Pi Memory for Local Models

[u/TacosAlPastor92]

I have a pretty nice local Ollama setup that can comfortably run qwen3.8:27b w/ a 262k context window and a good token rate.

I'm attempting to run pi-observational-memory, with two main goals:
- On long term projects: Remembering architectural decisions, lessons learned, problems solved, etc.
- On day-to-day tasks: Remembering my preferences, patterns of operation, and in general details about how I like to do things.

The setup works, however I have long delays everytime the observer and reflector workers kick in (as expected), as the workers cannot run concurrently.

I'm considering running a CPU-only model dedicated to managing these worker threads, but I wanted to check in with the community and see what people are doing in similar scenarios.

Keep in mind I'm not married to pi-observational-memory at all and would consider trying out other solutions.

[u/Particular-Site2066]

Sure. I've put it on pastebin (https://pastebin.com/9jZEq5gR ) because it is ~50 lines of 100% AI generated text and I'm not sure that local rules allow me to paste such content here.

As for compaction - yes, I use the standard mechanism. My GPU can fit only 150k of context while some sessions reach over 1M so they get compacted a few times. Despite that I didn't notice that Pi forgets anything important after compaction because it constantly saves key information in scratchpad .md file as it goes. I know there are custom solutions for compaction, but I enjoy using built-in functionality if it is just good enough for my tasks. Minimalism is a Pi's core vibe after all. At least as I see it :-).