Workflow Templates

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.

Who is this template for? This workflow template is designed for DevOps, Engineering, and Managed Service Provider professionals seeking alerts on various channels, with each channel being logically chosen based on the severity of the event. How it works Each time a new event occurs, the workflow runs (powered by TwentyCRM's native Webhooks feature). After filtering for the required data from the webhook, the filtered data is logged using Google Sheets. Based on the eventType from the webhook, we conditionally select a predefined messaging channel and send updates or alerts through it. Set up instructions Complete the Set up credentials step when you first open the workflow. You'll need a Google-OAuth2.0 with Gmail API & Google Sheets Scope, Slack with OAuth2.0 - chat:write scopes. Set up the Webhook in TwentyCRM, linking the On new TwentyCRM event Trigger with your TwentyCRM App. Set the correct channel to send to in the Post message in channel step. After testing your workflow, swap the Test URL to Production URL in TwentyCRM and activate your workflow. Template was created in n8n v1.63.4.

Unlock AI-Driven Research with Jina AI (No API Key Needed!) Following the success of Open Deep Research 1.0, we are excited to introduce an improved and fully free version: AI-Powered Research with Jina AI Deep Search. This workflow leverages Jina AI’s Deep Search API, a free and powerful AI research tool that requires no API key. It automates querying, analyzing, and formatting research reports, making AI-driven research accessible to everyone. Key Features No API Keys Required** - Start researching instantly without setup hassle. Automated Deep Search* - Uses Jina AI to fetch *relevant and high-quality information**. Structured AI Reports** - Generates clear, well-formatted research documents in markdown. Flexible and Customizable* - Modify the workflow to fit *your specific research needs**. Ideal for Researchers, Writers & Students** - Speed up your research workflow. Use Cases This workflow is particularly useful for: Researchers** - Quickly gather and summarize academic papers, online sources, and deep web content. Writers & Journalists** - Automate background research for articles, essays, and investigative reports. Students & Educators** - Generate structured reports for assignments, literature reviews, or presentations. Content Creators** - Find reliable sources for blog posts, videos, or social media content. Data Analysts** - Retrieve contextual insights from various online sources for reports and analysis. How It Works The user submits a research query via chat. The workflow sends the query to Jina AI’s Deep Search API. The AI processes and generates a well-structured research report. A code node formats the response into clean markdown. The final output is a structured, easy-to-read AI-generated report. Pre-Conditions & Requirements An n8n instance (self-hosted or cloud). No API keys needed** – Jina AI Deep Search is completely free. Basic knowledge of n8n workflow automation is recommended for customization. Customization Options This workflow is fully modular, allowing users to: Modify the query prompt to refine the research focus. Adjust the report formatting to match personal or professional needs. Expand the workflow by adding additional AI tools or data sources. Integrate it with other workflows in n8n to enhance automation. Users are free to connect it with other workflows, add custom nodes, or tweak existing configurations. Getting Started Setup Time: Less than 5 minutes Import the workflow into n8n. Run the workflow and input a research topic. Receive a fully formatted AI-generated research report. Try It Now! Start your AI-powered research with Jina AI Deep Search today! Get the workflow on n8n.io

How it works This workflow converts an HTML string into a polished PDF file using the powerful open-source Gotenberg service. It's designed to be a reusable utility in your automation stack. Receives Input: The workflow is triggered with a JSON object containing the full html code as a string and a desired file_name for the output. Prepares File: It converts the incoming HTML string into a binary index.html file, which is required for the API call. Calls Gotenberg API: It sends the HTML file to a running Gotenberg instance via an HTTP request. It also dynamically sets the output filename and embeds metadata (like Author, Title, and Creation Date) directly into the PDF. Returns PDF: The workflow outputs the final binary PDF file, ready to be saved, sent in an email, or used in the next step of your main workflow. Set up steps Setup time: ~3 minutes This workflow has one critical prerequisite: a running Gotenberg instance that your n8n can connect to. 1. Prerequisite: Run Gotenberg You need to have the Gotenberg service running. The easiest way is with Docker. Add the following service to your docker-compose.yml file (the same one you use for n8n): services: ... your n8n service ... gotenberg: image: gotenberg/gotenberg:8 restart: always Then, restart your stack with docker compose up -d. This makes Gotenberg available at the address http://gotenberg:3000 from within your n8n container. 2. Use as a Sub-Workflow This workflow is ready to be used as a sub-workflow. In your main workflow, add an Execute Sub-Workflow node. In the Workflow parameter, select this "Create PDF from HTML" workflow. Provide the input data in the required format: a JSON object with html and file_name keys.

This is a simple workflow that grabs HackerNews front-page headlines from today's date across every year since 2007 and uses a little AI magic (Google Gemini) to sort 'em into themes, sends a neat Markdown summary on Telegram. How it works Runs daily, grabs Hacker News front page for this day across every year since 2007. Pulls headlines & dates. Uses Google Gemini to sort headlines into topics & spot trends. Sends a Markdown summary to Telegram. Set up steps Clone the workflow. Add your Google Gemini API key. Add your Telegram bot token and chat ID. **Built on Day-01 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!**

How It works It's very important to come prepared to Sales calls. This often means a lot of manual research about the person you're calling with. This workflow delivers the latest news about businesses you are about to interact with each day. Scans Your Calendar**: Each morning, it reviews your Google Calendar for any scheduled meetings or calls with companies. Fetches Latest News**: For each identified company, it searches the web for the most recent and relevant news articles using newsapi.org Delivers Insights**: You receive personalized emails via Gmail, each dedicated to a company you're meeting with that day, containing a curated list of news headlines, brief descriptions, and direct links to full articles. Setup steps The workflow requires you to have the following accounts set up in their respective nodes: Google Calendar GMail Besides those, there are a few parameters in the node called Setup that can be used to tweak the workflow:

Who is this for? This workflow is designed for Customer Success Managers (CSM), sales, support, or marketing teams using HubSpot CRM who want to automate customer engagement tracking when new emails arrive. It’s ideal for businesses looking to streamline CRM updates without manual data entry. Problem Solved / Use Case Manually logging email interactions in HubSpot is time-consuming. This workflow automatically parses incoming emails, checks if the sender exists in HubSpot, and either: Creates a new contact + logs the email as an engagement (if the sender is new). Logs the email as an engagement for an existing contact. What This Workflow Does Triggers when a new email arrives in a connected IMAP inbox. Parses the email using AI (OpenAI) to extract structured data. Searches HubSpot for the sender’s email address. Updates HubSpot: Creates a contact (if missing) and logs the email as an engagement. Or logs the engagement for an existing contact. Setup Configure Email Account: Replace the default IMAP node with your email provider HubSpot Credentials: Add your HubSpot API key in the HubSpot nodes. OpenAI Integration: Ensure your OpenAI API key is set for email parsing. Customization Tips Improve AI Prompt**: Modify the OpenAI prompt to extract specific email data (e.g., customer intent). Add Filters**: Exclude auto-replies or spam by adding a filter node. Extend Functionality**: Use the parsed data to trigger follow-up tasks (e.g., Slack alerts, tickets). Need Help? Contact thomas@pollup.net for workflow modifications or help. Discover my other workflows here

How it works This workflow runs on a daily schedule. It starts by scraping real estate-related queries from Google using Apify. The organic search results are parsed and summarized into a single text block. That text is then sent to an AI model (GPT-4o) which extracts the top 3 pain points faced by real estate agents based on current online sentiment. The workflow compares today's insights with yesterday's data stored in Airtable to detect recurring or new pain points. Finally, it sends a summary notification via Telegram and stores the current day's insights into Airtable for trend tracking. How to set up Clone or import the workflow into your n8n instance. Get an Apify API token and insert it into the HTTP Request node. Create an Airtable base with a table containing two fields: "Date" (text) and "Summary" (long text). Copy the Base ID and Table ID into the Airtable nodes. Connect your Telegram bot and replace the chat ID in the Telegram node. Set up OpenAI credentials with GPT-4o or GPT-4o-mini for the LLM node. Run once manually to test, then activate the schedule trigger to run daily. (Optional) Extend the flow to generate cold outreach emails based on pain points, or sync to Notion/CRM.