I had similar problems to solve for trading advice, complex project reasoning etc.

I settled on simplicity, extended it to serve clear, useful purpose.

Started with markdown database, single fact per line, structured/parseable (subject VERB object). This can be easily diffed/reviewed etc in git. Then added optional metadata (confidence, tags, persisted comments for natural language, uncertainty for numeric values, context, timestamps/spans), querying, alternative sqlite3 backend, self describing VERBS, z3 solver etc.

Those kind of graph information systems are great to quickly structure knowledge in a way that LLMs and humans can use/act on/loop on. Creating ontology, linking, some rules and actions and kicking it so it all munches and spits out results that feed back in so it self evolves. It's very natural for llms to query/update/restructure those graphs (also good for humans because it's very terse, essential information only).

There is no need to create k8s style complexity/services/what-not, it all works well from single sqlite db file or bunch of markdown (.cave in my case) files.

It's also interesting to see how well local open weight models are dealing with information arranged this way.