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.

OpenAI Assistant is a powerful tool, but at the time of writing it doesn't automatically remember past messages from a conversation. This workflow demonstrates how to get around this, by managing the chat history in n8n and passing it to the assistant when required. This makes it possible to use OpenAI Assistant for chatbot use cases. Note that to use this template, you need to be on n8n version 1.28.0 or later.

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.

This n8n workflow automatically detects high‑spending hotel guests after checkout and emails them a personalized, one‑time reward offer. 🔧 What it does Watches Salesforce Guest__c custom object for checkout updates. Pulls guest spend data on optional paid amenities: Room Service Minibar Laundry Late Checkout Extra Bed Airport Transfer Calculates total spend to identify VIP guests (≥ $50). Uses AI to: Spot unused services. Randomly pick one unused service. Generate a realistic, short promo like: "Free late checkout on your next stay" Parses AI output into JSON. Sends a polished HTML email to the guest with their personalized offer. 📦 Key nodes Salesforce Trigger → monitors new checkouts. Salesforce → fetches detailed spend data. Function → sums up total amenity spend. IF → filters for VIP guests. LangChain LLM + Google Vertex AI → drafts the offer text. Structured Output Parser → cleans AI output. Brevo → delivers branded email. 📊 Example output > Subject: John, We Have Something Special for Your Next Stay > Offer in email: Enjoy a complimentary minibar selection on your next stay. ✨ Why it matters Rewarding guests who already spend boosts loyalty and repeat bookings — without generic discounts. The offer feels personal, relevant, and exclusive.

> 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.

Reduce human delays between malware detection and remediation in MSSP/SOC environments. This workflow automates full endpoint antivirus scanning immediately after high-severity endpoint infection wazuh alerts, closing the gap between alerting and action. Why Use This Workflow? Malware alerts are only effective if acted upon swiftly. Manual follow-ups are slow or often missed, letting threats persist. Automates detection, triage, scan initiation, and notification—all within one minute of alerting. Ensures consistent, auditable actions across endpoints running Linux or Windows. 🔑 Key Features Listens for high-severity Wazuh AV infection alerts (e.g., rule 52502). Uses GPT-4 for AI-powered alert summaries to speed triage and decision making. Extracts exact infected file paths using AI and regex for targeted scanning. Runs ClamAV/defender scans directly on endpoints via SSH with least-privilege credentials. Sends real-time scan results and remediation updates through Telegram, Slack, or email. Runs locally with limited permissions—no need for elevated Wazuh manager access. 🎯 Impact Eliminates manual lag—scans start automatically and immediately. Standardizes response playbooks for reliable, repeatable remediation. Reduces threat dwell time, minimizing risk exposure. Provides full event-to-remediation visibility via logs and notifications. 🚀 Get Started Configure Wazuh Manager to forward AV alerts to this n8n webhook. Import this workflow JSON into your n8n instance. Set up required credentials: OpenAI API, SSH access for ClamAV scanning, notification channels (Telegram/Slack/email). Activate the workflow and monitor alerts triggering automated scans and reports. 📂 Enjoy customizing Swap ClamAV with your preferred antivirus commands (e.g., Defender) as needed. Integrate with your existing communication or ticketing systems. Extend or adapt for multi-endpoint orchestration or other alert rules. Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast

Workflow: Publish to Contentful with Rich Text Formatting ⚡ About the Creators This workflow was created by Varritech Technologies, an innovative agency that leverages AI to engineer, design, and deliver software development projects 500% faster than traditional agencies. Based in New York City, we specialize in custom software development, web applications, and digital transformation solutions. If you need assistance implementing this workflow or have questions about content management solutions, please reach out to our team. 🏗️ Architecture Overview This workflow takes a JSON article payload, splits its markdown content into logical chunks, converts each chunk into Contentful Rich Text JSON via an AI agent, merges the resulting rich text nodes back into a single document, formats the entire entry according to Contentful's field schema, and finally publishes it to Contentful. Trigger → Executes when called by another workflow Split by Headings → Breaks markdown into ##-delimited chunks Markdown → Rich Text → AI agent converts each chunk to Contentful Rich Text JSON Combine Rich Text Objects → Aggregates all chunk outputs into one document Format Entry → Wraps metadata and rich-text content into Contentful schema Publish Entry → HTTP POST to Contentful API 📦 Node-by-Node Breakdown flowchart LR A[When Executed by Another Workflow] --> B[Split by Headings] B --> C[Markdown to Contentful format] C --> D[Combine Rich Text Objects] D --> E[Merge1] E --> F[Format1] F --> G[Create newly formatted Contentful Entry] 1. When Executed by Another Workflow Type: Execute Workflow Trigger Input Example: title, slug, category.id, description, keywords, content, metaTitle, metaDescription, readingTime, difficulty Purpose: Receives the JSON payload from the upstream workflow. 2. Split by Headings Type: Code Logic: Splits input.content into an array of markdown chunks at each second-level heading (##). Emits one item per chunk with index, slug, title, and contentChunk. 3. Markdown to Contentful format Type: LangChain Agent (+ OpenAI Chat model) System Prompt: Defines rules for generating valid Contentful Rich Text JSON (must include nodeType, data:{}, content:[], etc.). Provides examples for paragraphs, headings, lists, links, and images. User Prompt: Here is the markdown content to convert: Purpose: Converts each markdown chunk into an array of rich-text nodes. 4. Combine Rich Text Objects Type: Code Logic: Parses and merges all content arrays returned by the AI agent into one combined content array under a document root. 5. Merge1 Type: Merge Purpose: Joins the original item (with metadata) and the combined rich-text document into a single data stream. 6. Format1 Type: Code Logic: Maps workflow data into the Contentful entry schema by setting each field (title, slug, category link, description, keywords, rich-text content, metaTitle, metaDescription, readingTime, difficulty) under the appropriate locale and structure required by Contentful. 7. Create newly formatted Contentful Entry Type: HTTP Request Method: POST URL: https://api.contentful.com/spaces Headers: Authorization: Bearer token for Contentful Management API Content-Type: application/vnd.contentful.management.v1+json X-Contentful-Version: entry version number X-Contentful-Content-Type: content type ID Body: The formatted fields object produced by the previous node Purpose: Publishes the new entry with rich-text content to Contentful. 🔍 Design Rationale & Best Practices Chunked Conversion Splitting by headings prevents AI context limits and keeps conversions modular. Strict Rich Text Schema Enforcing nodeType, data, and content structure avoids validation errors on Contentful. Two-Phase Merge Separating "combine AI outputs" and "format entry" keeps transformations clear and testable. Idempotent Publish Uses explicit versioning and content type headers to ensure correct entry creation.

The Agent Decisioner is a dynamic, AI-powered routing system that automatically selects the most appropriate large language model (LLM) to respond to a user's query based on the query’s content and purpose. This workflow ensures dynamic, optimized AI responses by intelligently routing queries to the best-suited model. Advantages 🔁 Automatic Model Routing:** Automatically selects the best model for the job, improving efficiency and relevance of responses. 🎯 Optimized Use of Resources:** Avoids overuse of expensive models like GPT-4 by routing simpler queries to lightweight models. 📚 Model-Aware Reasoning:** Uses detailed metadata about model capabilities (e.g., reasoning, coding, web search) for intelligent selection. 📥 Modular and Extendable:** Easy to integrate with other tools or expand by adding more models or custom decision logic. 👨‍💻 Ideal for RAG and Multi-Agent Systems:** Can serve as the brain behind more complex agent frameworks or Retrieval-Augmented Generation pipelines. How It Works Chat Trigger: The workflow starts when a user sends a message, triggering the Routing Agent. Model Selection: The AI Agent analyzes the query and selects the best-suited model from the available options (e.g., Claude 3.7 Sonnet for coding, Perplexity/Sonar for web searches, GPT-4o Mini for reasoning). Structured Output: The agent returns a JSON response with the user’s prompt and the chosen model. Execution: The selected model processes the query and generates a response, ensuring optimal performance for the task. Set Up Steps Configure Nodes: Chat Trigger: Set up the webhook to receive user messages. Routing Agent (AI Agent): Define the system message with model strengths and JSON output rules. OpenRouter Chat Model: Connect to OpenRouter for model access. Structured Output Parser: Ensure it validates the JSON response format (prompt + model). Execution Agent (AI Agent1): Configure it to forward the prompt to the selected model. Connect Nodes: Link the Chat Trigger to the Routing Agent. Connect the OpenRouter Chat Model and Output Parser to the Routing Agent. Route the parsed JSON to the Execution Agent, which uses the chosen model via OpenRouter Chat Model1. Credentials: Ensure OpenRouter API credentials are correctly set for both chat model nodes. Test & Deploy: Activate the workflow and test with sample queries to verify model selection logic. Adjust the routing rules if needed for better accuracy. Need help customizing? Contact me for consulting and support or add me on Linkedin.