by Jonathan
This is the fourth workflow for the Mattermost Standup Bot. This workflow sends the team a message every morning to ask them three standup questions. What have you accomplished since your last report? What do you want to accomplish until your next report? Is anything blocking your progress? Once answered, the answers are sent to a Mattermost channel. The "Read Config" nodes will need to be updated to point to the ID of the "Standup Bot - Read Config" workflow and the "Override Config" node will need to point to "Standup Bot - Override Config"
by Eduard
An example workflow for a multilanguage Telegram bot. It allows adding many new languages to the bot without editing the workflow. Important note! Due to some breaking API changes in NocoDB some of its node options are not working at the moment (MAY 2022). These two nodes were replaced by HTTP request nodes. Functionality is still the same.
by rangelstoilov
This workflow goes through the teachable webhook request types and adds a user, updates him and tags him with #unsubscribe or removes the #unsubscribe tag. It also tags the user with the tag of the name of the course. Enjoy!
by Amjid Ali
๐ AI-Powered Business Performance Reporting Automation Unlock executive-level insights with ZERO manual work! This n8n template empowers you to automate your entire monthly business performance reporting using dynamic SQL queries, AI-driven analysis, and beautiful HTML dashboards โ all delivered directly to your inbox. ๐ฏ What This Automation Does ๐ Triggers automatically every month (5th of each month) ๐งฎ Fetches financial data from SQL (ERPNext or any database) ๐ Loops over cost centers to analyze each business unit individually ๐ Generates Profit & Loss reports, WIP, Employee stats, and vertical breakdowns ๐ค Uses Google Gemini 2.5 AI to perform advanced financial analysis ๐ Delivers a polished HTML report to your email inbox ๐ง Fully modular โ replace data source with Excel, Google Sheets, or APIs ๐งโ๐ซ Step-by-Step Video Tutorial ๐ฅ Watch the full tutorial on YouTube: ๐ Learn how each node works and see the AI-generated report in action. ๐ Useful Links ๐ Sign up for n8n Cloud (recommended for non-tech users): ๐ https://n8n.syncbricks.com ๐ Download the step-by-step Guidebook (Free): ๐ https://lms.syncbricks.com/books/n8n ๐ Explore the full course on n8n (includes templates, workflows, and AI integrations): ๐ https://lms.syncbricks.com/courses/n8n ๐ Requirements โ n8n (Self-hosted or Cloud) โ SQL Database (MySQL / PostgreSQL / ERPNext) โ Microsoft Outlook or Gmail (to send the report) โ Gemini API Key (for AI analysis) โ Basic understanding of your data schema ๐ก Why Use This Template? โฑ Saves 2-3 days of manual work every month ๐ Improves financial visibility across business units ๐ค Great for CFOs, COOs, Finance Analysts, and BI teams ๐ Scales across multiple divisions and companies ๐ง Leverages AI for actionable insights and recommendations ๐งฉ Customize It Your Way Replace the SQL nodes with: Excel / Google Sheets Airtable / APIs Custom Applications Swap the AI model: OpenAI GPT Claude DeepSeek Adjust the report structure or HTML style ๐ Get Started Now ๐ฏ Import the JSON template โ Connect your data โ Receive business insights via email. Donโt let manual reporting slow down your decision-making. ๐ Sign up for n8n Cloud ๐ Learn n8n with Amjid ๐ Download Guide Created by Amjid Ali | SyncBricksโข โ Automation for Everyone
by Luciano Gutierrez
Supabase AI Agent with RAG & Multi-Tenant CRUD Version: 1.0.0 n8n Version: 1.88.0+ Author: Koresolucoes License: MIT Description A stateful AI agent workflow powered by Supabase and Retrieval-Augmented Generation (RAG). Enables persistent memory, dynamic CRUD operations, and multi-tenant data isolation for AI-driven applications like customer support, task orchestration, and knowledge management. Key Features: ๐ง RAG Integration: Leverages OpenAI embeddings and Supabase vector search for context-aware responses. ๐๏ธ Full CRUD: Manage agent_messages, agent_tasks, agent_status, and agent_knowledge in real time. ๐ค Multi-Tenant Ready: Supports per-user/organization data isolation via dynamic table names and webhooks. ๐ Secure: Role-based access control via Supabase Row Level Security (RLS). Use Cases Customer Support Chatbots: Persist conversation history and resolve queries using institutional knowledge. Automated Task Management: Track and update task statuses dynamically. Knowledge Repositories: Store and retrieve domain-specific information for AI agents. Instructions 1. Import Template Go to n8n > Templates > Import from File and upload this workflow. 2. Configure Credentials Add your Supabase and OpenAI API keys under Settings > Credentials. 3. Set Up Multi-Tenancy (Optional) Dynamic Webhook Path**: Replace the default webhook path with /mcp/tool/supabase/:userId to enable per-user routing. Table Names**: Use a Set Node to dynamically generate table names (e.g., agent_messages_{{userId}}). 4. Activate & Test Enable the workflow and send test requests to the webhook URL. Tags AI Agent RAG Supabase CRUD Multi-Tenant OpenAI Automation Screenshots License This template is licensed under the MIT License.
by Lucas Perret
Job offers are a goldmine of information. Use them to boost your outreach results. They'll give you: more context to personalize your messaging a steady flow of new leads the right timing to contact your lead
by Angel Menendez
This workflow is triggered by a parent workflow initiated via a Slack shortcut. Upon activation, it collects input from a modal window in Slack and initiates a vulnerability scan using the Qualys API. Key Features Trigger:** Launched by a parent workflow through a Slack shortcut with modal input. API Integration:** Utilizes the Qualys API for vulnerability scanning. Data Conversion:** Converts XML scan results to JSON for further processing. Loop Mechanism:** Continuously checks the scan status until completion. Slack Notifications:** Posts scan summary and detailed results to a specified Slack channel. Workflow Nodes Start VM Scan in Qualys: Initiates the scan with specified parameters. Convert XML to JSON: Converts the scan results from XML format to JSON. Fetch Scan Results: Retrieves scan results from Qualys. Check if Scan Finished: Verifies whether the scan is complete. Loop Mechanism: Handles the repetitive checking of the scan status. Slack Notifications: Posts updates and results to Slack. Relevant Links Qualys API Documentation Qualys Platform Documentation Parent workflow link Link to Report Generator Subworkflow
by Luciano Gutierrez
Google Calendar AI Agent with Dynamic Scheduling Version: 1.0.0 n8n Version: 1.88.0+ Author: Koresolucoes License: MIT Description An AI-powered workflow to automate Google Calendar operations using dynamic parameters and MCP (Model Control Plane) integration. Enables event creation, availability checks, updates, and deletions with timezone-aware scheduling [[1]][[2]][[8]]. Key Features: ๐ Full Calendar CRUD: Create, read, update, and delete events in Google Calendar. โฐ Availability Checks: Verify time slots using AVALIABILITY_CALENDAR node with timezone support (e.g., America/Sao_Paulo). ๐ค AI-Driven Parameters: Use $fromAI() to inject dynamic values like Start_Time, End_Time, and Description [[3]][[4]]. ๐ MCP Integration: Connects to an MCP server for centralized AI agent control [[5]][[6]]. Use Cases Automated Scheduling: Book appointments based on AI-recommended time slots. Meeting Coordination: Sync calendar events with CRM/task management systems. Resource Management: Check room/equipment availability before event creation. Instructions 1. Import Template Go to n8n > Templates > Import from File and upload this workflow. 2. Configure Credentials Add Google Calendar OAuth2 credentials under Settings > Credentials. Ensure the calendar ID matches your target (e.g., ODONTOLOGIA group calendar). 3. Set Up Dynamic Parameters Use $fromAI('Parameter_Name') in nodes like CREATE_CALENDAR to inject AI-generated values (e.g., event descriptions). 4. Activate & Test Enable the workflow and send test requests to the webhook path /mcp/:tool/calendar. Tags Google Calendar Automation MCP AI Agent Scheduling CRUD Screenshots License This template is licensed under the MIT License. Notes: Extend multi-tenancy by adding :userId to the webhook path (e.g., /mcp/:userId/calendar) [[7]]. For timezone accuracy, always specify options.timezone in availability checks [[8]]. Refer to n8nโs Google Calendar docs for advanced field mappings.
by Francis Njenga
AI Content Generator Workflow Introduction This workflow automates the process of creating high-quality articles using AI, organizing them in Google Drive, and tracking their progress in Google Sheets. It's perfect for marketers, bloggers, and businesses looking to streamline content creation. With minimal setup, you can have a fully operational system to generate, save, and manage your articles in one cohesive workflow. How It Works Collect Inputs: Users fill out a form with details like article title, keywords, and instructions. Generate Content: AI creates an outline and writes the article based on user inputs. Organize Files: Saves the outline and final article in Google Drive for easy access. Track Progress: Updates Google Sheets with links to the generated content for tracking. Set Up Steps Time Required**: Approximately 15โ20 minutes to connect all integrations and test the workflow. Steps**: Connect Google Drive and Google Sheets: Authorize access to store files and update the spreadsheet. Set Up OpenAI Integration: Add your OpenAI API key for generating the outline and article content. Customize the Form: Modify the form fields to match the details you want to collect for each article. Test the Workflow: Run the workflow with sample inputs to ensure everything works smoothly. This workflow not only simplifies the process of article creation but also sets a foundation for expanding into additional automations, like posting to social media platforms.
by Askan
The News Site from Colt, a telecom company, does not offer an RSS feed, therefore web scraping is the choice to extract and process the news. The goal is to get only the newest posts, a summary of each post and their respective (technical) keywords. Note that the news site offers the links to each news post, but not the individual news. We collect first the links and dates of each post before extracting the newest ones. The result is sent to a SQL database, in this case a NocoDB database. This process happens each week thru a cron job. Requirements: Basic understanding of CSS selectors and how to get them via browser (usually: right click → inspect) ChatGPT API account - normal account is not sufficient A NocoDB database - of course you may choose any type of output target Assumptions: CSS selectors work on the news site The post has a date with own CSS selector - meaning date is not part of the news content "Warnings" Not every site likes to be scraped, especially not in high frequency Each website is structured in different ways, the workflow may then need several adaptations.
by Alberto Bordoni
What This Workflow Does: Generates original, research-based LinkedIn posts Combines AI insights with personal storytelling Includes human-in-the-loop selection & revision steps Automatically creates a conceptual image via DALLยทE 3 Sends the final post and image via email, ready to publish Perfect For: Professionals who want to share high-quality AI-assisted content Content creators balancing consistency and authenticity Consultants and solopreneurs building a personal brand Anyone who wants to turn AI research into personal, sharable stories ๐ WORKFLOW PROCESS OVERVIEW Step 1: ๐ Perplexity finds 3 recent, verifiable AI-related topics Step 2: ๐ง Email sent โ you choose your favorite topic Step 3: โ๏ธ OpenAI generates a LinkedIn post draft Step 4: ๐จ You review the post and approve or suggest changes Step 5: ๐ ๏ธ If needed, AI revises the post based on your feedback Step 6: ๐จ DALLยทE creates a conceptual image to match the content Step 7: ๐ฌ Final email sent โ post text + image ready to copy-paste on LinkedIn
by Joseph LePage
This n8n workflow is designed to automate the aggregation, processing, and reporting of community statistics related to n8n creators and workflows. Its primary purpose is to generate insightful reports that highlight top contributors, popular workflows, and key trends within the n8n ecosystem. Here's how it works and why it's important: How It Works Data Retrieval: The workflow fetches JSON data files from a GitHub repository containing statistics about creators and workflows. It uses HTTP requests to access these files dynamically based on pre-defined global variables. Data Processing: The data is parsed into separate streams for creators and workflows. It processes the data to identify key metrics such as unique weekly and monthly inserters/visitors. Ranking and Filtering: The workflow sorts creators by their weekly inserts and workflows by their popularity. It selects the top 10 creators and top 50 workflows for detailed analysis. Report Generation: Using AI tools like GPT-4 or Google Gemini, the workflow generates a Markdown report summarizing trends, contributors, and workflow statistics. The report includes tables with detailed metrics (e.g., unique visitors, inserters) and insights into why certain workflows are popular. Distribution: The report is saved locally or uploaded to Google Drive. It can also be shared via email or Telegram for broader accessibility. Automation: A schedule trigger ensures the workflow runs daily or as needed, keeping the reports up-to-date. Why It's Important Community Insights**: This workflow provides actionable insights into the n8n community by identifying impactful contributors and popular workflows. This fosters collaboration and innovation within the ecosystem. Time Efficiency**: By automating data collection, processing, and reporting, it saves significant time and effort for community managers or administrators. Recognition of Contributors**: Highlighting top creators encourages engagement and recognizes individuals driving value in the community. Trend Analysis**: The workflow helps uncover patterns in usage, enabling better decision-making for platform improvements or feature prioritization. Scalability**: With its modular design, this workflow can be easily adapted to include additional metrics or integrate with other tools.