I kept running into the same failure mode when switching between AI workers, browser sessions, or machines: the files were still there, but the exact working point was gone.
Conversation memory can tell a model what was said. RAG can retrieve relevant documents. Neither one is a durable answer to four operational questions:
I built Resume the Scene as a very small, model-agnostic project-memory layer for that gap.
GitHub: https://github.com/louisen0o0/resume-the-scene
The project keeps the handoff in files that live with the repository. A task state can look like this:
@task{id:#t42|goal:#g1|state:active|done:[#e69]|next:#a7}
@msg{op:resume|task:#t42|load:[#current,#e81]|skip:[#e69]|next:#a7}
The important part is not the syntax. The important part is that another worker can deterministically recover what to load, what to skip, and what to do next without replaying the prior chat.
Resume the Scene is intentionally thin:
.rsm frames;
There is no vector database requirement, no model-specific identity in the core protocol, and no requirement to archive prompts or responses.
The repository now includes a handoff example where one worker has already completed evidence #e2, and the next worker resumes from the same task state:
resume-scene checkpoint examples/handoff
resume-scene resume examples/handoff
The resume result tells the next worker to skip completed evidence and continue with the next action. That fixture is executed in CI on Python 3.11, 3.12, and 3.13.
The implementation is small enough to change. The state model is the part I want challenged before it grows.
If you regularly hand work between coding agents or long-lived AI sessions, I would especially like feedback on this question:
What state would you need to trust before allowing a different agent to continue a real project without replaying the previous conversation?
The project is MIT licensed and the roadmap is public in GitHub Issues.