As Retrieval-Augmented Generation (RAG) workflows scale, vector stores can quickly become cluttered with outdated or irrelevant content—slowing performance and reducing accuracy. This agentic app solves that by introducing a self-cleaning mechanism that keeps your knowledge base optimized over time.

Built in n8n, this agent:

• Continuously monitors document age and metadata in your vector store

• Automatically removes stale or unnecessary entries

• Keeps your RAG context streamlined for faster, more accurate responses

Whether you're managing client-facing AI tools or internal assistants, this solution ensures your vector data remains sharp, relevant, and ready for growth. Ideal for teams focused on scalability, maintainability, and long-term system health.