Workflow Templates

Automated Blog Monitoring System with RSS Feeds and Time-Based Filtering Overview This workflow provides a powerful yet simple foundation for monitoring blogs using RSS feeds. It automatically fetches articles from a list of your favorite blogs and filters them based on their publication date, separating new content from old. It is the perfect starting point for anyone looking to build a custom content aggregation or notification system without needing any API keys. This template is designed for developers, hobbyists, and marketers who want a reliable way to track new blog posts and then decide what to do with them. Instead of including a specific final step, this workflow intentionally ends with a filter, giving you complete freedom to add your own integrations. Use Cases Why would you need to monitor and filter blog posts? Build a Custom News Feed: Send new articles that match your interests directly to a Discord channel, Slack, or Telegram chat. Power a Newsletter: Automatically collect links and summaries from industry blogs to curate your weekly newsletter content. Create a Social Media Queue: Add new, relevant blog posts to a content calendar or social media scheduling tool like Buffer or Hootsuite. Archive Content: Save new articles to a personal database like Airtable, Notion, or Google Sheets to build a searchable knowledge base. How It Works Manual Trigger: The workflow starts when you click "Execute Workflow". You can easily swap this for a Schedule Trigger to run it automatically. Fetch RSS Feeds: It reads a list of RSS feed URLs that you provide in the "blogs to track" node. Process Each Feed: The workflow loops through each RSS feed individually. Filter by Date: It checks the publication date of every article and compares it to a timeframe you set (default is 60 days). Split New from Old: New articles are sent down the true path of the "Filter Out Old Blogs" node. Old articles are sent down the false path. This workflow leaves the true path empty so you can add your desired next steps. Setup and Customization This workflow requires minimal setup and is designed for easy customization. Add Your Blog Feeds: Find the "blogs to track" node. In the source_identifier field, replace the example URLs with the RSS feeds you want to monitor. // Add your target RSS feed URLs in this array ['https://blog.n8n.io/rss', 'https://zapier.com/blog/feeds/latest/'] Set the Time Filter: Go to the "max\_content\_age\_days" node. Change the value from the default 60 to your desired number of days. For example, use 7 to only get articles published in the last week. Customize Your Output (Required Next Step): This is the most important part\! Drag a new node and connect it to the true output of the "Filter Out Old Blogs" node. Example Idea: To save new articles to a Google Sheet, add a Split In Batches node followed by a Google Sheets node to append each new article as a new row.

Basic AI Chatbot that Retrieves Answers From Knowledge Base Using GraphRAG. Easiest setup, without vector database, external knowledge base, or OpenAI API keys. All you need is an InfraNodus graph with your knowledge. In this workflow, user sends a request to the InfraNodus GraphRAG system that will extract a reasoning ontology from a graph that you create (or that you can copy from our repository of public graphs) and generate a response directly to the user. How it works Receives a request from a user (via n8n or a publicly available URL chat bot if you replace the Chat Trigger with a webhook connected to the embeddable n8n Chat Widget that you can expose via a URL or add to any website. Sends the request to the knowledge graph in your InfraNodus account that contains a reasoning ontology represented as a knowledge graph. You can also use a standard graph — InfraNodus will use its underlying GraphRAG technology to generate the most relevant response. Sends the answer back to the user via chat or webhook (which is then delivered back via n8n chat widget Note: This is a simple example that will work well for occasionally providing responses to users. For a more advanced setup, you might want to build a more sophisticated workflow with AI agent node that would orchestrate among different InfraNodus expert graphs and chat memory, so the context of the conversation can be maintained. See our other workflows for examples. How to use • Just get an InfraNodus API key and add API authentication to your InfraNodus GraphRAG node. • In the same InfraNodus GraphRAG Nnode, provide the name of the graph you want to u. Note, these can be two different graphs ife for retrieval. Support If you wan to create your own reasoning ontology graphs, please, refer to this article on generating your own knowledge graph ontologies. You may also be interested to watch this video that explains the logic of this approach in detail: Help article on this specific workflow: Building expert ontology for InfraNodus GraphRAG n8n expert node.

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