Letta Code version: 0.27.8 (macOS) / 0.27.9 (Railway worker) Severity: High — silent data loss for agent learnings
On 2026-06-16, while making several Edit tool calls to memory block files in the agent's MemFS (for example reference/letta-code-remote-worker-integration.md and reference/team-learnings.md), the Letta memory harness did not auto-commit the changes. Running git status in the local Mac MemFS showed the modifications as unstaged, with 49 other recent commits also still unpushed to origin.
This means a multi-turn session that includes Edit-tool calls to memory blocks can leave the new learnings only in the working tree on the local machine. If the Mac restarts, the memfs resets, the harness re-clones from origin, or the user switches machines, those learnings are silently lost — even though the agent believes it has persisted them (the system prompt at the top of every conversation advertises an auto-commit/push workflow).