Workflow Templates

> 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.

0xdino Cyberrealistic Pony V125 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the 0xdino/cyberrealistic-pony-v125 model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Optional Parameters cfg** (number, default: 4): CFG scale seed** (integer, default: 0): Random seed (0 = random) steps** (integer, default: 40): Sampling steps width** (integer, default: 768): Image width height** (integer, default: 1152): Image height prompt** (string, default: score_9, score_8_up, score_7_up, super-detailed fashion portrait of a young woman in ripped denim shorts and ribbed tank top, colorful accessories, RAW photography style, soft cinematic lighting, dramatic shadows across her face and body, brown hair gently tousled, (fine-art editorial atmosphere), moody tone, high-resolution textures and rich natural detail, solo subject): Positive prompt denoise** (number, default: 0.98): Denoise strength scheduler** (string, default: karras): Scheduler type facerestore** (boolean, default: True): Enable face restoration sampler_name** (string, default: dpmpp_3m_sde): Sampler name How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: 0xdino/cyberrealistic-pony-v125 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

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.

Bulk Contact Deletion from HubSpot via Uploaded Excel / CSV File This workflow allows you to automate the deletion of HubSpot contacts based on email addresses provided in an uploaded Excel (.xlsx) file. It's ideal for bulk-cleaning outdated or invalid contact data. ✅ Prerequisites Before using this workflow, ensure you have the following: A valid HubSpot App Token with permissions to search and delete contacts. An Excel (.xlsx) file with a column labeled emails containing the contact emails to be deleted. n8n self-hosted or cloud environment with: Webhook node enabled and accessible. HubSpot node credentials configured. Basic familiarity with n8n node configuration for custom adjustments (optional). 📃Sample Document Download 🧠 n8n Workflow: Delete HubSpot Contacts from an Uploaded Excel File This n8n workflow allows you to upload an Excel file containing contact email addresses. It will check each one in HubSpot and delete the contact if it exists. 🔗 Workflow Overview 📥 1. Trigger via Webhook (POST) The workflow starts when a .xlsx file is uploaded via an HTTP POST request to the webhook. This Excel file should contain a column with contact email addresses. 📄 2. Extract Data from Excel The uploaded file is parsed, and its rows are converted into structured JSON items, making each email address available for further processing. 🧹 3. Normalize Data The data is cleaned and normalized — for example, mapping column headers (e.g., emails) into a standard email field, ensuring consistent downstream logic. 🔁 4. Loop Through Contacts Each row (contact) is processed individually using batch looping. This allows for fine-grained error handling and sequential processing. 🔎 5. Search for Contact in HubSpot For each contact, a search query is made in HubSpot based on the email address. The workflow only fetches the first result (if any). 🧪 6. Check if Contact Exists An IF condition checks whether the contact was found (i.e., if a HubSpot contact ID exists): ✅ Yes → proceed to delete the contact. ❌ No → skip deletion and continue to the next. 🗑️ 7. Delete Contact If a contact exists, it is deleted from HubSpot using its internal contact ID. 🛠️ 8. Optional Placeholder for Post-Processing A placeholder node named “Replace Me” is included for any custom logic you may want to add after the deletion step, such as: Logging Notifications Writing to external storage ✅ Use Cases Bulk delete old or bounced email addresses from HubSpot. Clean up contacts based on external suppression lists. Automate regular CRM hygiene processes. 💡 Suggested Enhancements ✍️ Log results to Google Sheets or a database 📬 Send completion report via email or Slack 🔁 Add retry logic for temporary API failures 🔍 Validate email format before making requests 📎 Requirements n8n (self-hosted or cloud) HubSpot App Token (set up in n8n credentials) Excel file (.xlsx) with a column for email 📦 Files No external files are required. All logic is contained within the n8n workflow. 🚀 Getting Started Deploy the workflow in n8n. Copy the webhook URL and use it in your app or API client (like Postman). Upload an Excel file containing contact emails via POST request. Watch as it searches and deletes matches in HubSpot.

This workflow is aimed at generating keywords for SEO and articles To get started, you need to use the workflow as it is. You just call the webhook URL with a query parameter as q={{ $keywords}} For example, you can call it using ?q=keyword research This will give you a list of keywords back as an array. This system can be used by SEO pros, content marketers and also social media marketers to generate relevant keywords for their user needs

This n8n template demonstrates how to add a tie form data to a new PDF. The idea is to automate the creation of a professional looking job posting. Use cases would be organizations who need to automate the creation of job postings. How it Works The trigger is a form that asks for job position, salary, office location, and responsiblities When the form is posted, it kicks off the workflow's next steps A Word document is downloaded from a Dropbox folder. This Word document is used as the template for the posting. The Word document is converted to base64. A call to Foxit's Document Generation endpoint includes the encoded Word document along with the form information. The resulting PDF is downloaded and converted from base64 into binary. At this point, the PDF is just there, but it could be emailed, sent to another workflow, etc. Requirements A Dropbox account. The workflow's first step points to a Word template. See our doc gen APIs for information on how to craft the Word doc, but the easiest way is to copy text like so: Job Position We are pleased to announce the opening of a new job, {{ jobPosition }}. This job pays ${{ salary }} per year and is in our {{ office }} location. The details of this job are: {{ responsibilities }} Foxit developer account (https://developer-api.foxit.com) Next Steps As mentioned above, you could do anything with the resulting PDF when done.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Brief Overview This automation template helps you track the latest job listings from the Y Combinator Jobs page. By using Scrapeless to scrape job listings, n8n to orchestrate the workflow, and Google Sheets to store the results, you can build a zero-code job tracking solution that runs automatically every 6 hours. How It Works Trigger on a Schedule: Every 6 hours, the workflow kicks off automatically. Scrape Job Listings: Scrapeless crawls the Y Combinator Jobs page and returns structured Markdown data. Extract & Parse Content: JavaScript nodes process the Markdown to extract job titles and links. Flatten Data: Each job becomes a single row with its title and link. Save to Google Sheets: New job listings are appended to your Google Sheet for easy viewing and sharing. Features No-code, automated job listing scraper. Scrapes and structures the latest Y Combinator job posts. Saves data directly to Google Sheets. Easy to schedule and run without manual effort. Extensible: Add Telegram, Slack, or email notifications easily in n8n. Requirements Scrapeless API Key: Scrapeless Service request credentials. Log in to the Scrapeless Dashboard Then click "Setting" on the left -> select "API Key Management" -> click "Create API Key". Finally, click the API Key you created to copy it. n8n Instance: Self-hosted or n8n.cloud account. Google Account: For Google Sheets API access. Target Site: This template is designed for the Y Combinator Jobs page but can be modified for other job boards. Installation Deploy n8n on your preferred platform. Import this workflow JSON file into your n8n workspace. Create and add your Scrapeless API Key in n8n’s credential manager. Connect your Google Sheets account in n8n. Update the target Google Sheet document URL and sheet name. Usage This automated job finder agent is ideal for: | Industry / Role | Use Case | |-------------------------------|--------------------------------------------------------------------------------------------| | Job Seekers | Automatically track newly posted startup jobs without manually visiting job boards. | | Recruitment Agencies | Monitor YC job postings and build a candidate-job matching system. | | Startup Founders / CTOs | Stay aware of which startups are hiring, for networking and market insights. | | Tech Media & Bloggers | Aggregate new job listings for newsletters, blogs, or social media sharing. | | HR & Talent Acquisition Teams | Monitor competitors’ hiring activity. | | Automation Enthusiasts | Example use case for learning web scraping + automation + data storage. | Output

Openai Clip Image Generator Description Official CLIP models, generate CLIP (clip-vit-large-patch14) text & image embeddings Overview This n8n workflow integrates with the Replicate API to use the openai/clip model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Optional Parameters text** (string, default: None): Input text to encode image** (string, default: None): Input image to encode How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: openai/clip API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Flow Start: The flow starts upon receiving an HTTP GET call. Webhook: Receives the HTTP GET call and triggers the flow. Database: Connects to the database (Customer Datastore) to retrieve all necessary information (getAllPeople). Data Processing: Variable Insertion: The retrieved data is inserted into a variable. Variable Aggregation: The variables are aggregated and prepared for use in FlutterFlow. Webhook Response: Sends the response back through the Webhook with the processed data ready for use in FlutterFlow.

Who is this for? For developers and organizations that need to protect PDF files with the password. What problem is this workflow solving? PDF file protection problem. What this workflow does Downloads the PDF file from the web. Protects PDF file with the password. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Change the password in the parameter UserPassword Optionally, additional Body Parameters can be added for the converter.

This workflow integrates Flowise Multi-Agent Chatflows into a custom-branded n8n chatbot, enabling real-time interaction between users and AI agents powered by large language models (LLMs). Key Advantages: ✅ Easy Integration with Flowise: Uses a low-code HTTP node to send user questions to Flowise's API (/api/v1/prediction/FLOWISE_ID) and receive intelligent responses. Supports multi-agent chatflows, allowing for complex, dynamic interactions. 🎨 Customizable Chatbot UI: Includes pre-built JavaScript for embedding the n8n chatbot into any website. Provides customization options such as welcome messages, branding, placeholder text, chat modes (e.g., popup or embedded), and language support. 🔐 Secure & Configurable: Authorization via Bearer token headers for Flowise API access. Clearly marked notes in the workflow for setting environment variables like FLOWISE_URL and FLOW_ID. How It Works Chat Trigger: The workflow starts with the When chat message received node, which acts as a webhook to receive incoming chat messages from users. HTTP Request to Flowise: The received message is forwarded to the Flowise node, which sends a POST request to a Flowise API endpoint (https://FLOWISEURL/api/v1/prediction/FLOWISE_ID). The request includes the user's input as a JSON payload ({"question": "{{ $json.chatInput }}"}) and uses HTTP header authentication (e.g., Authorization: Bearer FLOWSIE_API). Response Handling: The response from Flowise is passed to the Edit Fields node, which maps the output ($json.text) for further processing or display. Set Up Steps Configure Flowise Integration: Replace FLOWISEURL and FLOWISE_ID in the HTTP Request node with your Flowise instance URL and flow ID. Ensure the Authorization header is set correctly in the credentials (e.g., Bearer FLOWSIE_API). Embed n8n Chatbot: Use the provided JavaScript snippet in the sticky notes to embed the n8n chatbot on your website. Replace YOUR_PRODUCTION_WEBHOOK_URL with the webhook URL generated by the When chat message received node. Customize the chatbot's appearance and behavior (e.g., welcome messages, language, UI elements) using the createChat configuration options. Optional Branding: Adjust the sticky note examples to include branding details, such as custom messages, colors, or metadata for the chatbot. Activate Workflow: Toggle the workflow to "Active" in n8n and test the chat functionality end-to-end. Ideal Use Cases: Embedding branded AI assistants into websites. Connecting Flowise-powered agents with customer support chatbots. Creating dynamic, smart conversational flows with LLMs via n8n automation. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Overview This n8n workflow automates the process of crawling a website's sitemap to extract URLs, which is particularly useful for SEO analysis, website auditing, or content monitoring. By leveraging n8n's nodes, the workflow fetches the sitemap from a specified URL, processes the XML data, and extracts individual URLs, which can then be converted into a downloadable file or integrated with tools like Google Sheets. How It Works The workflow operates in a sequential manner, utilizing a series of nodes to fetch, parse, and process sitemap data: Trigger: Initiates when the user clicks "Test workflow" (Manual Trigger node). Set URL: Defines the base domain (e.g., https://phu.io.vn/) for the sitemap (Set URL node). Crawl Sitemap: Fetches the main sitemap file (sitemap.xml) from the specified domain using an HTTP request (Crawl sitemap node). Parse XML: Converts the sitemap XML into a JSON format for easier processing (XML node). Split Sitemap: Extracts individual sitemap entries (e.g., <sitemap> tags) from the parsed data (Split Out node). Crawl Sub-Sitemap: Fetches each sub-sitemap URL listed in the main sitemap (Crawl sitemap 2 node). Parse Sub-Sitemap XML: Converts the sub-sitemap XML into JSON (XML 2 node). Split URLs: Extracts individual URLs (e.g., <url> tags) from the sub-sitemap (Split Out 2 node). Convert to File: Saves the extracted URLs into a file for download or further use (Convert to File node). This workflow supports both single sitemap files and sitemap indexes that reference multiple sub-sitemaps, ensuring comprehensive URL extraction. How to Use To implement this workflow in n8n, follow these steps: Set Up n8n: Ensure you have an active n8n instance (Cloud, npm, or self-hosted). Refer to the n8n documentation for setup instructions. Import Workflow: Copy the JSON from the provided Extract Website URLs from Sitemap.XML for SEO Analysis.json file and import it into your n8n instance via the workflow editor. Configure the Domain: In the Set URL node, update the Domain parameter with the target website's base URL (e.g., https://example.com/). Alternatively, in the Crawl sitemap node, directly paste the full sitemap URL if known (e.g., https://example.com/sitemap.xml). Test the Workflow: Click "Test workflow" to execute the Manual Trigger node. Verify that the workflow fetches the sitemap and processes the URLs correctly. Download or Integrate: The Convert to File node generates a file containing the extracted URLs. Optionally, replace this node with a Google Sheets node to append URLs to a spreadsheet. Refer to the Google Sheets node documentation for setup. Save and Activate: Save the workflow and activate it for production use if needed, using a trigger like a schedule or webhook (see Trigger Node). Requirements n8n Instance**: An active n8n instance (version 1.0 or later recommended) on n8n Cloud, npm, or self-hosted (Docker). See Choose your n8n for details. Technical Knowledge**: Basic understanding of n8n's editor UI and node configuration. Familiarity with XML sitemaps is helpful but not mandatory. Permissions**: For self-hosted setups, ensure the n8n process has network access to fetch the sitemap URL. For Docker deployments, verify permissions as outlined in the n8n v1.0 migration guide. Optional**: If integrating with Google Sheets, valid Google Sheets credentials are required (see Credentials). Timeout Configuration**: The HTTP Request nodes (Crawl sitemap and Crawl sitemap 2) have a 10-second timeout. Adjust the timeout parameter in the node settings if dealing with slow-responding servers. FAQ Q: What happens if the sitemap is large or contains many sub-sitemaps? A: The workflow handles sitemap indexes by splitting and processing each sub-sitemap individually. For very large sitemaps, ensure your n8n instance has sufficient resources (memory and CPU) to avoid performance issues. See Scaling n8n for optimization tips. Q: Can I use this workflow with a specific sitemap URL instead of a domain? A: Yes, in the Crawl sitemap node, replace the url parameter ({{ $json.Domain }}sitemap.xml) with the direct sitemap URL (e.g., https://example.com/sitemap.xml). Update the node’s notes for clarity. Q: Why am I getting a timeout error? A: The HTTP Request nodes have a default timeout of 10 seconds. If the target server is slow, increase the timeout value in the options parameter of the Crawl sitemap or Crawl sitemap 2 nodes. Q: How can I save the URLs to Google Sheets instead of a file? A: Replace the Convert to File node with a Google Sheets node. Configure it with your Google Sheets credentials and map the loc field from the Split Out 2 node to the desired spreadsheet column. Refer to the Google Sheets node documentation. Q: Is this workflow compatible with older n8n versions? A: The workflow uses nodes compatible with n8n version 1.0 and later. For older versions, check for deprecated features (e.g., MySQL support) in the n8n v1.0 migration guide. Q: Can I automate this workflow to run periodically? A: Yes, replace the Manual Trigger node with a Schedule Trigger node to run the workflow at set intervals. See Trigger Nodes for configuration details. For further assistance, consult the n8n Community Forum or submit an issue on the n8n GitHub repository. Need help customizing? Contact me for consulting and support or add me on Facebook or email.