Filesystem for AI
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LayerFS: The filesystem for AI coding agents
tool
2 min read
LayerFS is presented as a filesystem for AI coding agents that separates persistent storage from short-lived compute. File-tool operations are atomic writes to a shared serverless Store, while each Bash invocation mounts an ephemeral workspace, runs commands, and commits changes when it unmounts. Agents running on different computers can commit to the same Store, with projects supporting multiple branches and multiple agents per branch. Every tool call creates a commit on its branch. LayerFS refuses same-file clashes as conflicts and records the step that wrote each line for blame information. Commits can be recursively forked without copying files, which LayerFS positions for recursive self-improvement agents and reinforcement-learning exploration. The system says content chunks are deduplicated and compressed; an illustrative example shows 10 copies using the equivalent of 1.05 copies after deduplication and 0.31 copies after compression. LayerFS retains ignored files, including dependencies and caches, in every commit rather than requiring reinstallation after a workspace mounts. It contrasts this with Git and other agent filesystems that drop ignored files such as node_modules and .cache. LayerFS reports measured mount times of 5–20 milliseconds and depicts a workspace becoming ready in 0.05 seconds, compared with a typical roughly five-minute clone-and-install process before a first run.