create a new one. Copy and store this securely — you'll need it in n8n. Google Drive API** Go to https://console.cloud.google.com/ Create a project and enable Google Drive API. Create OAuth 2.0 credentials and set the redirect URI to your n8n OAuth redirect (found in your n8n Google Drive node setup). Connect your Google account when adding credentials in n8n. 2. Workflow Nodes Overview Manual Trigger – Starts the workflow manually. Set Image Prompt – Stores the prompt text and base file name (e.g., “Make an image of an attractive woman standing in New York City”). Duplicate Rows (Code Node) – Creates multiple "runs" of the same prompt for variation. Loop Over Items – Processes each variation one at a time. Generate an image (OpenAI DALL·E 2) – Sends the prompt to OpenAI and retrieves an image. Upload to Google Drive – Saves each generated image to your chosen Google Drive folder. 3. Building the Workflow in n8n Step 1 — Manual Trigger Add a Manual Trigger node to start the workflow manually when testing. Step 2 — Set Image Prompt Add a Set node with two fields: Prompt → The image description text. Name → The base name for the saved file. Example: | Name | Value | |--------|---------------------------------------------------------------| | Prompt | Make an image of an attractive woman standing in New York City | | Name | woman-nyc | Step 3 — Duplicate Rows (Code Node) Use this JavaScript to create three copies of the prompt (run 1, run 2, run 3): const original = items[0].json; return [ { json: { ...original, run: 1 } }, { json: { ...original, run: 2 } }, { json: { ...original, run: 3 } }, ]; Step 4 — Loop Over Items Insert a Split in Batches node and set the batch size to 1. This ensures each prompt variation runs through the image generation process individually. Connect this node so it runs after the Duplicate Rows node. Step 5 — Generate Image Add the OpenAI Image Generation node and configure it as follows: Model**: dall-e-2 Prompt**: ={{ $json.Prompt }} Leave other options at their defaults unless you want to specify image size or style. Connect your OpenAI API credentials created in Step 1. This node will send the current prompt in the batch to OpenAI's DALL·E 2 model and return an AI-generated image. Step 6 — Upload to Google Drive Add a Google Drive node and configure it to store the generated image: File Name**: ={{ $('Set Image Prompt').item.json.Name }} - {{ $('Duplicate Rows').item.json.run }} Folder ID**: Select the target Google Drive folder where images should be saved. Connect your Google Drive OAuth2 API credentials. The node will upload each generated image to your chosen Google Drive location, with a unique filename for each variation. Running the Workflow Execute the workflow manually. The process will: Loop through each prompt variation. Generate an image using OpenAI DALL·E 2. Upload the image to Google Drive with a unique name. You will find all generated images in the selected Google Drive folder. Customization Tips Change the number of variations by editing the Duplicate Rows code. Adjust the prompt dynamically from other data sources like Google Sheets, webhooks, or forms. Schedule the workflow to run at specific times or trigger it via an API call. Created by Robert A. – Ynteractive Website: https://ynteractive.com Email: robert@ynteractive.com. An n8n automation workflow template by Robert Breen.

By Automate With Marc

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🚀 GPT-5 AI Lead Research & Auto-Email Agent – Instant Personalized Follow-Ups for Inbound Leads Description: Turn every inbound lead into a booked meeting — automatically. For step-by-step guide on how to build workflows like these, watch the free tutorial videos here: https://www.youtube.com/@Automatewithmarc This n8n workflow uses the latest GPT-5 model as your 24/7 AI research and email assistant. The moment a prospect submits your lead form, the workflow: Captures lead details (name, business URL, email, and inquiry). Researches the lead’s business online using the Tavily AI search tool for relevant context. Writes a highly-personalized email with GPT-5, including a compelling subject line, friendly tone, and clear call-to-action to book a meeting. Sends the email automatically via Gmail — no manual work required. Perfect for startups, agencies, SaaS companies, and B2B sales teams, this template ensures lightning-fast responses and higher conversion rates. Key Features & Benefits: 🔍 AI-powered research – pulls real, up-to-date insights on your leads before responding. ✍ Natural, persuasive copywriting – GPT-5 crafts emails that sound human, not robotic. ⏱ Instant follow-ups – zero delay from form submission to inbox. 📈 Boost booking rates – every email includes your scheduling link (e.g., Calendly). ⚙ No-code automation – easily customize tone, style, or meeting link. Ideal Use Cases: Automating inbound lead follow-up in marketing agencies. Personalized outreach for B2B SaaS sales teams. High-touch response for consultants and service providers. Included Integrations: Form Trigger – capture lead data. Tavily Search Tool – enrich lead profiles with live research. GPT-5 Agent – craft tailored responses with contextual awareness. Gmail – send professional follow-up emails instantly. 💡 Pro Tip: Pair this with your CRM in n8n to log every lead interaction and track conversions end-to-end. An n8n automation workflow template by Automate With Marc.

By Nima Salimi

Overview Automate your daily contact imports from NocoDB to Brevo.The workflow updates the record status in NocoDB at each step. For every email campaign, it’s essential to keep your Brevo contact list updated so you can send personalized and targeted emails. This flow automates that process. ✅ Tasks ⏰ Runs automatically every day 🗂 Fetches only new/unimported records from NocoDB 🔍 Checks for missing required fields 🚫 Filters out disposable/temporary emails 📬 Creates contacts in Brevo 📝 Updates NocoDB status after each step 🛠 How to Use 1️⃣ Set your schedule The Schedule Trigger node runs the flow daily adjust to your preferred time. 2️⃣ Prepare your table in NocoDB Your NocoDB table should contain at least: id first_name last_name email status (default: 0-not-imported) 3️⃣ Configure your credentials Connect your NocoDB API Token in the NocoDB nodes. Connect your Brevo API Key in the Brevo node. 4️⃣ Map your fields In the Brevo: Create Contact node, make sure first name, last name, and email match your NocoDB column names. 📌 Notes 🛡 Make sure your NocoDB project/table IDs match the ones in this template. 🚀 This workflow processes contacts one-by-one to avoid heavy API calls and rate limit issues with Brevo. ✅ status values: 0-not-imported → new record 1-empty-fields → missing required fields 2-disposal-email → disposable email detected 3-contact-created → successfully created in Brevo. An n8n automation workflow template by Nima Salimi.

By Charles

🚀 Daily IndieHackers Reddit Trend Analysis to Slack > Transform Reddit chaos into actionable startup intelligence > Get AI-powered insights from r/indiehackers delivered to your Slack every morning 🎯 Who's It For This template is designed for startup founders, growth teams, and product managers who need to: Stay ahead of indie hacker trends without manual Reddit browsing Understand what's working in the entrepreneurial community Get actionable insights for product and marketing decisions Keep their team informed about emerging opportunities Perfect for teams building products for entrepreneurs or anyone wanting to leverage community intelligence for competitive advantage. ✨ What It Does Transform your morning routine with automated intelligence gathering that delivers structured, AI-powered summaries of the hottest r/indiehackers discussions directly to your Slack channel. 🧠 Smart Analysis Features | Feature | Description | |---------|-------------| | 🔥 Hotness Scoring | Calculates engagement scores using time-decay algorithms | | 📊 Topic Extraction | Identifies key themes and trending subjects | | 💰 Traction Signals | Spots revenue, metrics, and growth indicators | | 🎯 Theme Clustering | Groups posts into actionable categories | | ⚡ Action Items | Generates specific recommendations for your team | 📱 Slack Integration Receive beautifully formatted messages with: Executive summaries and key takeaways Top 3 hottest posts with engagement metrics Interactive buttons for deeper exploration Team discussion prompts ⚙️ How It Works graph LR A[🕐 Daily 8AM Trigger] --> B[📱 Fetch Reddit Posts] B --> C[🔄 Process Data] C --> D[🤖 Gemini AI Analysis] D --> E[✨ Groq Slack Formatting] E --> F[💬 Deliver to Slack] 🔄 The Complete Process Step 1: Automated Trigger Every morning at 8 AM, the workflow springs into action Step 2: Reddit Data Collection Fetches the latest 5 posts from r/indiehackers with full metadata Step 3: Data Processing Structures raw Reddit data for optimal AI analysis Step 4: AI-Powered Analysis Gemini AI performs deep analysis calculating hotness scores, extracting topics, and identifying patterns Step 5: Slack Formatting Groq AI Agent transforms insights into beautiful Slack Block Kit messages Step 6: Team Delivery Your designated Slack channel receives the formatted analysis 🛠️ Requirements You'll need API access for: Reddit (OAuth2), Google Gemini, Groq, and Slack (OAuth2). All have free tiers available. 🚀 Setup Guide 1️⃣ Configure Your Credentials Add these credentials in n8n: Reddit OAuth2, Google Gemini, Groq, and Slack OAuth2. The workflow will guide you through each setup. 2️⃣ Customize the Schedule Default: Daily at 8:00 AM To modify: Edit the "Daily Schedule" cron trigger node // Example: Run at 9:30 AM { "triggerTimes": { "item": [{ "hour": 9, "minute": 30 }] } } 3️⃣ Set Your Slack Destination Open the "Send to Slack" node Select your target channel Configure notification preferences 4️⃣ Adjust Analysis Parameters Post Limit: Change from default 5 posts // In "Get many posts" Reddit node "limit": 10 // Recommended: 3-10 posts Context Customization: { "channel_type": "team", "audience": "Growth, Product, and Founders", "cta_link": "https://your-dashboard.com", "timeframe_label": "This Week" } 🎨 Customization Options 🔍 Analysis Focus Areas Transform the workflow for different insights: SaaS-Focused Analysis Add to Gemini prompt: "Focus on SaaS and B2B insights, prioritizing recurring revenue and product-market fit signals" Geographic Targeting Add: "Prioritize posts relevant to [your region/market]" Stage-Specific Insights Add: "Focus on [early-stage/growth-stage] startup challenges" 📈 Hotness Algorithm Tweaking Default Formula: (ups + 2*num_comments) * freshness_decay Emphasize Comments: (ups + 3*num_comments) * freshness_decay Include Upvote Ratio: (ups * upvote_ratio + 2*num_comments) * freshness_decay 🌐 Multi-Subreddit Analysis Expand beyond r/indiehackers: Additional Communities: r/startups r/entrepreneur r/SideProject r/buildinpublic r/nocode 💾 Data Storage Extensions Enhance with historical tracking: | Node Type | Purpose | Benefit | |-----------|---------|---------| | Google Sheets | Trend storage | Historical analysis | | Airtable | Advanced data management | Rich analytics | | Webhook | External analytics | Custom dashboards | 📊 Expected Output 📱 Daily Slack Message Structure 🚀 IndieHackers Trends — This Week 📋 TL;DR: [One-sentence key insight] 🔥 Hot Posts (Top 3) [Post Title] (Hotness: 8.7) Topics: SaaS launch, pricing strategy 💬 23 comments | 👍 156 ups | 📅 Posted 4 hours ago [Open Reddit Button] 🧭 Themes Summary Go-to-market tactics — 3 posts, hotness: 24.1 Product launches — 2 posts, hotness: 18.3 ✅ What to Do Now Test pricing page variations based on community feedback Consider cold email strategies mentioned in hot posts Validate product ideas using discussed frameworks [Open Dashboard Button] 💡 Pro Tips for Success 🎯 Optimization Strategies Week 1-2: Baseline Monitor output quality and team engagement Note which insights generate the most discussion Week 3-4: Refinement Adjust AI prompts based on feedback Fine-tune hotness scoring for your needs Month 2+: Advanced Usage Add historical trend analysis Create custom dashboards with stored data Build feedback loops for continuous improvement 🚨 Common Pitfalls to Avoid | Issue | Solution | |-------|---------| | API Rate Limits | Reduce post count or increase time intervals | | Poor Insight Quality | Refine prompts with specific examples | | Team Engagement Drop | Rotate focus areas and encourage thread discussions | | Information Overload | Limit to top 3 posts and key themes only | 🔧 Troubleshooting ❌ Common Issues & Solutions "Model not found" Error Cause: Gemini regional availability Fix: Check supported regions or switch to alternative AI model Slack Formatting Broken Cause: Invalid Block Kit JSON Fix: Validate JSON structure in AI Agent output Missing Reddit Data Cause: API credentials or rate limits Fix: Verify OAuth2 setup and check usage quotas AI Timeouts Cause: Too much data or complex prompts Fix: Reduce post count or simplify analysis requests ⚡ Performance Optimization Keep analysis under 10 posts for optimal speed Monitor execution times in n8n logs Add error handling nodes for production reliability Use webhook timeouts for external API calls 🌟 Advanced Use Cases 📈 Competitive Intelligence Modify prompts to track specific competitors or market segments mentioned in discussions 🎯 Product Validation Focus analysis on posts related to your product category for market research 📝 Content Strategy Use trending topics to inform your content calendar and thought leadership 🤝 Community Engagement Identify opportunities to participate in discussions and build relationships Ready to transform your startup intelligence gathering? 🚀 Deploy this workflow and start receiving actionable insights tomorrow morning!. An n8n automation workflow template by Charles.

By Sk developer

Automated DA PA Checker Workflow for SEO Analysis Description This n8n workflow collects a website URL via form submission, retrieves SEO metrics like Domain Authority (DA) and Page Authority (PA) using the Moz DA PA Checker API, and stores the results in Google Sheets for easy tracking and analysis. Node-by-Node Explanation On form submission – Captures the website input from the user to pass to the Moz DA PA Checker API. DA PA API Request – Sends the website to the Moz DA PA Checker API via RapidAPI to fetch DA, PA, spam score, DR, and organic traffic. If – Checks if the API request to the Moz DA PA Checker API returned a successful response. Clean Output – Extracts only the useful data from the Moz DA PA Checker API response for saving. Google Sheets – Appends the cleaned SEO metrics to a Google Sheet for record-keeping. Use Cases SEO Analysis** – Quickly evaluate a website’s DA/PA metrics for optimization strategies. Competitor Research** – Compare domain authority and organic traffic with competitors. Link Building** – Identify high-authority domains for guest posting and backlinks. Domain Purchase Decisions** – Check metrics before buying expired or auctioned domains. Benefits Automated Workflow** – From input to Google Sheets without manual intervention. Accurate Metrics* – Uses the trusted *Moz DA PA Checker API** for DA, PA, spam score, DR, and traffic. Instant Insights** – Get SEO scores in seconds for faster decision-making. Easy Integration** – Seamless connection between RapidAPI and Google Sheets for data storage. An n8n automation workflow template by Sk developer.

By 1Shot API

Swap Tokens with Li.Fi The growing popularity of agentic payments has lead to the development of protocols like x402 where agents and humans can pay for internet resources over standard http protocols using stablecoins. This workflow lets you run your own swap relayer where callers can provide an x402-compatible payment header and a desired destination network to instantly receive gas tokens. This setup is trust minimized - user's have the following guarantees: They will be the receiver of the swap. Only the amount of tokens the authorized will be swapped. Setup In order to run this relayer workflow, you will need an account on 1Shot API. You must then import the 1Shot Gas Station contract into your business for any chain you wish to support with your relayer. Next, follow the directions in the worflow sticky notes to update the Payment Configs for the tokens you wish to support swaps for. Lastly, distrubute your webhook api endpoint to your users. An n8n automation workflow template by 1Shot API.

By Carl Fung

✨ Intro This workflow shows how to go beyond a “plain” AI chatbot by: 🧠 Adding a Personality Layer — Link an extra LLM to inject a custom tone and style. Here, it’s Nova, a sassy, high-fashion assistant. You can swap in any personality without changing the main logic. 🎨 Custom Styling with CSS — Easily restyle the chatbot to match your brand or project theme. Together, these make your bot smart, stylish, and uniquely yours. ⚙️ How it Works 📥 Route Input Chat trigger sends messages to a Switch. If a Telegram video note exists → runs the audio path. Otherwise → runs the text path. 🎤 Audio Path Telegram Get a File → OpenAI Speech-to-Text → pass transcript to the agent. 💬 Text Path Chat text is normalized and sent to the agent. 🛠 Agent Brain Uses tools like Gmail 📧, Google Calendar 📅, Google Drive 📂, Airtable 📋, SerpAPI 🌐, Wikipedia 📚, Hacker News 📰, and Calculator ➗. 🧾 Memory Keeps the last 20 messages for context-aware replies. 💅 Optional Personality Polish An LLM Chain adds witty or cheeky tone on top of the agent’s response. 🛠 Setup Steps ⏱ Time Required ~10–15 minutes (+5 minutes for each Google/Airtable connection). 🔑 Connect Credentials OpenAI (and/or Anthropic) Telegram Bot Gmail, Google Calendar, Google Drive Airtable SerpAPI 📌 Configure IDs Set Airtable base/table. Set Calendar email. Adjust Drive search query defaults if needed. 🎙 Voice Optional Disable Telegram + Transcribe nodes if you only want text chat. 🎭 Choose Tone Edit Chat Trigger’s welcome text/CSS for custom look. Or disable persona chain for neutral voice. 🚀 Publish Activate workflow and share the chat URL. 💡 Detailed behavior notes are available as sticky notes inside the workflow. An n8n automation workflow template by Carl Fung.

By MattF

This workflow generates a weekly performance summary from Google Search Console, focused on brand-level SEO metrics and week-over-week trends. It provides a structured view of how each brand segment is performing, with clean formatting for quick insights. Key Features Sends a weekly email with a table showing clicks, impressions, CTR, and position — along with % change vs. the previous week. Highlights both brand and non-brand clicks separately. Color-coded % changes make it easy to spot wins (green) and losses (red) at a glance. It’s designed to give SEO teams a consistent overview of performance by brand, helping to track directional shifts and support deeper analysis when needed. How it works Runs weekly (e.g. every Monday) to compare “Last Week” vs. “2 Weeks Ago” from GSC data. Includes both brand + non-brand click breakdown. Calculates raw values and week-over-week % change for clicks, impressions, CTR, and position. Outputs a clean, formatted table with labeled rows and color-coded changes. Sends the table as part of a scheduled email (can also be adapted for Slack or other channels). Setup steps Requires connected Google Search Console data (per brand segment). Email delivery is included by default (customizable to other platforms). Update brand segmentation logic to match your tracking needs (e.g. domain, label, or custom filters). Typical setup time: ~5-10 minutes with structured input data. An n8n automation workflow template by MattF.

By Lakindu Siriwardana

📄 Automated Lease Renewal Offer by Email ✅ Features Automated Lease Offer Generation using AI (Ollama model). Duplicate File Check to avoid reprocessing the same customer. Personalized Offer Letter creation based on customer details from Supabase. PDF/Text File Conversion for formatted output. Automatic Google Drive Management for storing and retrieving files. Email Sending with generated offer letter attached. Seamless Integration with Supabase, Google Drive, Gmail, and AI LLM. ⚙️ How It Works Trigger: Workflow starts on form submission with customer details. Customer Lookup: Searches Supabase for customer data. Updates customer information if needed. File Search & Duplication Check: Looks for existing lease offer files in Google Drive. If duplicate found, deletes old file before proceeding. AI Lease Offer Creation: Uses the LLM Chain (offerLetter) to generate a customized lease renewal letter. File Conversion: Converts AI-generated text into a downloadable file format. Upload to Drive: Saves the new lease offer in Google Drive. Email Preparation: Uses Basic LLM Chain-email to draft the email body. Downloads the offer file from Drive and attaches it. Email Sending: Sends the renewal offer email via Gmail to the customer. 🛠 Setup Steps Supabase Connection: Add Supabase credentials in n8n. Ensure a customers table exists with relevant columns. 🔜Future Steps Add specific letter template (organization template). PDF offer letter. An n8n automation workflow template by Lakindu Siriwardana.

By Tomek

How it works Use Telegram to send in new phrases (flashcard front) You can also manually input phrase in the workflow itself ChatGPT generates provided phrase description (in English but you can change it) including multiple meanings & generates examples of using the phrase in a sample sentence (flashcard back) Steps to setup Provide your Telegram bot API key (optional) Provide your OpenAI key Provide Google Sheets credentials How to import flashcards from Google Sheets into Anki Use Google Sheets to Anki add-on: 1871608121 In Anki simply click Sync Decks and you're done :) Enjoy. An n8n automation workflow template by Tomek.

By Calistus Christian

What this workflow does Automatically triages risky AWS misconfigurations and alerts your team. Pipeline: Security Hub or AWS Config -> EventBridge rules -> SNS (HTTP) -> n8n Webhook -> Normalize -> AI Prioritizer -> Airtable (log) -> Gmail (email) Normalizes incoming findings (S3 / Security Groups / IAM / RDS) into a consistent JSON. Uses an LLM to assign a priority (P0–P3) with rationale and remediation steps. Upserts the finding into Airtable (avoids duplicates). Emails a compact incident summary to your inbox. This can be swapped for Microsoft Teams or Slack, etc. Category: Security / Cloud / Alerting Time to set up: ~10–15 minutes Difficulty: Beginner–Intermediate Cost: Mostly free (n8n CE + AWS SNS/EventBridge; OpenAI + Airtable/Gmail as used) What you’ll need An n8n instance reachable over HTTP. AWS account (one region) with permissions to create SNS topics and EventBridge rules. Security Hub** enabled (or AWS Config rules that emit compliance events). n8n credentials: OpenAI, Airtable, Gmail. Nodes used Webhook** (POST /aws-misconfig) Code:** SNS Handler (token check, confirm/unwrap) IF:** route mode === "confirm" vs notification HTTP Request:** SNS SubscriptionConfirmation (GET) Code:** Normalize Finding Message a model:** AI Prioritizer (JSON out) Airtable:** Create/Upsert Gmail:** Send message Edit Fields:** final JSON response Setup steps Import and activate the workflow in n8n. Webhook Respond: When Last Node Finishes -> First Entry JSON. Append a shared secret to the URL, e.g. ?token=MY_SUPER_TOKEN, and keep the check in the SNS Handler code node. Create an SNS topic (e.g., misconfig-events) in the same region as your EventBridge rules. Create EventBridge rules targeting the SNS topic: Rule A (Security Hub): source = aws.securityhub, detail-type = Security Hub Findings - Imported Rule B (AWS Config): source = aws.config, detail-type = Config Rules Compliance Change Create an SNS subscription with Protocol = HTTP and Endpoint = your production webhook URL: http://YOUR_HOST:5678/webhook/aws-misconfig?token=MY_SUPER_TOKEN (The workflow auto-confirms the subscription on first POST.) Configure Airtable (Upsert on Finding ID) and Gmail recipients. An n8n automation workflow template by Calistus Christian.

By Zain Ali

🧾 Who’s it for This workflow is designed for finance teams, accountants, and data analysts 📊 who want to interact with financial data from two PostgreSQL databases — one containing Profit & Loss data and another containing Balance Sheet data — using natural language chat. It’s perfect for those who need quick, AI-powered insights with the correct database automatically selected based on the question. ⚙️ How it works / What it does Chat Trigger 💬 – Starts the workflow when a chat message is received. AI Agent 🤖 – Processes the user’s question and decides: Profit & Loss DB → If the question is about revenue, costs, expenses, or profit. Balance Sheet DB → If the question is about assets, liabilities, or equity. PostgreSQL Query Nodes 🗄️ – P_L_Reports queries the financial_agent_pl_reports table. Balance_Sheets queries the financial_agent_balancesheets table. AI Model (OpenAI) 🧠 – Uses gpt-4.1-nano to interpret results and provide an easy-to-read answer. Memory Buffer 📝 – Keeps recent conversation context for a smoother chat experience. Table Output 📋 – Always formats the results as a clean, readable table with two decimal precision. 🛠️ How to set up Prepare Your Databases Feed your Profit & Loss and Balance Sheet data into PostgreSQL. Ensure the correct table structures are used: financial_agent_pl_reports → P&L data. financial_agent_balancesheets → Balance Sheet data. Configure the PostgreSQL Nodes Add connection credentials for both databases. Link P_L_Reports and Balance_Sheets nodes to the correct tables. Set Up the AI Agent Paste the provided system message into the AI Agent node (already configured in your workflow). Connect the Nodes Ensure Chat Trigger → AI Agent → DB Nodes → AI Model connections match your workflow. Deploy Save and activate the workflow. Start sending finance-related queries to test. 📋 Requirements n8n** (latest version recommended) PostgreSQL databases** with: financial_agent_pl_reports table (P&L data). financial_agent_balancesheets table (Balance Sheet data). OpenAI API credentials** with access to gpt-4.1-nano. Active Webhook/Chat Trigger** for receiving queries. 🎨 How to customize Expand AI Instructions** 🗒️ – Add more rules in the system message for different data sources or formatting styles. Change AI Model** 🧠 – Switch to a different OpenAI model for faster or more accurate results. Add More Databases** 🗄️ – Connect extra financial datasets, e.g., cash flow, sales analytics. Enhance Table Styling** 📊 – Use Markdown or HTML formatting for richer outputs. Refine Query Logic** 🔍 – Modify filtering logic to better match your reporting needs. An n8n automation workflow template by Zain Ali.

By Raphael De Carvalho Florencio

What this workflow is (About) This workflow turns a Telegram bot into an AI-powered lyrics assistant. Users send a command plus a lyrics URL, and the flow downloads, cleans, and analyzes the text, then replies on Telegram with translated lyrics, summaries, vocabulary, poetic devices, or an interpretation—all generated by AI (OpenAI). What problems it solves Centralizes lyrics retrieval + cleanup + AI analysis in one automated flow Produces study-ready outputs (translation, vocabulary, figures of speech) Saves time for teachers, learners, and music enthusiasts with instant results in chat Key features AI analysis** using OpenAI (no secrets hardcoded; uses n8n Credentials) Line-by-line translation, **concise summaries, vocabulary lists Poetic/literary device detection* and *emotional/symbolic interpretation** Robust ETL (extract, download, sanitize) and error handling Clear Sticky Notes documenting routing, ETL, AI prompts, and messaging Who it’s for Language learners & teachers Musicians, lyricists, and music bloggers Anyone studying lyrics for meaning, style, or vocabulary Input & output Input:* Telegram command with a public *lyrics URL** Output:** Telegram messages (Markdown/MarkdownV2), split into chunks if long How it works Telegram → Webhook** receives a user message (e.g., /get_lyrics <URL>). Routing (If/Switch)** detects which command was sent. Extract URL + Download (HTTP Request)** fetches the lyrics page. Cleanup (Code)** strips HTML/scripts/styles and normalizes whitespace. OpenAI (Chat)** formats the result per command (translation, summary, vocabulary, analysis). Telegram (Send Message)** returns the final text; long outputs are split into chunks. Error handling** replies with friendly guidance for unsupported/incomplete commands. Set up steps Create a Telegram bot with @BotFather and copy the bot token. In n8n, create Credentials → Telegram API and paste your token (no hardcoded keys in nodes). Create Credentials → OpenAI and paste your API key. Import the workflow and set a short webhook path (e.g., /lyrics-bot). Publish the webhook and set it on Telegram: https://api.telegram.org/bot<YOUR_BOT_TOKEN>/setWebhook?url=https://[YOUR_DOMAIN]/webhook/lyrics-bot (Optional) Restrict update types: curl -X POST https://api.telegram.org/bot<YOUR_BOT_TOKEN>/setWebhook \ -H "Content-Type: application/json" \ -d '{ "url": "https://[YOUR_DOMAIN]/webhook/lyrics-bot", "allowed_updates": ["message"] }' Test by sending /start and then /get_lyrics <PUBLIC_URL> to your bot. If messages are long, ensure MarkdownV2 is used and special characters are escaped. An n8n automation workflow template by Raphael De Carvalho Florencio.

By Jay Emp0

MCP Tool — Replicate (Flux) Image Generator → WordPress/Twitter Generates images via Replicate Flux models and uploads to WordPress (and optionally Twitter/X). Built to act as an MCP module that other agents/workflows call for on-demand image creation. Models configured in this workflow:\ black-forest-labs/flux-schnell, black-forest-labs/flux-dev, black-forest-labs/flux-1.1-pro Switch rationale: lower cost 💰, broader model choice 🎯, full control of parameters ⚙️ Leonardo API credits cannot be used in the web UI 🙅‍♂️; separate spend for API vs UI Links: 📜 Prior Leonardo-based workflow: https://n8n.io/workflows/6363-generate-and-upload-images-with-leonardo-ai-wordpress-and-twitter/ 📰 Blog automation consuming these images: https://n8n.io/workflows/6734-ai-blog-automation-publish-hourly-seo-articles-to-wordpress-and-twitter-v3/ 📥 Inputs | Field | Type | Description | | ------ | ------ | --------------------------------- | | prompt | string | Text description for the image | | slug | string | Filename slug for WP media | | model | string | One of the configured Flux models | Example: { "prompt":"Joker watching a Batman movie on his laptop", "slug":"joker-watching-batman", "model":"black-forest-labs/flux-dev" } 📤 Output { "public_image_url": "https://your-wp.com/wp-content/uploads/2025/08/img-joker-watching-batman.webp", "wordpress": {...}, "twitter": {...} } 🔄 Flow Trigger with prompt, slug, model Build model payload (quality/steps/ratio/output format) Call Replicate: POST /v1/models/{model}/predictions (Prefer: wait) Download the generated image URL Upload to WordPress (returns public URL) Optional: upload to Twitter/X Return URL + metadata 🤖 MCP Use at Scale (emp0.com) Operational pattern: I currently use this setup for my blog where i generate 300 posts/month, each with 4 images (banner + 2 to 3 inline images) → 1,000 images/month produced by this MCP. 💡 Hybrid Cost-Optimized Setup: High-priority images* (banners, main visuals): Generated using *Flux Dev** on Leonardo for slightly better prompt adherence. Low-priority images* (inline blog visuals): Generated using *Flux Schnell** on Replicate for maximum cost efficiency. 💰 Pricing Comparison (per image) Leonardo per-image cost uses API Basic math: $9 / 3,500 credits = $0.0025714 per credit. Flux Schnell (Leonardo)** = 7 credits Flux Dev (Leonardo)** = 7 credits Flux 1.1 Pro equivalent in Leonardo* = *Leonardo Phoenix** based on my experience = 10 credits | Flux Model | Replicate | Leonardo API* | | ------------------------ | ------------------------- | ------------------------------- | | flux-schnell | $0.0030 (=$3/1,000) | $0.0180 (7 × $0.0025714) | | flux-dev | $0.0250 | $0.0180 (7 × $0.0025714) | | flux-1.1-pro / Phoenix | $0.0400 | $0.0257 (10 × $0.0025714) | Replicate pricing: https://replicate.com/pricing\ Leonardo pricing: https://leonardo.ai/pricing/\ Leonardo API usage: https://docs.leonardo.ai/docs/commonly-used-api-values 📊 Monthly Cost Example (1,000 images/month) Mix: 300 ×flux-dev on Leonardo, 700 ×flux-schnell on Replicate. | Platform/Model | Images | Price per Image | Total | | ------------------------ | ------ | --------------- | ---------- | | Leonardo flux-dev | 300 | $0.0180 | $5.40 | | Replicate flux-schnell | 700 | $0.0030 | $2.10 | | Total Monthly Spend | 1000 | — | $7.50 | 💵 If using Leonardo for both: 300 × $0.0180 = $5.40 700 × $0.0180 = $12.60 Total = $18.00** Savings: $10.50/month (≈58% lower) with the hybrid setup. 📌 Notes More Replicate models can be added in Code1 node. Parameters tuned for aspect ratio, inference steps, quality, guidance. Leonardo credit model is API-only; credits are not spendable in Leonardo's web UI. An n8n automation workflow template by Jay Emp0.

By Dr. Firas

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Automate Content Publishing to TikTok, YouTube, Instagram, Facebook via Blotato 🎯 Who is this for? This workflow is perfect for: Content creators who post daily to multiple platforms Marketing teams managing brand presence across channels Solo entrepreneurs and social media managers looking to scale their output Anyone tired of uploading content manually across apps 💡 What problem is this solving? Managing content across platforms is time-consuming. You need to: Track posts per platform Upload videos manually Adapt captions and posting time Avoid repetitive mistakes This workflow solves all of that by centralizing everything in one place (Google Sheets) and automating it via Blotato. ⚙️ What this workflow does Every hour, this workflow will: Check your Google Sheet for any post marked as "TO GO" Select one item at a time (avoids spam and overposting) Extract media from a shared Google Drive link Upload the media to Blotato Publish it automatically to: TikTok YouTube Shorts Instagram Facebook Update the post status in your Sheet to "Posted" 🧰 Setup Before running this template, make sure you have: ✅ A Blotato account (Pro plan required for API key) 🔑 Generated your Blotato API key (Settings > API > Generate) 📦 Enabled Verified Community Nodes in n8n Admin Panel 🧩 Installed the Blotato node via the community nodes list 🛠 Created a Blotato credential in n8n using your API key ☁️ Made sure your media folder in Google Drive is set to Anyone with the link can view 📌 Followed the 3 setup steps in the brown sticky notes inside the workflow 🛠 How to customize this workflow Add new platform nodes (LinkedIn, Threads, Pinterest, etc.) using Blotato Adjust the scheduling frequency from hourly to daily or weekly Add an approval layer (Slack/Telegram) before publishing Customize your captions dynamically using GPT or formulas in Sheets Use tags, categories, or campaign tracking for analytics 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube. An n8n automation workflow template by Dr. Firas.

By Garri

Description This workflow is designed to automate the security reputation check of domains and IP addresses using multiple APIs such as VirusTotal, AbuseIPDB, and Google DNS. It assesses potential threats including malicious and suspicious scores, as well as email security configurations (SPF, DKIM, DMARC). The analysis results are processed by AI to produce a concise assessment, then automatically updated into Google Sheets for documentation and follow-up. How It Works Automatic Trigger – The workflow runs periodically via a Schedule Trigger. Data Retrieval – Fetches a list of domains from Google Sheets with status "To do". Domain Analysis – Uses VirusTotal API to get the domain report, perform a rescan, and check IP resolutions. IP Analysis – Checks IP reputation using AbuseIPDB. Email Security Validation – Verifies SPF, DKIM, and DMARC configurations via Google DNS. AI Assessment – Analysis data is processed by AI to produce a short summary in Indonesian. Data Update – The results are automatically updated to Google Sheets, changing the status to "Done" or adding notes if potential threats are found. How to Setup Prepare API Keys Sign up and obtain API keys from VirusTotal and AbuseIPDB. Set up access to Google Sheets API. Configure Credentials in n8n Add VirusTotal API, AbuseIPDB API, and Google Sheets OAuth credentials in n8n. Prepare Google Sheets Create a sheet with columns No, Domain, Customer, Keterangan, Status. Ensure initial data has the status "To do". Import Workflow Upload the workflow JSON file into n8n. Set Schedule Trigger Define the checking interval as needed (e.g., every 1 hour). Test Run Run the workflow manually to ensure all API connections and Google Sheets output work properly. An n8n automation workflow template by Garri.

By iamvaar

Execution video: Youtube Link I built an AI voice-triggered RAG assistant where ElevenLabs’ conversational model acts as the front end and n8n handles the brain....here’s the real breakdown of what’s happening in that workflow: Webhook (/inf) Gets hit by ElevenLabs once the user finishes talking. Payload includes user_question. Embed User Message (Together API - BAAI/bge-large-en-v1.5) Turns the spoken question into a dense vector embedding. This embedding is the query representation for semantic search. Search Embeddings (Supabase RPC) Calls matchembeddings1 to find the top 5 most relevant context chunks from your stored knowledge base. Aggregate Merges all retrieved chunk values into one block of text so the LLM gets full context at once. Basic LLM Chain (LangChain node) Prompt forces the model to only answer from the retrieved context and to sound human-like without saying “based on the context”.... Uses Google Vertex Gemini 2.5 Flash as the actual model. Respond to Webhook Sends the generated answer back instantly to the webhook call, so ElevenLabs can speak it back. You essentially have: Voice → Text → Embedding → Vector Search → Context Injection → LLM → Response → Voice. An n8n automation workflow template by iamvaar.

By Garri

Description This workflow is an n8n-based automation that allows users to download TikTok/Reels videos without watermarks simply by sending the video link through a Telegram Bot. It uses a Telegram Trigger to receive the link from the user, then makes an HTTP request to a third-party API (tiktokio.com) to process and retrieve the download link. The workflow filters the results to find the Download without watermark link, downloads the video in MP4 format, and sends it back to the user directly in their Telegram chat. Key features: Supports the best available video quality (bestvideo+bestaudio). Automatically removes watermarks. Instant response directly in Telegram chat. Fully automated — no manual downloads required. How It Works Telegram Trigger The user sends a TikTok or Reels link to the Telegram bot. The workflow captures and stores the link for processing. HTTP Request – MediaDL API The link is sent via POST method to https://mediadl.app/api/download. The API processes the link and returns video file data. Wait Delay The workflow waits a few seconds to ensure the API response is fully ready. Edit Fields Extracts the video file URL from the API response. Additional Wait Delay Adds a short pause to avoid connection errors during the download process. HTTP Request – Proxy Download Downloads the MP4 video file directly from the filtered URL. Send Video via Telegram The downloaded video is sent back to the user in their Telegram chat. How to Set Up Create & Configure a Telegram Bot Open Telegram and search for BotFather. Send /newbot → choose a name & username for your bot. Copy the Bot Token provided — you’ll need it in n8n. Prepare Your n8n Environment Log in to your n8n instance (self-hosted or n8n Cloud). Go to Credentials → create new Telegram API credentials using your Bot Token. Import the Workflow In n8n, click Import and select the PROJECT_DOWNLOAD_TIKTOK_REELS.json file. Configure the Telegram Nodes In the Telegram Trigger and Send Video nodes, connect your Telegram API credentials. Configure the HTTP Request Nodes Ensure the Download2 and HTTP Request nodes have the correct URL and headers (pre-configured for mediadl.app). Make sure the responseFormat is set to file in the final download node. Activate the Workflow Toggle Activate in the top right corner of n8n. Test by sending a TikTok or Reels link to your bot — you should receive the no-watermark video in return. An n8n automation workflow template by Garri.

By Rully Saputra

Who’s it for This workflow is ideal for marketing teams, growth analysts, and business owners who need regular Google Analytics insights without manually digging through data. It’s also perfect for organizations that want to ensure positive performance updates reach stakeholders quickly while negative trends get immediate attention from the internal team. How it works / What it does The workflow runs weekly on a set schedule, pulls key performance metrics from Google Analytics, and aggregates the data into a clean summary. An AI Agent (powered by Google Gemini and connected to Simple Memory for historical context) analyzes the data, generates actionable insights, and classifies the sentiment as Positive, Negative, or Neutral. Positive sentiment → Automatically emailed to stakeholders via Gmail. Negative sentiment → Sent instantly to a designated Telegram group for faster response. This ensures wins are celebrated, and issues are addressed promptly. How to set up Configure the Schedule Trigger for your preferred reporting day/time. Connect the Google Analytics node with your property ID and metrics/dimensions. Set up the AI Agent with Google Gemini/others model API credentials. Connect Gmail and Telegram accounts to their respective nodes. Adjust sentiment routing rules. Requirements Google Analytics account with API access Google Gemini API key Gmail account with OAuth connection Telegram bot token and group chat ID How to customize the workflow Modify the AI prompt to include custom KPIs or industry-specific recommendations. Change the schedule frequency (daily, monthly, or on-demand). Add Neutral sentiment handling (e.g., log to Google Sheets). Extend with Slack, Discord, or other notification channels. An n8n automation workflow template by Rully Saputra.

By Basil Irfan

🚀 LinkedIn Lead-Gen Flywheel – Apify → GPT-4o → Google Sheets → Phantombuster What this workflow does Collect audience specs – simple web-form asks for your ideal company profile. Generate a laser-targeted Apollo search URL with GPT-4o (no manual filtering). Scrape the matching leads via an Apify actor (returns clean JSON). Craft hyper-personalized icebreakers for each lead using GPT-4o (ultra-short, human-sounding). Log everything to Google Sheets – name, LinkedIn URL, company site, summary, and the icebreaker. (Optional) Auto-launch Phantombuster to fire off those connection requests at scale. Why it matters Zero grunt work:** audience research, scraping, copy-writing, and outreach all run hands-free. Punchy personalization:** micro-icebreakers outperform canned intros, boosting accept rates. Scales with you:** flip a switch to go from 10 to 1 000+ connections/day. Node rundown | Step | Node | Key Inputs | Key Outputs | |------|------|-----------|-------------| | 1 | Form Trigger | Audience description | description_of_company | | 2 | OpenAI (GPT-4o) | Audience text | SearchUrl | | 3 | HTTP Request – Apify | SearchUrl, APIFY_TOKEN | Lead JSON | | 4 | OpenAI (GPT-4o) | Lead JSON | Icebreaker | | 5 | Google Sheets | Lead + Icebreaker | Row append/update | | 6 | Aggregate | Sheet rows | Batched output | | 7 | HTTP Request – Phantombuster | PHANTOM_KEY, AGENT_ID | Launch status | Prerequisites OpenAI API key** (GPT-4o access recommended) Apify API token** with access to actor id Google Service Account creds** shared with your target sheet Phantombuster API key** and Agent ID for your LinkedIn connector Active Apollo account to open the generated search URL (only required for debugging) Setup (5-minute sprint) Import the workflow into n8n. Add the required credentials in Credentials → OpenAI, Apify, Google Sheets, Phantombuster. Paste your Phantombuster Agent ID into the HTTP Request node URL. Publish the Form Trigger URL—this is where you (or your SDRs) describe the target audience. Hit Execute Workflow once to verify data flows end-to-end. Customization tips Titles & keywords:** tweak the prompt in the first GPT-4o node to lock in different roles or industries. Icebreaker style:** adjust the second GPT-4o prompt to match your brand voice. Data columns:** map extra fields from Apify into Google Sheets as needed. Skip outreach:** disable the Phantombuster node if you only want the leads + icebreakers. An n8n automation workflow template by Basil Irfan.

By Oneclick AI Squad

This n8n workflow automates email blasts with follow-ups and response tracking by reading contact data from a Google Sheet daily, looping through contacts to send personalized emails based on follow-up stages via Gmail, updating the sheet with status changes, and monitoring replies for logging. Why Use It This workflow streamlines email marketing campaigns by automating personalized email distribution, managing follow-up sequences, and tracking responses without manual intervention, saving time, improving engagement, and providing actionable insights into contact interactions. How to Import It Download the Workflow JSON: Obtain the workflow file from the n8n template or create it based on this document. Import into n8n: In your n8n instance, go to "Workflows," click the three dots, select "Import from File," and upload the JSON. Configure Credentials: Set up Gmail and Google Sheets credentials in n8n. Run the Workflow: Activate the scheduled trigger and test with a sample Google Sheet. System Architecture Email Blast Pipeline**: Daily Trigger - 9 AM: Initiates the workflow daily at 9 AM via Cron. Read Contact Data from Google Sheet: Fetches contact details from the sheet. Loop Through Contacts: Processes each contact individually. Determine Follow-Up Stage: Identifies the current stage for each contact. Send Main/Follow-Up Email: Delivers the appropriate email via Gmail. Update Sheet Status: Updates the Google Sheet with the latest status. Response Tracking Flow**: Check Gmail for Replies: Monitors Gmail for email responses. Log Responses: Records responses in the Google Sheet. Google Sheet File Structure Sheet Name**: EmailCampaign Range**: A1:F10 (or adjust based on needs) | A | B | C | D | E | F | |------------|------------|---------------|---------------|---------------|---------------| | name | email | stage | last_email_date | status | response | | John Doe | john@example.com | Initial | 2025-08-07 | Pending | | | Jane Smith | jane@example.com | Follow-Up 1 | 2025-08-06 | Sent | "Interested" | | Bob Jones | bob@example.com | Follow-Up 2 | 2025-08-05 | Replied | "Follow up later" | Columns**: name: Contact’s full name. email: Contact’s email address for sending emails. stage: Current follow-up stage (e.g., Initial, Follow-Up 1, Follow-Up 2). last_email_date: Date of the last email sent. status: Current status (e.g., Pending, Sent, Replied). response: Logged response from the contact (updated after reply detection). Customization Ideas Adjust Schedule**: Change the Cron trigger to hourly or weekly based on campaign needs. Add Email Templates**: Customize email content for different stages or audiences. Incorporate SMS**: Add WhatsApp or SMS follow-ups using additional nodes. Enhance Tracking**: Integrate a dashboard (e.g., Google Data Studio) for real-time campaign analytics. Automate Segmentation**: Add logic to segment contacts by industry or interest for targeted emails. Requirements to Run This Workflow Google Sheets Account**: For storing and managing contact data and responses. Gmail Account**: For sending emails and checking replies (requires IMAP enabled). n8n Instance**: With Google Sheets and Gmail connectors configured. Cron Service**: For scheduling the daily trigger. Internet Connection**: To access Google Sheets and Gmail APIs. API Credentials**: Gmail OAuth2 and Google Sheets API credentials set up in n8n. Notes Ensure the Google Sheet is shared with the n8n service account or has appropriate permissions. Test the workflow with a small contact list to verify email delivery and response logging. Adjust the stage logic in the "Determine Follow-Up Stage" node to match your campaign structure. An n8n automation workflow template by Oneclick AI Squad.

By Oneclick AI Squad

The workflow is triggered manually with user input, searches LinkedIn profiles, processes the results using AI, generates connection recommendations, and delivers them via email. It leverages AI to enhance networking opportunities based on insights from profiles. Good to Know Each email is personalized with the user’s name and recommended connections. Recommendations are based on LinkedIn search results and AI analysis. The system ensures data privacy by processing inputs securely. Email notifications include a curated list of potential connections. How it Works Profile Analysis Workflow Get User Data from Email**: Manually inputs user email and profile information to initiate the workflow. Your Profile Information**: Provides initial user data for LinkedIn search. Search LinkedIn Profiles**: Queries LinkedIn via an API (e.g., SerpAPI) to gather profile data. Process LinkedIn Search Results**: Extracts relevant details from search results. AI Recommendation Workflow AI Profile Analysis**: Uses an AI model (e.g., Ollama Model) to analyze profile data and suggest connections. Create Recommendations**: Generates a curated list of potential connections. Create Final Recommendations**: Refines and formats the recommendation list. Create Email**: Prepares a personalized email with the connection list. Send Email**: Delivers the email to the user. Excel Sheet Structure No persistent Excel sheet is required**; data is processed in-memory and emailed directly. However, optional logging can be set up: Optional Log Sheet (Recommendations): Timestamp: Date and time of recommendation generation. User Email: User’s email address. Profile Name: User’s LinkedIn profile name. Industry: User’s industry. Recommended Connections: List of suggested connections. Sent Status: Whether the email was sent successfully. How to Use Import the Workflow into your n8n instance and configure email integration. Provide User Data: Manually enter the user’s email and profile information in the "Get User Data from Email" node. Configure API Credentials: Set up SerpAPI for LinkedIn searches and email service (e.g., SMTP). Run the Workflow: Execute manually to test the process. Monitor Emails: Check the user’s inbox for the curated connection list. Optional Logging: Set up a Google Sheet to log recommendations if desired. Requirements SerpAPI**: For LinkedIn profile searches. Email Service Integration**: Gmail, SMTP, or similar for email delivery. Ollama Model**: For AI-based profile analysis. n8n Instance**: With SerpAPI, email, and function nodes. Customizing this Workflow Expand Data Sources**: Integrate additional platforms (e.g., Xing) for broader searches. Enhance AI**: Train the Ollama Model for more specific connection criteria (e.g., job role, location). Add Notifications**: Include Slack or SMS alerts for admin tracking. Customize Email**: Adjust the email template for branding or additional details. Automate Trigger**: Replace manual input with a scheduled trigger or webhook. An n8n automation workflow template by Oneclick AI Squad.

By Oneclick AI Squad

This automated n8n workflow enables an AI-powered movie recommendation system on WhatsApp. Users send messages like "I want to watch a horror movie" or "Where can I watch the Jumanji movie?" The workflow uses AI to interpret the request, searches relevant APIs (e.g., TMDb, JustWatch), and replies with movie recommendations or streaming platform availability via WhatsApp. Fundamental Aspects WhatsApp Webhook Trigger**: Initiates the workflow when a WhatsApp message is received. Analyze WhatsApp Message**: Uses AI (e.g., Ollama Model) to interpret the user's intent and extract request type. Check Request Type**: Determines if the request is for a movie genre or a specific movie title. Check Where Request**: Identifies if the request includes a "where to watch" query. Extract Movie Title**: Extracts the movie title from the message if specified. Extract Genre**: Identifies the movie genre from the message if specified. Search Specific Movie Title**: Queries an API (e.g., TMDb) for details about a specific movie. Search Movies by Genre**: Queries an API (e.g., TMDb) for movies matching the genre. Get Streaming Availability**: Queries an API (e.g., JustWatch) for streaming platforms. Format Streaming Response**: Prepares the response with streaming platform details. Format Genre Recommendations**: Prepares the response with genre-based movie recommendations. Prepare WhatsApp Message**: Formats the final response for WhatsApp. Send WhatsApp Response**: Sends the recommendation or streaming info back to the user via WhatsApp. Setup Instructions Import the Workflow into n8n: Download the workflow JSON and import it via the n8n interface. Configure API Credentials: Set up WhatsApp Business API credentials with a valid phone number and token. Configure TMDb API key (e.g., https://api.themoviedb.org). Configure JustWatch API key (e.g., https://api.watchmode.com). Set up AI model credentials (e.g., Ollama Model). Run the Workflow: Activate the webhook trigger and test with a WhatsApp message. Verify Responses: Check WhatsApp for accurate movie recommendations or streaming info. Adjust Parameters: Fine-tune API endpoints or AI model as needed. Features AI Interpretation**: Uses AI to analyze user intents (genre or movie title). API Integration**: Searches TMDb for movie details and JustWatch for streaming availability. Real-Time Responses**: Sends instant replies via WhatsApp. Custom Recommendations**: Provides genre-based or specific movie recommendations. Technical Dependencies WhatsApp Business API**: For receiving and sending messages. TMDb API**: For movie details and genre searches. JustWatch API**: For streaming availability. Ollama Model**: For AI-based message analysis. n8n**: For workflow automation and integration. Customization Possibilities Add More APIs**: Integrate additional movie databases (e.g., IMDb). Enhance AI**: Train the Ollama Model for better intent recognition. Support More Languages**: Add multilingual support for WhatsApp responses. Add Email Alerts**: Include email notifications for admin monitoring. Customize Responses**: Adjust the format of recommendations or streaming info. An n8n automation workflow template by Oneclick AI Squad.

By Automate With Marc

🧠 RAG-Based Customer Support Agent (GPT-5 + Telegram) Description: This workflow builds a powerful Retrieval-Augmented Generation (RAG) Customer Support Agent that interacts with users directly through Telegram using the GPT-5 model. It combines real-time conversational capabilities with context-aware responses by leveraging vector search via Pinecone, making it ideal for automated, intelligent support systems. Watch Video Tutorial Build on Workflows Like These: https://www.youtube.com/@Automatewithmarc 💬 Key Features: Telegram Integration: Listens to customer queries via the Telegram Trigger node and sends back intelligent responses in the same chat. GPT-5 Agent (LangChain): A powerful AI agent node orchestrates the conversation using OpenAI's GPT-5 model. Contextual Memory: A Memory Buffer stores the last 15 interactions per user to provide more personalized and coherent multi-turn conversations. RAG with Pinecone: Integrates with Pinecone to fetch relevant answers from your “Customer FAQ” vector namespace, enabling grounded and accurate responses. Embeddings Generation: Uses OpenAI’s Embeddings node to process and vectorize documents for retrieval. End-to-End AI Pipeline: Connects all components from input to output, providing seamless and intelligent customer support. 🔧 Tech Stack: GPT-5 via OpenAI API Pinecone vector store (namespace: Customer FAQ) Telegram Bot API LangChain agent, memory, and embedding tools n8n self-hosted or cloud instance 📌 Ideal Use Cases: Automated customer support for e-commerce, SaaS, or community support FAQ bots with up-to-date product or policy documents Multilingual support agents (customizable via GPT-5) 🛠️ Setup Instructions: Set up your Telegram bot and insert credentials. Add your OpenAI and Pinecone API keys. Upload or index your support documents into the Customer FAQ namespace on Pinecone. Deploy and test your Telegram bot. An n8n automation workflow template by Automate With Marc.

By Humble Turtle

Github Deployer Agent Overview The Github Deployer Agent is an intelligent automation tool that integrates with Slack to streamline code deployment workflows. Powered by Anthropic's Claude 3.5 and Tavily for web search, it enables seamless, context-aware file pushes to a GitHub repository with minimal user input. Capabilities Accepts natural language via Slack Automatically pushes code to a default GitHub repository Uses Claude 3.5 for code generation and decision-making Leverages Tavily for real-time web search to enhance context Supports folder structure hints to ensure clean and organized repositories Required Connections To operate correctly, the following integrations must be in place: Slack API Token with permission to read messages and post responses GitHub Personal Access Token with repo write permissions Tavily API Key for external search functionality Claude 3.5 API Access via Anthropic Detailed configuration instructions are provided in the workflow Example Input From Slack, you can send messages like: "Generate a basic README.md for my Python project and store it in the root directory." Customising This Workflow You can tailor the workflow by: Modifying default folder paths or repository settings Integrate Jira node to use issue keys as default folder naming Add slack file upload option. An n8n automation workflow template by Humble Turtle.

By Thiago Vazzoler Loureiro

Description This workflow automates the creation of a Retrieval-Augmented Generation (RAG) pipeline using content from the GLPI Knowledge Base. It retrieves and processes FAQ articles directly via the GLPI API, cleans and vectorizes the content using pgvector in PostgreSQL, and prepares the data for use by LLM-powered AI agents. What Problem Does This Solve? Manually building a RAG pipeline from a GLPI knowledge base requires integrating multiple tools, cleaning data, and managing embeddings—tasks that are often complex and repetitive. This subworkflow simplifies the entire process by automating data retrieval, transformation, and vector storage, allowing you to focus on building intelligent support agents or chatbots powered by your internal documentation. Features Connects to GLPI via API to fetch FAQ articles Cleans and normalizes content for better embedding quality Generates vector embeddings using Google Gemini (or another model) Stores embeddings in a PostgreSQL database with pgvector Fully modular: easily integrate with any RAG-ready LLM pipeline Prerequisites Before using this subworkflow, make sure you have: A GLPI instance installed on a Linux server with API access enabled A PostgreSQL database with the pgvector extension installed An OpenAI API key (or alternative embedding provider) n8n instance (self-hosted or cloud) Suggested Usage This subworkflow is intended to be part of a larger AI pipeline. Attach it to a scheduled workflow (e.g. daily sync) or use it in response to updates in your GLPI base. Ideal for internal support bots, IT documentation assistants, and help desk AI agents that rely on up-to-date knowledge. An n8n automation workflow template by Thiago Vazzoler Loureiro.

By Mary Newhauser

RAG over a PDF with Weaviate This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. Who it's for This workflow is the simplest possible implementation of RAG with Weaviate in n8n. It's intended to act as an extendable template for RAG over your own documents. Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use OpenAI, but feel free to switch out the models as you like. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How it works Part 1: Manually upload data In this example, we manually upload a 100+ page article from arXiv called "A Survey of Large Language Models". But you can replace this with your own more advanced data pipeline, if you wish. Part 2: Embed and load data into Weaviate collection Here, we generate embeddings for the full-text of the article and store them in Weaviate. Part 3: Perform RAG over PDF file with Weaviate In this part of the workflow, you can enter your query by running the Chat Node and get a RAG response grounded in context via the Question and Answer Chain node. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Upload a PDF file you want to ask questions about. Execute the rest of the workflow. An n8n automation workflow template by Mary Newhauser.

By Marth - Business Automation

How It Works & Setup Guide for the Automated Candidate Management & Feedback System This guide will walk you through setting up your n8n workflow. By the end, you'll have a fully automated system for managing your recruitment pipeline. How It Works: The Workflow Explained This workflow is designed in three logical phases to handle the entire post-interview process automatically. Phase 1: Trigger & Feedback Loop: The workflow triggers when an interview ends on your Google Calendar. It immediately sends a Slack message to the interviewer with a link to the feedback form. After a 2-hour wait, it checks if the feedback has been submitted. If not, it sends a reminder. Once feedback is received, it logs the data in Airtable and uses an If node to determine if the candidate has passed or failed. Phase 2: Automated Communication: Based on the candidate's status, the workflow sends a personalized and professional email using Gmail. For candidates who pass, it sends a follow-up invitation. For those who don't, it sends a polite rejection email crafted by a Code node. If a candidate is in the final stage and passes, the workflow automatically generates and sends an offer letter for signature via DocuSign. Phase 3: Onboarding & Reporting: Once a candidate accepts the offer (by signing the document), the workflow is triggered to create a new task list in Trello for the HR team. It sends a personalized welcome email to the new hire and a notification to the team on Slack. Finally, a Cron Trigger runs every Friday to collect all candidate data, calculate key recruitment metrics, log them in Google Sheets, and send a summary report to your team on Slack. Step-by-Step Setup Guide Follow these steps to configure the workflow in your n8n instance. Step 1: Prerequisites Before you begin, ensure you have the following accounts and a workspace set up: n8n Google Calendar, Google Sheets, Gmail Airtable Slack Trello DocuSign Step 2: Database & Form Preparation Airtable: Create a new Airtable base with two tables: Candidates Table: Create columns for Candidate Name, Email, Interviewer ID, Interview Date, and Status. Feedback Table: Create columns for Candidate Name, Overall Score, and Comments. Feedback Form: Create a feedback form (e.g., using Google Forms or Typeform) that collects the candidate's name, the interviewer's name, and a score/comments. Step 3: Import the Workflow In your n8n instance, click "New" and select "Import from File". Import the .json file you purchased. The entire workflow, with all nodes, will appear on your canvas. Step 4: Configure Credentials Click on any node with a red "!" icon (e.g., the Google Calendar Trigger or Slack node). In the right-hand panel, click "Create new credential". Follow the on-screen instructions to connect your accounts. Repeat this process for all nodes that require credentials. Step 5: Node-Specific Configuration Now, let's configure the specific details for each node to ensure it works for your company. Google Calendar Trigger: Click on the node and in the Calendar ID field, enter the ID of the calendar you use for scheduling interviews. Airtable Nodes: For every Airtable node in the workflow, enter the correct Base ID and Table Name (Candidates or Feedback) that you created in Step 2. Trello Node: Enter the Board ID and the specific List ID where you want new onboarding tasks to be created. Gmail Nodes: Customize the Subject and HTML Body of the emails to match your company's tone and branding. DocuSign Node: Enter your Account ID and the Template ID for your offer letter. Ensure your offer letter template includes the anchorString (e.g., /s1/) that the workflow uses to place the signature tag. Environment Variables: In your n8n settings, go to Environment Variables and add the following: FEEDBACK_FORM_URL: The URL of your feedback form. SCHEDULING_LINK: The URL for candidates to schedule their next interview. REPORTS_DASHBOARD_URL: A link to your Google Sheets report or a separate dashboard. Step 6: Final Step - Activating the Workflow Once all nodes are configured, click "Save" at the top of the canvas. Click the "Active" toggle in the top right corner. The workflow is now live! Final Tip: It's a good practice to test the system once by creating a test interview event on your calendar to ensure all steps run as expected. An n8n automation workflow template by Marth - Business Automation.

By Eumentis

What It Does This workflow automatically runs when a new email is received in the user's Gmail account. It sends the email content to OpenAI (GPT-4.1-mini), which intelligently determines whether the message requires action. If the email is identified as actionable, the workflow sends a structured alert message to the user in Microsoft Teams. This keeps the user informed of high-priority emails in real time without the need to manually check every message. The workflow does not log any execution data, ensuring that email content remains secure and unreadable by others. How It Works Trigger on New Email**: The workflow is triggered automatically when a new email is received in the user's Gmail account.  Email Evaluation with OpenAI**: The email content is sent to GPT-4.1-MINI, which evaluates whether the message requires user action.  Filter Actionable Emails**: Only emails identified as actionable by the AI are allowed to proceed through the rest of the workflow.  Send Notification to Teams**: For actionable emails, the workflow sends a structured alert message to the user in Microsoft Teams chat via a Power Automate webhook. Prerequisites Gmail IMAP Credentials  OpenAI API Key  Microsoft Teams Webhook URL  Power Automate Flow to send message to Teams chat  How to Set It Up 1. Set Up Power Automate Workflow 1.1 Open Workflow Power Automate in Microsoft Teams Open the Workflow app from Microsoft Teams.  If it's not already added, go to Apps → search "Workflow" → click Add → open it. 1.2 Create a New Flow Click New Flow → select Create from blank. 1.3 Add a Trigger: When a Teams webhook request is received In the trigger setup, set Who can trigger the flow? to Anyone.  After saving the flow, a webhook URL will be generated — this URL will be used in n8n workflow. 1.4 Add Action: Parse JSON Set Content to: Body  Use the following schema: { "type": "object", "properties": {    "from": {      "type": "string"    },    "receivedAt": {      "type": "string"    },    "subject": {      "type": "string"    },    "message": {      "type": "string"    } } } 1.5 Add Action: Get an @mention token for a user Set the User field to the Microsoft Teams email address of the person to notify (e.g. yourname@domain.com). 1.6 Add Action: Post message in a chat or channel In this action, configure the following: Post as: Flow bot  Post in: Chat with Flow bot  Recipient: Your Microsoft Teams email address (e.g., yourname@domain.com) Paste the following code into the Message (in code view): Hello @{outputs('Get_an_@mention_token_for_a_user')?['body/atMention']}, You have received a new email at your email address @{body('Parse_JSON')?['recipientEmail']} that requires your attention: From: @{body('Parse_JSON')?['sender']} Received On: @{body('Parse_JSON')?['date']} Subject:  @{body('Parse_JSON')?['subject']} Please review the message at your earliest convenience. Click here to search this mail in your mailbox 1.7 Save and Enable the Flow Click Save. Turn the flow On. The webhook URL is now active and available in the first trigger step, copy it to use in n8n. Need help with the setup? Feel free to contact us 2. Configure IMAP Email Trigger First, enable 2‑Step Verification in your Google Account and generate an App Password for n8n. Then, in the IMAP node → Create Credential to connect using the following details:  • User: your Gmail address  • Password: the App Password  • Host: imap.gmail.com  • Port: 993  • SSL/TLS: Enabled Follow the n8n documentation to complete the setup. 3. Configure OpenAI Integration Add your OpenAI API key as a credential in n8n. Follow the n8n documentation to complete the setup. 4. Set Up HTTP Request to Trigger Power Automate Workflow Paste generated Webhook URL from the Power Automate workflow into the URL field of the HTTP Request node. 5. Disable Execution Logging for Privacy To ensure that email content is not stored in logs and remains fully secure, you can disable execution logging in n8n: In the n8n Workflow Editor, click on the three dots (•••) in the top right corner and select Settings. In the settings panel: Set Save manual executions to: Do not save Set Save successful production executions to: Do not save Set Save failed production executions to: Do not save if you also want to avoid logging errors Save the changes. Refer to the official n8n documentation for more details: 6. Activate the Workflow Set the workflow status to Active in n8n so it runs automatically when a new mail is received in Gmail. Need Help? Contact us for support and custom workflow development. An n8n automation workflow template by Eumentis.

By Humble Turtle

Architecture Agent Overview The Architect Agent listens to Slack messages and generates full data architecture blueprints in response. Powered by Claude 3.5 (Anthropic) for reasoning and design, and Tavily for real-time web search, this agent creates production-ready data pipeline scaffolds on-demand — transforming natural language prompts into structured data engineering solutions. Capabilities Understands and interprets user requests from Slack Designs end-to-end data pipelines architectures using industry best practices. Outputs include High-level architecture diagrams Required Connections To operate correctly, the following integrations must be in place: Slack API Token with permission to read messages and post responses Tavily API Key for external search functionality Claude 3.5 API Access via Anthropic Detailed configuration instructions are provided in the workflow Setup time <15 minutes Example input: "Create a data pipeline orchestrated by Airflow, running on a Docker image. It should connect to a MySQL database, load in the data into a PostgreSQL DB (incremental load) and then transform the data into business-oriented tables also in the PostgreSQL database. Create an example setup with raw sales data." Customising this workflow Try saving outputs to Google Drive to store all your architecture blueprints. An n8n automation workflow template by Humble Turtle.

By Hunyao

What it does Automatically monitors multiple subreddits daily, identifies trending posts with high engagement, and delivers AI-powered summaries directly to your inbox. Never miss important discussions in your favorite communities again. Perfect for Investors tracking market sentiment, researchers monitoring industry discussions, content creators finding trending topics, or anyone wanting curated Reddit insights without endless scrolling. Apps used Reddit, OpenRouter (GPT-4o mini), Gmail How it works Triggers daily at your chosen time across all specified subreddits Fetches hot posts from the last 24 hours with scores above 30 upvotes Sorts posts by engagement score to prioritize trending content Extracts post content plus top-level comments for full context Generates concise AI summaries for each high-value thread Compiles summaries into a clean HTML email digest Delivers the digest to your Gmail inbox with clickable Reddit links Setup Configure these three essential settings: Schedule time: Set your preferred daily delivery time in the Schedule Trigger node. **Replace with your preferred hour (currently 6 AM). Note: Times display in your workflow timezone Topic and subreddits: In the "Set Topic, Subreddits and Email Address" node, **replace with your topic name (e.g., "Investing") and replace with your subreddit array (e.g., ["investing", "stocks"]) Email recipient: **Replace with your Gmail address in the same node Credentials Reddit OAuth2 for API access, OpenRouter API key for AI summaries, Gmail OAuth2 for email delivery If you have any questions in running the workflow, feel free to reach out to me at my youtube channel: https://www.youtube.com/@lifeofhunyao. An n8n automation workflow template by Hunyao.

By inderjeet Bhambra

Who is this for? This workflow is designed for travel bloggers, content creators, social media managers, and anyone who wants to transform their travel photos into engaging written narratives. It's perfect for travelers looking to create compelling stories from their photo collections without spending hours crafting content manually, families wanting to document memorable trips, and digital nomads who need to produce travel content efficiently. What problem is this workflow solving? Converting travel photos into engaging stories is time-consuming and requires both creative writing skills and the ability to analyze visual content meaningfully. This workflow solves the challenge of: Transforming visual memories into compelling written narratives Organizing photos chronologically to create logical story flow Generating professional-quality travel content without writing expertise Analyzing photo content to extract meaningful themes and emotions Creating day-by-day structured narratives from unorganized photo collections Reducing the time spent on manual content creation for travel documentation What this workflow does This AI-powered photo storyteller takes your travel photos and automatically generates immersive, first-person travel narratives. The workflow: Accepts multiple photos through a webhook endpoint Uses OpenAI Vision API (GPT-4o) to analyze each photo's content, emotions, and themes Automatically organizes photos chronologically by date and timestamp Groups photos by travel days and extracts daily themes Leverages GPT-4.1 (minimum required) to craft engaging, first-person travel stories with creative day titles Generates structured narratives with sensory details, cultural observations, and emotional insights Outputs JSON formatted content ready for formatting Creates day-by-day story structure with memorable moments and reflective conclusions Setup Required Credentials: OpenAI API key configured in n8n for both Vision Analysis and Story Generation nodes Ensure you have sufficient OpenAI credits for image analysis and text generation Webhook Configuration: The workflow creates a webhook endpoint at /tripteller-upload Configure your photo upload interface to POST photos array to this endpoint Photos should be sent as base64 encoded data with filename and metadata Photo Requirements: Supported formats: Standard image formats (JPEG, PNG, etc.) Photos should include timestamp metadata for chronological organization Caution Do not upload all photos at once. Start with a small number of photos, like 5 at a time. How to customize this workflow to your needs Story Style Customization: Modify the system prompt in the "Generate Travel Story" node to adjust writing tone (nostalgic, adventurous, poetic, etc.) Customize the story structure by editing the output format requirements Add specific cultural or geographical context prompts for location-specific storytelling Photo Analysis Enhancement: Adjust the Vision Analysis node prompt to focus on specific elements (architecture, food, people, landscapes) Modify the grouping logic in the "Group Photos by Day" node for different time-based organization Add location extraction from EXIF data for geographical context Output Format Adjustment: Customize the final response structure in the "Format Final Response" node Add integration with publishing platforms (blog APIs, social media, etc.) Include additional metadata like location tags, travel duration, or trip statistics Performance Optimization: Adjust the execution timeout based on your typical photo volume Modify the parallel processing approach for large photo collections Add progress tracking for longer processing workflows. An n8n automation workflow template by inderjeet Bhambra.

By Aziz B

Overview This workflow acts as an AI-powered research assistant that takes a topic from the user, performs multi-step intelligent research, and stores the final report in Notion. It uses advanced search, content extraction, and AI summarization to deliver a high-quality research report—fully automated from query to publication. How It Works User Interaction** The workflow starts by asking the user what topic they want to research. A “Strategy Agent” asks 2–3 clarifying questions to refine the scope. Once the user confirms, it creates a Notion database page with the research title. Search Query Generation** Generates up to 3 relevant search queries for the given topic. Data Gathering** (Loop over each query) Sends the query to Tavily Search API to find the most relevant blogs/articles. Picks the top-matched link and uses Tavily again to extract its content. Repeats the process for all 3 queries. Report Compilation** Aggregates extracted content from all sources. A Final Report Agent creates a well-structured research report in Markdown. Converts Markdown → HTML → splits into chunks. Pushes each chunk into the Notion report page. Delivery** Sends the final Notion report link back to the user. How to Use This workflow is triggered via Webhook. Attach the provided webhook URL** to any application, form, or chatbot to collect the user’s topic. Once triggered, the workflow will run automatically and deliver the research link without any manual steps. Requirements To use this workflow, you’ll need: n8n account** (self-hosted or cloud) Notion account** with a database where reports will be stored Tavily API Key** – for search & content extraction OpenRouter API key* *or OpenAI API key – for AI agents & report generation Google Gemini API Key** – for converting Markdown to HTML and splitting content for Notion Notion database ID connected in n8n. An n8n automation workflow template by Aziz B.

By Calistus Christian

How it works • Webhook → urlscan.io → GPT-4o mini → Gmail • Payload example: { "url": "https://example.com" } • urlscan.io returns a Scan ID and raw JSON. • AI node classifies the scan as malicious / suspicious / benign, assigns a 1-10 risk score, and writes a two-sentence summary. • Gmail sends an alert that includes the URL, Scan ID, AI verdict, screenshot link, and full report link. Set-up steps (~5 min) • Create three credentials in n8n urlscan.io API key OpenAI API key (GPT-4o mini access) Gmail OAuth (or SMTP) • Replace those fields in the nodes, or reference env vars like {{ $env.OPENAI_API_KEY }}. • Switch the Webhook to Production → copy the live URL. • Test with: curl -X POST <your-webhook-url> \ -H "Content-Type: application/json" \ -d '{ "url": "https://example.com" }'. An n8n automation workflow template by Calistus Christian.

By Robert Breen

This guide walks you through building an intelligent AI Agent in n8n that routes tasks to the appropriate sub-agent using the new @n8n/n8n-nodes-langchain agent framework. You’ll create a Manager Agent that evaluates user input and delegates it to either an Email Agent or a Data Agent—each with its own role, memory, and OpenAI model. This is perfect for use cases where you want a single entry point but intelligent branching behind the scenes. 🔧 Step 1: Set Up the Manager Agent Start by dragging in an Agent node and name it something like ManagerAgent. This agent will act as the “brain” of your system, analyzing the user's input and determining whether it should be handled by the email-writing sub-agent or the data-summary sub-agent. Open the node’s settings and paste the following into the System Message: You are an AI Manager that delegates tasks to specialized agents. Your job is to analyze the user's message and decide whether it requires: An EmailAgent for writing outreach, follow-up, or templated emails, or A DataAgent for tasks involving data summaries, metrics, or analysis. Send the instructions to the sub agents. This instruction gives the Manager Agent clarity on what roles exist and what types of tasks belong to each one. 🧠 Step 2: Add Memory to the Manager Agent Drag in a Memory (BufferWindow) node and label it Manager Memory. Connect it to the ai_memory input of the Manager Agent. This ensures the agent can remember recent inputs and outputs from the user and agents during the conversation. No extra configuration is needed in this memory node—just connect it to the agent. 🔌 Step 3: Connect a Language Model to the Manager Agent Next, add a Language Model node and choose OpenAI Chat Model. Select a model like gpt-4o-mini or gpt-4, depending on what you have access to. Under Credentials, connect your OpenAI API key. If you haven’t created this credential yet: Click "OpenAI API" under Credentials. Choose "Create New". Paste your OpenAI API key (found at https://platform.openai.com/account/api-keys). Save it and return to the workflow. Once the model is set, connect it to the ai_languageModel input of the Manager Agent. ✉️ Step 4: Create the Email Agent Tool Now you’ll create a specialized sub-agent that only writes emails. Add an Agent Tool node and call it EmailAgent. In the tool’s settings, describe its job clearly. For example: Writes professional, friendly, or action-oriented emails based on instructions. Then scroll down to the System Message section and enter the following: You are a professional Email Writing Assistant. You write polished, effective emails for tasks such as outreach, follow-ups, and client communication. Follow the instruction provided exactly and return only the email content. Use a warm, business-appropriate tone. For the text input field, use the expression: {{ $fromAI('Prompt__User_Message_', ``, 'string') }} This allows the Email Agent to receive exactly what the Manager Agent wants it to handle. Add another Memory node and link it to this tool to help it maintain short-term context. Then add a second Language Model node, configured just like the first one (you can even clone it), and connect it to the EmailAgent. Finally, connect this entire EmailAgent setup back to the ManagerAgent by attaching it to its ai_tool input. 📊 Step 5: Create the Data Agent Tool Repeat the same steps, but this time for data summaries and analysis. Add another Agent Tool node and name it DataAgent. In the Tool Description, write something like: Responds to instructions requiring metrics, summaries, or data analysis explanations. For its input text field, you can use: {{json.query}} If desired, provide a system message that gives the agent more detailed instruction on how to behave: You are a helpful Data Analyst. Summarize trends, explain metrics, and break down data clearly based on user instructions. As with the EmailAgent, you’ll also need: A dedicated Memory node A dedicated Language Model node A connection to the ai_tool input of the Manager Agent Now the Manager Agent has two tools it can delegate to: one for communication and one for insights. 🧪 Step 6: Test Your AI Agent System Deploy the workflow and start testing by sending prompts like: > “Write a cold outreach email to a software company.” The ManagerAgent should route that to the EmailAgent. Then try: > “Summarize how our lead volume changed last month.” The DataAgent should receive that task. If routing isn’t working as expected, double-check your system messages and input bindings in each agent tool. ✅ You’re Done! You now have a modular, multi-agent AI system powered by n8n. The Manager Agent delegates intelligently, each sub-agent is optimized for its role, and all of them benefit from context memory. For more advanced setups, you can chain tools, add additional memory types, or use retrieval (RAG) tools for external document support. An n8n automation workflow template by Robert Breen.

By Ludwig

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Bypass Cloudflare Turnstile for Web Scraping with n8n How It Works This workflow automatically solves Cloudflare Turnstile CAPTCHAs for legitimate web scraping and automation tasks. It extracts the Turnstile sitekey from target webpages, submits solving requests to 2captcha, and returns bypass tokens that can be used in automated form submissions or data collection workflows. Set Up Steps Create a 2captcha Account: Sign up and get your API clientKey for CAPTCHA solving. Import the Workflow into n8n: Add the workflow to your n8n instance. Configure Authentication: Set up custom HTTP authentication with your clientKey in the credential settings. Test the Workflow: Run with the demo URL to verify Turnstile detection and solving works correctly. Replace the sample destination_url with your target website and start automating! Setup should take just a few minutes with your API credentials ready. Getting Started Once configured, the workflow can extract sitekeys from any Turnstile-protected webpage and return solved tokens for seamless automation. The API documentation provides additional technical details for advanced use cases. Customizing the Workflow The workflow targets a demo page for testing. To adapt it, simply update the destination_url variable with your target website. You can also modify the sitekey extraction logic for sites that implement Turnstile differently, or integrate this as a subworkflow into larger scraping operations. API Reference Uses the TurnstileTaskProxyless task type with REST-based HTTPS communication. Each solve costs approximately $0.002 USD and typically completes within 30-90 seconds depending on queue load. An n8n automation workflow template by Ludwig.

By Stephan Koning

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. **Alternatively, you can delete the community node and use the HTTP node instead. ** Most email agent templates are fundamentally broken. They're stateless—they have no long-term memory. An agent that can't remember past conversations is just a glorified auto-responder, not an intelligent system. This workflow is Part 1 of building a truly agentic system: creating the brain. Before you can have an agent that replies intelligently, you need a knowledge base for it to draw from. This system uses a sophisticated parser to automatically read, analyze, and structure every incoming email. It then logs that intelligence into a persistent, long-term memory powered by mem0. The Problem This Solves Your inbox is a goldmine of client data, but it's unstructured, and manually monitoring it is a full-time job. This constant, reactive work prevents you from scaling. This workflow solves that "system problem" by creating an "always-on" engine that automatically processes, analyzes, and structures every incoming email, turning raw communication into a single source of truth for growth. How It Works This is an autonomous, multi-stage intelligence engine. It runs in the background, turning every new email into a valuable data asset. Real-Time Ingest & Prep: The system is kicked off by the Gmail Trigger, which constantly watches your inbox. The moment a new email arrives, the workflow fires. That email is immediately passed to the Set Target Email node, which strips it down to the essentials: the sender's address, the subject, and the core text of the message (I prefer using the plain text or HTML-as-text for reliability). While this step is optional, it's a good practice for keeping the data clean and orderly for the AI. AI Analysis (The Brain): The prepared text is fed to the core of the system: the AI Agent. This agent, powered by the LLM of your choice (e.g., GPT-4), reads and understands the email's content. It's not just reading; it's performing analysis to: Extract the core message. Determine the sentiment (Positive, Negative, Neutral). Identify potential red flags. Pull out key topics and keywords. The agent uses Window Buffer Memory to recall the last 10 messages within the same conversation thread, giving it the context to provide a much smarter analysis. Quality Control (The Parser): We don't trust the AI's first draft blindly. The analysis is sent to an Auto-fixing Output Parser. If the initial output isn't in a perfect JSON format, a second Parsing LLM (e.g., Mistral) automatically corrects it. This is our "twist" that guarantees your data is always perfectly structured and reliable. Create a Permanent Client Record: This is the most critical step. The clean, structured data is sent to mem0. The analysis is now logged against the sender's email address. This moves beyond just tracking conversations; it builds a complete, historical intelligence file on every person you communicate with, creating an invaluable, long-term asset. Optional Use: For back-filling historical data, you can disable the Gmail Trigger and temporarily connect a Gmail "Get Many" node to the Set Target Email node to process your backlog in batches. Setup Requirements To deploy this system, you'll need the following: An active n8n instance. Gmail** API credentials. An API key for your primary LLM (e.g., OpenAI). An API key for your parsing LLM (e.g., Mistral AI). An account with mem0.ai for the memory layer. An n8n automation workflow template by Stephan Koning.

By Margo Rey

AI-Powered Email Generation with MadKudu sent via Outreach.io This workflow researches prospects using MadKudu MCP, generates personalized emails with OpenAI, and syncs them to Outreach with automatic sequence enrollment. Its for SDRs and sales teams who want to scale personalized outreach by automating research and email generation while maintaining quality. ✨ Who it's for Sales Development Representatives (SDRs) doing cold outreach Business Development teams needing personalized emails at scale RevOps teams wanting to automate prospect research workflows Sales teams using Outreach for email sequences 🔧 How it works 1. Input Email & Research: Enter prospect email via chat trigger. Extract email and generate comprehensive account brief using MadKudu MCP account-brief-instructions. 2. Deep Research & Email Generation: AI Agent performs 6 research steps using MadKudu MCP tools: Account details (hiring, partnerships, tech stack, sales motion, risk) Top users in the account (for name-dropping opportunities) Contact details (role, persona, engagement) Contact web search (personal interests, activities) Contact picture web search (LinkedIn profile insights) Company value prop research AI generates 5 different email angles and selects the best one based on relevance. 3. Outreach Integration: Checks if prospect exists in Outreach by email. If exists: Updates custom field (custom49) with generated email. If new: Creates new prospect with email in custom field. Enrolls prospect in specified email sequence (ID 781) using mailbox (ID 51). Waits 30 seconds and verifies successful enrollment. 📋 How to set up Set your OpenAI credentials Required for AI research and email generation. Create a n8n Variable to store your MadKudu API key named madkudu_api_key Used for the MadKudu MCP tool to access account research capabilities. Create a n8n Variable to store your company domain named my_company_domain Used for context in email generation and value prop research. Create an Oauth2 API credential to connect your Outreach account Used to create/update prospects and enroll in sequences. Configure Outreach settings Update Outreach Mailbox ID (currently set to 51) in the "Configure Outreach Settings" node. Update Outreach Sequence ID (currently set to 781) in the same node. Adjust custom field name if using different field than custom49. 🔑 How to connect Outreach In n8n, add a new Oauth2 API credential and copy the callback URL Now go to Outreach developer portal Click "Add" to create a new app In Feature selection add Outreach API (OAuth) In API Access (Oauth) set the redirect URI to the n8n callback Select the following scopes accounts.read, accounts.write, prospects.read, prospects.write, sequences.read Save in Outreach 7.Now enter the Outreach Application ID into n8n Client Id and the Outreach Application Secret into n8n Client secret Save in n8n and connect via Oauth your Outreach Account ✅ Requirements MadKudu account with access to API Key Outreach Admin permissions to create an app OpenAI API Key 🛠 How to customize the workflow Change the research steps Modify the AI Agent prompt to adjust the 6 research steps or add additional MadKudu MCP tools. Update Outreach configuration Change Mailbox ID (51) and Sequence ID (781) in the "Configure Outreach Settings" node. Update custom field mapping if using different field than custom49. Modify email generation Adjust the prompt guidelines, tone, or angle priorities in the "AI Email Generator" node. Change the trigger Swap the chat trigger for a Schedule, Webhook, or integrate with your CRM to automate prospect input. An n8n automation workflow template by Margo Rey.

By Oriol Seguí

AI-Powered Automatic Analysis of YouTube Product Reviews With Apify This n8n workflow automates the process of searching, transcribing, and analyzing product reviews extracted from YouTube videos, generating a clear, ready-to-use report in HTML format and/or email. 📌 Usage and How It Works Enter the product name (via webhook or manually). The workflow searches YouTube videos related to the product using Apify. Extracts the text (transcription) from the selected videos. Merges and separates each video's content to maintain context. Analyzes the texts with GPT-4o-mini to obtain: Strengths (pros, advantages, what users value most). Weaknesses (cons, problems, criticisms). Other relevant points mentioned in the reviews. Returns an HTML report (via email or webhook), ready for reading or presentation. The entire process is fully automated and multi-language (language configured in the LANG node). 💡 Why Buy This Workflow Save hours of work** searching and manually analyzing reviews. Reliable results**: the analysis is based only on what is said in the videos, without inventing data. Complete integration**: scraping, transcription, processing, and delivery, all in a single flow. Multi-language** configurable in seconds. Low cost per use** (see below). Configurable**: Designed to be easy to set up and adapt. 🎯 Who It’s For E-commerce and online stores** wanting to know product pros/cons before selling. Marketing and SEO agencies** looking for real user insights. Brands** seeking genuine feedback on their products. Market researchers** needing aggregated opinions. n8n enthusiasts** wanting to see a practical and complete example of advanced automation. 💰 Usage Costs Approximate cost:* $0.03 USD per report *(GPT-4o-mini + Apify). Apify* offers *$5 free without a credit card, which equals **~130 free reports before incurring any costs. After that, you only pay for what you use. 🔧 Technologies Used Apify** for video search and transcript extraction. OpenAI GPT-4o-mini** for text analysis and report generation. 📬 Basic Support For questions or basic support, email: oriolrotllant3@gmail.com Example result. An n8n automation workflow template by Oriol Seguí.

By Michael Muenzer

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Generates relevant keywords and questions from a a customer profile. Keyword data is enriched from ahref and everything is stored in a Google Sheet. This is great for market and customer research. Understanding search intent for a well defined audience and gives relevant actionable data in a fraction of time that manual research takes. How it works We'll define a customer profile in the 'Data' node We use an OpenAI LLM to fetch relevant search intent as keywords and questions We use an SEO MCP server to fetch keyword data from ahref free tooling The fetched data is stored in the Google sheet Set up steps Copy Google Sheet template and add it in all Google Sheet nodes Make sure that n8n has read & write permissions for your Google sheet. Add your list of domains in the first column in the Google sheet Add MCP credentials for seo-mcp Add OpenAI API credentials. An n8n automation workflow template by Michael Muenzer.

By Vitorio Magalhães

Auto-publish NASA APOD to LinkedIn with AI translation and hashtags Transform NASA's daily astronomical wonders into engaging LinkedIn content automatically. This workflow fetches NASA's Astronomy Picture of the Day, translates it to Brazilian Portuguese using AI, generates strategic hashtags, and publishes everything to your LinkedIn profile with the stunning space image attached. Who's it for Content creators, astronomy enthusiasts, science communicators, and anyone wanting to share high-quality educational content consistently on LinkedIn. Perfect for Portuguese-speaking professionals who want to engage their network with fascinating space discoveries while building their personal brand as a science advocate. How it works The workflow runs on a daily schedule and handles the complete content pipeline automatically. It fetches the latest NASA APOD through the official API, including both the image and detailed explanation. The English description gets professionally translated to selected language using Google Gemini 2.5 Flash, while maintaining scientific accuracy and terminology. Smart hashtag generation combines fixed branding tags with content-specific ones, mixing Portuguese and English for maximum reach. The final post includes the NASA image, translated description, and strategic hashtags, then gets published to your LinkedIn profile automatically. How to set up You'll need accounts for Google AI Studio (free), LinkedIn Developer (free), and a Telegram bot for notifications. The setup takes about 15 minutes and uses only free services and APIs. First, create your Google AI Studio account and get an API key for the AI translation services. Then set up a LinkedIn OAuth2 application to enable posting permissions. Create a Telegram bot through BotFather and get your chat ID for notifications. Configure the Settings node with your Telegram chat ID and preferred language. The workflow comes with all prompts and configurations ready to use. Test each component individually before activating the daily automation. Requirements LinkedIn account with posting permissions Google AI Studio API key (free tier available) Telegram bot token and your chat ID Basic understanding of OAuth2 setup for LinkedIn NASA API key (optional - demo key included) All services used have generous free tiers, making this workflow completely free to operate indefinitely. How to customize the workflow The centralized Settings node makes customization simple. Change the target language from Brazilian Portuguese to any other language by updating the translate_to_language variable. Modify the posting schedule in the CRON trigger to match your preferred timing. Customize the post template in the "Create Final Post Text" node to match your personal brand voice. Adjust the hashtag strategy by editing the AI prompt in the "Generate Hashtags" node. Add additional social platforms by duplicating the LinkedIn publisher with different credentials. The AI prompts can be fine-tuned for different writing styles or specific astronomical topics. You can also extend the workflow to include additional content processing, image enhancements, or cross-posting to multiple platforms while maintaining the core NASA APOD automation. An n8n automation workflow template by Vitorio Magalhães.

By Robert Breen

This workflow introduces beginners to one of the most fundamental concepts in n8n: looping over items. Using a simple use case—generating LinkedIn captions for content ideas—it demonstrates how to split a dataset into individual items, process them with AI, and collect the output for review or export. ✅ Key Features 🧪 Create Dummy Data**: Simulate a small dataset of content ideas. 🔁 Loop Over Items**: Process each row independently using the SplitInBatches node. 🧠 AI Caption Creation**: Automatically generate LinkedIn captions using OpenAI. 🧰 Tool Integration**: Enhance AI output with creativity-injection tools. 🧾 Final Output Set**: Collect the original idea and generated caption. 🧰 What You’ll Need ✅ An OpenAI API key ✅ The LangChain nodes enabled in your n8n instance ✅ Basic knowledge of how to trigger and run workflows in n8n 🔧 Step-by-Step Setup 1️⃣ Run Workflow Node**: Manual Trigger (Run Workflow) Purpose**: Manually start the workflow for testing or learning. 2️⃣ Create Random Data Node**: Create Random Data (Code) What it does**: Simulates incoming data with multiple content ideas. Code**: return [ { json: { row_number: 2, id: 1, Date: '2025-07-30', idea: 'n8n rises to the top', caption: '', complete: '' } }, { json: { row_number: 3, id: 2, Date: '2025-07-31', idea: 'n8n nodes', caption: '', complete: '' } }, { json: { row_number: 4, id: 3, Date: '2025-08-01', idea: 'n8n use cases for marketing', caption: '', complete: '' } } ]; 3️⃣ Loop Over Items Node**: Loop Over Items (SplitInBatches) Purpose**: Sends one record at a time to the next node. Why It Matters**: Loops in n8n are created using this node when you want to iterate over multiple items. 4️⃣ Create Captions with AI Node**: Create Captions (LangChain Agent) Prompt**: idea: {{ $json.idea }} System Message**: You are a helpful assistant creating captions for a LinkedIn post. Please create a LinkedIn caption for the idea. Model**: GPT-4o Mini or GPT-3.5 Credentials Required**: OpenAI Credential Go to: OpenAI API Keys Create a key and add it in n8n under credentials as “OpenAi account” 5️⃣ Inject Creativity (Optional) Node**: Tool: Inject Creativity (LangChain Tool) Purpose**: Demonstrates optional LangChain tools that can enhance or manipulate input/output. Why It’s Cool**: A great way to show chaining tools to AI agents. 6️⃣ Output Table Node**: Output Table (Set) Purpose**: Combines original ideas and generated captions into final structure. Fields**: idea: ={{ $('Create Random Data').item.json.idea }} output: ={{ $json.output }} 💡 Educational Value This workflow demonstrates: Creating dynamic inputs with the Code node Using SplitInBatches to simulate looping Sending dynamic prompts to an AI model Using Set to structure the output data Beginners will understand how item-level processing works in n8n and how powerful looping combined with AI can be. 📬 Need Help or Want to Customize This? Robert Breen Automation Consultant | AI Workflow Designer | n8n Expert 📧 robert@ynteractive.com 🌐 ynteractive.com 🔗 LinkedIn 🏷️ Tags n8n loops OpenAI LangChain workflow training beginner LinkedIn automation caption generator. An n8n automation workflow template by Robert Breen.

By Luís Philipe Trindade

What's up Guys. I'm Luís 🙋🏻‍♂️ Let me make one thing clear up front: this isn't just another WhatsApp summary workflow. It’s a fully structured automation built for people who actually need to stay informed without wasting time and with total control over what gets summarized. What this workflow does: Receives messages via webhook from Evolution API Checks if the message is from a group or an individual Routes messages by type: text or audio (with automatic transcription using OpenAI) Stores everything in a Google Sheet organized by group, sender, timestamp and message sended Creates a Control Panel with a checkbox for each group. So, you decide which groups should receive summaries (this is the main differentiator about this workflow) Collects all messages from yesterday, groups them by chat, and sends them to GPT to generate a summary Sends the summary in a clean, formatted in Whatsapp every morning (fully automated). 🧩 How the flow is structured This workflow is strategically divided into two independent parts to ensure clarity, organization, and easy scalability: Part 1 – Message Capture and Storage Triggered via webhook, this part: Receives messages from Evolution API Checks if the message is from a group Distinguishes between text and audio (with automatic transcription) Stores the message in Google Sheets -Checks if the group exists in the control tab If it doesn't, it creates a new row with a checkbox so you can enable/disable summaries for that group Part 2 – Summary Generation and Delivery Scheduled to run daily at 08:00 AM or choose your preferred trigger time Pulls all messages from the previous day Groups them by chat and checks if that group is enabled for summaries Sends the messages to OpenAI to generate a digest Delivers the summary directly into the WhatsApp group using Evolution API This structure makes the flow easier to manage, customize, and scale — plug in other tools without breaking the logic. Tools used: ✅ Evolution API (WhatsApp connection API non-official) ✅ Google Sheets (template provided) ✅ OpenAI (for transcription and summarization) How to set it up: Set up the webhook on Evolution and connect it to n8n Use the included Google Sheets template. Click here to make your copy 👉🏻 [[Template] Log - Group Summary](https://docs.google.com/spreadsheets/d/1ymkWd0thcFRTtWdNrenUg1k8lAmn19ebznSHtvKHaoE/edit?usp=sharing) Connect your Google Sheets credentials Add your OpenAI API key (Optional) Customize the prompt and choose your preferred trigger time Why this workflow stands out: 📊 *Real control panel: enable or disable summaries per group with a single click* 🔍 Fully traceable and modular logic with clear branching and error handling ⚙️ Built for scale. Ideal for teams, communities, or educational groups 📬 Automatically delivers structured daily insights straight to your Whatsapp Groups ✅ Works on both n8n Cloud and Self-hosted 🔐 100% secure. No hacks. No shortcuts. Want to adapt this flow for your business, team, or community? 📩 Custom requests: WhatsApp me at +5534992569346 Português <> PT-BR Fala, galera! Eu sou o Luís 🙋🏻‍♂️ Eu já vou deixar uma coisa clara: esse não é só mais um fluxo de resumo do WhatsApp. É uma automação completa, estruturada do início ao fim, feita pra quem realmente precisa se manter informado sem perder tempo e com controle total sobre o que vai ou não pro resumo. O que esse fluxo faz: Recebe mensagens via webhook da Evolution API Verifica se a mensagem é de grupo ou contato individual Separa as mensagens por tipo: texto ou áudio (com transcrição automática via OpenAI) Armazena tudo no Google Sheets, organizado por grupo, autor, horário e conteúdo Cria um Painel de Controle com checkbox para cada grupo — você decide quais grupos vão ou não receber o resumo (esse é o grande diferencial do fluxo) Coleta todas as mensagens do dia anterior, agrupa por grupo e envia para a IA gerar o resumo Envia o resumo formatado direto no grupo do WhatsApp todas as manhãs (100% automático) 🧩 Como o fluxo está estruturado Esse fluxo foi estrategicamente dividido em duas partes independentes, garantindo clareza, organização e escalabilidade: Parte 1 – Captura e Armazenamento das Mensagens Ativado por webhook: Recebe mensagens da Evolution API Verifica se é de grupo Separa entre texto e áudio (com transcrição automática) Armazena a mensagem no Google Sheets Verifica se o grupo já existe na aba de controle Caso não exista, cria uma nova linha com checkbox para ativar ou não os resumos daquele grupo Parte 2 – Geração e Envio do Resumo Agendado para rodar todo dia às 08:00 (ou no horário que você quiser) Coleta todas as mensagens do dia anterior Agrupa por grupo e valida se o grupo está habilitado no painel de controle Envia as mensagens para o OpenAI gerar o resumo Entrega o resumo diretamente no grupo via Evolution API Essa estrutura torna o fluxo muito mais fácil de manter, adaptar e escalar — pode integrar novas ferramentas sem bagunçar nada Ferramentas utilizadas: ✅ Evolution API (conexão com o WhatsApp, API não oficial) ✅ Google Sheets (modelo incluso) ✅ OpenAI (para transcrição e geração do resumo) Como configurar: Configure o webhook no Evolution e conecte ao n8n Use a planilha modelo que acompanha esse fluxo. Faça sua cópia clicando aqui 👉🏻 [[Template] Log - Group Summary](https://docs.google.com/spreadsheets/d/1ymkWd0thcFRTtWdNrenUg1k8lAmn19ebznSHtvKHaoE/edit?usp=sharing) Conecte suas credenciais do Google Sheets Adicione sua chave da OpenAI (Opcional) Personalize o prompt da IA e defina o melhor horário de execução Por que esse fluxo se destaca: 📊 *Painel de controle real: ative ou desative os resumos por grupo com 1 clique* 🔍 Lógica rastreável e modular, com ramificações claras e tratamento de exceções ⚙️ Pronto pra escalar. Ideal para times, comunidades ou grupos educacionais 📬 Entrega automática de resumos diários direto nos grupos do WhatsApp ✅ Compatível com n8n Cloud e Self-hosted 🔐 100% seguro. Sem gambiarra. Sem atalhos. Quer adaptar esse fluxo para seu negócio, time ou comunidade? 📩 Solicitações personalizadas: me chama no WhatsApp +5534992569346. An n8n automation workflow template by Luís Philipe Trindade.

By Robert Breen

This workflow automates invoice creation using Google Sheets for structured input and Google Docs for templated output — all built inside n8n. 🛠️ Step-by-Step Instructions ### Step 1: Manual Trigger Start the workflow manually for testing or development purposes. ### Step 2: Google Sheets — Load Invoice Data Pulls invoice data from a Google Sheet. 📄 Sheet URL: Copy This Sheet Expected Columns**: Company From Company To Terms Invoice Description Amount > 🔑 Credentials Required: > Connect to Google Sheets OAuth2 API in n8n. > Be sure your sheet is shared with the connected Google account. ### Step 3: Get Invoice Template — Load Google Doc Loads a static Google Docs template containing placeholder values. 🧾 Template URL: Copy This Template Required Placeholders** in the document: FromCompany# ToCompany# Terms# Invoice# Description# Amount# > 🔑 Credentials Required: > Connect to Google Docs OAuth2 API in n8n. ### Step 4: Create New Doc — Make Invoice File Creates a new Google Doc by duplicating the invoice template. Title Format**: Invoice: {{ $json.Invoice }} Destination Folder ID**: 1TnDibwPPPUm3VbmETiqWDVhtaUTLJ6mn (You can change this to your own Google Drive folder) > 🔐 Make sure your Google Docs credential has write access to this folder. ### Step 5: Merge — Combine Data Merges the loaded document and spreadsheet row together for downstream updates. ### Step 6: Insert Content into Doc (Optional) You can insert additional content here if needed. For example, a note, header, or footer pulled from your database or a custom field. ### Step 7: Input Invoice Details — Replace Fields Uses Google Docs API to replace all placeholders from the original template with the actual values. Replacements: | Placeholder | Replaced With | |----------------|------------------------------| | FromCompany# | Company From from sheet | | ToCompany# | Company To from sheet | | Terms# | Terms from sheet | | Invoice# | Invoice number | | Description# | Description of service | | Amount# | Amount of invoice | 📤 Final Output Each row from the Google Sheet results in a completed, branded Google Doc invoice stored in your Drive. 🙋 Need Help? Robert Breen Automation Consultant 🌐 ynteractive.com 📧 robert.j.breen@gmail.com 🔗 LinkedIn 🔒 Required APIs | Service | Purpose | |------------------|--------------------------| | Google Sheets API | Pull structured invoice data | | Google Docs API | Load & modify invoice documents | | n8n OAuth2 | Connect both services securely | Let me know if you'd like a follow-up step to export invoices as PDFs or auto-email them to clients!. An n8n automation workflow template by Robert Breen.

By Trung Tran

🎧 IT Voice Support Automation Bot – Telegram Voice Message to JIRA ticket with OpenAI Whisper > Automatically process IT support requests submitted via Telegram voice messages by transcribing, extracting structured data, creating a JIRA ticket, and notifying relevant parties. 🧑‍💼 Who’s it for Internal teams that handle IT support but want to streamline voice-based requests. Employees who prefer using mobile/voice to report incidents or ask for support. Organizations aiming to integrate conversational AI into existing support workflows. ⚙️ How it works / What it does A user sends a voice message to a Telegram bot. The system checks whether it’s an audio message. If valid, the audio is: Downloaded Transcribed via OpenAI Whisper Backed up to Google Drive The transcription and file metadata are merged. The merged content is processed through an AI Agent (GPT) to extract structured request info. A JIRA ticket is created using the extracted data. The IT team is notified via Slack (or other channels). The requester receives a Telegram confirmation message with the JIRA ticket link. If the input is not audio, a polite rejection message is sent. 📌 Key Features Supports voice-based ticket creation Accurate transcription using Whisper Context-aware request parsing using GPT-4.1 mini Fully automated ticket creation in JIRA Notifies both IT and the original requester Cloud backup of original voice messages (Google Drive) 🛠️ Setup Instructions Prerequisites | Component | Required | |----------|----------| | Telegram Bot & API Key | ✅ | | OpenAI Whisper / Transcription Model | ✅ | | Google Drive Credentials (OAuth2) | ✅ | | Google Sheets or other storage (optional) | ⬜ | | JIRA Cloud API Access | ✅ | | Slack Bot or Webhook | ✅ | Workflow Steps Telegram Voice Message Trigger: Starts the flow when a user sends a voice message. Is Audio Message?: If false → reply "only voice is supported" Download Audio: Download .oga file from Telegram. Transcribe Audio: Use OpenAI Whisper to get text transcript. Backup to Google Drive: Upload original voice file with metadata. Merge Results: Combine transcript and metadata. Pre-process Output: Clean formatting before AI extraction. Transcript Processing Agent: GPT-based agent extracts: Requester name, department Request title & description Priority & request type Submit JIRA Request Ticket: Create ticket from AI-extracted data. Setup Slack / Email / Manual Steps: Optional internal routing or approvals. Inform Reporter via Telegram: Sends confirmation message with JIRA ticket link. 🔧 How to Customize Replace JIRA with Zendesk, GitHub Issues, or other ticketing tools. Change Slack to Microsoft Teams or Email. Add Notion/Airtable logging. Enhance agent to extract department from user ID or metadata. 📦 Requirements | Integration | Notes | |-------------|-------| | Telegram Bot | Used for input/output | | Google Drive | Audio backup | | OpenAI GPT + Whisper | Transcript & Extraction | | JIRA | Ticketing platform | | Slack | Team notification | Built with ❤️ using n8n. An n8n automation workflow template by Trung Tran.

By Humble Turtle

Manage Jira Issues with Natural Language via Telegram and GPT-4o Overview The Jira Agent is an AI-powered assistant that allows users to interact with Jira directly through messaging platform Telegram. It leverages OpenAI's GPT-4o model to interpret natural language commands and perform various Jira-related actions. On Telegram, it enables users to create Jira stories by triggering a guided form when prompted with "create story." Additionally, it provides more extensive functionality, including creating, updating, searching, and transitioning Jira issues through natural language commands. How it works Normal interaction Using messages as "Please give all my issues". Standardized process of creating stories: Message: "create story" Open the Form that Telegram responds back to you Fill in the essential story information in the form The story automatically gets created in your backlog. Required Connections To use the Jira Agent effectively, users need access to: A Telegram account, Telegram setup involves deploying the bot and starting a chat; story creation is triggered with a simple text command. A connected Jira workspace Permissions to create and modify Jira issue Access to GPT-4o API-key Detailed configuration instructions are provided in the workflow Setup Time <15 minutes Customising this workflow Try adding more details to the form for more complete Jira ticket creation. Try connecting a Google Calendar node to plan your work. An n8n automation workflow template by Humble Turtle.