I would like to migrate away from Claude Code and use Pi as my "peimary" harness. I really like in particular how it manages conversation trees and branches.
But I think I didn't do a good job in customizing it for my work. While nothing dramatic, I think the LLM I was using did a better job on Claude Code than on Pi a couple of time when I tried giving it the same work.
I was not sure how to improve on it though.
https://github.com/gitsense/pi-brains
which is designed around the Pi philosophy of less is better by focusing on ondemand context/guidance. I won't bloat the context unless the LLM needs to do something I know it will need better guidance with. I have a demo repo for this at https://github.com/gitsense/gsc-rules-demos
One of the examples is, if I know the agent is reading a specific file, I will inject additional context. So if the agent never needs to do something in a certain file or directory, I don't need to pollute the context with "what it may need to know".
I was using LLM collaboratively to help me setting up and document a home server. I was using DeepSeek for that matter. I tried some tasks on Claude Code and some on Pi.
Subjectively, I felt that it was marginally "smarter" on Claude Code. It would figure things out better, that sort of thing.
I am still using Pi btw. My current set up is using MiMo on Pi as a planner, ans DS in Claude Code to validate/execute the plan.
I may try moving it all to Pi, but I wonder if I should learn how to better configure the things there.
Honestly if the difference was marginal, I would move to Pi. I just tried layzpi and I had the agent write/compile a hello world in c and it required 20k in tokens. My minimal setup required 5.3k in tokens.
People will say the cost is minimal since this is cached but 15k is a lot since that needs to be reasoned by the LLM. I haven't looked at Claude Code but I read somewhere the system prompt is like 20k so I can see how Claude Code might have seem smarter as what you are working was probably addressed in the system prompt.
For me the bloat is not worth it since I am more interested in the LLM being able to reason better.
At least I am learning to build modular so I can reuse parts like image gen, audio gen (STT/TTS), knowledge management. I have probably built 4 of these systems in the last year, each one gets better and lasts longer until I brick the crap out of it. Super fun.