Agents & Automations

Image Creation with OpenAI and Telegram In the realm of automation and artificial intelligence, n8n offers a sophisticated platform for seamlessly integrating AI algorithms to enhance image creation and communication processes. This innovative workflow leverages the capabilities of OpenAI and Telegram to facilitate creative image generation and streamline communication channels, ultimately enhancing user engagement and interaction. How to Use: 1. Set Up Credentials: Configure credentials for the Telegram account and OpenAI API to enable seamless integration. 2. Configure Nodes: - Telegram Trigger Node: Set up the node to initiate the workflow based on incoming messages from users on Telegram. - OpenAI Node: Utilize advanced AI algorithms to analyze text content from messages and generate intelligent responses. - Telegram Node: Send processed data, including images and responses, back to users on Telegram for seamless communication. - Merge Node: Organize and combine processed data for efficient handling and integration within the workflow. - Aggregate Node: Aggregate all item data, including binaries if specified, for comprehensive reporting and analysis purposes. 3. Run Workflow: Initiate the workflow to leverage AI-enhanced image processing and communication capabilities for enhanced user interactions. 4. Monitor Execution: Keep an eye on the workflow execution for any errors or issues that may occur during processing. 5. Customize Workflow: Tailor the workflow nodes, parameters, or AI models to align with specific business objectives and user engagement strategies. Experience Benefits: Embrace the power of AI-driven image processing and interactive communication on Telegram to elevate user engagement and satisfaction levels. By following these steps, businesses can unlock the transformative potential of AI integration in image creation and communication workflows using n8n. Elevate your user engagement strategies and deliver exceptional experiences to your audience through innovative AI-driven solutions. Embark on a journey of innovation and efficiency with AI integration in image creation and communication workflows using n8n!

This n8n template monitors an Outlook mailbox for invoices, automatically parses/extracts data from them, and then uploads the output to an Excel Workbook. One of my top workflow requests, this template can save many hours of manual labor for you or your finance/accounts team. How it works A scheduled trigger is set to fetch recent Outlook messages to the Accounts Receivable mailbox. Each message is analyzed to determine whether or not it is from a supplier and is issuing/contains an invoice. For each valid message, the attachments are downloaded and non-invoice documents are filtered out via AI Vision classification. Invoices are then processed through an AI vision model again to extract the details. The extracted data can then be used for reconciliation or otherwise. For this demonstration, we'll just append the row to an Excel sheet for now. How to use Ensure your Microsoft365 credential points to the correct mailbox. If a shared folder is used, toggle the "shared folder" option to "on" and for the principal ID, use the email address. If you receive lots of other types of messages such as replies and forwards, you may want to implement additional checks to prevent processing invoices twice. The "remove duplicates" node can help with this. Customizing this workflow Note the assumption for this template is that all invoices will come as a PDF attachment. In real life, this is rarely the case! Adding in document conversion to cover all invoice formats. Human feedback is also an important factor in AI workflows. Try tagging emails as a way to notify team members that the invoice was processed.

Tags: Sustainability, CSRD, Reporting, ESG, Compliance, Automation Context Hey! I'm Samir, a Supply Chain Engineer and Data Scientist from Paris, founder of LogiGreen Consulting. We help companies automate sustainability workflows using AI, Data Analytics, and No-Code tools like N8N. Sustainability Reporting meets Automation with n8n! 📬 For business inquiries, you can add me on Here. What is a CSRD XHTML Report? Under the Corporate Sustainability Reporting Directive (CSRD), companies must publish their ESG disclosures in a machine-readable XHTML format, embedding XBRL tags that make the report structured and standardized. These files must follow strict formatting and tagging rules to ensure compliance, traceability, and accessibility for both regulators and analysts. Who is this template for? This workflow is designed for sustainability teams, ESG consultants, or developers who want to automatically check the structure and format of CSRD reports submitted in XHTML. How does it work? This N8N workflow automates the audit process: 📤 Input Node → Uploads or fetches the XHTML file via URL or Webhook. 🧪 Validates Structure → Uses a custom code node to parse HTML and identify required tags (e.g., <ix:nonNumeric>, namespaces). 📋 Outputs a Report → Returns a summary report of errors, warnings, and key metadata (like entity name, reporting period). 📤 Export Option → Save the results in Google Sheets or send via email. Prerequisite A sample XHTML file that you can find in my GitHub Repository. Google Sheets API and OpenAI API credentials. Next Steps Follow the sticky notes inside each node to adjust parsing rules or extend validation to specific XBRL tags relevant to your sector (e.g., GHG emissions, water usage). Notes This workflow includes an example XHTML file to test the validator. You can plug this into your internal systems or even extend it with AI to auto-summarize the sustainability report. This workflow has been created with N8N 1.82.1. Submitted: April 3rd, 2025

How to automate advanced SEO keyword research using AI and N8N workflows to save time and uncover high-value search terms for any content creation. Using n8n and DataForSEO.

Enhance Security Operations with the Qualys Slack Shortcut Bot! Our Qualys Slack Shortcut Bot is strategically designed to facilitate immediate security operations directly from Slack. This powerful tool allows users to initiate vulnerability scans and generate detailed reports through simple Slack interactions, streamlining the process of managing security assessments. Workflow Highlights: - Interactive Modals: Utilizes Slack modals to gather user inputs for scan configurations and report generation, providing a user-friendly interface for complex operations. - Dynamic Workflow Execution: Integrates seamlessly with Qualys to execute vulnerability scans and create reports based on user-specified parameters. - Real-Time Feedback: Offers instant feedback within Slack, updating users about the status of their requests and delivering reports directly through Slack channels. Operational Flow: - Parse Webhook Data: Captures and parses incoming data from Slack to understand user commands accurately. - Execute Actions: Depending on the user's selection, the workflow triggers other sub-workflows like 'Qualys Start Vulnerability Scan' or 'Qualys Create Report' for detailed processing. - Respond to Slack: Ensures that every interaction is acknowledged, maintaining a smooth user experience by managing modal popups and sending appropriate responses. Setup Instructions: 1. Verify that Slack and Qualys API integrations are correctly configured for seamless interaction. 2. Customize the modal interfaces to align with your organization's operational protocols and security policies. 3. Test the workflow to ensure that it responds accurately to Slack commands and that the integration with Qualys is functioning as expected. Need Assistance? Explore our Documentation or get help from the n8n Community for more detailed guidance on setup and customization. Deploy this bot within your Slack environment to significantly enhance the efficiency and responsiveness of your security operations, enabling proactive management of vulnerabilities and streamlined reporting. To handle the actual processing of requests, you will also need to deploy these two sub-workflows: - Qualys Start Vulnerability Scan - Qualys Create Report To simplify deployment, use this Slack App manifest to quickly create an app with the correct permissions.

Use case When working with multiple teams, bugs must get in front of the right team as quickly as possible to be resolved. Normally, this includes a manual grooming of new bugs that have arrived in your ticketing system (in our case, Linear). We found this way too time-consuming. That's why we built this workflow. What this workflow does This workflow triggers every time a Linear issue is created or updated within a certain team. For us at n8n, we created one general team called Engineering where all bugs get added in the beginning. The workflow then checks if the issue meets the criteria to be auto-moved to a certain team. In our case, that means that the description is filled, that it has the bug label, and that it's in the Triage state. The workflow then classifies the bug using OpenAI's GPT-4 model before updating the team property of the Linear issue. If the AI fails to classify a team, the workflow sends an alert to Slack. Setup - Add your Linear and OpenAI credentials. - Change the team in the Linear Trigger to match your needs. - Customize your teams and their areas of responsibility in the Set me up node. Please use the format [Teamname][Description/Areas of responsibility]. Also, make sure that the team names match the names in Linear exactly. - Change the Slack channel in the Set me up node to your Slack channel of choice. How to adjust it to your needs - Play around with the context that you're giving to OpenAI, to make sure the model has enough knowledge about your teams and their areas of responsibility. - Adjust the handling of AI failures to your needs. How to enhance this workflow At n8n, we use this workflow in combination with some others. For example, we have the following enhancements: - We're using an automation that enables everyone to add new bugs easily with the right data via a /bug command in Slack.

Who is this for? This workflow is for all sales reps and lead generation managers who need to prepare their prospecting activities and find relevant information to personalize their outreach. Use Case This workflow allows you to do account research with the web using AI. It has the potential to replace manual work done by sales reps when preparing their prospecting activities by searching complex information available online. What this workflow does The advanced AI module has two capabilities: 1. Research Google using SerpAPI 2. Visit and get website content using a sub-workflow From an unstructured input like a domain or a company name, it will return the following properties: - domain - company LinkedIn URL - cheapest plan - has free trial - has enterprise plan - has API - market (B2B or B2C) The strength of n8n here is that you can adapt this workflow to research whatever information you need. You just have to specify it in the prompt and detail the output format in the "Structured Output Parser" module. Detailed instructions and video guide can be found by following this link.

This workflow demonstrates three distinct approaches to chaining LLM operations using Claude 3.7 Sonnet. Connect to any section to experience the differences in implementation, performance, and capabilities. What you'll find: 1️⃣ Naive Sequential Chaining The simplest but least efficient approach - connecting LLM nodes in a direct sequence. Easy to set up for beginners but becomes unwieldy and slow as your chain grows. 2️⃣ Agent-Based Processing with Memory Process a list of instructions through a single AI Agent that maintains conversation history. This structured approach provides better context management while keeping your workflow organized. 3️⃣ Parallel Processing for Maximum Speed Split your prompts and process them simultaneously for much faster results. Ideal when you need to run multiple independent tasks without shared context. Setup Instructions:API Credentials: Configure your Anthropic API key in the credentials manager. This workflow uses Claude 3.7 Sonnet, but you can modify the model in each Anthropic Chat Model node, or pick an entirely different LLM. For Cloud Users: If using the parallel processing method (section 3), replace {{ $env.WEBHOOK_URL }} in the "LLM steps - parallel" HTTP Request node with your n8n instance URL. Test Data: The workflow fetches content from the n8n blog by default. You can modify this part to use a different content or a data source. Customization: Each section contains a set of example prompts. Modify the "Initial prompts" nodes to change the questions asked to the LLM. Compare these methods to understand the trade-offs between simplicity, speed, and context management in your AI workflows!

This n8n template watches an Outlook shared inbox for support messages and creates an equivalent issue item in JIRA. How it works A scheduled trigger fetches recent Outlook messages from a shared inbox that collects support requests. These support requests are filtered to ensure they are only processed once, and their HTML body is converted to markdown for easier parsing. Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority, and summarizes a title and description of the original request. Finally, the AI-generated values are used to create an issue in JIRA to be actioned. How to use Ensure the messages fetched are solely support requests; otherwise, you'll need to classify messages before processing them. Specify the labels and priorities to use in the system prompt of the AI agent. Requirements - Outlook for incoming support - OpenAI for LLM - JIRA for issue management Customizing this workflow Consider automating more steps after the issue is created, such as attempting issue resolution or capacity planning.

Name: AI-Powered Research Agent using Perplexity Sonar Description: This workflow acts as an AI-powered research assistant using the Perplexity Sonar model. When triggered by another workflow, it sends a user-defined prompt to the Perplexity API to retrieve up-to-date search results. The response is then parsed into a clean format for downstream processing. How it Works:Trigger: Activated from another workflow via Execute Workflow Trigger.Prompt Setup: Sets a system role message and user query dynamically.API Call: Sends a POST request to Perplexity's /chat/completions endpoint with your credentials.Response Handling: Extracts the message content from the API response.Output: Returns the result, ready for display or further processing.Requirements: - A Perplexity AI API Key - Set up authentication via Header Auth with Bearer token - Ensure your account allows outbound HTTP requests in n8nCustomization Tips: - Modify the system prompt to suit your research domain - Chain this workflow with other automation like blog creation, summaries, etc. - Replace the output handling logic to fit into Google Sheets, Notion, or Telegram

Who this template is for This template is for every engineer who wants to automate their code reviews or just get a second opinion on their PR. How it works This workflow will automatically review your changes in a Gitlab PR using the power of AI. It will trigger whenever you comment with +0 to a Gitlab PR, get the code changes, analyze them with GPT, and reply to the PR discussion. Set up Steps - Set up webhook of note_events in Gitlab repository (see here on how to do it) - Configure ChatGPT credentials - Note "+0" in MergeRequest to trigger automatic review by ChatGPT

Master Facebook Ads copy creation using Google Sheets and ChatGPT by applying the effective Pain, Agitate, Solution (PAS) copywriting formula. Optimize your ad results while simplifying your social media marketing approach.

How It Works This N8N workflow creates an automated system for discovering high-potential real estate investment opportunities. The workflow runs on a customizable schedule to scan the market for properties that match your specific criteria, then alerts your team about the most promising leads. The process follows these steps: - Connects to BatchData API on a regular schedule to search for properties matching your parameters. - Compares new results with previous scans to identify new listings and property changes. - Applies intelligent filtering to focus on high-potential opportunities (high equity, absentee owners, etc.). - Retrieves comprehensive property details and owner information for qualified leads. - Delivers formatted alerts through multiple channels (email and Slack/Teams). Each email alert includes detailed property information, owner details, equity percentage, and a direct Google Maps link to view the property location. The workflow also posts concise notifications to your team's communication channels for quick updates. Who It's For This workflow is designed for: - Real Estate Investors: Find off-market properties with high equity and motivated sellers. - Real Estate Agents: Identify potential listing opportunities before they hit the market. - Property Acquisition Teams: Streamline the lead generation process with automated scanning. - Real Estate Wholesalers: Discover properties with significant equity spreads for potential deals. - REITs and Property Management Companies: Monitor market changes and expansion opportunities. The workflow is especially valuable for professionals who want to: - Save hours of manual market research time. - Get early notifications about high-potential properties. - Access comprehensive property and owner information in one place. - Focus their efforts on the most promising opportunities. About BatchData BatchData is a powerful property data platform for real estate professionals. Their API provides access to comprehensive property and owner information across the United States, including: - Property details (bedrooms, bathrooms, square footage, year built, etc.). - Valuation and equity estimates. - Owner information (name, mailing address, contact info). - Transaction history and sales data. - Foreclosure and distressed property status. - Demographic and neighborhood data. The platform specializes in providing accurate, actionable property data that helps real estate professionals make informed decisions and identify opportunities efficiently. BatchData's extensive database covers millions of properties nationwide and is regularly updated to ensure data accuracy. The API's flexible search capabilities allow you to filter properties based on numerous criteria, making it an ideal data source for automated lead generation workflows like this one.

Overview This workflow is designed for automated fact-checking of texts. It uses AI models to compare a given text with a list of facts and identify potential discrepancies or hallucinations. Components 1. Input The workflow can be initiated in two ways: a) Manually via the "When clicking 'Test workflow'" trigger b) By calling from another workflow via the "When Executed by Another Workflow" triggerRequired inputs: - facts: A list of verified facts - text: The text to be checked 2. Text Preparation The "Code" node splits the input text into individual sentences. It takes into account date specifications and list elements. 3. Fact Checking Each sentence is individually compared with the given facts. It uses the "bespoke-minicheck" Ollama model for verification. The model responds with "Yes" or "No" for each sentence. 4. Filtering and Aggregation Sentences marked as "No" (not fact-based) are filtered. The filtered results are aggregated. 5. Summary A larger language model (Qwen2.5) creates a summary of the results. The summary contains: - Number of incorrect factual statements - List of incorrect statements - Final assessment of the article's accuracyUsage Ensure the "bespoke-minicheck" model is installed in Ollama (ollama pull bespoke-minicheck). Prepare a list of verified facts. Enter the text to be checked. Start the workflow. The results are output as a structured summary. Notes The workflow ignores small talk and focuses on verifiable factual statements. Accuracy depends on the quality of the provided facts and the performance of the AI models.Customization Options The summarization function can be adjusted or removed to return only the raw data of the issues found. The AI models used can be exchanged if needed. This workflow provides an efficient method for automated fact-checking and can be easily integrated into larger systems or editorial workflows.

Instantly access NFT metadata, collections, traits, contracts, and ownership details from OpenSea! This workflow integrates GPT-4o-mini AI, OpenSea API, and n8n automation to provide structured NFT data for traders, collectors, and investors. How It Works - Receives user queries via Telegram, webhooks, or another connected interface. - Determines the correct API tool based on the request (e.g., user profile, NFT metadata, contract details). - Retrieves data from OpenSea API (requires API key). - Processes the information using an AI-powered NFT insights engine. - Returns structured insights in an easy-to-read format for quick decision-making. What You Can Do with This Agent 🔹 Retrieve OpenSea User Profiles → Get user bio, links, and profile info. 🔹 Fetch NFT Collection Details → Get collection metadata, traits, fees, and contract info. 🔹 Analyze NFT Metadata → Retrieve ownership, rarity, and trait-based pricing. 🔹 Monitor NFTs Owned by a Wallet → Track all NFTs under a specific account. 🔹 Retrieve Smart Contract Data → Get blockchain contract details for an NFT collection. 🔹 Identify Valuable Traits → Fetch NFT trait insights and rarity scores.Example Queries You Can Use ✅ "Get OpenSea profile for 0xA5f49655E6814d9262fb656d92f17D7874d5Ac7E." ✅ "Retrieve details for the 'Azuki' NFT collection." ✅ "Fetch metadata for NFT #5678 from 'Bored Ape Yacht Club'." ✅ "Show all NFTs owned by 0x123... on Ethereum." ✅ "Get contract details for NFT collection 'CloneX'."Available API Tools & Endpoints 1️⃣ Get OpenSea Account Profile → /api/v2/accounts/{address_or_username} (Retrieve user bio, links, and image) 2️⃣ Get NFT Collection Details → /api/v2/collections/{collection_slug} (Get collection-wide metadata) 3️⃣ Get NFT Metadata → /api/v2/chain/{chain}/contract/{address}/nfts/{identifier} (Retrieve individual NFT details) 4️⃣ Get NFTs Owned by Account → /api/v2/chain/{chain}/account/{address}/nfts (List all NFTs owned by a wallet) 5️⃣ Get NFTs by Collection → /api/v2/collection/{collection_slug}/nfts (Retrieve all NFTs from a specific collection) 6️⃣ Get NFTs by Contract → /api/v2/chain/{chain}/contract/{address}/nfts (Retrieve all NFTs under a contract) 7️⃣ Get Payment Token Details → /api/v2/chain/{chain}/payment_token/{address} (Fetch info on payment tokens used in NFT transactions) 8️⃣ Get NFT Traits → /api/v2/traits/{collection_slug} (Retrieve collection-wide trait data)Set Up Steps 1. Get an OpenSea API Key Sign up at OpenSea API and request an API key. 2. Configure API Credentials in n8n Add your OpenSea API key under HTTP Header Authentication. 3. Connect the Workflow to Telegram, Slack, or Database (Optional) Use n8n integrations to send alerts to Telegram, Slack, or save results to Google Sheets, Notion, etc. 4. Deploy and Test Send a query (e.g., "Azuki latest sales") and receive instant NFT market insights! Unlock powerful NFT analytics with AI-powered OpenSea insights—start now!

Este flujo de trabajo analiza los sentimientos de los comentarios proporcionados por los usuarios y los envía a un canal de Mattermost. Tipo de nodo de activación de Typeform: Cada vez que un usuario envía una respuesta al Typeform, este nodo activador inicia el flujo de trabajo. El nodo devuelve la respuesta que el usuario ha enviado en el formulario. Nodo de AWS Comprehend: Este nodo analiza el sentimiento de la respuesta que el usuario ha proporcionado y otorga un puntaje. Nodo IF: El nodo IF utiliza los datos proporcionados por el nodo de AWS Comprehend y verifica si el sentimiento es negativo. Si el sentimiento es negativo, obtenemos el resultado como verdadero; de lo contrario, falso. Nodo de Mattermost: Si el puntaje es negativo, el nodo IF devuelve verdadero y se ejecuta la rama verdadera del nodo IF. Conectamos el nodo de Mattermost con la rama verdadera del nodo IF. Siempre que el puntaje del análisis de sentimientos sea negativo, se ejecuta el nodo y se publica un mensaje en un canal de Mattermost. NoOp: Este nodo es opcional, ya que la ausencia de este nodo no afectará el funcionamiento del flujo de trabajo. Este flujo de trabajo puede ser utilizado por Gerentes de Producto para analizar los comentarios del producto. El flujo de trabajo también puede ser utilizado por Recursos Humanos para analizar comentarios de empleados. Incluso puedes usar este nodo para realizar un análisis de sentimientos de Tweets. Para realizar un análisis de sentimientos de Tweets, reemplaza el nodo de activación de Typeform con el nodo de Twitter. Nota: Necesitarás un nodo de activación o un nodo de inicio para iniciar el flujo de trabajo. En lugar de publicar un mensaje en Mattermost, puedes guardar los resultados en una base de datos o en una hoja de Google, o Airtable. Reemplaza el nodo de Mattermost con (o agrégalo después del nodo de Mattermost) el nodo de tu elección para agregar el resultado a tu base de datos.

Build a powerful AI agent from scratch that can help manage your social media, draft emails, and schedule meetings. Perfect for entrepreneurs, professionals, and anyone looking to automate their daily tasks using AI or for their AI agency.

Watch for every new product in WooCommerce and generate engaging descriptions using ChatGPT.Compile markdown and update products seamlessly in WooCommerce.

Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! Purpose: This workflow enables automated and AI-driven topic monitoring, delivering concise article summaries directly to a Slack channel in a structured and easy-to-read format. It allows users to stay informed on specific topics of interest effortlessly, without manually checking multiple sources, ensuring a time-efficient and focused monitoring experience. Target Audience: This workflow is designed for: - Industry professionals looking to track key developments in their field. - Research teams who need up-to-date insights on specific topics. - Companies aiming to keep their teams informed with relevant content. How It Works:Trigger: A Scheduler initiates the workflow at regular intervals (default: every hour). Data Retrieval: - RSS feeds are fetched using the RSS Read node. - Previously monitored articles are checked in Google Sheets to avoid duplicates. Content Processing: - The article relevance is assessed using OpenAI (GPT-4o-mini). - Relevant articles are scraped using Jina AI to extract content. - Summaries are generated and formatted for Slack. Output: - Summaries are posted to the specified Slack channel. - Article metadata is stored in Google Sheets for tracking. Key APIs and Nodes Used: - Scheduler Node: Triggers the workflow periodically. - RSS Read: Fetches the latest articles from defined RSS feeds. - Google Sheets: Stores monitored articles and manages feed URLs. - OpenAI API (GPT-4o-mini): Classifies article relevance and generates summaries. - Jina AI API: Extracts the full content of relevant articles. - Slack API: Posts formatted messages to Slack channels. This workflow provides an efficient and intelligent way to stay informed about your topics of interest, directly within Slack.

Automate product extraction from Product Hunt using Browse AI, conveniently syncing to Google Sheets while sharing findings directly on Slack.

🤖 AI Agent Web Search using SearchApi & LLMWho is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does - Connects an AI agent node to SearchApi.io as an integrated search tool. - Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). - Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. - Ensures responses include clearly cited sources for validation and further exploration. Setup 1. Install the SearchApi community node: - Open Settings → Community Nodes inside your self‑hosted n8n instance. - Fill npm Package Name with @searchapi/n8n-nodes-searchapi. - Accept the risk prompt, and hit Install. - It should now appear as a node when you search for it. 2. API Configuration: - Set up your SearchApi.io credentials in n8n. - Add your preferred LLM provider credentials (e.g., OpenRouter API). 3. Input Requirements: - Provide the YouTube video ID (e.g., wBuULAoJxok). 4. Connect LLM Integration: - Configure the summarization chain with your chosen model and parameters for text splitting. How to customize this workflow to your needs - Integrate additional nodes to structure or store search results (e.g., saving to databases, Notion, Google Sheets). - Extend chatbot capabilities to integrate with messaging platforms (Slack, Discord) or email notifications. - Adjust search parameters and filters within the AI agent node to tailor information retrieval. Example UsageInput: User asks, "What are the latest developments in AI regulation?" Output: AI retrieves, summarizes, and cites recent, authoritative articles and news sources from the web.