I kept rebuilding checkpointing, retries, and run tracking for agents. So I built an open-source runtime around them.

I built a small open-source run backend for agent workflows. It’s an early alpha designed around three problems I kept hitting while bringing long-running agents into production: 1. Seeing what an agent run is doing right now. 2. Checkpointing completed steps so a failed run can resume instead of starting over. 3. Streaming workflow progress back to the UI from the same run state. You keep your existing code. Wrap meaningful steps with `run.step()`. Tidebase stores checkpoints, run state, events, and recovery attempts in Postgres. The dashboard shows the run timeline, current state, completed steps, failed steps, and retry attempts. If a run fails halfway, rerunning with the same run id skips completed steps. Optional recovery webhooks can call back into your app to resume the workflow. Would this replace any retry/status/checkpointing plumbing you have today? What would be missing before you could try it on a real workflow? If checkpointing/live run state worked well, what would you expect this tool to handle next?