Is it similar to rtk? Where the output of tool calls is compressed? Or does it actively compress your history once in a while?
If it's the latter, then users will pay for the entire history of tokens since the change uncached: https://platform.claude.com/docs/en/build-with-claude/prompt...
How is this better?
This is a bit more akin to distill - https://github.com/samuelfaj/distill
Advantage of SML in between some outputs cannot be compressed without losing context, so a small model does that job. It works but most of these solutions still have some tradeoff in real world applications.
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We do both:
We compress tool outputs at each step, so the cache isn't broken during the run. Once we hit the 85% context-window limit, we preemptively trigger a summarization step and load that when the context-window fills up.
> we preemptively trigger a summarization step and load that when the context-window fills up.
How does this differ from auto compact? Also, how do you prove that yours is better than using auto compact?