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2,085 stars · Rust

How to use Layerfs: what you can do with it

What is Layerfs? LayerFS is a Rust filesystem layer that gives each AI agent a complete, writable Workspace over shared committed history, saving work as deduplicated Commits that can be branched, rewound and merged.

How do I use it? Install the dsh-layerfs plugin for DeepSeek Harness by following the LayerFS × DSH guide in dsh/README.md; then start a dsh session on your project and have each agent fork its own Branch; let each agent test its Branch in a Sandbox and commit what it changed. Full steps below.

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What do you want to do?

3 use cases

  • Run parallel agents on separate Branches of one project

    Fork a Branch per agent, test in Sandboxes, then merge and publish to main.

    View prompt
    Using the LayerFS repo at https://github.com/Ephemeral-AI-Lab/layerfs, set up the dsh-layerfs plugin for <project path> and fork one Branch per agent. Report the Branch names and confirm the Work and History tabs list each one. Verify that the agents' Commits do not appear on main until a Branch is merged.

  • Rewind a project to an earlier Commit and branch from it

    Keep every agent step as a Commit, then fork a new Branch from any earlier point.

    View prompt
    Using the LayerFS repo at https://github.com/Ephemeral-AI-Lab/layerfs, find the last good Commit on <branch name> and fork a new Branch from it. Report the Commit ID you chose and confirm the original Branch still exists. Verify that the new Branch starts with the files from that Commit.

  • Let the newest agent edit win on chosen files

    Name files in a .lww rule so concurrent edits do not stop a merge.

    View prompt
    Using the LayerFS repo at https://github.com/Ephemeral-AI-Lab/layerfs, add a .lww rule for <file list> in <project path>, then merge two Branches that both edit those files. Report which edit was kept. Verify that an unnamed file changed on both Branches still produces a Conflict.

Quick start with Layerfs

  1. Check you have what it needs: A DeepSeek Harness (dsh) setup with the dsh-layerfs plugin, as described in the LayerFS × DSH guide; A project you want agents to work on; Read the LayerFS 1.0.0 status note: the Store runs in its Disposable profile with no crash or power-loss durability, so keep a copy of anything you cannot rebuild.
  2. Open Ephemeral-AI-Lab/layerfs on GitHub and read the README and license.
  3. Install the dsh-layerfs plugin for DeepSeek Harness by following the LayerFS × DSH guide in dsh/README.md.
  4. Start a dsh session on your project and have each agent fork its own Branch.
  5. Let each agent test its Branch in a Sandbox and commit what it changed.
  6. Open the Work and History tabs to check each Branch as it grows.

These steps come from our top use case, Run parallel agents on separate Branches of one project, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forDevelopers and teams running several AI coding agents on one project who need isolated working trees, a reviewable history and cheap snapshots.
  • Why it is hot2.1k stars, launched 77 days ago
  • LanguageRust
  • Sourcegithub.com/Ephemeral-AI-Lab/layerfs · Back to Hot repos

Requirements

  • A DeepSeek Harness (dsh) setup with the dsh-layerfs plugin, as described in the LayerFS × DSH guide
  • A project you want agents to work on
  • Read the LayerFS 1.0.0 status note: the Store runs in its Disposable profile with no crash or power-loss durability, so keep a copy of anything you cannot rebuild

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Questions about Layerfs

What is Layerfs?

LayerFS is a Rust filesystem layer that gives each AI agent a complete, writable Workspace over shared committed history, saving work as deduplicated Commits that can be branched, rewound and merged. It is an open-source project on GitHub: https://github.com/Ephemeral-AI-Lab/layerfs.

How do I use Layerfs?

Install the dsh-layerfs plugin for DeepSeek Harness by following the LayerFS × DSH guide in dsh/README.md; then start a dsh session on your project and have each agent fork its own Branch; let each agent test its Branch in a Sandbox and commit what it changed. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Layerfs for?

Developers and teams running several AI coding agents on one project who need isolated working trees, a reviewable history and cheap snapshots.

What can I do with Layerfs?

3 use cases so far, for example: Run parallel agents on separate Branches of one project; Rewind a project to an earlier Commit and branch from it; Let the newest agent edit win on chosen files. Each has steps and a copy-paste agent prompt.

What do I need to run Layerfs?

A DeepSeek Harness (dsh) setup with the dsh-layerfs plugin, as described in the LayerFS × DSH guide; A project you want agents to work on; Read the LayerFS 1.0.0 status note: the Store runs in its Disposable profile with no crash or power-loss durability, so keep a copy of anything you cannot rebuild.

What are alternatives to Layerfs?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is Layerfs safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.