Workflow Templates

This workflow enriches new Pipedrive organization's data by adding a note to the organization object in Pipedrive. It assumes there is a custom "website" field in your Pipedrive setup, as data will be scraped from this website to generate a note using OpenAI. Then, a notification is sent in Slack. ⚠️ Disclaimer This workflow uses a scraping API. Before using it, ensure you comply with the regulations regarding web scraping in your country or state. Important Notes The OpenAI model used is GPT-4o, chosen for its large input token capacity. However, it is not the cheapest model if cost is very important to you. The system prompt in the OpenAI Node generates output with relevant information, but feel free to improve or modify it according to your needs. How It Works Node 1: Pipedrive Trigger - An Organization is Created This is the trigger of the workflow. When an organization object is created in Pipedrive, this node is triggered and retrieves the data. Make sure you have a "website" custom field in Pipedrive (the name of the field in the n8n node will appear as a random ID and not with the Pipedrive custom field name). Node 2: ScrapingBee - Get Organization's Website's Homepage Content This node scrapes the content from the URL of the website associated with the Pipedrive Organization created in Node 1. The workflow uses the ScrapingBee API, but you can use any preferred API or simply the HTTP request node in n8n. Node 3: OpenAI - Message GPT-4o with Scraped Data This node sends HTML-scraped data from the previous node to the OpenAI GPT-4o model. The system prompt instructs the model to extract company data, such as products or services offered and competitors (if known by the model), and format it as HTML for optimal use in a Pipedrive Note. Node 4: Pipedrive - Create a Note with OpenAI Output This node adds a Note to the Organization created in Pipedrive using the OpenAI node output. The Note will include the company description, target market, selling products, and competitors (if GPT-4o was able to determine them). Node 5 & 6: HTML To Markdown & Code - Markdown to Slack Markdown These two nodes format the HTML output to Slack Markdown. The Note created in Pipedrive is in HTML format, as specified by the System Prompt of the OpenAI Node. To send it to Slack, it needs to be converted to Markdown and then to Slack Markdown. Node 7: Slack - Notify This node sends a message in Slack containing the Pipedrive Organization Note created with this workflow.

This workflow allows you to recognize a folder with receipts or invoices (make sure your files are in .pdf, .png, or .jpg format). The workflow can be triggered via the "Test workflow" button, and it also monitors the folder for new files, automatically recognizing them. Video Demo https://youtu.be/mGPt7fqGQD8 1. n8n import glitch After import, the trigger node "When clicking 'Test workflow'" might be disconnected. You need to connect it via 2 arrows to "Google Sheets1" and "Google Drive" nodes. So, the workflow has 2 triggers - via button, and via Google Sheets "new file" event - both of these triggers should be connected to 2 nodes. Here is how it should look like: https://ocr.oakpdf.com/n8n_fix.png 2. Set up RapidAPI HTTP auth key Create new "HTTP header" n8n credential and paste your RapidAPI key from https://rapidapi.com/restyler/api/receipt-and-invoice-ocr-api into it. https://ocr.oakpdf.com/n8n_api_key.png Make sure "HTTP Request" node uses this credential. 3. Set up your Google Auth You need a Google connection to work with your Google Sheets and Google Drive accounts: https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/#finish-your-n8n-credential 4. Set up Google Sheets Copy this Google Sheets document: https://docs.google.com/spreadsheets/d/1G0w-OMdFRrtvzOLPpfFJpsBVNqJ9cfRLMKCVWfrTQBg/edit?usp=sharing Custom document formats and advanced usage Email: contact@scrapeninja.net Linkedin: https://www.linkedin.com/in/anthony-sidashin/

This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations**: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens**: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company**: The name of the company Website**: The website URL of the company These two fields are required at this step. Business Area**: The business area deduced by OpenAI from the scraped data Offer**: The offer deduced by OpenAI from the scraped data Value Proposition**: The value proposition deduced by OpenAI from the scraped data Business Model**: The business model deduced by OpenAI from the scraped data ICP**: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information**: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.

Telegram AI Assistant: Summarize Links & Generate Images On Demand This workflow turns any Telegram chat into a smart assistant. By typing simple commands like /summary or /img, users can trigger powerful AI actions—directly from Telegram. ✨ What It Does This automation listens for specific commands in Telegram messages: /help: Sends a help menu explaining available commands. /summary <link>: Fetches a webpage, extracts its content, and summarizes it using OpenAI into 10–12 bullet points. /img <prompt>: Sends the image prompt to OpenAI and replies that the request has been received (designed for future integration with image APIs). 📦 Features ✅ Works instantly in Telegram 🧠 Uses OpenAI for text summarization and image prompt processing 🌐 Scrapes and cleans raw article text before summarizing 📤 Replies directly to the same Telegram thread 🔧 Easily expandable to support more commands 🔧 Use Cases Research Summaries**: Quickly condense articles or reports shared in chat. Content Review**: Get team-friendly TL;DRs of long blog posts or product pages. Creative Brainstorming**: Share visual ideas via /img and get quick prompts logged. Customer Support**: Offer instant answers in group chats (with further extension). Daily Digest Bot**: Connect to news feeds and auto-summarize updates. 🚀 Getting Started Clone this workflow and connect your Telegram Bot. Insert your OpenAI credentials. Deploy and test by messaging /summary https://example.com in your Telegram group or DM. Expand with new commands or connect Stability.ai or other services for real image generation. 🔗 Author & Resources Built by Yaron Been Follow more automations at nofluff.online

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score

The 🌐🤖 AI Agent Chatbot with Jina.ai Webpage Scraper workflow is a powerful automation designed to integrate real-time web scraping capabilities into an AI-driven chatbot. Here's how it works and why it's important: How It Works 💬 Chat Trigger: The workflow begins when a user sends a chat message, triggering the "When chat message received" node. 🧠 AI Agent Processing: The input is passed to the "Jina.ai Web Scraping Agent," which uses advanced AI logic to interpret the user’s query and determine the information needed. 🌐 Web Scraping: The agent utilizes the "HTTP Request" node to scrape real-time data from a user-provided URL, enabling the chatbot to fetch and analyze live website content. 🗂️ Memory Management: The "Window Buffer Memory" node ensures context retention by storing and managing conversational history, allowing for seamless interactions. 🤖 Language Model Integration: The scraped data is processed using the "gpt-4o-mini" language model, which generates clear, accurate, and contextually relevant responses for the user. Why It's Cool ⏱️ Real-Time Information Retrieval**: This workflow empowers users to access up-to-date web content directly through a chatbot, eliminating manual web searches. ✨ Enhanced User Experience**: By combining web scraping with conversational AI, it delivers precise answers tailored to user queries in real time. 🔄 Versatility**: It can be applied across various domains, such as customer support, research, or data analysis, making it a valuable tool for businesses and individuals alike. ⚙️ Automation Efficiency**: Automating web scraping and response generation saves time and effort while ensuring accuracy.

Pizza Ordering Chatbot with OpenAI - Menu, Orders & Status Tracking Introduction This workflow template is designed to automate order processing for a pizza store using OpenAI and n8n. The chatbot acts as a virtual assistant to handle customer inquiries related to menu details, order placement, and order status tracking. Features The chatbot provides an interactive experience for customers by performing the following functions: Menu Inquiry: When a customer asks about the menu, the chatbot responds with a list of available pizzas, prices, and additional options. Order Placement: If a customer places an order, the chatbot confirms order details, provides a summary, informs the customer that the order is being processed, and expresses gratitude. Order Status Tracking: If a customer asks about their order status, the chatbot retrieves details such as order date, pizza type, and quantity, providing real-time updates. Prerequisites Before setting up the workflow, ensure you have the following: OpenAI account** (Sign up here) OpenAI API key** to interact with GPT-3.5 n8n instance** running locally or on a server (Installation Guide) Configuration Steps Step 1: Set Up OpenAI API Credentials Log in to OpenAI's website. Navigate to API Keys under your account settings. Click Create API Key and copy the key for later use. Step 2: Configure OpenAI Node in n8n Open n8n and create a new workflow. Click Add Node and search for OpenAI. Select OpenAI from the list. In the OpenAI node settings, click "Create New" under the Credentials section. Enter a name for the credentials (e.g., "PizzaBot OpenAI Key"). Paste your API Key into the field. Click Save. Step 3: Set Up the Chatbot Logic Connect the AI Agent Builder Node to the OpenAI Node and HTTP Request Node. Configure the OpenAI Node with the following settings: Model: gpt-3.5-turbo Prompt: Provide dynamic text based on customer inquiries (e.g., "List available pizzas," "Place an order for Margherita pizza," "Check my order status"). Temperature: Adjust based on desired creativity (recommended: 0.7). Max Tokens: Limit response length (recommended: 150). Add multiple HTTP Request Node: For Get Products: Fetch stored menu data and return details. For Order Product: Capture order details, generate an order ID, and confirm with the customer. For Get Order: Retrieve order details based on the order ID and display progress. Step 4: Testing and Deployment Click Execute Workflow to test the chatbot. Open the Chat Message node, then copy the chat URL to access the chatbot in your browser. Interact with the chatbot by asking different queries (e.g., "What pizzas do you have?" or "I want to order a Pepperoni pizza"). Verify responses and adjust prompts or configurations as needed. Deploy the workflow and integrate it with a messaging platform (e.g., Telegram, WhatsApp, or a website chatbot). Conclusion This n8n workflow enables a fully functional pizza ordering chatbot using OpenAI's GPT-3.5. Customers can view menus, place orders, and track their order status efficiently. You can further customize the chatbot by refining prompts, adding new features, or integrating with external databases for order management. 🚀 Happy automating!

This template is designed for community managers, developers, and enthusiasts who want to monitor and capture discussions on the n8n community forum by specific keywords. By tracking new topics that match your provided keyword, the workflow automatically logs them into a Google Sheet, helping you keep a pulse on trending discussions and manage community insights effortlessly. How it Works Keyword Monitoring: The workflow continuously monitors the n8n community forum for new topics containing your specified keyword. Data Extraction: When a matching topic is found, relevant details such as id, title, URL, and posting time are extracted. Google Sheets Integration: The extracted information is automatically appended as a new row in your Google Sheet, providing an organized log of community discussions. Alert Option: Optionally, you can add notifications (e.g., Slack messages or emails) to alert you when a new topic is captured, ensuring you never miss an important update. Set Up Steps Import the Workflow: Download and import the workflow into your n8n instance. Configure Keyword Monitoring: In the HTTP Request node, set your desired keyword to filter the n8n community topics. Set Up Google Sheets: Connect your Google Sheets account and specify the target sheet where the new topics should be logged. Customize Data Fields: Adjust the data extraction node parameters if you wish to capture additional details from each topic. Deploy and Test: Deploy the workflow and perform a test run to confirm that new topics matching the keyword are correctly added to your Google Sheet. Set Up Messaging Channels (Optional): Connect to Slack, Email or other providers to get instant messages when new topics are added into your Google Sheet. This template streamlines the process of tracking community discussions, ensuring you have timely and organized insights to enhance your community engagement and strategic decision-making. Enjoy seamless monitoring and easy data management with this ready-to-use n8n workflow template!

How many times have you missed a meeting or forgotten an appointment because a calendar reminder got lost in the noise? Traditional notifications are often dry, easy to ignore, or scattered across different apps—leaving you scrambling at the last minute. This smart Google Calendar workflow fixes that by sending you a clear, friendly reminder exactly 1 hour before your event starts—delivered through Telegram as if a personal assistant were looking out for you. Powered by AI, it transforms cold calendar alerts into warm, conversational nudges you won't ignore. Why This Works Better: ✅ No More Overlooked Alerts – Consolidates reminders into one clear, accessible place (Telegram), so you never miss them. ✅ Friendly & Engaging – AI transforms robotic calendar entries into natural, human-like reminders that are harder to ignore. ✅ Works Everywhere – Whether you're on your phone, laptop, or tablet, you’ll get the same clear notification, no matter the platform. How It Works Scheduled Trigger: The workflow starts with a Schedule Trigger node that runs every minute to check for upcoming events. Google Calendar Check: The "Get upcoming event" node queries Google Calendar for events starting within the next hour (between timeMin and timeMax). Duplicate Prevention: The "Already sent?" node ensures reminders are not sent multiple times for the same event by filtering out duplicates. AI-Powered Reminder: The "Secretary Agent" node, powered by GPT-4, crafts a friendly and professional reminder message. It includes event details like name, description, location, start/end time, and creator, formatted in a conversational tone. Telegram Notification: The final "Send reminder" node delivers the reminder via Telegram, ensuring the user receives it in a clear and accessible format. Set Up Steps Configure Schedule Trigger: Set the interval (e.g., every minute) to check for events. Connect Google Calendar: Link your Google Calendar account and specify the calendar to monitor. Set Up AI Agent: Customize the "Secretary Agent" with the provided system message to ensure reminders are warm, professional, and detailed. Link Telegram: Add your Telegram credentials and specify the CHAT_ID where reminders will be sent. Activate Workflow: Ensure the workflow is active and set to the correct timezone (e.g., Europe/Rome). Why It’s Useful Never Miss an Event**: Traditional calendar reminders can be easy to overlook, especially when scattered across platforms. This workflow consolidates reminders into a single, accessible channel (Telegram). Clear and Friendly**: The AI agent transforms generic calendar alerts into personalized, conversational reminders, making them harder to ignore. Cross-Platform Accessibility**: By delivering reminders via Telegram, users receive them in a consistent format, regardless of the device or platform they’re using. No more missed events due to unclear notifications! Need help customizing? Contact me for consulting and support or add me on Linkedin.

About the Automation Staying on top of competitor pricing changes can be a full-time job. Manual price tracking is time-consuming and prone to errors, especially when dealing with complex pricing structures and multiple subscription tiers. Paid competitor price monitoring tools like Competera, Visualping and Fluxguard can be expensive. What if you could automate this process and get instant alerts when competitors adjust their pricing? How to easily monitor competitor pricing With this automation, you'll learn how to set up automated price monitoring system using Airtop's built-in node in n8n. By the end, your system will automatically track competitor pricing changes and notify you of any modifications. What You'll Need A free Airtop API Key Google Sheets account with a copy of this sheet URLs of competitors' pricing pages Understanding the Process This automation continuously monitors competitor pricing pages and compares them against your baseline data. The workflow: Tracks all different pricing plans (monthly, yearly, etc.). Monitors feature changes across different tiers. Detects and logs pricing structure modifications. Alerts you via Slack when changes are detected Setting Up Your Automation We've created a ready-to-use blueprint for seamless price monitoring. Here's how to get started: Connect your Google Sheets Set up your Airtop API connection Define update frequency Customization Options Enhance the basic template with these popular modifications: Add other notification channels (Email, Telegram, etc.). Include feature comparison tracking. Set up threshold-based alerts for significant price changes Track historical pricing trends Real-World Applications Case Study 1: A B2B SaaS company can use this automation to track competitors' pricing changes. When they identify a market-wide pricing shift, they can adjust their strategy proactively within minutes. Case Study 2: An online Ecommerce retailer automates monitoring of 100+ competitor products, maintaining optimal pricing positions and increasing profit margins. Best Practices To ensure accurate tracking: Include detailed baseline data for each pricing tier Specify both monthly and annual pricing clearly List all features included in each plan Update your baseline data whenever you verify changes Include any promotional pricing or special offers Document currency and regional variations if applicable Example Structure in Google Sheets: Competitor: Acme Tools Basic Plan: Monthly: $29 Annual: $290 ($24.17/mo) Features: 5 users, 10GB storage, basic support Pro Plan: Monthly: $79 Annual: $790 ($65.83/mo) Features: 20 users, 50GB storage, priority support What's Next? After setting up your price monitoring automation, consider the following: Creating automated competitive analysis reports Setting up market trend analysis Implementing automatic pricing recommendations Expanding monitoring to feature changes Happy monitoring!

> Disclaimer: this workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes. This template is intended for use by those interested in learning more about Agentic AI workflow development, as well as those interested in learning how to use the Couchbase Search Vector Store node for practical applications. This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search. How it Works This template contains two workflows: The Data Ingestion workflow uses the following nodes Webhook node (to listen for HTTP requests) OpenAI Embeddings node (to generate embeddings on document insertion) Note: You’ll need to configure OpenAI credentials for this node Couchbase Vector node (configured for document insertion) Default Data Loader and Recursive Character Text Splitter The Chat Application workflow uses the following nodes Chat Trigger node AI Tools Agent node connect to: Gemini (as the Chat Model, for generating responses) Note: You will have to configure Gemini credentials for this node Simple Memory (as the Memory, to maintain conversation context) Couchbase Search Vector node (as the Tool, for search) OpenAI Embeddings node (as the Embedding model for the Couchbase Search Vector node, to convert queries to vectors) Note: You’ll need to configure OpenAI credentials for this node Set up Setting up this workflow is easy and only takes around 10 minutes. Prerequisites A Couchbase Cluster running the Search Service, and corresponding database access credentials Be sure the Couchbase cluster allows the incoming IP address for n8n Create a Vector Search Index using this index definition Create a bucket (called travel-agent), scope (called vectors), and collection (called points-of-interest) in your Cluster OpenAI API Key Gemini API Key Steps Configure all necessary credentials (Couchbase, OpenAI, and Gemini) Select your bucket, scope, and collection for each of the Couchbase vector nodes Ingest data, either using the cURL statements found on the sticky note within the workflow, or using this shell script to ingest 6 points of interest Open the chat and test out your travel agent! Customization and Next Steps This workflow template can be made more robust by enhancing the data model to include more information about each point of interest. For example, the addition of price ranges, ideal seasons to visit, activity types, and accomodation options can help inform the LLM further about each destination, and in turn allow it to provide a more tailored response and be more helpful for travel planning. Alternatively, the data model could be entirely re-configured to suit a wide variety of other use cases. This template can serve as a building block for all sorts of AI Agent applications using RAG and is not limited to only travel recommendations.

This workflow builds a conversational AI chatbot agent using Claude 3.7 Sonnet model with the new . It enhances standard LLM capabilities with Anthropic’s features: Web Search and Think: Real-time web search**, to answer up-to-date factual queries. A “Think” function, to support internal reasoning and memory-like behavior by Anthropic. A memory buffer, allowing the agent to maintain conversation history. A system prompt defining clear ethical, functional, and formatting rules for interaction. When a user sends a message (trigger), the chatbot evaluates the query, optionally performs a web search if needed, processes the result using Claude, and responds accordingly. ✅ Advantages 🧠 Enhanced Reasoning Abilities** The Think tool allows the agent to simulate deep thought processes or contextual memory storage, improving conversational intelligence. 🌐 Real-Time Knowledge via Web Search** The integrated web_search tool enables the agent to fetch the latest information from the internet, making it ideal for dynamic or news-driven use cases. 🧾 Contextual Responses with Memory Buffer** The inclusion of a memory buffer allows the agent to maintain state across messages, improving dialogue flow and continuity. 🛡️ Built-in Ethical Guidelines** The system prompt enforces privacy, factual integrity, neutrality, and ethical response generation, making the agent safe for public or enterprise use. How It Works Chat Trigger: The workflow begins when a chat message is received via a webhook. This triggers the AI Agent to process the user's query. AI Agent Processing: The AI Agent analyzes the query to determine if it requires information from the website or external sources. It follows a structured approach: For website-related queries, it uses the provided context. For external information, it employs the web_search tool to fetch up-to-date data from the internet. The Think tool is used for internal reasoning or caching thoughts without altering data. Language Model: The Anthropic Chat Model (Claude 3.7 Sonnet) generates responses based on the analyzed query, incorporating website context or web search results. Memory: A simple memory buffer retains context from previous interactions to maintain continuity in conversations. Output: The final response is delivered to the user, excluding internal processes like web searches or reasoning steps. Set Up Steps Configure Nodes: Chat Trigger: Set up the webhook to receive user messages. AI Agent: Define the system message and rules for handling queries. Anthropic Chat Model: Select the Claude 3.7 Sonnet model and configure parameters like maxTokensToSample. Memory: Initialize the memory buffer to store conversation context. Tools: web_search: Configure the HTTP request to the Anthropic API for web searches, including headers and authentication. Think: Set up the tool for internal reasoning. Connect Nodes: Link the Chat Trigger to the AI Agent. Connect the Anthropic Chat Model, Memory, and Tools (web_search and Think) to the AI Agent. Credentials: Ensure the Anthropic API credentials are correctly configured for both the chat model and the web_search tool. Need help customizing? Contact me for consulting and support or add me on Linkedin.