Memory

Memory is context that persists across AI conversations. It allows the AI to remember decisions, terminology, and patterns specific to your workspace or team.

How Memory Works

Memory is stored as files in cloud storage via the sync API. The AI reads and writes memory during interactions using the cosmo_workspaces_update_memory, cosmo_workspaces_get_memory, and cosmo_workspaces_list_memory tools. You can view and edit memory files directly.

Workspace memory files are persisted server-side at the path workspaces/{workspaceId}/memory/{fileName} in your org's blob container, and indexed in the database with kind: WORKSPACE_MEMORY for fast listing.

File names must match ^[A-Za-z0-9 _-]+\.md$ (a bare markdown filename, no path separators or ..) — both the client and server reject anything else, since fileName originates from LLM tool-call input. Fetching a workspace memory file that doesn't exist returns 404 Not Found; an existing-but-empty file returns 200 with content: '', so "missing" and "empty" stay distinguishable.

Types of Memory

• Workspace Memory — scoped to a single workspace (decisions, terminology, context). Supports multiple named files (e.g. project-context.md, jira-rules.md).
• Team Memory — scoped to a team, shared across all workspaces in that team (conventions, patterns)
• Hub Memory — org-wide (preferences, conventions, cross-workspace patterns)