The problem with CAG is not just that it hogs memory, but to keep it fresh you have to keep re-indexing. If the corpus is large and dynamic, it can easily fall out of date and, at runtime, blow out the context window.

GraphRAG has some promise. NVidia has a playbook for converting text into a knowledge graph: https://build.nvidia.com/spark/txt2kg

It'll probably have the same issues with reindexing, but that will be a common problem, until someone comes up with better incremental training/indexing.