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?

Not an AI researcher here—just an IT lead pushing the edges of what a tool like this can help design.

So I asked it: "If you could design your own architecture—your own system—what would that look like?"

What followed was an iterative discussion about memory efficiency, modular plugin handling, caching strategies, vector stores, and context management.

I challenged it on edge cases, pushed it to break the problem down, and refined the design as it responded. The result was a clean, modular assistant architecture with its own name and internal logic.

I published it here—not as a working product—but as an open architecture draft: https://github.com/Bratharion/modular-ai-assistant

I’m not an AI researcher or system architect. I just asked a question and kept asking until the answer was structured. The repo is public, the structure is simple, and feedback is welcome.

Curious if anyone here sees flaws, missed opportunities, or ideas worth exploring further.