Building a Self-Learning RAG Agent That Updates Its Own Knowledge Daily

Most AI agents only know what you told them at setup time. This walkthrough shows how to build one that automatically ingests new content from blogs and RSS feeds every day, then stores that knowledge in a vector database so it stays current on any topic you choose.

How the Workflow Works

The automation runs on a schedule trigger, firing once per day. It pulls articles from up to 20 different bloggers or content sources that you configure through RSS feeds.

After fetching new articles, the workflow scrapes each blog post, strips out the HTML, and cleans the raw text. The cleaned content then gets uploaded directly into the agent's knowledge base using embeddings through OpenAI.

Once the knowledge base is updated, you can ask the agent questions. It pulls the most relevant, recent information and returns actionable tips specific to your topic. The demo shows it surfacing campaign feature changes and remarketing strategy warnings from recent marketing blog posts.

Adapting to Any Topic

The same architecture works for any niche. Swap the RSS feeds to follow cybersecurity blogs, SaaS industry news, medical research updates, or whatever domain matters to you. The agent's knowledge compounds daily without any manual work.

Key Takeaways

• A scheduled n8n workflow can scrape, clean, and embed blog content into a RAG agent daily
• RSS feeds from 20+ sources keep the agent's knowledge current across any topic
• The agent surfaces critical information and actionable recommendations on demand

Resources

• n8n -- Open-source workflow automation platform used to build the agent
• OpenAI Embeddings API -- Powers the vector storage for the agent's knowledge base