Workflow Templates

4 nodes2 views

Automate Instagram Complaint Handling with Claude AI, Tickets & SLA Management

by Oneclick AI Squad

This enterprise-grade n8n workflow automates the Instagram complaint handling process — from detection to resolution — using Claude AI, dynamic ticket assignment, and SLA enforcement. It converts customer complaints in comments into actionable support tickets with auto-assignment, escalation alerts, and full audit trails, ensuring timely responses and improved customer satisfaction with zero manual intervention. Key Features Real-time Instagram polling** for new comments AI-powered complaint detection* using *Claude 3.5 Sonnet** for sentiment and issue classification Automatic ticket creation** in Google Sheets (or integrable with Zendesk/Jira) Round-robin assignment** to team members from a dynamic roster SLA timer and monitoring** (e.g., 24-hour response window with escalation at 80% elapsed) Escalation engine** notifies managers via Slack if near breach Multi-channel notifications:** Slack for assignees and escalations Audit-ready:** Logs ticket details, assignments, and actions Scalable triggers:** Webhook or scheduled polling Workflow Process | Step | Node | Description | | ---- | ----------------------------------- | -------------------------------------------------------- | | 1 | Schedule Trigger | Runs every 15 minutes or via webhook (/complaint-handler) | | 2 | Get Instagram Posts | Fetches recent posts from Instagram Graph API | | 3 | Get Comments | Retrieves comments for the latest post | | 4 | Loop Over Comments | Processes each comment individually to avoid rate limits | | 5 | Detect Complaint (Claude AI) | Uses AI to classify if complaint, extract issue/severity | | 6 | IF Complaint | Branches: Proceed if yes, end if no | | 7 | Get Team Members | Loads team roster from TeamMembers sheet | | 8 | Assign Ticket | Sets assignee via round-robin logic | | 9 | Create Ticket (Google Sheet) | Appends new ticket with details and SLA due date | | 10 | Notify Assignee (Slack) | Alerts assigned team member | | 11 | Wait for SLA Check | Delays to near-SLA-breach point (e.g., 20 hours) | | 12 | Check Ticket Status | Looks up ticket status in sheet | | 13 | IF SLA Breach Near | Checks if unresolved; escalates if yes | | 14 | Escalate to Manager (Slack) | Notifies manager for urgent action | | 15 | End (Non-Complaint Path) | Terminates non-complaint branches | Setup Instructions 1. Import Workflow Open n8n → Workflows → Import from Clipboard Paste the JSON workflow 2. Configure Credentials | Integration | Details | | ----------------- | -------------------------------------------------- | | Instagram API | Access token from Facebook Developer Portal | | Claude AI | Anthropic API key for claude-3-5-sonnet-20241022 | | Google Sheets | Service account with spreadsheet access | | Slack | Webhook or OAuth app | 3. Update Spreadsheet IDs Ensure your Google Sheets include: SupportTickets TeamMembers 4. Set Triggers Webhook:** /webhook/complaint-handler (for real-time Instagram notifications if set up) Schedule:** Every 15 minutes 5. Run a Test Use manual execution to confirm: Ticket creation in sheet Slack notifications SLA wait and escalation logic (simulate by shortening wait time) Google Sheets Structure SupportTickets | ticketId | commentText | user | createdAt | assignedTo | status | slaDue | issueType | severity | |--------------|-------------|----------|--------------------|--------------------|--------|--------------------|---------------|----------| | TKT-12345678 | Sample complaint text | user123 | 2023-10-01T12:00:00Z | john@team.com | Open | 2023-10-02T12:00:00Z | Product Issue | Medium | TeamMembers | name | email | |-----------|-------------------| | John Doe | john@team.com | | Jane Smith| jane@team.com | System Requirements | Requirement | Version/Access | | --------------------- | ---------------------------------------------- | | n8n | v1.50+ (AI integrations supported) | | Claude AI API | claude-3-5-sonnet-20241022 | | Instagram Graph API| Business account access token | | Google Sheets API | https://www.googleapis.com/auth/spreadsheets | | Slack Webhook | Required for notifications | Optional Enhancements Integrate Zendesk/Jira for professional ticketing instead of Google Sheets Add email notifications to customers acknowledging complaints Use sentiment thresholds for prioritizing high-severity tickets Connect Twilio for SMS escalations Enable multi-platform support (e.g., Twitter/Facebook comments) Add reporting dashboard via Google Data Studio Implement auto-resolution for simple complaints using AI responses Result: A single automated system that detects, tickets, assigns, and enforces SLAs on Instagram complaints — with full AI intelligence and zero manual work. Explore More AI Workflows: Get in touch with us for custom n8n automation!

3 nodes

Scrape Hotel Listings with Prices from Booking.com using Brightdata & AI

by phil

This workflow automates the search and extraction of hotel data from Booking.com. Triggered by a chat message, it uses a combination of web scraping with Bright Data's Web Scraper and AI-powered data processing with OpenRouter to deliver a concise, human-friendly list of hotels. The final output is a clean and formatted report, making it a valuable tool for travelers, event planners, and business professionals who need to quickly find accommodation options. Who's it for This template is ideal for: Event Planners:** Quickly identify and compare hotel options for conferences, meetings, or group travel. Travel Agents:** Efficiently research and provide clients with a curated list of accommodations based on their specified destination. Business Travelers:** Instantly find and assess hotel availability and pricing for upcoming trips. Individuals:** Streamline the hotel search process for personal vacations or short-term stays. How it works The workflow is triggered by a chat message containing a city name from an n8n chat application. It uses Bright Data to initiate a web scraping job on Booking.com for the specified city. The workflow continuously checks the status of the scraping job. Once the data is ready, it downloads the snapshot. The extracted data is then passed to a custom AI agent powered by OpenRouter. This AI agent uses a calculator tool to convert prices and an instruction prompt to refine and format the raw data. The final output is a well-presented list of hotels, ready for display in the chat application. How to set up Bright Data Credentials: Sign up for a Bright Data account and create a Web Scraper dataset. In n8n, create new Bright Data API credentials and copy your API key. OpenRouter Credentials: Create an account on OpenRouter and get your API key. In n8n, create new OpenRouter API credentials and paste your key. Chat Trigger Node: Configure the "When chat message received" node. Copy the production webhook URL to integrate with your preferred chat platform. Requirements An active n8n instance. A Bright Data account with a Web Scraper dataset. An OpenRouter account with API access. How to customize this workflow Search Parameters:** The "Initiate batch extraction from URL" node can be modified to change search criteria, such as check-in/check-out dates, number of adults and children, or property type. Output Format:** Edit the "Human Friendly Results" node's system message to change the format of the final report. You can modify the prompt to generate a JSON object, a CSV, or a different text format. Price Conversion:** The "Calculator" tool can be adjusted to perform different mathematical operations or currency conversions by modifying the AI agent's prompt. . Phil | Inforeole | Linkedin 🇫🇷 Contactez nous pour automatiser vos processus

Create a new user in Notion based on the signup form submission

by Will Stenzel

This workflow recieves webhook data from a form submission and creates a new user (with name and email) if necessary. It also add the current semester of the program to the relation for the user.

1 nodes1567 views

Chat with GitHub Issues using OpenAI and Redis Vector Search

by Tihomir Mateev

Chat with Your GitHub Issues Using AI 🤖 Ever wanted to just ask your repository what's going on instead of scrolling through endless issue lists? This workflow lets you do exactly that. What Does It Do? Turn any GitHub repo into a conversational knowledge base. Ask questions in plain English, get smart answers powered by AI and vector search. "Show me recent authentication bugs"** → AI finds and explains them "What issues are blocking the release?"** → Instant context-aware answers "Are there any similar problems to #247?"** → Semantic search finds connections you'd miss The Magic ✨ Slurp up issues from your GitHub repo (with all the metadata goodness) Vectorize everything using OpenAI embeddings and store in Redis Chat naturally with an AI agent that searches your issue database Get smart answers with full conversation memory Quick Start You'll need: OpenAI API key (for the AI brain) Redis 8.x (for vector search magic) GitHub repo URL (optional: API token for speed) Get it running: Drop in your credentials Point it at your repo (edit the owner and repository params) Run the ingestion flow once to populate the database Start chatting! Tinker Away 🔧 This is your playground. Here are some ideas: Swap the data source**: Jira tickets? Linear issues? Notion docs? Go wild. Change the AI model**: Try different GPT models or even local LLMs Add custom filters**: Filter by labels, assignees, or whatever matters to you Tune the search**: Adjust how many results come back, tweak relevance scores Make it public**: Share the chat interface with your team or users Auto-update**: Hook it up to webhooks for real-time issue indexing Built with n8n, Redis, and OpenAI. No vendor lock-in, fully hackable, 100% yours to customize.

intermediate

7 nodes1 views

Automate Invoice Creation & Smart Reminders with Jotform, QuickBooks & Outlook AI

by AppUnits AI

Generate Invoices and Send Reminders for Customers with Jotform, QuickBooks and Outlook This workflow automates the entire process of receiving a product/service order, checking or creating a customer in QuickBooks Online (QBO), generating an invoice, emailing it — all triggered by a form submission (via Jotform), and sending invoice reminders. How It Works Receive Submission Triggered when a user submits a form. Collects data like customer details, selected product/service, etc. Check If Customer Exists Searches QBO to determine if the customer already exists. If Customer Exists:* *Update** customer details (e.g., billing address). If Customer Doesn’t Exist:* *Create** a new customer in QBO. Get The Item Retrieves the selected product or service from QBO. Create The Invoice Generates a new invoice for the customer using the item selected. Send The Invoice Automatically sends the invoice via email to the customer. Store The Invoice In DB Stores the needed invoice details in the DB. Send Reminders Every day at 8 AM, the automation checks each invoice to decide whether to: send a reminder email, skip and send it later, or delete the invoice from the DB (if it's paid or all reminders have been sent). Who Can Benefit from This Workflow? Freelancers** Service Providers** Consultants & Coaches** Small Businesses** E-commerce or Custom Product Sellers** Requirements Jotform webhook setup, more info here QuickBooks Online credentials, more info here Email setup, update email nodes (Send reminder email & Send reminders sent summary), more info about Outlook setup here Create data table with the following columns: invoiceId (string) remainingAmount (number) currency (string) remindersSent (number) lastSentAt (date time) Update Add reminders config node so update the data table id and intervals in days (default is after 2 days, then after 3 days and finally after 5 days ) LLM model credentials

5 nodes

Scan Single URLs for Security Vulnerabilities with GPT-4 (JS, PHP, Python)

by Javier Rieiro

Overview This workflow automates static security analysis for JavaScript, PHP, and Python codebases. It’s designed for bug bounty hunters and security researchers who need fast, structured, and AI-assisted vulnerability detection across multiple sources. Features 🤖 AI-Powered Analysis: Specialized agents for each language: AI JavaScript Expert AI PHP Expert AI Python Expert Each agent detects only exploitable vulnerabilities (AST + regex heuristics). Returns strict JSON with: { "results": [ { "url": "file or URL", "code": "lines + snippet", "severity": "medium|high|critical", "vuln": "vulnerability type" } ] } 🧩 Post-Processing: Cleans, formats, and validates JSON results. Generates HTML tables with clear styling for quick visualization. Output ✅ JSON vulnerability reports per file. 📊 HTML table summaries grouped by language and severity. Usage Import the workflow into n8n. Configure credentials: OpenAI API key GitHub API Key Google Drive API Key Run via the provided webhook form. Select analysis mode and input target. View structured vulnerability reports directly in n8n or Google Drive. Notes Performs static analysis only (no code execution). Detects exploitable findings only; ignores low-impact issues.

5 nodes

Generate AI Images in Bulk with Freepik, Google Sheets & Drive

by Robert Breen

This n8n workflow automates bulk AI image generation using Freepik's Text-to-Image API. It reads prompts from a Google Sheet, generates multiple variations of each image using Freepik's AI, and automatically uploads the results to Google Drive with organized file names. This is perfect for content creators, marketers, or designers who need to generate multiple AI images in bulk and store them systematically. Key Features: Bulk image generation from Google Sheets prompts Multiple variations per prompt (configurable duplicates) Automatic file naming and organization Direct upload to Google Drive Batch processing for efficient API usage Freepik AI-powered image generation Step-by-Step Implementation Guide Prerequisites Before setting up this workflow, you'll need: n8n instance (cloud or self-hosted) Freepik API account with Text-to-Image access Google account with access to Sheets and Drive Google Sheet with your prompts Step 1: Set Up Freepik API Credentials Go to Freepik API Developer Portal Create an account or sign in Navigate to your API dashboard Generate an API key for Text-to-Image service Copy the API key and save it securely In n8n, go to Credentials → Add Credential → HTTP Header Auth Configure as follows: Name: "Header Auth account" Header Name: x-freepik-api-key Header Value: Your Freepik API key Step 2: Set Up Google Credentials Google Sheets Access: Go to Google Cloud Console Create a new project or select existing one Enable Google Sheets API Create OAuth2 credentials In n8n, go to Credentials → Add Credential → Google Sheets OAuth2 API Enter your OAuth2 credentials and authorize with spreadsheets.readonly scope Google Drive Access: In Google Cloud Console, enable Google Drive API In n8n, go to Credentials → Add Credential → Google Drive OAuth2 API Enter your OAuth2 credentials and authorize Step 3: Create Your Google Sheet Create a new Google Sheet: sheets.google.com Set up your sheet with these columns: Column A: Prompt (your image generation prompts) Column B: Name (identifier for file naming) Example data: | Prompt | Name | |-------------------------------------------|-------------| | A serene mountain landscape at sunrise | mountain-01 | | Modern office space with natural lighting | office-02 | | Cozy coffee shop interior | cafe-03 | Copy the Sheet ID from the URL (the long string between /d/ and /edit) Step 4: Set Up Google Drive Folder Create a folder in Google Drive for your generated images Copy the Folder ID from the URL when viewing the folder Note: The workflow is configured to use a folder called "n8n workflows" Step 5: Import and Configure the Workflow Copy the provided workflow JSON In n8n, click Import from File or Import from Clipboard Paste the workflow JSON Configure each node as detailed below: Node Configuration Details: Start Workflow (Manual Trigger) No configuration needed Used to manually start the workflow Get Prompt from Google Sheet (Google Sheets) Document ID**: Your Google Sheet ID (from Step 3) Sheet Name**: Sheet1 (or your sheet name) Operation**: Read Credentials**: Select your "Google Sheets account" Double Output (Code Node) Purpose**: Creates multiple variations of each prompt JavaScript Code**: const original = items[0].json; return [ { json: { ...original, run: 1 } }, { json: { ...original, run: 2 } }, ]; Customization**: Add more runs for additional variations Loop (Split in Batches) Processes items in batches to manage API rate limits Options**: Keep default settings Reset**: false Create Image (HTTP Request) Method**: POST URL**: https://api.freepik.com/v1/ai/text-to-image Authentication**: Generic → HTTP Header Auth Credentials**: Select your "Header Auth account" Send Body**: true Body Parameters**: Name: prompt Value: ={{ $json.Prompt }} Split Responses (Split Out) Field to Split Out**: data Purpose**: Separates multiple images from API response Convert to File (Convert to File) Operation**: toBinary Source Property**: base64 Purpose**: Converts base64 image data to file format Upload Image to Google Drive (Google Drive) Operation**: Upload Name**: =Image - {{ $('Get Prompt from Google Sheet').item.json.Name }} - {{ $('Double Output').item.json.run }} Drive ID**: My Drive Folder ID**: Your Google Drive folder ID (from Step 4) Credentials**: Select your "Google Drive account" Step 6: Customize for Your Use Case Modify Duplicate Count: Edit the "Double Output" code to create more variations Update File Naming: Change the naming pattern in the Google Drive upload node Adjust Batch Size: Modify the Loop node settings for your API limits Add Image Parameters: Enhance the HTTP request with additional Freepik parameters (size, style, etc.) Step 7: Test the Workflow Ensure your Google Sheet has test data Click Execute Workflow on the manual trigger Monitor the execution flow Check that images are generated and uploaded to Google Drive Verify file names match your expected pattern Step 8: Production Deployment Set up error handling for API failures Configure appropriate batch sizes based on your Freepik API limits Add logging for successful uploads Consider webhook triggers for automated execution Set up monitoring for failed executions Freepik API Parameters Basic Parameters: prompt: Your text description (required) negative_prompt: What to avoid in the image guidance_scale: How closely to follow the prompt (1-20) num_inference_steps: Quality vs speed trade-off (20-100) seed: For reproducible results Example Enhanced Body: { "prompt": "{{ $json.Prompt }}", "negative_prompt": "blurry, low quality", "guidance_scale": 7.5, "num_inference_steps": 50, "num_images": 1 } Workflow Flow Summary Start → Manual trigger initiates the workflow Read Sheet → Gets prompts and names from Google Sheets Duplicate → Creates multiple runs for variations Loop → Processes items in batches Generate → Freepik API creates images from prompts Split → Separates multiple images from response Convert → Transforms base64 to binary file format Upload → Saves images to Google Drive with organized names Complete → Returns to loop for next batch Contact Information Robert A Ynteractive For support, customization, or questions about this workflow: 📧 Email: rbreen@ynteractive.com 🌐 Website: https://ynteractive.com/ 💼 LinkedIn: https://www.linkedin.com/in/robert-breen-29429625/ Need help implementing this workflow or want custom automation solutions? Get in touch for professional n8n consulting and workflow development services.

4 nodes1 views

Smart Chat Routing Between Gemini and GPT Models Based on Query Complexity

by Daniel

Adaptive LLM Router for Optimized AI Chat Responses Elevate your AI chatbots with intelligent model selection: automatically route simple queries to cost-effective LLMs and complex ones to powerful ones, balancing performance and expenses seamlessly. What It Does This workflow listens for chat messages, uses a lightweight Gemini model to classify query complexity, then selects and routes to the optimal LLM (Gemini 2.5 Pro for complex, OpenAI GPT-4.1 Nano for simple) to generate responses—ensuring efficient resource use. Key Features Complexity Classifier** - Quick assessment using Gemini 2.0 Flash Dynamic Model Switching** - Routes to premium or budget models based on needs Chat Trigger** - Webhook-based for real-time conversations Current Date Awareness** - Injects $now into system prompt Modular Design** - Easy to add more models or adjust rules Cost Optimization** - Reserves heavy models for demanding tasks only Perfect For Chatbot Developers**: Build responsive, cost-aware AI assistants Customer Support**: Handle routine vs. technical queries efficiently Educational Tools**: Simple facts vs. in-depth explanations Content Creators**: Quick ideas vs. detailed writing assistance Researchers**: Basic lookups vs. complex analysis Business Apps**: Optimize API costs in production environments Technical Highlights Harnessing n8n's LangChain nodes, this workflow demonstrates: Webhook triggers for instant chat handling Agent-based classification with strict output rules Conditional model selection for AI chains Integration of multiple LLM providers (Google Gemini, OpenAI) Scalable architecture for expanding model options Ideal for minimizing AI costs while maximizing response quality. No coding required—import, configure credentials, and deploy!

4 nodes

Create & Publish SEO Content with AI Agents & Google Sheets Tracking

by Rahul Joshi

📊 Description Enhance your support, onboarding, and internal knowledge workflows with an intelligent RAG-powered chatbot that responds using live data stored in Google Sheets. 🤖📚 Built for teams that rely on structured datasets, this workflow fetches sheet content, enriches it with AI reasoning, and produces precise, context-aware answers — without requiring a full vector database setup. Whether you're managing FAQs, product data, SOPs, or internal documentation, this automation keeps responses consistent, up-to-date, and always aligned with your source of truth. 🚀 What This Template Does 1️⃣ Trigger – Starts automatically or via manual execution to answer user queries. 2️⃣ Extract – Receives a question and fetches the relevant sheet ID and target range. 3️⃣ Retrieve – Pulls knowledge from your Google Sheet (FAQ list, product catalog, SOPs, etc.). 4️⃣ Process – Sends the question and sheet content to an AI model to perform Retrieval-Augmented Generation. 5️⃣ Generate – Creates a concise, accurate, context-grounded answer based on the sheet data. 6️⃣ Output – Returns a clean final response ready for apps, chatbots, or integrations. Key Benefits ✅ Guarantees answers that stay aligned with your internal documents ✅ Eliminates hallucinations using RAG-style grounding ✅ Works with any Google Sheet — no database required ✅ Easy to extend into customer support, onboarding flows, or SOP assistants ✅ Fast, lightweight, and highly reliable for high-volume Q&A Features Google Sheets integration for real-time document retrieval OpenAI (or any LLM) prompt-based reasoning RAG-style answer generation without vector embeddings Modular design for easy reuse in other workflows Clean JSON output suitable for chat interfaces or APIs Requirements Google Sheets OAuth2 credentials OpenAI API key or any compatible LLM provider Question input (from webhook, chatbot, form, CRM, etc.) Target Audience Support teams responding to repetitive customer FAQs 💬 Operations teams managing SOP-based workflows 📘 Product teams needing accurate data-driven responses 📊 Agencies building custom AI assistants for clients 🤝 Internal helpdesk automations for teams 🛠️

intermediate

7 nodes

Automatically Assign Jira Service Management Reporter from Forwarded Emails

by Websensepro

Automatically Assign Jira Service Management Reporter from Forwarded Emails This workflow solves a common problem in Jira Service Management: when an email is forwarded to create a ticket, Jira often sets the forwarding system (e.g., support@yourcompany.com) as the reporter, not the original customer. This template automates the process of parsing the original sender's details from the email body and correctly assigning them as the ticket's reporter. If the customer doesn't exist in Jira, a new customer profile is created automatically before the ticket is assigned. What it Does Triggers on New Issue: The workflow starts when a new issue is created in a specified Jira project. Filters Forwarded Emails: An If node checks if the issue was created by one of your internal forwarding email addresses. The workflow only proceeds for these specific issues. Parses Details: A Code node uses regular expressions to parse the issue description (the forwarded email's body) and extract the original sender's name and email address. Searches for Existing Customer: An HTTP Request node checks if a customer with the extracted email already exists in your Jira Service Desk. Creates New Customer: If the customer is not found, another HTTP Request node creates a new customer profile in Jira Service Management. Assigns Reporter: Finally, a Jira node updates the issue's "Reporter" field to the existing or newly created customer, ensuring the ticket is correctly associated with the original sender. Use Cases Shared Support Inboxes**: Automatically process emails sent to a general support inbox (e.g., support@company.com) that are then forwarded to Jira. Departmental Forwarding**: Handle tickets forwarded from specific departments (e.g., sales@company.com or billing@company.com) and assign the original sender correctly. Personal Email Forwarding**: Useful when a team member forwards a customer email from their personal inbox to the Jira Service Management-connected address. Customization The Parse Details From Description node uses a regular expression (regex) to find the sender's email. The default regex is designed for standard forwarded emails that look like this: From: John Doe <john.doe@example.com> If your email client forwards emails in a different format, you may need to adjust the regex in the Code node. For example, if your format is From: [john.doe@example.com], you would need to update the regex pattern to match this structure. Troubleshooting Reporter Not Being Updated**: Verify that the forwarding email addresses in the Filter Forwarding Emails node are correct. Check the body of the Jira ticket's description to ensure the forwarded email content is present and in a format the regex can parse. Customer Not Found/Created**: Ensure your Jira API credentials have the necessary permissions to search for and create customers in Jira Service Management. Workflow Not Triggering**: Confirm that the Jira Trigger is correctly configured for the right project and that the webhook is active in your Jira instance. Requirements An n8n instance (self-hosted or cloud). Jira Software Cloud API credentials with Service Management permissions. How to Set Up Connect Credentials: In the Jira Trigger, Jira, and HTTP Request nodes, select your Jira Software Cloud API credentials. Configure Trigger: In the Jira Trigger node, select the Jira project you want this workflow to monitor. Set Filter Emails: In the Filter Forwarding Emails (If) node, replace the placeholder email addresses with the internal email addresses that forward mail to Jira. Update Jira Domain: In both HTTP Request nodes (Search for Existing Customer and Create Customer), replace the YOUR_JIRA_DOMAIN placeholder with your actual Atlassian domain (e.g., my-company.atlassian.net). Activate Workflow: Save and activate the workflow.

3 nodes

Auto-Generate Sales Presentations from Claap Calls with GPT-4o and Google Slides

by Robin Bonduelle

Template presentation This template generates a sales follow-up presentation in Google Slides after a sales call recorded in Claap. The workflow is simplified to showcase the main use case. You can customize and enrich this workflow by connecting to the CRM, researching data online or adding more files in the presentation. The presentation template used in this workflow is available here. Workflow configuration Create a webhook in Claap, by following this article. Edit the labels that trigger the workflow and route on the relevant presentation. Fill your Open AI credentials by creating an API Key in OpenAI Platform Edit the presentation personalization details (user set as editor, content, title) Fill your Slack credentials by following steps in this video.