Compaction is painful if you run just one local LLM, the best way to avoid it is to keep context as small as possible.

One trick I find useful is to have one model with two KV caches running and while first cache has produced tokens, second cache immediately summarizes them during input tokens are being generated (tools time), then harness switches to the second KV cache which takes newly produced input tokens while KV in first cache is getting replaced with compacted summary tokens. This is a kind of ping pong, so we trade more space for less time. Still experimenting but it looks it works, and nice bonus it improves GPU utilization. Btw I have my own harness and model serving code, but it can be easily implemented in any other harness and model server.

I tend to ask a line of questions to the LLM as first step to a point the context is enough for me. Then i ask for the work i need but then get back to previous context using /tree.

It's like asking the questions needed to get the answer you need from the LLM, can be either an answer for a plan/todo or a task to code/change something.