Workflow Templates

This workflow is especially suitable for founders and operators offering services to their clients and regularly scheduling sales or project update meetings. How it works When a booking is created, rescheduled, or canceled in cal.com, this workflow syncs the meeting and contact data into Notion. When a new booking is scheduled: Creates a meeting in the dedicated Notion database. Here we can customize all the information to include on the meeting page (e.g., mapping the answers to custom questions). Finds the Contact(s) in the dedicated Notion database (based on the email). If the Contact(s) exists, it links the contact(s) to the newly created meeting. If the Contact(s) doesn’t exist, it creates the contact(s) and links them to the newly created meeting. When a booking is rescheduled: The automation finds the event in Notion (based on the “cal id” property) It updates the event date and time in Notion When a booking is cancelled: The automation deletes the event in Notion (i.e., it archives the page, which remains available in the Trash for 30 days) Requirements A Cal account and API key. A Notion account and connection with access to all the databases involved (Meetings, Contacts). Find all your connections, manage their access, or create a new connection on your Notion Integrations page. A Meetings and Contacts database in Notion, both accessible by the Integration (see step 2 above). The database names don't matter. You will input your database IDs in the workflow. Find a Notion database ID in the URL between the slash characters. Notion database column specifications In the Meetings database, these are required properties: Event time (date) cal id (text) Contacts (relation) Name In the Contacts database, these are required properties: Name Email Meetings (relation) Read the essay and watch the video for a detailed walkthrough.

🗂️ Bulk File Upload to Google Drive with Folder Management How it works User submits files and target folder name via form Workflow checks if folder exists in Drive Creates folder if needed or uses existing one Processes and uploads all files maintaining structure Set up steps (Est. 10-15 mins) Set up Google Drive credentials in n8n Replace parent folder ID in search query with your Drive folder ID Configure form node with: Multiple file upload field Folder name text field Test workflow with sample files 💡 Detailed configuration steps and patterns are documented in sticky notes within the workflow. Perfect for: Bulk file organization Automated Drive folder management File upload automation Maintaining consistent file structures

What is it This workflow aims to build a simple bot that will send a message to a telegram channel every time there is a new saved item to the Reader. This workflow can be easily modify to support other way of sending the notification, thanks to existing n8n nodes. Warning: This is only for folks who already have access to the Reader, it won't work if you don't Also, this workflow use a file to store the last update time in order to not sync everything everytime. Setup The config node : It contains the telegram channel id It also contains the file used as storage To get the header auth, you have to : Go to the reader Open the devtools, Option + ⌘ + J (on macOS), or Shift + CTRL + J (on Windows/Linux) Go to network and find a profile_details/ request, click on it Go to Request Headers Copy the value for "Cookie" In n8n, set the name of the Header auth account to Cookie and the value with the one you copied before

This flexible template scrapes business listings for any industry and location, perfect for sales teams, marketers, and researchers. Good to know Works with any business category (restaurants, contractors, retailers, etc.) Fully customizable search parameters Results automatically organized in Google Sheets Built-in delay ensures scraping completes before data collection How it works Trigger: Manual or scheduled start Apify Configuration: Sets scraping parameters (industry, location, data fields) Scraping Execution: Runs the web scraping job Data Processing: Cleans and structures the raw data Storage: Saves results to your Google Sheets What is Apify? Apify is a webscraping tool, in this workflow the data is scraped from a google maps scraper: https://apify.com/compass/crawler-google-places How to use Apify Small # Lead Generation (Purple) https://apify.com/compass/crawler-google-places Add location and industry to scrape (Apify) Add the number of leads to output (Apify) Copy over the JSON file into N8N Copy & paste API endpoint "Get Run URL" in N8N Apify Large # Lead Generation (Grey) Configure the Manual Trigger When clicking 'Execute workflow' node is ready to use as-is This triggers the entire lead generation process Setup "Start Results (Apify)" Node Get Your Apify API Information Go to Apify.com and create a free account Navigate to Settings → Integrations → API tokens Copy your API token Find the Google Maps scraper actor ID: Configure the HTTP Request (start results) Method: POST URL: Replace "enter apify (get run)" with: https://api.apify.com/v2/acts/nwua9Gu5YrADL7ZDj/runs?token=YOUR_API_TOKEN C. Customize the JSON Body Parameters In the JSON body, modify these key fields: Location & Search: "locationQuery": Change "Toronto" to your target city "searchStringsArray": Change ["barber"] to your business type Examples: ["restaurants"], ["dentists"], ["contractors"] Configure the HTTP Request (start results) Method : Get Url: enter the get dataset URL from Apify Split Out Node Select fields to append in the google sheet Test the Configuration Click Execute workflow to test Check that the Apify job starts successfully Note the job ID returned for the next section This section initiates the scraping process and should complete in 30-60 seconds depending on your lead count. Setup Google Sheets Create a new Google Sheet with these columns: title (business name) address (full address) state (state/province) neighborhood (area/district) phone (contact number) emails (email addresses) Copy your Google Sheets document ID for workflow configuration Requirements Apify account Google Sheets document Google OAuth credentials Customization Options For different use cases: Lead Gen: Get business leads Local SEO: Collect competitor data Market Research: Analyze industry trends Advanced mofications: Add email enrichment Integrate with CRM systems Set up automatic daily runs

This n8n workflow automates SEO keyword research by querying the Ahrefs API for keyword data and related keyword insights. The enriched data is then processed by an AI agent to format a response and provide valuable SEO recommendations. Perfect for SEO specialists, content marketers, digital agencies, and anyone looking to gain valuable insights into keyword opportunities to boost their rankings. ⚙️ How This Workflow Works This workflow guides you through the entire SEO keyword research process, from entering the initial keyword to receiving detailed insights and related keyword suggestions. 1. 🗣️ User Input (Keyword Query) The user enters a keyword they want to research. This input is captured by the Chat Input Node, ready for analysis. 2. 🤖 AI Agent (Input Verification) The AI Agent reviews the keyword input for any grammatical errors or extra commentary. If necessary, it cleans the input to ensure a seamless query to the API. 3. 🔑 Ahrefs API (Keyword Data Retrieval) The cleaned keyword is sent to the Ahrefs Keyword Tool API. This retrieves a detailed report including metrics like search volume, keyword difficulty, and CPC. 4. 💡 Related Keywords Extraction (Using JavaScript Function) The workflow uses a JavaScript function to extract main keyword data and 10 related keywords data from the Ahrefs response. You can tweak the script to adjust the number of related keywords or the level of detail you want. 5. 🧠 AI Agent (Text Formatting) The aggregated data, including both the main keyword and related keywords, is sent to an AI agent. The AI agent formats the data into a concise, readable format that can be shared with the user. 6. 📨 Final Response The formatted text is delivered to the user with keyword insights, recommendations, and related keyword suggestions. ✅ Smart Retry & Error Handling Each subworkflow includes a fail-safe mechanism to ensure: ✅ Proper error handling for any issues with the API request. 🕒 Failed API requests are retried after a customizable period (e.g., 2 hours or 1 day). 💬 User input validation prevents any incorrect or malformed queries from being processed. 📋 Ahrefs API Setup To use this workflow, you’ll need to set up your Ahrefs API credentials: 🔑 Ahrefs API Sign up for an Ahrefs account and get your key here: Ahrefs Keyword Tool API Once signed up, you'll receive an API key, which you’ll use in the x-rapidapi-key header in n8n. Ensure you check the Ahrefs Keyword Tool API documentation for more details on available parameters. 📥 How to Import This Workflow Copy the json code. Open your n8n instance. Open a new workflow. Paste anywhere inside the workflow. Voila. 🛠️ Customization Options Adjust the number of related keywords extracted (default is 10). Customize the AI agent response formatting or add specific recommendations for users. Modify the JavaScript function to extract different metrics from the Ahrefs API. 🧪 Use Case Example Trying to optimize your blog post around a specific keyword? Query a broad keyword, like “SEO tips”. Get related keyword data and search volume insights. Use the AI agent to provide keyword recommendations and additional topics to target. 💥 Boost your content strategy with fresh keywords and relevant search data!

This n8n template imports an XLSX containing terms dates for a university, extracts the relevant events using AI and converts the events to an ICS file which can be imported into iCal, Google Calendar or Outlook. Manually adding important term dates to your calendar by hand? Stop! Automate it with this simple AI/LLM-powered document understanding and extraction template. This cool use-case can be applied to many scenarios where Excel files are predominantly used. How it works The term dates excel file (xlsx) are imported into the workflow from the university's website using the http request node. To parse the excel file, we use an external service - Cloudflare's Markdown Conversion Service. This converts the excel's sheets into markdown tables which our LLM can read. To extract the events and their dates from the markdown, we can use the Information Extractor node for structured output. LLMs are great for this use-case because they can understand the layout; one row may have many data points. With our data, there are endless possibilities to use it! But for this demonstration, we'll generate an ICS file so that we can import the extracted events into our calendar. We use the Python code node to combine the events into the ICS spec and the "Convert to File" node to create the ICS binary. Finally, let's distribute the ICS file by email to other students or instructors who may also find this incredibly helpful for the upcoming semester! How to use Ensure you're downloading the correct excel file and amend the URL parameter of the "Get Term Dates Excel" as necessary. Update the gmail node with your email or other emails as required. Alternatively, send the ICS file to Google Drive or a student portal. Requirements Cloudflare Account is required to use the Markdown Conversion Service. Gemini for LLM document understanding and extraction. Gmail for email sending. Customising the workflow This template should work for other Excel files which - for a university - there are many. Some will be more complicated than others so experiment with different parsers and extraction tools and strategies.

📰 Comprehensive Reuters News Intelligence System With Brightdata & Telegram Alerts A powerful n8n automation workflow that scrapes the latest Reuters news articles using Bright Data's web scraping capabilities and delivers intelligent news summaries directly to your Telegram chat. 📋 Overview This workflow provides an automated news intelligence solution that monitors Reuters for breaking news, analyzes content using Claude AI, and delivers personalized news alerts. Perfect for journalists, researchers, traders, and anyone who needs real-time access to Reuters content with AI-powered insights. ✨ Key Features 🎯 Form-Based Input: Easy web form to specify keywords and news type preferences 🤖 AI-Powered Processing: Uses Claude 4 Sonnet for intelligent content analysis 🌐 Professional Scraping: Leverages Bright Data's Reuters dataset for reliable data extraction 📱 Telegram Integration: Instant notifications delivered to your preferred chat ⏰ Smart Waiting: Built-in delays to ensure data processing completion 🔄 Status Monitoring: Automatic scraping status checks with retry logic 📊 Data Formatting: Clean, structured output with essential article fields 🚀 Scalable Design: Handles multiple articles with batch processing 🎯 What This Workflow Does Input Keywords**: Search terms for Reuters articles (e.g., "Election", "Gas shocks", "Technology") News Type**: Sorting preference (newest, oldest, relevance) Form Submission**: Web-based interface for easy interaction Processing Form Trigger: Captures user input via web form interface AI Agent Orchestration: Claude processes requirements and coordinates actions Bright Data Request: Initiates Reuters scraping with specified keywords Status Monitoring: Checks scraping progress with smart retry logic Data Retrieval: Fetches completed article data when ready Content Processing: Extracts and formats essential article information Telegram Delivery: Sends structured news updates to specified chat Output Data Points | Field | Description | Example | |-------|-------------|---------| | article_title | The main headline of the article | "Global Energy Markets Face Uncertainty" | | headline | Reuters display headline | "Oil Prices Surge Amid Supply Concerns" | | description | Article summary/meta description | "Energy markets react to geopolitical tensions..." | | content | Full article body text | "LONDON (Reuters) - Oil prices jumped 3%..." | | article_url | Direct link to Reuters article | "https://reuters.com/business/energy/..." | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Bright Data account with Reuters dataset access Telegram bot and channel setup Claude API access (Anthropic) 15-20 minutes for complete setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON content and click Import Save the workflow with a descriptive name Step 2: Configure Bright Data Integration Set up Bright Data credentials: In n8n: Credentials → + Add credential → HTTP Header Auth Name: "Bright Data API" Add header: Authorization: Bearer YOUR_BRIGHT_DATA_API_KEY Test the connection Configure Reuters dataset: Ensure access to dataset ID: gd_lyptx9h74wtlvpnfu Verify Reuters scraping permissions in Bright Data dashboard Check monthly quota and usage limits Step 3: Configure Anthropic Claude Integration Set up Anthropic credentials: In n8n: Credentials → + Add credential → Anthropic API Enter your Anthropic API key Test the connection Update model settings: Open "Anthropic Chat Model" node Verify model is set to: claude-sonnet-4-20250514 Adjust temperature and other parameters if needed Step 4: Configure Telegram Notifications Create Telegram Bot: Message @BotFather on Telegram Use /newbot command and follow instructions Save the bot token provided Get Chat ID: Add your bot to desired channel/group Send a test message Visit: https://api.telegram.org/bot{BOT_TOKEN}/getUpdates Find your chat ID in the response Set up Telegram credentials: In n8n: Credentials → + Add credential → Telegram API Enter bot token from BotFather Test the connection Update Telegram node: Open "Telegram" node Replace DEMO_CHAT_ID with your actual chat ID Customize message format if needed Step 5: Configure Web Form Set up form trigger: Open "On form submission" node Note the webhook URL provided Customize form title and fields if needed Test form functionality: Access the webhook URL in your browser Fill out test form with sample keywords Verify form submission triggers workflow Step 6: Update Node Configurations Update HTTP Request nodes: Replace BRIGHT_DATA_API_KEY with actual credentials reference Verify dataset ID matches your Bright Data setup Check request parameters and headers Configure Data Formatting: Open "Data Formatting" node Review JavaScript code for field extraction Modify output fields if additional data needed Step 7: Test & Activate Run initial test: Submit form with test keywords (e.g., "Technology") Monitor workflow execution in n8n Check for Telegram message delivery Verify data flow: Confirm Bright Data snapshot creation Check status monitoring functionality Validate final data formatting Activate workflow: Toggle workflow to "Active" status Monitor for any execution errors Set up error notifications if needed 📖 Usage Guide Submitting News Requests Access the form: Navigate to your webhook URL Form title: "Reuters News Intelligence" Fill required fields: Keywords: Enter search terms (e.g., "Climate Change", "Tech Earnings") News Type: Select sorting preference: newest: Most recent articles first oldest: Historical articles first relevance: Best matching articles Submit and wait: Click submit to trigger workflow Expect 1-3 minutes for processing Check Telegram for article delivery Understanding the Process The workflow follows this sequence: Form submission triggers Claude AI agent Claude coordinates all scraping and processing steps Bright Data scrapes Reuters with your keywords System waits for scraping completion (60 seconds) Status check confirms data readiness Article data is retrieved and formatted Telegram message delivers final results Reading Telegram Results Each article includes: Clickable URL** to full Reuters article Headline** for quick scanning Description** with article summary Content preview** with key details 🔧 Customization Options Modifying Search Parameters Edit the "HTTP Request" node to adjust: { "keyword": "Your search terms", "sort": "newest|oldest|relevance", "limit_per_input": "2-10 articles" } Customizing Telegram Messages Update the "Telegram" node message format: 🗞️ {{ $json.heading }} 📖 {{ $json.description }} 🔗 Read Full Article 📅 Retrieved: {{ $now.format('YYYY-MM-DD HH:mm') }} Adding Email Notifications Add "Email" node after "Data Formatting" Configure SMTP credentials Create HTML email template with article data Connect to same input as Telegram node Enhancing AI Processing Modify the MCP Agent prompt to: Request specific article sections Add sentiment analysis Include market impact assessment Generate executive summaries Extract key quotes and statistics Adding Data Storage Include database storage by: Adding "Postgres" or "MySQL" node Creating articles table with schema Storing full article data for analysis Building historical news database 🚨 Troubleshooting Common Issues & Solutions 1. "Bright Data snapshot failed" Cause**: Invalid API key or dataset access Solution**: Verify credentials and dataset permissions in Bright Data dashboard 2. "No articles found" Cause**: Keywords too specific or no matching content Solution**: Try broader search terms, check Reuters availability 3. "Telegram message not sent" Cause**: Invalid bot token or chat ID Solution**: Re-verify bot setup with @BotFather, confirm chat ID 4. "Workflow timeout" Cause**: Bright Data scraping taking too long Solution**: Increase timeout in "sleep tool" or add retry logic 5. "Data formatting errors" Cause**: Unexpected response structure from Bright Data Solution**: Check "Data Formatting" node logs, adjust parsing logic 6. "Claude API errors" Cause**: API key issues or rate limiting Solution**: Verify Anthropic credentials, check usage limits Advanced Troubleshooting Monitor execution logs** in n8n for detailed error messages Test individual nodes** by running them separately Verify JSON structures** ensure data flows correctly between nodes Check rate limits** for both Bright Data and Claude API Add error handling** implement try-catch logic for robust operation 📊 Use Cases & Examples 1. Financial News Monitoring Goal: Track market-moving Reuters financial news Keywords: "earnings", "fed rates", "market outlook" Instant alerts for breaking financial news Support trading and investment decisions 2. Competitive Intelligence Goal: Monitor industry-specific news for business insights Keywords: Company names, industry terms Track competitor mentions and market developments Generate competitive analysis reports 3. Crisis Communications Goal: Stay informed during breaking news events Keywords: "breaking", location names, event types Rapid response to developing situations Crisis management team notifications 4. Research & Academia Goal: Gather news data for academic research Keywords: Research topics, geographic regions Build datasets for media analysis Track news coverage patterns over time ⚙ Advanced Configuration Scaling for High Volume To handle larger news monitoring needs: Increase batch processing: Modify limit_per_input parameter Add parallel processing branches Implement queue management Add rate limiting: Insert delays between requests Monitor API usage quotas Implement exponential backoff Database integration: Store articles in PostgreSQL/MySQL Add deduplication logic Create search and filter capabilities Multi-Channel Distribution Expand beyond Telegram: Slack integration: Add Slack webhook node Format messages for team channels Include interactive buttons Email newsletters: Compile daily/weekly summaries HTML formatting with images Subscriber management API endpoints: Create webhook responses Build news API for other systems Real-time data streaming AI Enhancement Options Leverage Claude's capabilities further: Sentiment analysis: Add sentiment scoring to articles Track market sentiment trends Generate mood indicators Summarization: Create executive summaries Extract key points Generate abstracts Classification: Categorize articles by topic Tag with relevant industries Priority scoring system 📈 Performance & Limits Expected Performance Single request**: 60-120 seconds average processing time Articles per request**: 2-10 (configurable) Data accuracy**: 95%+ for standard Reuters articles Success rate**: 90%+ for accessible content Daily capacity**: Limited by Bright Data quotas Resource Usage Memory**: ~200MB per execution API calls**: 1 Bright Data + 1 Claude + 1 Telegram per execution Bandwidth**: ~5-10MB per article scraped Execution time**: 1-3 minutes per request Scaling Considerations Rate limiting**: Respect API quotas and limits Error handling**: Implement comprehensive retry logic Data validation**: Verify article quality and completeness Cost monitoring**: Track API usage across services Performance optimization**: Cache common requests when possible 🤝 Support & Community Getting Help n8n Community**: community.n8n.io Bright Data Support**: Contact through dashboard Anthropic Documentation**: docs.anthropic.com Telegram Bot API**: core.telegram.org/bots Contributing Share workflow improvements with the community Report issues and suggest enhancements Create variations for specific news sources Document best practices and optimizations 📋 Quick Setup Checklist Before You Start ☐ n8n instance running (self-hosted or cloud) ☐ Bright Data account with Reuters dataset access ☐ Anthropic API key for Claude access ☐ Telegram bot created via @BotFather ☐ 20 minutes for complete setup Setup Steps ☐ Import Workflow - Copy JSON and import to n8n ☐ Configure Bright Data - Set up API credentials and test ☐ Configure Claude - Add Anthropic API credentials ☐ Setup Telegram - Create bot and get chat ID ☐ Update Credentials - Replace all demo values with real ones ☐ Test Form - Submit test request and verify flow ☐ Check Telegram - Confirm message delivery ☐ Activate Workflow - Turn on for production use Ready to Use! 🎉 Your workflow form URL: https://your-n8n-instance.com/webhook/your-webhook-id 🎯 Happy News Monitoring! This workflow provides a solid foundation for automated Reuters news intelligence. Customize it to fit your specific monitoring needs and use cases. The combination of Bright Data's reliable scraping, Claude's AI analysis, and Telegram's instant delivery creates a powerful news monitoring solution.

Smart Document Parser for Invoices, Logs or Sensor Reports (PDF/Image to Google Sheets) This n8n workflow automatically parses documents such as invoices, sensor logs or structured PDFs/images (including scanned docs or CSVs), extracts key fields like totals, dates and customer/vendor info using OCR and AI, and writes the structured output into Google Sheets. Who’s it for Finance or Ops teams automating invoice processing. SaaS platforms parsing uploaded reports or documents. Anyone needing a no-code backend for PDF/image/CSV document parsing. AI-powered data capture pipelines. How it works Webhook Trigger receives file uploads (/uploadDoc) Switch Node checks the file type: If image → Use Tesseract OCR If PDF → Use PDF parser If CSV → Extract as-is Extracted text is passed to: Google Gemini or Gemini Flash AI model Prompt extracts fields like invoice_id, total, customer_name, etc. JSON string is parsed and cleaned Data is appended to Google Sheets using appendOrUpdate How to set up Create a Google Sheet with columns like: invoice_id, invoice_date, due_date, customer_name, vendor_name, subtotal, tax_total, total, currency Connect: Google Sheets OAuth Google Gemini (PaLM API key) for LLM parsing Deploy the webhook endpoint: /uploadDoc Upload sample files (PDFs, images, CSVs) to test Review and map sheet columns in the Invoice Data node Requirements | Tool | Purpose | | ------------- | --------------------------------- | | n8n | Automation framework | | Google Sheets | To store structured output | | Tesseract OCR | For scanned image text extraction | | Google Gemini | For natural language parsing | How to customize Add extraction for line items using structured prompts. Change prompt to extract sensor readings, log types, or custom keys. Add support for other file types (e.g., XLSX, DOCX). Add Slack/Email notifications on success/failure. Swap Gemini with OpenAI or Hugging Face if preferred. Add‑ons Save uploaded files to Google Drive or S3 Add auth for secure uploads Use charting/dashboard nodes to visualize extracted data Integrate with billing/accounting software Use Case Examples | Scenario | What Happens | | ----------------------- | ------------------------------------------------------- | | Invoice Upload (PDF) | Extracts totals, customer, tax data into a Google Sheet | | Scanned Receipt (Image) | OCR + LLM extracts structured data | | Log File (CSV) | Parses and logs entries into Sheets | Common troubleshooting | Issue | Possible Cause | Solution | | --------------------------------- | ----------------------- | ------------------------------------------- | | Webhook not triggered | URL or method mismatch | Use correct POST URL /uploadDoc | | Text is blank | OCR failed | Check image quality or Tesseract config | | Gemini model not returning JSON | Prompt formatting issue | Ensure prompt ends with valid JSON schema | | Sheet not updated | Invalid Sheet ID or tab | Double-check sheet credentials and tab name | Need Help? Need help fine-tuning the Gemini prompt for better field accuracy? Want to extract full tables, multi-page invoices or convert PDFs to JSON lines? Our automation team at WeblineIndia can help you extend this into a full-blown document automation pipeline.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. AI dental appointment booking with Google Calendar and Sheets Who's it for This workflow is perfect for dental practices, medical offices, and healthcare providers who want to automate their appointment scheduling process. It's ideal for practices that receive high volumes of appointment requests and want to reduce manual booking while maintaining accurate patient records. What it does This AI-powered voice agent handles complete appointment booking workflows for "Pearly Whites Dental." When patients call or submit requests, the system: Analyzes the request using Google Gemini AI to understand patient needs Checks calendar availability in real-time via Google Calendar integration Automatically finds and offers up to 2 available appointment slots when the preferred time isn't available Books confirmed appointments directly to the practice calendar Logs all patient information (name, insurance, concerns) to Google Sheets for record-keeping Maintains conversation context across interactions for natural dialogue flow The workflow operates in Central Time Zone and assumes standard business hours (8 AM - 5 PM, excluding lunch). How it works The system receives webhook requests containing patient interaction data. The AI agent processes this information and determines which tools to use based on the request type. For availability checks, it intelligently searches multiple time slots in 30-minute increments until finding suitable options. All appointments are automatically formatted as "Dental Appointment | [Patient Name]" and logged with complete patient details. Requirements Google Calendar API access with OAuth2 credentials Google Sheets API access for patient data logging Google Gemini API key for AI processing Webhook endpoint for receiving requests Pre-configured Google Calendar and Sheets document How to set up Configure Google Calendar credentials in the calendar tool nodes Set up Google Sheets integration with your patient tracking spreadsheet Add your Google Gemini API key to the language model node Update the calendar ID in both calendar nodes to match your practice calendar Modify the Google Sheets document ID to point to your patient records sheet Test the webhook endpoint to ensure proper request processing How to customize the workflow Adjust business hours** by modifying the availability checking logic in the system prompt Change appointment duration** by updating the end time calculation (currently set to 1 hour) Modify patient data fields** by updating the Google Sheets column mapping Update practice name** by changing "Pearly Whites Dental" references in the system prompt Customize response format** by adjusting the AI agent's instructions for different appointment types

📋 Description This template creates an intelligent AI assistant for WhatsApp that can: Respond naturally** to messages using Google Gemini AI Remember previous conversations** for each user Access a knowledge base** for answering frequently asked questions Automatically save** all conversations for long-term memory 🛠️ Requirements 1. WAMM.pro Account (FREE tier available) What is WAMM.pro?** - A platform that enables WhatsApp automation using proprietary API technology Free tier:** 50 messages/month PRO tier:** Unlimited messages + advanced features Link:** wamm.pro 2. Pinecone Account (for AI memory) For storing conversations and knowledge base Free tier available 3. Google AI Account (for Gemini) For the conversational AI model 4. OpenAI Account (for embeddings) For generating memory vectors 🚀 Step-by-step Setup Step 1: WAMM.pro Configuration Create account at wamm.pro Account Manager → Add WhatsApp profile Scan QR code with your WhatsApp Note down: Instance ID and Access Token Step 2: Webhook Configuration In WAMM.pro: Integrations → Webhooks → Messages Webhooks Add Webhook with the n8n URL Required configuration: From others: ✅ Relevant + ✅ Without media + ✅ Exclude no text To others: ✅ Relevant + ✅ Without media + ✅ Exclude no text To myself: ✅ None (to avoid responding to own messages) Step 3: Pinecone Configuration Create 2 indexes: historywa - for conversation memory knowledge - for knowledge base Index settings: Dimensions: 3072 Metric: cosine Embedding model: text-embedding-3-large Step 4: n8n Configuration Configure credentials: WAMM: Instance ID + Access Token Pinecone: API Key Google Gemini: API Key OpenAI: API Key for embeddings 🔧 How it Works Workflow Flow: 📱 WhatsApp Message ↓ (webhook) 🎯 AI Agent (Gemini) ↓ (uses tools) 📚 Memory Tool + Knowledge Tool ↓ (response generated) 📤 WAMM Send Message ↓ (saves) 💾 Pinecone Memory Storage Available AI Tools: Memory Tool - Searches previous conversations with the user Knowledge Tool - Searches the general knowledge base Special Features: Natural conversations** - AI doesn't mention "searching history" Persistent context** - Remembers names, preferences, previous conversations User language detection** - Automatically responds in user's language Organized memory** - Each user has their own memory space 📊 Benefits ✅ Zero maintenance - Runs automatically ✅ Scalable - Supports multiple users simultaneously ✅ Intelligent memory - Uses similarity search for relevant context ✅ Extensible - Easy to add new features ✅ Cost-effective - Free tiers available for all services 🎯 Use Cases Automated customer support** with memory Personal assistant** for WhatsApp Business chatbot** with specific knowledge Conversation automation** with persistent context 🔒 Security Data** stored in Pinecone as vector embeddings No plain text** message storage Each user** has separate memory space API keys** secured in n8n credentials 📈 Possible Extensions CRM** integrations Scheduling** and reminders Advanced multi-language** support Analytics** and conversation reports Custom knowledge bases** per user 💡 Tip: For optimal results, populate the knowledge base with frequently asked questions specific to your business!

This n8n template demonstrates how to automate invoice data extraction from PDF attachments received via Gmail. Using LlamaParse and Gemini LLM, this workflow parses structured fields like PO numbers, line items, tax amounts, and totals — and stores them neatly into a Google Sheet. Perfect for use cases such as: 💼 Finance teams managing vendor invoices 📊 Bookkeeping workflows 🔄 Automating monthly reconciliation Good to Know At the time of writing, LlamaParse and Gemini may involve API usage costs depending on your subscription tier. Check LlamaIndex Pricing and Gemini Pricing for updated info. LlamaParse provides Markdown-formatted parsed output which is then passed to an LLM for structured field extraction. Gemini models may be geo-restricted. If you encounter "model not found" errors, your region might not be supported. How it Works Trigger: Watches your Gmail for new emails with PDF attachments. Email Filter: Ensures we only parse fresh emails not already labeled as "invoice synced". LlamaParse Upload: Uploads the PDF to LlamaParse’s parsing endpoint. Status Polling: Periodically checks whether the parsing is complete. Download Markdown: Once ready, it fetches the parsed invoice in Markdown format. AI Parsing with Gemini: Sends the Markdown to Gemini LLM to extract structured JSON (like PO number, line items, taxes, etc.) using a predefined schema. Google Sheets Upload: Stores extracted data into a predefined spreadsheet. Labeling: Marks the email as “invoice synced” to avoid reprocessing. How to Use The trigger is based on Gmail, but you can replace this with a webhook or manual trigger for testing. Setup Instructions Gmail API Enable Gmail API in Google Cloud Console. Connect your Gmail account in n8n credentials. Allow read + modify access. Google Sheets Create a new Google Sheet with the following headers (row 1): Date | Vendor Name | Invoice Number | PO Number | Line Items | Subtotal | Tax | Total Amount Connect Google Sheets in n8n and paste the Sheet ID in the node. You can customise the google sheet basis your requirement. LlamaParse Get a LlamaIndex API Key from LlamaIndex. Use the LlamaParse upload and polling nodes to process your PDFs. Gemini (via Vertex AI) Set up Gemini access in GCP. Use the Gemini 2.5 Model. Construct a structured prompt to extract required fields. Labeling Create a Gmail label named "Invoice Synced" for tracking processed emails. Requirements Gmail account with API access LlamaParse (LlamaIndex) account with API Key Google Sheets API credentials Access to Gemini 2.5 model via Google Vertex AI Customising This Workflow This template is just the beginning. You can expand it to: Auto-generate invoices back to vendors Run duplicate checks before inserting into Sheets Integrate with accounting tools like Zoho, QuickBooks, or Tally Trigger Slack/Email notifications on specific vendors or high invoice amounts

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Overview When applying for freelance jobs on Upwork, minutes matter. The first quality application is more often than not the one that's ultimately selected. Subscribers to Upwork's Freelancer Plus receive email job alerts, but filters are very limited. As a result, it takes a lot of time to manually go through each email and determine if each job fits your criteria. This workflow scans your Gmail every few minutes, finds all Upwork job alerts, scores them based on your profile/preferences, and sends a Slack channel message for jobs that are strong potential matches. How it works Scans Gmail for Upwork job alerts every few minutes Extracts all available job data from each email Scores the job based on profile information and criteria you provide Sends a Slack notification for all jobs that meet a given score threshold Disclaimers This workflow polls Gmail for new messages every 10 minutes. A workflow execution will be used each time, regardless of whether the Gmail scan finds anything. You may want to adjust this frequency based on the amount of workflow executions you want to use. The AI matching process is based only on the information included in the email body (job title, description snippet and metadata). It is against Upwork's Terms of Service to scrape a full job posting. Despite this, the quality of the results in our testing is high for most use cases. Required Setup Subscribe to Upwork's Freelancer Plus plan to enable job alerts ($19.99/mo at the time of this posting) Create Gmail and Open Router (or an LLM provider of your choice) credentials and select them in the Gmail / LLM Model nodes Create a Slack app that has at least the chat:write.public and channels:read scopes, install it into your workspace, and use your apps OAuth Token to create a Slack API credential in n8n IMPORTANT: In the "Opportuntity Scorer" node, replace the text in between the <my_profile> tags with your freelancer bio. For best results, include as much detail as possible about your skillset, experience, tool familiarity, and job preferences. Update the filter with your notification threshold preference(s) and update the Slack channel to send notifications to in the last Slack node If you have any questions or feedback about this workflow, or would like me to build custom workflows for your business, email me at n8n@paperjam.agency.