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Bratharion: A modular architecture for building efficient LLM assistants

Over the past few months I’ve been using ChatGPT as a tool in my technical work. I became curious—what would a more efficient assistant architecture look like if designed with an LLM’s limitations and strengths in mind?

So I started exploring that question with the model itself.

Through iterative discussions—challenging assumptions, refining structure, and applying real-world IT constraints—I ended up with a proposed architecture that focuses on:

- Modular plugin design - Layered memory and vector search - Stateless LLM interaction with cached context reconstruction - Microservice-based handlers for real-world tools

I published the result on GitHub as a concept-only system: https://github.com/Bratharion/modular-ai-assistant

It’s not implemented yet, but I’d love feedback on the structure itself. Is this kind of hybrid architecture viable? What would you add, remove, or rethink?

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