Workflow Templates

AI Content Generator Workflow Introduction This workflow automates the process of creating high-quality articles using AI, organizing them in Google Drive, and tracking their progress in Google Sheets. It's perfect for marketers, bloggers, and businesses looking to streamline content creation. With minimal setup, you can have a fully operational system to generate, save, and manage your articles in one cohesive workflow. How It Works Collect Inputs: Users fill out a form with details like article title, keywords, and instructions. Generate Content: AI creates an outline and writes the article based on user inputs. Organize Files: Saves the outline and final article in Google Drive for easy access. Track Progress: Updates Google Sheets with links to the generated content for tracking. Set Up Steps Time Required**: Approximately 15–20 minutes to connect all integrations and test the workflow. Steps**: Connect Google Drive and Google Sheets: Authorize access to store files and update the spreadsheet. Set Up OpenAI Integration: Add your OpenAI API key for generating the outline and article content. Customize the Form: Modify the form fields to match the details you want to collect for each article. Test the Workflow: Run the workflow with sample inputs to ensure everything works smoothly. This workflow not only simplifies the process of article creation but also sets a foundation for expanding into additional automations, like posting to social media platforms.

The News Site from Colt, a telecom company, does not offer an RSS feed, therefore web scraping is the choice to extract and process the news. The goal is to get only the newest posts, a summary of each post and their respective (technical) keywords. Note that the news site offers the links to each news post, but not the individual news. We collect first the links and dates of each post before extracting the newest ones. The result is sent to a SQL database, in this case a NocoDB database. This process happens each week thru a cron job. Requirements: Basic understanding of CSS selectors and how to get them via browser (usually: right click → inspect) ChatGPT API account - normal account is not sufficient A NocoDB database - of course you may choose any type of output target Assumptions: CSS selectors work on the news site The post has a date with own CSS selector - meaning date is not part of the news content "Warnings" Not every site likes to be scraped, especially not in high frequency Each website is structured in different ways, the workflow may then need several adaptations.

This n8n workflow is designed to analyze email headers received via a webhook. The workflow splits into two main paths based on the presence of the received and authentication results headers. In the first path, if received headers are present, the workflow extracts IP addresses from these headers and then queries the IP Quality Score API to gather information about the IP addresses, including fraud score, abuse history, organization, and more. Geolocation data is also obtained from the IP-API API. The workflow collects and aggregates this information for each IP address. In the second path, if authentication-results headers are present, the workflow extracts SPF, DKIM, and DMARC authentication results. It then evaluates these results and sets fields accordingly (e.g., SPF pass/fail/neutral). The paths merge their results, and the workflow responds to the original webhook with the aggregated analysis, including IP information and authentication results. Potential issues during setup include ensuring proper configuration of the webhook calls with header authentication, handling authentication and API keys for the IP Quality Score API, and addressing any discrepancies or errors in the logic nodes, such as handling SPF, DKIM, and DMARC results correctly. Additionally, thorough testing with various email header formats is essential to ensure accurate analysis and response.

Using Mistral API, you can use this n8n workflow to automate the process of: collecting, filtering, analyzing, and summarizing news articles from multiple sources. The sources come from pre-built RSS feeds and a custom DuckDuckGo node, which you can change if you need. It will deliver the most relevant news of the day in a concise manner. ++How It Works++** The workflow begins each weekday at noon. The news are gathered from RSS feeds and a custom DuckDuckGo node, using HTTPS GET when needed. News not from today or containing unwanted keywords are filtered out. The first AI Agent will select the top news from their titles alone and generate a general title & summary. The next AI Agent will summarize the full content of the selected top news articles. The general summary and title will be combined with the top 10 news summaries into a final output. ++Requirements++ An active n8n instance (self-hosted or cloud). Install the custom DuckDuckGo node: n8n-nodes-duckduckgo-search A Mistral API key Configure the Sub-Workflow for the content which requires HTTP GET requests. It is provided in the template itself. ++Fair Notice++ This is an older version of the template. There is a superior updated version which isn't restricted to tech news, with enhanced capabilities such as communication through different channels (email, social media) and advanced keyword filtering. It was recently published in n8n. You can find it here. If you are interested or would like to discuss specific needs, then feel free to contact us.

Workflow Information 📌 Purpose 🎯 The intention of this workflow is to integrate New Shopify Orders into MS Dynamics Business Central: Point-of-Sale (POS):** POS orders will be created in Business Central as Sales Invoices given no fulfillment is expected. Web Orders:** This type of orders will be created as Business Central Sales Orders. How to use it 🚀 Edit the "D365 BC Environment Settings" node with your own account values (Company Id, Tenanant Id, Tax & Discount Items). Go to the "Shopify" node and edit the connection with your environment. More help here. Go to the "Lookup Customers" node to edit the Business Central connection details with your environment settings. Set the required filters on the "Shopify Order Filter" node. Edit the "Schedule Trigger" node with the required frequency. Useful Workflow Links 📚 Step-by-step Guide/ Integro Cloud Solutions Business Central REST API Documentation Video Demo Need Help? Contact me at: ✉️greg.lopez@integrocloudsolutions.com 📥 https://www.linkedin.com/in/greg-lopez-08b5071b/

Build a Better AI Chatbot for Your Zendesk Knowledge Portal Simple setup, no vector database needed. Uses GraphRAG to enhance user's prompts and provide high-quality and relevant up-to-date responses from your Zendesk knowledge base. Can be embedded on your Zendesk portal, also accesible via a URL. Can be customized and branded in your style. See example at support.noduslabs.com or a screenshot below: Also, compare it to the original Zendesk AI chatbot available at our other website https://infranodus.com — you will see that the quality of responses in this custom chatbot is much better than in the native Zendesk one, plus you save subscription because you won't need to activate their chat option, which is $25 per agent. Workflow Overview In this workflow, we use the n8n AI Agent Node with a custom prompt that: 1) First consults an "expert" graph from the InfraNodus GraphRAG system using the official InfraNodus GraphRAG node that will extract a reasoning ontology and a general context about your product from the graph that you create manually or automatically as described on our support portal. 2) The augmented user prompt is converted by AI agent node in a Zendesk search query that retrieves the most relevant content using their search API via n8n HTTP node. Both the results from the graph and the search results are combined and shown to the user How it works Receives a request from a user via a webhook that connects to the custom n8n chat widget. The request goes to the AI Agent node from n8n with a custom prompt (provided in the workflow) that orchestrates the following procedure: Sends the request to the knowledge graph in your InfraNodus account using the official InfraNodus GraphRAG node that contains a reasoning ontology represented as a knowledge graph based on your Zendesk knowledge support portal. Read more on how to generate this ontology here. Based on the results from InfraNodus, it reformulates the original prompt to include the reasoning logic as well as provide a fuller context to the model. Sends the request to the Zendesk search API using the n8n custom HTTP node with an enhanced search query to retrieve high-quality results. Combines Zendesk search results with InfraNodus ontology to generate a final response to the user. Sends the response back to the webhook, which is then picked up by the n8n chat widget that is shown to the user wherever the widget is embedded (e.g. on your own support portal). How to use • Get an InfraNodus API key and add it into InfraNodus GraphRAG node. • Edit the InfraNodus Graph node to provide the name of the graph that you will be using as ontology (you need to create it in InfraNodus first. • Edit the AI Agent (Support Agent) prompt to modify our custom instructions for your particular use case (do not change it too much as it works quite well and tells the agent what it should do and in what sequence). • Add the API key for your Zendesk account. In order to get it, go to your support portal Admin > Apps & Integrations > API Tokens. Usually it's located at https://noduslabs.zendesk.com/admin/apps-integrations/apis/api-tokens where instead of noduslabs you need to put the name of your support portal. Note: the official n8n Zendesk node does not have an endpoint to search and extract articles from support portal, so we use the custom HTTP node, but you can still connect to it via the Zendesk API key you have installed in your n8n. Support & Tutorials If you wan to create your own reasoning ontology graphs, please, refer to this article on generating your own knowledge graph ontologies. Specifically for this use case: Building ontology for your n8n AI chat bot. You may also be interested to watch this video that explains the logic of this approach in detail: Our support article for this workflow with real-life example: Building an embeddable AI chatbot agent for your Zendesk knowledge portal. To get support and help, contact us via support.noduslabs.com Learn more about InfraNodus at www.infranodus.com

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. 2. This is the second pipeline to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Setting Up Cluster (Class) Centres & Cluster (Class) Threshold Scores for Anomaly Detection Preparatory workflow to set cluster centres and cluster threshold scores so anomalies can be detected based on these thresholds. Here, we're using two approaches to set up these centres: the "distance matrix approach" and the "multimodal embedding model approach".

Quick Overview This workflow receives a webhook request with a target business vertical and location, finds matching companies via Google Places API, scrapes their websites with Firecrawl to extract and validate contact emails, deduplicates against Supabase, stores new prospects, and sends a single digest via Microsoft Outlook plus a notification webhook. How it works Receives a POST webhook request containing a business vertical, location, and optional result limit. Searches Google Places Text Search for matching local businesses and keeps operational results that include a website while filtering out competitor categories. Scrapes each business homepage and a likely contact page with Firecrawl to extract a preferred email address and detect parked or low-content domains. Validates the extracted email domain by checking DNS MX records via Google DNS-over-HTTPS. Collects all processed businesses, fetches existing prospects from Supabase, and removes entries that match existing website URLs or verified emails. Inserts the new, emailable prospects into a Supabase table and sends a single HTML digest email through Microsoft Outlook. Posts a run summary to a separate webhook endpoint (for a Telegram relay) and returns a JSON response to the original webhook caller. Setup Create credentials for Google Places API (HTTP Header Auth with your API key), Firecrawl, Supabase, and Microsoft Outlook OAuth2. Replace the Supabase REST URLs (YOUR-PROJECT.supabase.co) and ensure a pilot_prospects table exists with fields to store website_url, verified_email, and the inserted prospect payload. Update the webhook path and send POST requests like {"vertical":"dentists","location":"Orlando FL","limit":20} to trigger the workflow. Set the digest recipient/sender addresses in the Microsoft Outlook email node. Replace the notification webhook URL used for the Telegram relay with your own endpoint (and configure that endpoint/bot to post to your target chat).

This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. Here’s how it functions: Once configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications. Benefits End-to-End Automation** No manual interaction is needed: documents are read, processed, and made queryable with minimal setup. Scalable and Modular** The workflow uses subflows and batching, making it easy to scale and customize. Multi-Model Support** Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelligent answering—taking advantage of the strengths of each. Real-Time Q\&A** With RAG integration, users can query document content through natural language and receive accurate responses grounded in the PDF data. Light or Full Mode** Users can choose to index full page content or only summarized text, optimizing for either performance or richness. How It Works PDF Processing with Mistral OCR: The workflow starts by uploading a PDF file to Mistral's API, which performs OCR to extract text and metadata. The extracted content is split into manageable chunks (e.g., pages or sections) for further processing. Vector Storage in Qdrant: The extracted text is converted into embeddings using OpenAI's embedding model. These embeddings are stored in a Qdrant vector database, enabling efficient similarity searches for RAG. Question-Answering with RAG: When a user submits a question via a chat interface, the workflow retrieves relevant text chunks from Qdrant using vector similarity. A language model (Google Gemini) generates answers based on the retrieved context, providing accurate and context-aware responses. Optional Summarization: The workflow includes an optional summarization step using Google Gemini to condense the extracted text for faster processing or lighter RAG usage. Set Up Steps To deploy this workflow in n8n, follow these steps: Configure Qdrant Database: Replace QDRANTURL and COLLECTION in the "Create collection" and "Refresh collection" nodes with your Qdrant instance details. Ensure the Qdrant collection is configured with the correct vector size (e.g., 1536 for OpenAI embeddings) and distance metric (e.g., Cosine). Set Up Credentials: Add credentials for: Mistral Cloud API (for OCR processing). OpenAI API (for embeddings). Google Gemini API (for chat and summarization). Google Drive (if sourcing PDFs from Drive). Qdrant API (for vector storage). PDF Source Configuration: If using Google Drive, specify the folder ID in the "Search PDFs" node. Alternatively, modify the workflow to accept PDFs from other sources (e.g., direct uploads or external APIs). Customize Text Processing: Adjust chunk size and overlap in the "Token Splitter" node to optimize for your document type. Choose between raw text or summarized content for RAG by toggling between the "Set page" and "Summarization Chain" nodes. Test the RAG: Trigger the workflow manually or via a chat message to verify OCR, embedding, and Qdrant storage. Use the "Question and Answer Chain" node to test query responses. Optional Sub-Workflows: The workflow supports execution as a sub-workflow for batch processing (e.g., handling multiple PDFs). Need help customizing? Contact me for consulting and support or add me on Linkedin.

Create Video with HeyGen and Upload to YouTube Overview This workflow automates the process of creating an AI-generated avatar video using HeyGen and directly uploading it to YouTube. By sending text input via a webhook, the workflow generates a video with a chosen avatar and voice, waits for processing, downloads the completed file, and publishes it to your configured YouTube channel. This template is ideal for automating content creation pipelines, such as daily news updates, explainer videos, or narrated scripts, without manual intervention. Use Case Marketing teams**: Automate explainer or promotional video creation from text input. Content creators**: Generate AI-based avatar videos for YouTube directly from scripts. Organizations**: Streamline video generation for announcements, product updates, or tutorials. Instead of recording and editing videos manually, this template allows you to feed text content into a webhook and have a ready-to-publish video on your YouTube channel within minutes. How It Works Webhook Trigger: The workflow starts when text content and a title are sent to the webhook endpoint. Code Node: Cleans and formats the input text by removing unnecessary newlines and returns it with the title. Set Node: Prepares HeyGen parameters, including API key, avatar ID, voice ID, title, and content. HeyGen API Call: Sends the request to generate a video with the provided avatar and voice. Wait Node: Pauses briefly to allow HeyGen to process the video. Video Status Check: Polls HeyGen to check whether the video has finished processing. Conditional Check: If the video is still processing, it loops back to wait. Once complete, it moves forward. Download Node: Retrieves the generated video file. YouTube Upload Node: Uploads the video to your YouTube channel with the provided title and default settings. Requirements HeyGen API Key**: Required to authenticate with HeyGen’s video generation API. HeyGen Avatar & Voice IDs**: Unique identifiers for the avatar and voice you want to use. YouTube OAuth2 Credentials**: Connected account for video uploads. Setup Instructions Import the Workflow: Download and import this template JSON into your n8n instance. Configure the Webhook: Copy the webhook URL from n8n and use it to send requests with title and content. Example payload: { "title": "Tech News Update", "content": "Today’s top story is about AI advancements in video generation..." } Add HeyGen Credentials: Insert your HeyGen API key in the Set Node under x-api-key. Provide your chosen avatar_id and voice_id from HeyGen. To find your HeyGen avatar_id and voice_id, first retrieve your API key from the HeyGen dashboard. With this key, you can use HeyGen’s API to look up available options: run a GET request to https://api.heygen.com/v2/avatars to see a list of avatars along with their avatar_id, and then run a GET request to https://api.heygen.com/v2/voices to see a list of voices with their voice_id. Once you’ve identified the avatar and voice you want to use, copy their IDs and paste them into the Set HeyGen Parameters node in your n8n workflow. Set Up YouTube Credentials: Connect your YouTube account in n8n using OAuth2. Ensure proper permissions are granted for video uploads. To set up YouTube credentials in n8n, go to the Google Cloud Console, enable YouTube Data API v3, and create an OAuth Client ID (choose Web Application and add the redirect URI: https://<your-n8n-domain>/rest/oauth2-credential/callback). Copy the Client ID and Client Secret, then in n8n create new credentials for YouTube OAuth2 API. Enter the values, authenticate with your Google account to grant upload permissions, and test the connection. Once complete, the YouTube node will be ready to upload videos automatically. Activate the Workflow: Once configured, enable the workflow. Sending a POST request to the webhook with title and content will trigger the full process. Notes You can adjust video dimensions (default: 1280x720) in the HeyGen API request. Processing time may vary depending on script length. The workflow uses a wait-and-poll loop until the video is ready. Default YouTube upload category is Education (28) and region is US. These can be customized in the YouTube node.

This workflow acts as a central API gateway for all technical indicator agents in the Binance Spot Market Quant AI system. It listens for incoming webhook requests and dynamically routes them to the correct timeframe-based indicator tool (15m, 1h, 4h, 1d). Designed to power multi-timeframe analysis at scale. 🎥 Watch Tutorial: 🎯 What It Does Accepts requests via webhook with a token symbol and timeframe Forwards requests to the correct internal technical indicator tool Returns a clean JSON payload with RSI, MACD, BBANDS, EMA, SMA, and ADX Can be used directly or as a microservice by other agents 🛠️ Input Format Webhook endpoint: POST /webhook/indicators Body format: { "symbol": "DOGEUSDT", "timeframe": "15m" } 🔄 Routing Logic | Timeframe | Routed To | | --------- | -------------------------------- | | 15m | Binance SM 15min Indicators Tool | | 1h | Binance SM 1hour Indicators Tool | | 4h | Binance SM 4hour Indicators Tool | | 1d | Binance SM 1day Indicators Tool | 🔎 Use Cases | Use Case | Description | | -------------------------------------------------- | ------------------------------------------------------ | | 🔗 Used by Binance Financial Analyst Tool | Automatically triggers all indicator tools in parallel | | 🤖 Integrated in Binance Quant AI System | Supports reasoning, signal generation, and summaries | | ⚙️ Can be called independently for raw data access | Useful for dashboards or advanced analytics | 📤 Output Example { "symbol": "DOGEUSDT", "timeframe": "15m", "rsi": 56.7, "macd": "Bearish Crossover", "bbands": "Stable", "ema": "Price above EMA", "adx": 19.4 } ✅ Prerequisites Make sure all the following workflows are installed and operational: Binance SM 15min Indicators Tool Binance SM 1hour Indicators Tool Binance SM 4hour Indicators Tool Binance SM 1day Indicators Tool OpenAI credentials (for any agent using LLM formatting) 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company All architectural routing logic and endpoint structuring is IP-protected. No unauthorized rebranding or resale permitted. 🔗 Need help? Connect on LinkedIn – Don Jayamaha

This workflow will gather data every minute from the GitHub (https://github.com), Docker (https://www.docker.com/), npm (https://www.npmjs.com/) and Product Hunt (https://www.producthunt.com/) website APIs and display select information on a Smashing (https://smashing.github.io/) dashboard. For convenience sake, the dashboard piece can be easily downloaded as a docker container (https://hub.docker.com/r/tephlon/n8n_dashboard) and installed into your docker environment.