RLMs store working context in memory within a code environment; in the Prime Agent example, the trajectory and context remain on disk and can be referenced after compaction.

“always stored in some memory inside of this code environment so this could be a file system like this could be like i'll give an example prime agent the trajectory of prime agent like the context even the when you compact and do all these things is stored on disk so the model can always reference its original context even if it's compacted and all of its tools are run inside of let's say like a python repl or a bash repl”