save-memory

You can save session context by capturing working directory, Git repository details, and OS environment to create a memory episode. It generates a structured summary of goals, activities, progress, and next steps for continuous workflow review.

The memory manager invokes a Python script to store your session context as a memory episode. It automatically extracts machine identifiers and Git details, organizing the data with optional tags and detail levels into a personal memory store.

Yes, the memory persistence mechanism explicitly applies to Git repositories. It collects Git repository details alongside your working directory and machine context to build a coherent memory episode for your development sessions.

Saving session progress to a personal memory system ensures continuity across work sessions. By generating structured summaries of goals and next steps, you reduce repetitive setup and can easily review past episodes in your memory store.

Tags and detail levels are optional parameters used when saving memory episodes. Configuring them allows you to control the persistence granularity and organize the generated session summaries within the memory-manager agent framework.

The memory store relies on invoking a specific Python script and the memory-manager agent. Limitations include dependency on this script being present and the requirement to have an active development session with identifiable Git and OS environment metadata.