One thing that matters in practice is memory lifecycle. Shared memory tends to drift unless entries have retention rules (TTL, dedupe, and merge policy) plus periodic compaction.
Are you planning a policy layer so users can control what persists as raw events versus summarized state?
policy driven persistence and compaction is planned, and parts of the lifecycle are already live.
I use both OpenAI Codex and Claude Code daily on the same codebase. The biggest pain — they don't share memory. Claude fixes a bug, Codex repeats it next session. Every session starts from zero.
So I built Open Timeline Engine — a local-first engine that captures your decisions, mines patterns, and gives any MCP agent shared memory.
It also support gated autonomy