Workflow Templates

Community Node Disclaimer: This workflow uses KlickTipp community nodes. How It Works AI Agent and KlickTipp Tools Integration via Telegram: This component connects a large language model (LLM), such as Claude or OpenAI, to the KlickTipp contact management platform through Telegram messaging. The AI Agent interprets natural language queries received from Telegram and dynamically maps them to KlickTipp API operations, enabling intuitive and automated contact handling through a familiar messaging interface. Key Features Telegram & LLM Interaction Setup: Captures messages received via Telegram bot as an alternative to the chat message node. Maintains conversation state using a memory buffer tied to Telegram chat IDs. Interprets user input using an LLM (Claude or OpenAI). Routes interpreted commands to specific KlickTipp tools based on detected intent. Sends responses back to Telegram users with operation results. KlickTipp Integration: Complete set of KlickTipp API endpoints included: Contact Management:** Add, update, get, list, delete, and unsubscribe contacts. Contact Tagging:** Tag, untag, list tagged contacts. Tag Operations:** Create, get, update, delete, list tags. Opt-In Processes:** List and retrieve opt-in process details. Data Fields:** List and get custom data fields. Redirects:** Retrieve redirect URLs. Use Cases Supported: Query contact information via email or name through Telegram messages. Identify and segment contacts by city, region, or behavior via Telegram commands. Create or update contacts from data provided in Telegram messages. Dynamically apply or remove tags to initiate campaigns through Telegram bot interactions. Automate targeted outreach based on contact attributes using Telegram as the control interface. Setup Instructions Install and Configure Nodes: Set up a Telegram bot using BotFather and obtain the bot token. Configure the Telegram Trigger node in n8n with your bot token. Configure the LLM model (e.g., OpenAI or Claude) and memory node if used. Connect all required KlickTipp nodes and authenticate using valid API credentials. Activate the workflow. Define Tagging and Field Mapping: Identify which fields and tags are relevant to your use cases. Ensure necessary tags and custom fields are already created in KlickTipp. Workflow Logic: Trigger via Telegram: A message is received by the Telegram bot and passed to the AI Agent. Query Handling via LLM Agent: AI interprets the natural language input and determines the action. Contact Search & Segmentation: Searches contacts using identifiers (email, address) or criteria. Data Operations: Retrieves, updates, or manages contact and tag data based on interpreted command. Campaign Preparation: Applies tags or sends campaign triggers depending on query results. Response via Telegram: Sends formatted results back to the Telegram user. Benefits: Mobile-First Interface:** Users can manage KlickTipp contacts directly from Telegram on any device. AI-Powered Automation:** Reduces manual contact search and tagging efforts through intelligent processing. Scalable Integration:** Built-in support for full range of KlickTipp operations allows diverse use-case handling. Data Consistency:** Ensures structured data flows between Telegram, AI, and KlickTipp, minimizing errors. Testing and Deployment: Use defined Telegram messages such as: “Tell me something about the contact with email address X” “Tag all contacts from region Y” “Send campaign Z to customers in area A” Validate expected actions in KlickTipp after message execution and confirm responses in Telegram. Notes: Customization:** Adjust tag logic, AI prompts, and contact field mappings based on project needs. Extensibility:** The template can be expanded with further logic for Google Sheets input or campaign feedback loops Resources: Use KlickTipp Community Node in n8n Automate Workflows: KlickTipp Integration in n8n

Effortlessly track your expenses with MoneyMate, an n8n workflow that transforms receipts into organized financial insights. Upload a photo or text via Telegram, and let MoneyMate extract key details—store info, transaction dates, items, and totals—using Google Vision OCR and AI-powered parsing via OpenRouter. It categorizes expenses (e.g., Food & Beverages, Transport, Household) and delivers a clean, emoji-rich summary back to your Telegram chat. Handles zero-total errors with a friendly nudge to double-check inputs. Perfect for freelancers, small business owners, or anyone seeking hassle-free expense management. No database required, ensuring privacy and simplicity. Deploy MoneyMate and take control of your finances today! Key Features 📱 Telegram Integration: Input via photo or text, receive summaries instantly. 📸 Receipt Scanning: Converts receipt images to text using Google Vision API. 🤖 AI Parsing: Categorizes transactions with OpenRouter’s AI analysis. 🛡️ Privacy-First: Processes data on-the-fly without storage. ⚠️ Smart Error Handling: Catches zero totals with user-friendly prompts. 📊 Flexible Categories: Supports Income/Expense and custom expense types. Ideal For Budget-conscious individuals** managing personal finances. Entrepreneurs** tracking business expenses. Teams** needing quick, automated expense reporting. Pre-Requirements n8n Instance:** A running n8n instance (cloud or self-hosted). Credentials:** Telegram: A bot token and webhook setup (obtained via BotFather). For more information, please refer to Telegram bots creation Google Cloud: A service account with Google Vision API enabled and API key. For more informations, please refer to Google cloud Vision OpenRouter: An account with API access for AI language model usage. Telegram Bot:* A configured *Telegram** bot to receive inputs and send summaries. Setup Instructions Import Workflow:* Copy the *MoneyMate** workflow JSON and import it into your n8n instance using the "Import Workflow" option. Set Up Telegram Bot:* Create a bot via BotFather on *Telegram** to get a token and set up a webhook. For detailed steps, refer to n8n’s Telegram setup guide. Configure Credentials:** In the Telegram Trigger, Send Error Message, and Send Expense Summary nodes, add Telegram API credentials with your bot token. In the Get Telegram File and Download Image nodes, ensure Telegram API credentials are linked. In the Google Vision OCR node, add Google Cloud credentials with Google Vision API access. In the OpenRouter AI Model node, set up OpenRouter API credentials. Test the Workflow:* Send a test receipt photo or text (e.g., "Lunch 50,000 IDR") via *Telegram** and verify the summary in your chat. Activate:** Enable the workflow in n8n to run automatically for each input. Customization Options Add Categories:* Modify the *AI Categorizer* node to include new expense types (e.g., *Entertainment**). Change Output Format:* Adjust the *Format Summary Message** node to include more details like taxes or payment methods. Switch AI Model:* In the *OpenRouter AI Model* node, select a different *OpenRouter** model for better parsing. Store Data:* Add a *Google Sheets* node after *Parse Receipt Data** to save expense records. Enhance Errors:* Include an email notification node after *Check Invalid Input** for failed inputs. Why Choose MoneyMate? Save time, reduce manual entry, and gain clarity on your spending with MoneyMate’s AI-driven workflow. Ready to streamline your finances? Get MoneyMate now! Made by: khmuhtadin Need a custom? contact me on LinkedIn or Web

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: retrieved document relevance (i.e. whether the information retrieved from a vector store is relevant to the question). The workflow takes a question and checks whether the information retrieved to answer it is relevant. To run this workflow, you need to insert documents into a vector data store, so that they can be retrieved by the agent to answer questions. You can do this by running the top part of the workflow once. The main workflow works as follows: We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info We make sure that the agent outputs the list data from the tools that it used If we’re evaluating (i.e. the execution started from the evaluation trigger), we calculate the relevance metric using AI to compare the retrieved documents with the question We pass this information back to n8n as a metric If we’re not evaluating we avoid calculating the metric, to reduce cost

This n8n workflow automates the process of parsing and extracting data from PDF invoices. With this workflow, accounts and finance people can realise huge time and cost savings in their busy schedules. Read the Blog: https://blog.n8n.io/how-to-extract-data-from-pdf-to-excel-spreadsheet-advance-parsing-with-n8n-io-and-llamaparse/ How it works This workflow will watch an email inbox for incoming invoices from suppliers It will download the attached PDFs and processing them through a third party service called LlamaParse. LlamaParse is specifically designed to handle and convert complex PDF data structures such as tables to markdown. Markdown is easily to process for LLM models and so the data extraction by our AI agent is more accurate and reliable. The workflow exports the extracted data from the AI agent to Google Sheets once the job complete. Requirements The criteria of the email trigger must be configured to capture emails with attachments. The gmail label "invoice synced" must be created before using this workflow. A LlamaIndex.ai account to use the LlamaParse service. An OpenAI account to use GPT for AI work. Google Sheets to save the output of the data extraction process although this can be replaced for whatever your needs. Customizing this workflow This workflow uses Gmail and Google Sheets but these can easily be swapped out for equivalent services such as Outlook and Excel. Not using Excel? Simple redirect the output of the AI agent to your accounting software of choice.

Who is this for? This workflow is ideal for HR teams, startups, and enterprises that want to handle employee interactions through WhatsApp and automate responses using LLM (OpenAI) and intelligent routing. What problem is this workflow solving? Managing WhatsApp messages manually can be time-consuming and error-prone. This workflow solves that by: Auto-classifying messages using LLM Routing them to the right AI-powered agent Automating leave approvals, attendance, HR FAQs, complaints, and candidate shortlisting Delivering final responses interactively via WhatsApp What this workflow does WhatsApp Trigger captures incoming messages LLM Classification analyzes message intent and outputs category (1–5) Switch Node routes the message to the correct agent: 1 → Leave Agent 2 → HR FAQ Chatbot 3 → Attendance Agent 4 → Complaint/Request Agent 5 → Shortlisting Agent Each agent performs specific tasks using tools like: Google Sheets (fetch dept head emails, JD/applicants, logs) Google Calendar (schedule meetings) Vector Search (for policy embeddings) OpenAI (transcription, classification, chatbot) Final WhatsApp Response node sends updates and interactive options to the user Setup Connect WhatsApp API (e.g., via Twilio or WhatsApp Business Cloud API) Configure OpenAI credentials Set up Google Sheets with: Employee data JD and applicants info Policy documents (for embedding) Prepare Google Calendar access Create a vector store with embedded company policy docs How to customize this workflow to your needs Update the LLM prompt to suit your company’s categories or expand to more intents Replace sample sheets with your organization’s actual data Train your own policy embeddings if needed Add/modify agents (e.g., Payroll Bot, IT Support Bot) by cloning an existing pattern Adjust the Switch Node if you add more classifications With this modular and intelligent setup, you can turn your WhatsApp into a smart HR & operations assistant powered by AI, accessible 24/7.

Automated Financial Reporting Using Google Vision OCR, Telegram & Google Sheets This workflow automates the process of recording financial transactions from photos of receipts or shopping receipts. Users simply send an image of the receipt via Telegram. The image is processed using the Google Vision API to detect text, then extracted and structured by LLM via OpenRouter. The final result is saved to Google Sheets and also displayed to the user via a Telegram bot. 🧾 Google Sheets Template Create a Google Sheet using this template: Financial Reporting 🛠️ Key Features The workflow starts when a user sends a photo of a receipt to the Telegram bot. The image is converted to text using the Google Vision API's OCR. Data processing with LLM (OpenRouter) helps identify and structure transaction elements such as: date, vendor name & address, receipt/invoice number, item list (product name, quantity, unit price, total), and transaction category. Cleaned and structured data is automatically recorded to Google Sheets per item. The system also sends a summary of the recording results in an easy to read text format. Users can also send text messages to the bot to query stored transaction data, which will be answered by a Google Sheets-based AI Agent. 🔧 Requirements Active Telegram Bot + API Token Google Vision API Key OpenRouter Account + API Key Google Sheets connected to n8n 🧩 Setup Instructions Replace all API keys and tokens with your own in the relevant nodes. Google Vision API Key: Set in 'Set Vision API' node. Telegram Bot Token: Set in 'Set Telegram Token' node and all Telegram nodes. OpenRouter API Key: Set in all OpenRouter nodes. Google Sheets: Connect your own Google Sheets credential. Use the provided Google Sheets template or your own. Activate the workflow after configuration. (Optional) Review sticky notes for step-by-step explanations.

How it works This template is a hands-on, practical exam designed to test your understanding of the fundamental JSON data types. It's the perfect way to solidify your knowledge after learning the basics. Think of it as the "driver's test" that comes after the "theory lesson". You'll be given a series of tasks, and the workflow will automatically check your answers, providing instant feedback. The test is broken down into six sequential challenges, each focusing on a core data type: String: Writing text values correctly. Number: Using integers and decimals. Boolean: Working with true and false. Null: Representing a non-existant value. Array: Creating ordered lists of data. Object: Building nested key-value structures. For each challenge, you'll modify a Set node with the correct JSON syntax. When you execute the workflow, a corresponding IF node will validate your input. A green path means you passed and can move to the next challenge. A red path means you need to try again! Set up steps Setup time: < 1 minute This workflow is a self-contained test and requires no setup or credentials. Read the instructions on the main sticky note to understand the goal. Start with the first challenge, "Test - String". Activate and modify the node according to the instructions on the purple sticky note next to it. Click "Execute Workflow". If the execution path is green, you've passed! You can move on to the next "Test" node in the sequence to continue. If the path is red, read the hint in the error message and try again. Repeat the process until you reach the final success message. Good luck!

This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremental updates to documents in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering. Here is a clear and professional description in English of the n8n workflow “Create a RAG with Qdrant and update single files”, including its benefits: Benefits Efficient RAG Setup** Seamlessly integrates OpenAI, Qdrant, and Google Drive to create a scalable RAG pipeline. Single File Update** You can replace the vector representation of a single file without reprocessing the entire collection—ideal for maintaining document freshness. Flexible File Source** Works with Google Drive, allowing document management and updates from a familiar interface. How It Works This workflow is designed to create a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as a document source. It consists of four main phases: Collection Setup**: Creates or clears a Qdrant collection to store vectorized documents. Configures the collection with cosine distance metrics and other parameters. Document Processing**: Retrieves files from a specified Google Drive folder. Downloads and processes each file (text extraction, chunking, and embedding using OpenAI). Stores the embeddings in Qdrant for vector search. Single-File Update**: Allows updating or deleting a specific file in the Qdrant collection by referencing its Google Drive ID. Re-embeds the file and updates the vector store. RAG Querying**: Uses a chat trigger to receive user questions. Retrieves relevant documents from Qdrant using vector similarity. Generates answers using Google Gemini as the language model. Set Up Steps Configure Qdrant: Replace QDRANTURL and COLLECTION in the "Create collection" and "Clear collection" HTTP nodes. Ensure Qdrant API credentials are correctly set in the credentials section. Google Drive Integration: Specify the Google Drive folder ID in the "Get files" node. Ensure Google Drive OAuth credentials are configured. OpenAI and Gemini Keys: Add OpenAI API credentials for embeddings (used in "Embeddings OpenAI" nodes). Configure Google Gemini credentials for the chat model. Single-File Update: Set the file_id in the "Edit Fields3" node to target a specific Google Drive file for updates. Testing: Trigger the workflow manually to populate the Qdrant collection. Use the chat interface to test RAG responses. Need help customizing? Contact me for consulting and support or add me on Linkedin.

⚠️ DISCLAIMER: This workflow uses the HDW LinkedIn community node, which is only available on self-hosted n8n instances. It will not work on n8n.cloud. Overview This workflow automates the entire LinkedIn lead generation process from finding prospects that match your Ideal Customer Profile (ICP) to sending personalized messages. It uses AI to analyze lead data, score potential clients, and prioritize your outreach efforts. Key Features AI-Driven Lead Generation**: Convert ICP descriptions into LinkedIn search parameters Comprehensive Data Enrichment**: Analyze company websites, LinkedIn posts, and news Intelligent Lead Scoring**: Prioritize leads based on AI analysis of intent signals Automated Outreach**: Connect with prospects and send personalized messages Requirements Self-hosted n8n instance with the HDW LinkedIn community node installed OpenAI API access (for GPT-4o) Google Sheets access HDW API key (available at app.horizondatawave.ai) LinkedIn account Setup Instructions 1. Install Required Nodes Ensure the HDW LinkedIn community node is installed on your n8n instance Command: npm install n8n-nodes-hdw (or use this instruction) 2. Configure Credentials OpenAI**: Add your OpenAI API key Google Sheets**: Set up Google account access HDW LinkedIn**: Configure your API key from horizondatawave.ai 3. Set Up Google Sheet Create a new Google Sheet with the following columns (or copy template): Name, URN, URL, Headline, Location, Current company, Industry, etc. The workflow will populate these columns automatically 4. Customize Your ICP Use chat to provide the AI Agent with your Ideal Customer Profile Example: "Target marketing directors at SaaS companies with 50-200 employees" 5. Adjust Scoring Criteria Modify the lead scoring prompt in the "Company Score Analysis" node to match your specific product/service Tune the evaluation criteria based on your unique business needs 6. Configure Message Templates Update the HDW LinkedIn Send Message node with your custom message How It Works ICP Translation: AI converts your ICP description into LinkedIn search parameters Lead Discovery: Workflow searches LinkedIn using these parameters Data Collection: Results are saved to Google Sheets Enrichment: System collects additional data about each lead: Company website analysis Lead's LinkedIn posts Company's LinkedIn posts Recent company news Intent Analysis: AI analyzes all data to identify buying signals Lead Scoring: Leads are scored on a 1-10 scale based on likelihood of interest Connection Requests: Top-scoring leads receive connection requests Follow-Up: When connections are accepted, automated messages are sent Customization Search Parameters**: Adjust the AI Agent prompt to refine your target audience Scoring Criteria**: Modify scoring prompts to highlight indicators relevant to your product Message Content**: Update message templates for personalized outreach Schedule**: Configure when connection requests and messages are sent Rate Limits & Best Practices LinkedIn has connection request limits (approximately 100-200 per week) The workflow includes safeguards to avoid exceeding these limits Consider spacing your outreach for better response rates Note: Always use automation tools responsibly and in accordance with LinkedIn's terms of service.

The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp. Set up steps: Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables in the workflow. Use the Create collection HTTP request node to initialize the collection in Qdrant. Vectorize Documents: Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. Use the Embeddings OpenAI node to generate embeddings for the downloaded files. Store the vectorized documents in Qdrant using the Qdrant Vector Store node. Configure Webhooks: Ensure both Verify and Respond webhooks have the same URL. Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. Set Up AI Agent: Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. Test Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received. 🎯 Core Features 📥 1. Automatic Message Type Detection Using the Input type node, the bot detects whether the user has sent: Text Voice messages Images Files (PDF) Other unsupported content 💬 2. Smart Text Message Handling Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt. Replies are concise, accurate, and formatted for mobile readability. 🖼️ 3. Image Analysis & Description Images are downloaded, converted to base64, and analyzed by an image-aware AI model. The output is a rich, structured description, designed for visually impaired users or visual content interpretation. 🎙️ 4. Voice Message Transcription & Reply Audio messages are downloaded and transcribed using OpenAI Whisper. The transcribed text is analyzed and answered by the AI. Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message. 📄 5. PDF Document Extraction & Summary Only PDFs are allowed (filtered via MIME type). The document’s content is extracted and combined with the user's message. The AI then provides a relevant summary or answer. 🧠 6. Contextual Memory Each user has a personalized session ID with a memory window of 10 interactions. This ensures a more natural and contextual conversation flow. How It Works Thisworkflow is designed to handle incoming WhatsApp messages and process different types of inputs (text, audio, images, and PDF documents) using AI-powered analysis. Here’s how it functions: Trigger: The workflow starts with the **WhatsApp Trigger node, which listens for incoming messages (text, audio, images, or documents). Input Routing: The **Input type (Switch node) checks the message type and routes it to the appropriate processing branch: Text: Directly forwards the message to the AI agent for response generation. Audio: Downloads the audio file, transcribes it using OpenAI, and sends the transcription to the AI agent. Image: Downloads the image, analyzes it with OpenAI’s GPT-4 model, and generates a detailed description. PDF Document: Downloads the file, extracts text, and processes it with the AI agent. Unsupported Formats: Sends an error message if the input is not supported. AI Processing: The **AI Agent1 node, powered by OpenAI, processes the input (text, transcribed audio, image description, or PDF content) and generates a response. Response Handling**: For audio inputs, the AI’s response is converted back into speech (using OpenAI’s TTS) and sent as a voice message. For other inputs, the response is sent as a text message via WhatsApp. Memory: The **Simple Memory node maintains conversation context for follow-up interactions. Setup Steps To deploy this workflow in n8n, follow these steps: Configure WhatsApp API Credentials: Set up WhatsApp Business API credentials (Meta Developer Account). Add the credentials in the WhatsApp Trigger, Get Image/Audio/File URL, and Send Message nodes. Set Up OpenAI Integration: Provide an OpenAI API key in the Analyze Image, Transcribe Audio, Generate Audio Response, and AI Agent1 nodes. Adjust Input Handling (Optional): Modify the Switch node ("Input type") to handle additional message types if needed. Update the "Only PDF File" IF node to support other document formats. Test & Deploy: Activate the workflow and test with different message types (text, audio, image, PDF). Ensure responses are correctly generated and sent back via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.