We’ve found that dreaming has been a useful tool for helping us give Claude effective memory. In this post, we share how we’ve come to think about agent memory and walk through several evaluations we’ve used to explore agent memory, including the use of dreaming, and what each component contributes to memory organization.

Memory can be a prediction about what the future will need

Memory is, occasionally, a prediction. By writing down a rule, an agent predicts that the pattern it’s describing will be relevant again. For instance, writing down a verified fact is a prediction that an agent in a future session will want to rely on that fact without double-checking it. Over time, Claude has become much better at making these kinds of predictions.

Memory can support a learning loop here. If Claude gets a question wrong, it can record the failure, come up with theories about what went wrong, test those theories, and turn what it finds into a rule about how to answer in the future.

Different models achieved different levels of success at creating their own learning loops. Claude Sonnet 4.6 got as far as generating theories about why an answer was wrong. Its memory directory contained a scattering of files, each listing several untested guesses.

Claude Opus 4.7 consolidated its notes into a single reference document and began marking which facts it had checked and which were still unconfirmed. Claude Sonnet 5 marked each answer as confirmed after the grader had checked it, and wrote a correction when it had answered wrong.