macrodata-memory-maintenance

Automated end-of-day memory distillation extracts actionable insights from daily interactions and logs them to a journal. This process refreshes agent state files and prunes completed items to maintain accurate, lean context without manual intervention.

Pruning stale information from state files involves removing completed items and outdated context during a maintenance cycle. This housekeeping step prevents context drift and keeps search indices healthy for quick retrieval.

Memory maintenance updates workspace and identity state by refreshing multiple state files to reflect current context. It applies distillation across daily sessions, ensuring the agent's identity, topics, and workspace remain synchronized.

Yes, journal logging and state updates are handled through internal tooling without external dependencies. The maintenance cycle updates state files, prunes stale data, and logs a journal summary autonomously within the agent's environment.

Memory maintenance should run at the end of daily sessions to distill today's conversations. Running it nightly ensures the agent's state files, journal, and search indices are refreshed and ready for the next operational cycle.