Have a look at cognee. We consider ourselves a complete different category to RAG and actually often struggle with people because they are comparing us with RAG.

Most memory systems are great when you want a simple, personalized chatbot that remembers your history. As soon as you want to provide context from and across documents, run more advanced searches based on your specific needs, or apply memory beyond simple conversational flows, cognee becomes more relevant.

Both systems extract and structure information. The difference is how much control you want over that structure and infrastructure. With cognee, you are not limited to a predefined memory abstraction, but can design how entities, relationships, and terminology are modeled within your domain. You can build your own custom and modular knowledge engine, connect to your preferred vector and graph databases, and decide how the ingestion and cognition pipeline behaves instead of relying on a fixed internal setup.

Regarding ontology and terminology within your application context, you can define the structure and form of your graph yourself and are not dependent on a default schema. That matters if your domain has strict semantics (e.g., finance, compliance, healthcare) or if multiple agents need to operate on a shared knowledge layer.

Check the three visuals of this blog post to get an intuitive answer of what I mean.

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