Proxmox AI agent with n8n and generative AI integration
Proxmox AI Agent with n8n and Generative AI Integration This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters. Buy My Book: Mastering n8n on Amazon Full Courses & Tutorials: http://lms.syncbricks.com Watch Video on Youtube How It Works Trigger Mechanism The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat). Interact with the AI agent conversationally. AI-Powered Parsing A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation. API Call Generation The AI parses the input and generates structured JSON output, which includes: response_type: The HTTP method (GET, POST, PUT, DELETE). url: The Proxmox API endpoint to execute. details: Any required payload parameters for the API call. Proxmox API Execution The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as: Retrieving cluster or node information. Creating, deleting, starting, or stopping VMs. Migrating VMs between nodes. Updating or resizing VM configurations. Response Formatting The workflow formats API responses into a user-friendly summary. For example: Success messages for operations (e.g., "VM started successfully"). Error messages with missing parameter details. Extensibility You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports: Telegram/Slack integration for real-time notifications. Backup and restore workflows. Cloud monitoring extensions. --- Key Features Multi-Channel Input: Use chat, email, or custom triggers to communicate with the AI agent. Low-Code Automation: Easily customize the workflow to suit your Proxmox environment. Generative AI Integration: Supports advanced AI models for precise command interpretation. Proxmox API Compatibility: Fully adheres to Proxmox API specifications for secure and reliable operations. Error Handling: Detects and informs you of missing or invalid parameters in your requests. --- Example Use Cases Create a Virtual Machine Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1." Action: Sends a POST request to Proxmox to create the VM with specified configurations. Start a VM Input: "Start VM 105 on node psb2." Action: Executes a POST request to start the specified VM. Retrieve Node Details Input: "Show the memory usage of psb3." Action: Sends a GET request and returns the node's resource utilization. Migrate a VM Input: "Migrate VM 202 from psb1 to psb3." Action: Executes a POST request to move the VM with optional online migration. --- Pre-Requisites Proxmox API Configuration Enable the Proxmox API and generate API keys in the Proxmox Data Center. Use the Authorization header with the format: PVEAPIToken=<user>@<realm>!<token-id>=<token-value> n8n Setup Add Proxmox API credentials in n8n using Header Auth. Connect a generative AI model (e.g., Google Gemini) via the relevant credential type. Access the Workflow Import this template into your n8n instance. Replace placeholder credentials with your Proxmox and AI service details. --- Additional Notes This template is designed for Proxmox 7.x and above. For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow. Always test with a non-production Proxmox environment before deploying in live systems. Start with n8n Learn n8n with Amjid Get n8n Book What is Proxmox
By Amjid Aliđđ ď¸Generate SEO-optimized WordPress content with AI powered perplexity research
Generate SEO-Optimized WordPress Content with Perplexity Research Who is This For? This workflow is ideal for content creators, marketers, and businesses looking to streamline the creation of SEO-optimized blog posts for WordPress. It is particularly suited for professionals in the AI consulting and workflow automation industries. --- What Problem Does This Workflow Solve? Creating high-quality, SEO-friendly blog posts can be time-consuming and challenging, especially when trying to balance research, formatting, and publishing. This workflow automates the process by integrating research capabilities, AI-driven content creation, and seamless WordPress publishing. It reduces manual effort while ensuring professional-grade output. --- What This Workflow Does Research: Gathers detailed insights from Perplexity AI based on user-provided queries. Content Generation: Uses OpenAI models to create structured blog posts, including titles, slugs, meta descriptions, and HTML content optimized for WordPress. Image Handling: Automatically fetches and uploads featured images to WordPress posts. Publishing: Drafts the blog post directly in WordPress with all necessary formatting and metadata. Notification: Sends a success message via Telegram upon completion. --- Setup Guide Prerequisites: A WordPress account with API access. OpenAI API credentials. Perplexity AI API credentials. Telegram bot credentials for notifications. Steps: Import the workflow into your n8n instance. Configure API credentials for WordPress, OpenAI, Perplexity AI, and Telegram. Customize the form trigger to define your research query. Test the workflow using sample queries to ensure smooth execution. --- How to Customize This Workflow to Your Needs Modify the research query prompt in the "Form Trigger" node to suit your industry or niche. Adjust content generation guidelines in the "Copywriter AI Agent" node for specific formatting preferences. Replace the image URL in the "Set Image URL" node with your own source or dynamic image selection logic.
By Joseph LePageQualifying appointment requests with AI & n8n forms
This n8n template builds upon a simple appointment request form design which uses AI to qualify if the incoming enquiry is suitable and/or time-worthy of an appointment. This demonstrates a lighter approach to using AI in your templates but handles a technically difficult problem - contextual understanding! This example can be used in a variety of contexts where figuring out what is and isn't relevant can save a lot of time for your organisation. How it works We start with a form trigger which asks for the purpose of the appointment. Instantly, we can qualify this by using a text classifier node which uses AI's contextual understanding to ensure the appointment is worthwhile. If not, an alternative is suggested instead. Multi-page forms are then used to set the terms of the appointment and ask the user for a desired date and time. An acknowledgement is sent to the user while an approval by email process is triggered in the background. In a subworkflow, we use Gmail with the wait for approval operation to send an approval form to the admin user who can either confirm or decline the appointment request. When approved, a Google Calendar event is created. When declined, the user is notified via email that the appointment request was declined. How to use Modify the enquiry classifier to determine which contexts are relevant to you. Configure the wait for approval node to send to an email address which is accessible to all appropriate team members. Requirements OpenAI for LLM Gmail for Email Google Calendar for Appointments Customising this workflow Not using Google Mail or Calendar? Feel free to swap this with other services. The wait for approval step is optional. Remove if you wish to handle appointment request resolution in another way.
By JimleukAutomated daily workflow backup to GitHub
This workflow provides a robust solution for automatically backing up all your n8n workflows to a designated GitHub repository on a daily basis. By leveraging the n8n API and GitHub API, it ensures your workflows are version-controlled and securely stored, safeguarding against data loss and facilitating disaster recovery. How it works The automation follows these key steps: Scheduled trigger: The workflow is initiated automatically every day at a pre-configured time. List existing backups: It first connects to your GitHub repository to retrieve a list of already backed-up workflow files. This helps in determining whether a workflow's backup file needs to be created or updated. Retrieve n8n workflows: The workflow then fetches all current workflows directly from your n8n instance using the n8n REST API. Process and prepare: Each retrieved workflow is individually processed. Its data is converted into JSON format. This JSON content is then encoded to base64, a format suitable for GitHub API file operations. Commit to GitHub: For each n8n workflow: A standardized filename is generated (e.g., workflow-name-tag.json). The workflow checks if a file with this name already exists in the GitHub repository (based on the list fetched in step 2). If the file exists: It updates the existing file with the latest version of the workflow. If it's a new workflow (file doesn't exist): A new file is created in the repository. Each commit is timestamped for clarity. This process ensures that you always have an up-to-date version of all your n8n workflows stored securely in your GitHub version control system, providing peace of mind and a reliable backup history. Pre-requisites Before you can use this template, please ensure you have the following: An active n8n instance (self-hosted or cloud). A GitHub account. A GitHub repository created where you want to store the workflow backups. A GitHub Personal Access Token with repo scope (or fine-grained token with read/write access to the specific backup repository). This token will be used for GitHub API authentication. n8n API credentials (API key) for your n8n instance. Set up steps Setting up this workflow should take approximately 10-15 minutes if you have your credentials ready. Import the template: Import this workflow into your n8n instance. Configure n8n API credentials: Locate the "Retrieve workflows" node. In the "Credentials" section for "n8n API", create new credentials (or select existing ones). Enter your n8n instance URL and your n8n API Key (you can create your n8n api key in the settings of your n8n instance) Configure GitHub credentials: Locate the "List files from repo" node (and subsequently "Update file" / "Upload file" nodes which will use the same credential). In the "Credentials" section for "GitHub API", create new credentials. Select OAuth2/Personal Access Token authentication method. Enter the GitHub Personal Access Token you generated as per the pre-requisites. Specify repository details: In the "List files from repo", "Update file", and "Upload file" GitHub nodes: Set the Owner: Your GitHub username or organization name. Set the Repository: The name of your GitHub repository dedicated to backups. Set the Branch (e.g., main or master) where backups should be stored. (Optional) Specify a Path within the repository if you want backups in a specific folder (e.g., n8n_backups/). Leave blank to store in the root. Adjust schedule (Optional): Select the "Schedule Trigger" node. Modify the trigger interval (e.g., change the time of day or frequency) as needed. By default, it's set for a daily run. Activate the workflow: Save and activate the workflow. Explanation of nodes Here's a detailed breakdown of each node used in this workflow: Schedule trigger Type: n8n-nodes-base.scheduleTrigger Purpose: This node automatically starts the workflow based on a defined schedule (e.g., daily at midnight). List files from repo Type: n8n-nodes-base.github Purpose: Connects to your specified GitHub repository and lists all files, primarily to check for existing workflow backups. Aggregate Type: n8n-nodes-base.aggregate Purpose: Consolidates the list of file names obtained from the "List files from repo" node into a single item for easier lookup later in the "Check if file exists" node. Retrieve workflows Type: n8n-nodes-base.n8n Purpose: Uses the n8n API to fetch a list of all workflows currently present in your n8n instance. Json file Type: n8n-nodes-base.convertToFile Purpose: Takes the data of each workflow (retrieved by the "Retrieve workflows" node) and converts it into a structured JSON file format. To base64 Type: n8n-nodes-base.extractFromFile Purpose: Converts the binary content of the JSON file (from the "Json file" node) into a base64 encoded string. This encoding is required by the GitHub API for file content. Commit date & file name Type: n8n-nodes-base.set Purpose: Prepares metadata for the GitHub commit. It generates: commitDate: The current date and time for the commit message. fileName: A standardized file name for the workflow backup (e.g., my-workflow-vps-backups.json), typically using the workflow's name and its first tag. Check if file exists Type: n8n-nodes-base.if Purpose: A conditional node. It checks if the fileName (generated by "Commit date & file name") is present in the list of files aggregated by the "Aggregate" node. This determines if the workflow backup already exists in GitHub. Update file Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does* exist, this node updates that existing file in your GitHub repository with the latest workflow content (base64 encoded) and a commit message. Upload file Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does not* exist, this node creates and uploads a new file to your GitHub repository with the workflow content and a commit message. Customization Here are a few ways you can customize this template to better fit your needs: Backup path: In the GitHub nodes ("List files from repo", "Update file", "Upload file"), you can specify a Path parameter to store backups in a specific folder within your repository (e.g., workflows/ or daily_backups/). Filename convention: Modify the "Commit date & file name" node (specifically the expression for fileName) to change how backup files are named. For example, you might want to include the workflow ID or a different date format. Commit messages: Customize the commit messages in the "Update file" and "Upload file" GitHub nodes to include more specific information if needed. Error handling: Consider adding error handling branches (e.g., using the "Error Trigger" node or checking for node execution failures) to notify you if a backup fails for any reason. Filtering workflows: If you only want to back up specific workflows (e.g., those with a particular tag or name pattern), you can add a "Filter" node after "Retrieve workflows" to include only the desired workflows in the backup process. Backup frequency: Adjust the "Schedule Trigger" node to change how often the backup runs (e.g., hourly, weekly, or on specific days). Template was created in n8n v1.92.2
By HugoAutomatic Gmail categorization and labeling with AI
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Who is this for? If your inbox is full of unread emails, this workflow is for you. Instead of reading through them one by one, let AI do the sorting. It reads your emails and flags only what needs action. What does it solve? This workflow reads your unread Gmail emails and uses AI to decide whatâs important â and whatâs not. It labels emails that need your attention, identifies receipts, and trashes everything else. No more manual reading. Just an inbox that uses AI to take care of itself. How it works Every hour, the workflow runs automatically. It searches for unread emails in your Gmail inbox. For each email: It extracts the content and sends it to OpenAI. The AI returns one of four labels: Action, Receipt, Informational or Spam. Based on the label: Emails are marked with the appropriate label. Or moved to trash it is spam. It marks the email as read once processed. How to set up? Connect these services in your n8n credentials: Gmail (OAuth2) OpenAI (API key) Create the Gmail labels: In your Gmail account, create these labels exactly as written: Action, Receipt, and Informational The workflow will apply these labels based on AI classification. How to customize this workflow to your needs Change the AI prompt to detect more types of emails like Meeting or Newsletter. Add more branches to the Switch node to apply custom logic. Change the schedule to fit your workflow. By default, it runs every hour, but you can update this in the Schedule Trigger node.
By Matt Chong | n8n CreatorRun weekly inventories on Shopify sales
This workflow is scheduled to run every week, when it gets all your Shopify orders, calculates their sales value, stores the data in Google Sheets, and sends a notification message to a Slack channel.
By LorenaProcess Shopify new orders with Zoho CRM and Harvest
This workflow is triggered when a new order is created in Shopify. Then: the order information is stored in Zoho CRM, an invoice is created in Harvest and stored in Trello, if the order value is above 50, an email with a discount coupon is sent to the customer and they are added to a MailChimp campaign for high-value customers; otherwise, only a "thank you" email is sent to the customer. Note that you need to replace the List ID in the Trello node with your own ID (see instructions in our docs). Same goes for the Account ID in the Harvest node (see instructions here).
By LorenaBeginner Outlook calendar summary with OpenAI
A step-by-step demo that shows how to pull your Outlook calendar events for the week and ask GPT-4o to write a short summary. Along the way youâll practice basic data-transform nodes (Code, Filter, Aggregate) and see where to attach the required API credentials. --- 1ď¸âŁ Manual Trigger â Run Workflow | Why | Lets you click âExecuteâ in the n8n editor so you can test each change. | | --- | --- | --- 2ď¸âŁ Get Outlook Events â Get many events Node type: Microsoft Outlook â Event â Get All Fields selected: subject, start API setup (inside this node): Click Credentials ⸠Microsoft Outlook OAuth2 API If you havenât connected before: Choose âMicrosoft Outlook OAuth2 APIâ â âCreate Newâ. Sign in and grant the Calendars.Read permission. Save the credential (e.g., âMicrosoft Outlook accountâ). Output: A list of events with the raw ISO start time. > Teaching moment: Outlook returns a full dateTime string. Weâll normalize it next so itâs easy to filter. --- 3ď¸âŁ Normalize Dates â Convert to Date Format js // Code node contents return $input.all().map(item => { const startDateTime = new Date(item.json.start.dateTime); const formattedDate = startDateTime.toISOString().split('T')[0]; // YYYY-MM-DD return { json: { ...item.json, startDateFormatted: formattedDate } }; }); 4ď¸âŁ Filter the Events Down to This Week After weâve normalised the start date-time into a simple YYYY-MM-DD string, we drop in a Filter node. Add one rule for every day you want to keepâfor example 2025-08-07 or 2025-08-08. Rows that match any of those dates will continue through the workflow; everything else is quietly discarded. Why weâre doing this: we only want to summarise tomorrowâs and the following dayâs meetings, not the entire calendar. --- 5ď¸âŁ Roll All Subjects Into a Single Item Next comes an Aggregate node. Tell it to aggregate the subject field and choose the option âOnly aggregated fields.â The result is one clean item whose subject property is now a tidy list of every meeting title. Itâs far easier (and cheaper) to pass one prompt to GPT than dozens of small ones. --- 6ď¸âŁ Turn That List Into Plain Text Insert a small Code node right after the aggregation: js return [{ json: { text: items .map(item => JSON.stringify(item.json)) .join('\n') } }]; Need a Hand? Iâm always happy to chat automation, n8n, or Outlook API quirks. Robert Breen â Automation Consultant & n8n Instructor đ§ robert@ynteractive.comâ|âLinkedIn
By Robert BreenRetrieve a Monday.com row and all data in a single node
This workflow is a building block designed to be called from other workflows via an Execute workflow node. When called from another workflow, and given the JSON input of a "pulse" field with the ID to pull from monday, this workflow will return: The items name and ID All column data, indexable by the column name All column data, indexable by the column's ID string All board relation columns, with their data and column values All subitems, with their data and column values For example: ++Prerequisites++ A monday.com account and credential A workflow that needs to get detailed data from a monday.com row The pulse id of the monday.com row to retreive data from. ++Setup++ Import the workflow Configure all monday nodes with your credentials and save the workflow Copy the workflow ID from it's URL In a different workflow, add an Edit Fields node, to output the field "pulse", with the monday item you want to retrieve. Feed the Edit Fields node with your pulse into an Execute workflow node, and paste the workflow ID from above into it This "pulse" field will tell the workflow what pulse to retreive. This can be populated by an expression in your workflow There is an example of the Edit Fields and Execute Workflow nodes in the template
By Joey DâAnnaAutomatically create daily list of todos in Todoist
Use case Automatically create todo items in Todoist every morning. This workflow has two flows At 5am, delete any uncompleted tasks every morning At 5:10 am, copy all template tasks into Inbox In each template task, set the due dates and days to add the task. You can do that like this days:mon,tues; due:8pm which will add the task every Monday and Tuesday and make it due at 8pm. How to setup Add Todoist creds Create a template list to copy from in Todoist. Add days and due times on each task as necessary. Set the projects to copy from and to write to in each Todoist node
By MutasemGenerate complete SEO strategy reports with SerpAPI data and GPT-4 Agent team
Overview This workflow deploys a fully autonomous "AI SEO Agency" inside your n8n instance. Unlike simple chatbots, this is a hierarchical agent swarm. A "Director" agent acts as the project manager, using live market data to orchestrate a team of 6 specialized AI workers. It takes a single user prompt and turns it into a comprehensive, professional-grade SEO strategy report. Key Features Hierarchical Architecture: A "Manager" agent delegates tasks to "Worker" agents. Live Market Research: Uses SerpApi to fetch real-time Google Search results and competitor data. Conversational Memory: Remembers business details across the chat session. 6 Expert Specialists: Includes dedicated agents for Keyword Research, Technical SEO, Link Building, Analytics, Local SEO, and Content Writing. How it works Analysis: The SEO Director Agent receives your request and consults its memory. Live Research: The Director uses the SerpApi tool to perform live Google searches on the target website and niche to gather context. Delegation: Based on the research, the Director dynamically assigns tasks to the relevant Specialist Agents (e.g., calling the "Technical Specialist" for site speed issues or the "Keyword Specialist" for content ideas). Synthesis: The Director compiles the outputs from all specialists into one cohesive, actionable strategy report. Set up steps Estimated time: 5 minutes OpenAI Keys: Add your OpenAI API Key to all 7 "OpenAI Chat Model" nodes (1 for the Director, 6 for the Specialists). SerpApi Key: Open the "SEO Director Agent" node. Under "Tools" > "SerpApi," add your SerpApi Key (free tier available). This enables the live Google Search capability. Run: Toggle the workflow to "Active" and start chatting! About the Creator Built by Pixril. We specialize in building advanced, production-ready AI agents for n8n. Find more professional workflows in our shop: https://pixril.etsy.com
By PixrilChat with internal documents using Ollama, Supabase Vector DB & Google Drive
đ Chat with Internal Documents (RAG AI Agent) â Features Answers should given only within provided text. Chat interface powered by LLM (Ollama) Retrieval-Augmented Generation (RAG) using Supabase Vector DB Multi-format file support (PDF, Excel, Google Docs, text files) Automated file ingestion from Google Drive Real-time document update handling Embedding generation via Ollama for semantic search Memory-enabled agent using PostgreSQL Custom tools for document lookup with context-aware chat âď¸ How It Works đĽ Document Ingestion & Vectorization Watches a Google Drive folder for new or updated files. Deletes old vector entries for the file. Uses conditional logic to extract content from PDFs, Excel, Docs, or text Summarizes and preprocesses content. (if needed) Splits and embeds the text via Ollama. Stores embeddings in Supabase Vector DB đŹ RAG Chat Agent Chat is initiated via Webhook or built-in chat interface. User input is passed to the RAG Agent. Agent queries the User_documents tool (Supabase vector store) using the Ollama model to fetch relevant content. If context is found, it answers directly. Otherwise, it can call tools or request clarification. Responses are returned to the user, with memory stored in PostgreSQL for continuity. đ Supabase Database Configuration Create a Supabase project at https://supabase.com and go to the SQL editor. Create a documents table with the following schema: id - int8 content - text metadata - jsonb embedding - vector Generate an API Key
By Lakindu Siriwardana