Workflow Templates

Create a reusable “photos to post” queue from your Lightroom Cloud album—ideal for Lightroom-to-Instagram automation with n8n. It discovers new photos, stores clean metadata in a Data Table, and generates AI alt text to power on-brand captions and accessibility. Use it together with “Lightroom Image Webhook (Direct JPEG for Instagram)” and “Instagram Auto-Publisher for Lightroom Photos (AI Captions).” What it’s for Automate Lightroom to Instagram; centralize photo data for scheduled IG posting; prep AI-ready alt text and metadata for consistent, hands-free publishing. Parameters to set Lightroom Cloud credentials (client/app + API key) Album/collection ID to monitor in Lightroom Cloud Data Table name for the posting queue (e.g., Photos) AI settings: language/tone for alt text (concise, brand-aware) Image analysis URL: public endpoint of Workflow 2 (Lightroom Image Webhook) Works best with Workflow 2: Lightroom Image Webhook (Direct JPEG for Instagram) Workflow 3: Instagram Auto-Publisher for Lightroom Photos (AI Captions) Learn more & stay in the loop Want the full story (decisions, trade-offs, and tips) behind this Lightroom Cloud → Instagram automation? 👉 Read the write-up on my blog: camilleroux.com If you enjoy street & urban photography or you’re curious how I use these n8n workflows day-to-day: 👉 Follow my photo account on Instagram: @camillerouxphoto 👉 Follow me on other networks: links available on my site (X, Bluesky, Mastodon, Threads)

AI Chat Bot workflow for WordPress & Webhook Live Chats This workflow powers a versatile AI chatbot that can be integrated into any live chat interface, such as our free Forerunner™ AI Chat Bot for WordPress. It's designed to automate customer support and lead generation by handling a variety of user queries independently. The setup process is straightforward and typically takes less than five minutes. This involves connecting your preferred Large Language Model (LLM) and a live chat platform to the workflow via webhooks. How the Workflow Works The core of this workflow is an AI Agent that acts as the brain of the chatbot. It processes user input and generates responses based on predefined rules and your chosen language model. User Input: When a user sends a message through your live chat, it's sent to the workflow via a webhook. This message is then passed to the AI Agent for processing. AI Response Generation: The AI Agent analyzes the message, retrieves relevant conversational history from the Simple Memory node to maintain context, and uses the selected Large Language Model (e.g., OpenAI, Gemini, or Claude) to formulate a response. Conditional Logic: After the response is generated, the workflow uses an If node to check if the conversation should end. If the response contains the specific tag [END_OF_CONVERSATION], the workflow prepares to end the chat. Otherwise, the conversation continues. Send to Client: The final response is then sent back to the live chat interface, where it is displayed to the user. This completes the loop, allowing the chatbot to engage in a continuous conversation until the task is complete.

This workflow automates the process of generating stylized product photos for e-commerce by combining real product shots with creative templates. It enables the creation of a complete set of images for an SKU from a single product photo and a set of reusable templates. The workflow uses Google Gemini (Nano Banana) for image editing and Airtable as the data source. Example use case. An apparel brand can use this workflow to turn plain product photos (e.g., socks on a white background) into lifestyle images that match their brand aesthetic. By combining each product photo with predefined templates and reference images, the workflow generates a variety of stylized results automatically - ready for marketing or online stores. How it works This workflow expects the following Airtable table setup: "Product Images"** - contains original product photos, one per record. "Reference Images"** - contains reference images for templates, one per record. "Templates"** - contains reusable generation templates. Each template includes a text prompt and up to three reference images. "Jobs"** - contains batch generation jobs. Each job references multiple product images and multiple templates. "Results"** - contains the generated outputs. Each result includes a generated image, references to the job, product image, and template, and a status field (pending, approved, rejected). The workflow is triggered by a webhook that receives a job ID from Airtable. It then: Fetches the job record. Retrieves the associated product images and templates (each with its text prompt and reference images). Downloads all required product and reference images. For each product-template combination, sends these images and the prompt to Google Gemini to generate new AI-edited images. Saves the generated images back into Airtable. NOTE: A separate workflow should handle the human-in-the-loop approval process and any regeneration of rejected results. Requirements Airtable Personal Access Token Google Gemini API key Setup Ensure all required Airtable tables exist. Configure parameters in the parameters node: Set Airtable Base ID Set ID of the attachment field in the "Results" table (where the generated images will be uploaded) Configure credentials for all Airtable nodes. Set Google Gemini API key for the "Generate..." nodes.

Who’s it for This workflow is designed for researchers, content creators, and AI agents who need to quickly scrape structured web data and capture full-page screenshots for further use. It’s especially useful for automating competitive research, news monitoring, and content curation. How it works The workflow uses the Firecrawl API integrated with n8n to perform web searches and return results in structured formats (Markdown and screenshots). It includes: A search agent that transforms natural language queries into Firecrawl-compatible search strings. HTTP requests to retrieve results from specific sites (e.g., YouTube, news outlets) or across the web. Automatic capture of full-page screenshots alongside structured text. Integration with the OpenAI Chat Model for enhanced query handling. How to set up Import this workflow into your n8n instance. Add and configure your Firecrawl API credentials. Add your OpenAI credentials for natural language query parsing. Trigger the workflow via the included chat input or modify it to run on schedule. Requirements A Firecrawl account with an active API key. n8n self-hosted or cloud instance. OpenAI account if you want to enhance search queries. How to customize the workflow Update the search queries to focus on your preferred sites or keywords. Adjust the number of results with the limit parameter. Extend the workflow to store screenshots in Google Drive, Notion, or your database. Replace the chat trigger with any other event trigger (webhook, schedule, etc.).

Description: Guarantee that only fully compliant stories and tasks make it into your release with this n8n automation template. The workflow monitors Jira for issue updates and link changes, validates whether each story meets the Definition of Done (DoD), and automatically flags non-compliant items. It also creates a tracking record in Monday.com for unresolved blockers and sends Slack alerts summarizing readiness status for every version. Perfect for release managers, QA leads, and engineering teams who need an automated guardrail for production readiness. ✅ What This Template Does (Step-by-Step) 🎯 Jira Webhook Trigger: Activates automatically when an issue is updated or linked in Jira — ideal for continuous readiness validation. 📋 Fetch Full Issue Details: Retrieves the complete issue payload, including custom fields, status, and Definition of Done flags. 🔄 Batch Processing (1-by-1): Ensures each issue is validated individually, allowing precise error handling and clean audit trails. ✅ Check Definition of Done (DoD): Evaluates whether the customfield_DoD field is marked as true — a key signal of readiness for release. ⚠️ Flag Non-Compliant Issues: If DoD isn’t met, marks the issue as “Non-Compliant” with the reason “Definition of Done not met.” 📊 Create Tracking Record in Monday.com: Logs non-compliant issues to a dedicated Release Issues board for visibility and coordination with cross-functional teams. 📢 Send Slack Notifications: Posts to the #release-updates channel summarizing compliant vs non-compliant items per version, helping the team take timely action. 🧠 Key Features 🚦 Real-time Jira readiness validation ✅ Automated DoD enforcement before release 📊 Monday.com tracker for all non-compliant issues 📢 Slack summary notifications for release teams ⚙️ Batch-wise validation for scalable QA 💼 Use Cases 🚀 Enforce Definition of Done across linked Jira stories 📦 Automate pre-release checks for every version increment 🧩 Provide visibility into blockers via Monday.com dashboard 📢 Keep engineering and QA teams aligned on release status 📦 Required Integrations Jira Software Cloud API – to monitor issue updates and retrieve details Monday.com API – to log and track non-compliant items Slack API – for real-time release alerts 🎯 Why Use This Template? ✅ Eliminates manual pre-release validation ✅ Reduces release delays due to missed criteria ✅ Keeps all stakeholders aligned on readiness status ✅ Creates a transparent audit trail of compliance

This workflow automatically converts Telegram text or voice messages into Notion tasks by using AI to extract the task name and due date, allowing users to approve or decline tasks directly in Telegram before they are created. It is designed for students managing assignments, professionals tracking tasks from messaging apps, productivity enthusiasts looking to automate task entry, and teams using Notion for organized task management. Detailed Use Case This template turns casual Telegram messages and voice notes into confirmed Notion tasks by handling transcription, AI extraction, validation, and user approval. Typical scenarios include: Students:** Record or type assignment details after class → transcribe, extract title + due date → confirm → save to Notion. Managers:** Capture meeting action items in chat → extract tasks and deadlines → verify with one tap → add to project database. Freelancers:** Log client requests by voice → confirm parsed due date → create a tracked task in Notion. Productivity users / teams:** Quickly funnel ad-hoc requests from Telegram into a single Notion workspace without manual copy/paste. Key benefits: automated transcription, structured extraction (TaskName + TaskDue), quick user approval in Telegram, and reliable Notion mapping for immediate tracking. Prerequisites for this Workflow n8n instance** Active and publicly reachable for webhook triggers. Telegram bot** Create via BotFather, copy Bot Token, and get chat ID. Transcription API** I use Google Gemini Free Tier, but you can use Groq, OpenAI, or any other provider. Add your API key / credentials in n8n. Notion integration & database** Integration token added to n8n. Database structure must include: Title (Title property) → Task Name Date (Date property) → Task Due Date Share the database with the integration and copy the Database ID. n8n node setup basics** Telegram Trigger node for messages. Telegram Get File node (Download = true) for voice notes. Transcription node connected to your chosen provider. AI Extractor node to get TaskName and TaskDue. Notion node with mapped properties (TaskName → Title, TaskDue → Date). For any doubts or questions, contact: contact@abhiman.io or connect on LinkedIn: https://www.linkedin.com/in/abhimangs/

Telegram AI Bot Workflow An intelligent Telegram bot powered by Google Gemini AI that provides smart responses to both text messages and images. Features Multi-Modal Input**: Handles both text messages and image uploads AI-Powered Responses**: Uses Google Gemini 2.5 Flash for intelligent reply generation Image Analysis**: Automatically analyzes uploaded images and responds with descriptions Conversation Memory**: Maintains context across 20 messages per conversation Formatted Output**: Delivers well-structured, Telegram-friendly responses How It Works User sends a message (text or image) to the Telegram bot Workflow routes the input based on message type Images are downloaded and analyzed using Gemini Vision AI AI agent processes the input with conversation context Formatted response is sent back to the user instantly Requirements Telegram Bot Token (from @BotFather) Google Gemini API Key n8n instance with webhook capability Perfect for building interactive AI assistants, customer support bots, or educational tools on Telegram.

✅ What problem does this workflow solve? Salon staff often spend hours juggling appointment calls, managing bookings manually, and keeping track of customer preferences. This workflow automates your entire salon appointment system via WhatsApp, delivering a personalized and human-like booking experience using AI, memory, and Google Sheets. 💡 Main Use Cases 💁‍♀️ Offer personalized stylist recommendations by remembering customer preferences and past visits. 📅 Provide real-time availability and salon opening hour information. 📝 Book and update appointments directly from customer chat. 🔁 Simplify appointment changes by recalling previous booking details. 🧠 Enable context-aware, memory-driven conversations across multiple interactions. 🧠 How It Works – Step-by-Step 1. 📲 Chat Message Trigger The workflow is triggered whenever a customer sends a message to your WhatsApp salon assistant. 2. 🧠 Memory Buffer for Context Management The assistant uses a Memory Buffer to: Recognize returning customers Avoid repeating questions Maintain conversation flow across multiple sessions This enables a seamless and intelligent dialogue with each customer. 3. 💇 Stylist & Service Lookup When the customer asks for stylist suggestions, available time slots, or services: Extracts request details using AI Queries a Google Sheet containing: Stylist availability Service types Salon opening hours Returns personalized recommendations based on preferences and availability 4. ✅ Appointment Booking Collects all necessary info: Date, time, selected service, stylist, contact info Stores the appointment in Google Sheets Sends a confirmation message to the customer in WhatsApp 5. 🔄 Modify or Cancel Bookings Customers can update or cancel appointments Bot matches records by phone number Modifies or deletes the appointment in the sheet accordingly 🧩 Integrations Used WhatsApp Integration** (via Twilio, Meta API, or other connector) OpenAI/GPT Model** for natural conversation flow and extraction Google Sheets** as a simple and effective appointment database Memory Buffer** for ongoing context across chats 👤 Who can use this? Perfect for: 💇‍♀️ Salons and barbershops 💅 Spas and beauty centers 🧖‍♀️ Wellness studios 🛠 Developers building vertical AI assistants for SMBs If you're looking to modernize your booking process and impress customers with an AI-powered, memory-enabled WhatsApp bot—this workflow delivers. 🚀 Benefits ⏰ Save time for your staff 🧠 Offer truly personalized experiences 📲 Book appointments 24/7 via WhatsApp 📋 Keep all records organized in Google Sheets 🧘 Reduce human error and double bookings 📦 Ready to Launch? Just configure: ✅ Your WhatsApp number + webhook integration ✅ Google Sheet with stylist and service data ✅ OpenAI key for AI-powered conversation ✅ Memory Buffer to enable smart replies And your salon will be ready to offer automated, intelligent booking—right from a simple WhatsApp chat.

CTO Agent with Engineering Team Description Complete AI-powered engineering department with a Chief Technology Officer (CTO) agent orchestrating specialized engineering team members for comprehensive software development and technical operations. Overview This n8n workflow creates a comprehensive engineering department using AI agents. The CTO agent analyzes technical requests and delegates tasks to specialized agents for software architecture, DevOps, security, quality assurance, backend development, and frontend development. Features Strategic CTO agent using OpenAI O3 for complex technical decision-making Six specialized engineering agents powered by GPT-4.1-mini for efficient execution Complete software development lifecycle coverage from architecture to deployment Automated DevOps pipelines and infrastructure management Security assessments and compliance frameworks Quality assurance and test automation strategies Full-stack development capabilities Team Structure CTO Agent**: Technical leadership and strategic delegation (O3 model) Software Architect Agent**: System design, patterns, technology stack decisions DevOps Engineer Agent**: CI/CD pipelines, infrastructure automation, containerization Security Engineer Agent**: Application security, vulnerability assessments, compliance QA Test Engineer Agent**: Test automation, quality strategies, performance testing Backend Developer Agent**: Server-side development, APIs, database architecture Frontend Developer Agent**: UI/UX development, responsive design, frontend frameworks How to Use Import the workflow into your n8n instance Configure OpenAI API credentials for all chat models Deploy the webhook for chat interactions Send technical requests via chat (e.g., "Design a scalable microservices architecture for our e-commerce platform") The CTO will analyze and delegate to appropriate specialists Receive comprehensive technical deliverables Use Cases Full Stack Development**: Complete application architecture and implementation System Architecture**: Scalable designs for microservices and distributed systems DevOps Automation**: CI/CD pipelines, containerization, cloud deployment strategies Security Audits**: Vulnerability assessments, secure coding practices, compliance Quality Assurance**: Test automation frameworks, performance testing strategies Technical Documentation**: API documentation, system diagrams, deployment guides Requirements n8n instance with LangChain nodes OpenAI API access (O3 for CTO, GPT-4.1-mini for specialists) Webhook capability for chat interactions Optional: Integration with development tools and platforms Cost Optimization O3 model used only for strategic CTO decisions GPT-4.1-mini provides 90% cost reduction for specialist tasks Parallel processing enables simultaneous agent execution Code template library reduces redundant development work Integration Options Connect to development platforms (GitHub, GitLab, Bitbucket) Integrate with project management tools (Jira, Trello, Asana) Link to monitoring and logging systems Export to documentation platforms Contact & Resources Website**: nofluff.online YouTube**: @YaronBeen LinkedIn**: Yaron Been Tags #SoftwareEngineering #TechStack #DevOps #SecurityFirst #QualityAssurance #FullStackDevelopment #Microservices #CloudNative #TechLeadership #EngineeringAutomation #n8n #OpenAI #MultiAgentSystem #EngineeringExcellence #DevAutomation #TechInnovation

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This n8n workflow template helps you automatically convert unstructured contact information—such as customer details copied from emails, web forms, or chat messages—into clean, structured JSON using an AI agent. What It Does: Accepts unstructured contact data via a Webhook (as form-data under the key prompt) Uses AI to intelligently extract key fields such as: Company Name First Name Last Name Address City Country Phone Fax Email Parses and formats the extracted data into a valid JSON object Prepares the output for seamless integration into systems like: Dolibarr Other ERP/CRM platforms Any service that consumes JSON via API or webhook Use Cases: Automate manual data entry from emails into your ERP system Clean and normalize contact data from various input sources Reduce human error in your customer onboarding workflows This template saves you time and ensures consistency across your business systems. Simply connect your systems and let the automation handle the rest.

This workflow demonstrates how to create a new deployment when new content gets added to the database. This example workflow can be used when building a JAMstack site. Webhook node: This node triggers the workflow when new content gets added. For this example, we have configured the webhook in GraphCMS. Netlify node: This node will start the build process and deploy the website. You will have to select your site from the Site ID dropdown list. To identify the deployment, we are passing a title.

Description Automated workflow that creates Jira issues directly from Streamlit form submissions. Receives webhook data, validates and transforms it to Jira's API schema, creates the issue, and returns the ticket details to the frontend application. Context Bridges the gap between lightweight Streamlit prototypes and enterprise Jira workflows. Enables rapid ticket creation while maintaining Jira as the authoritative source of truth. Includes safety mechanisms to prevent duplicate submissions and malformed requests. Target Users Product Managers building internal request portals. Engineering Managers creating demo applications. Teams requiring instant Jira integration without complex UI development. Project Manager using Jira pour mangement and reporting. Organizations wanting controlled ticket creation without exposing Jira directly. Technical Requirements n8n instance (cloud or self-hosted) with webhook capabilities Jira Cloud project with API token and issue creation permissions Streamlit application configured to POST to n8n webhook endpoint Optional: Custom field IDs for Story Points (typically customfield_10016) Workflow Steps Webhook Trigger - Receives POST from Streamlit with ticket payload. Deduplication Guard - Filters out ping requests and rapid duplicate submissions. Data Validation - Ensures required fields are present and properly formatted. Schema Transformation - Maps Streamlit fields to Jira API structure. Jira API Call - Creates issue via REST API with error handling. Response Formation - Returns success status with issue key and URL. Key Features Duplicate submission prevention. Rich text description formatting for Jira. Configurable priority and issue type mapping. Story points integration for agile workflows. Comprehensive error handling and logging. Clean JSON response for frontend feedback. Validation Testing Ping/test requests are ignored without creating issues. First submission creates Jira ticket with proper formatting. Rapid resubmission is blocked to prevent duplicates. All field types (priority, labels, due dates, story points) map correctly. Error responses are handled gracefully. Expected Output Valid Jira issue created in specified project JSON response: {ok: true, jiraKey: "PROJ-123", url: "https://domain.atlassian.net/browse/PROJ-123"} No orphaned or duplicate tickets. Audit trail in n8n execution logs. Implementation Notes Jira Cloud requires accountId for assignee (not username). Date format must be YYYY-MM-DD for due dates. Story Points field ID varies by Jira configuration. Enable response output in HTTP node for debugging. Consider rate limiting for high-volume scenarios. Tutorial video: Watch the Youtube Tutorial video How it works ⏰ Trigger: Webhook fires when the app submits. 🧹 Guard: Ignore pings/invalid, deduplicate rapid repeats. 🧱 Prepare: Normalize to Jira’s field model (incl. Atlassian doc description). 🧾 Create: POST to /rest/api/3/issue and capture the key. 🔁 Respond: Send { ok, jiraKey, url } back to Streamlit for instant UI feedback. About me : I'm Yassin, IT Project Manager, Agile & Data specialist. Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin