I did try to use those for some stuff: * Login requirement is something else * It's closed source last I checked * Pretty slow/unstable

There are many better namespace/container based options, VMs may be moderately more secure but when you more or less trust your agent and code you can do with lesser containment. And with the recent CVEs in kvm honestly there isn't a huge deal of difference vs namespaces.

(I'm building https://xbin.dev/ for some time now for managing my personal code/apps, a project which started specifically after Docker Sandboxes broke on me some time ago)

> Each agent runs inside a dedicated microVM with your dev environment

What's a "microVM" and what's the security model here compared to using real virtual machines with actual constraints on breakouts?

Is it marketing fluff?

Incus/LXD has had VM's for a long time now.

    incus launch images:ubuntu/26.04 my-ubuntu-vm --vm
    incus exec my-ubuntu-vm -- bash

The login is annoying but, lacking an open source alternative, this has been my daily driver for a while now because it works great out of the box with two key features: outbound firewall and secret injection with placeholders.

I run it with superset and then each git worktree is mounted in a sandbox that is configured for each repo i work in.

Closest open source I have seen is https://earendil-works.github.io/gondolin but the DX is not as polished. https://exe.dev/ would be perfect but it does not come with outbound firewall.

Does anyone have a better alternative?

What is the main benefit over doing something like:

    docker run --runtime krun --rm -it -v $(pwd):/workspace/$(basename $(pwd)) -w /workspace/$(basename $(pwd)) --network restricted-net ghcr.io/openai/codex-universal:latest sh

I just made my own devcontainer that I copy on any project and load whatever harness I want in that repo. Harnesss' config and auth are simply mounted from the host, so no setup required at all.

https://github.com/iodize6399/ai-devcontainer/tree/main/.dev...

Wow, I hope one day Linux will be able to support the exclusive MacOs/Windows technology of Docker Sandboxes.

(it's in the doc, but kinda strange to not see some instructions on the main page, probably distro related)

I used this for a while then decided to build my own suites that pack individual harness and respective host state (config, plugins, skills, etc.) into an image. Works better and much flexible in my opinion.

I started building my own isolated and security-hardened docker image for OpenCode about half a year ago. Been using it daily.

https://github.com/pkhamre/opencode-docker

I got excited for this not because this didn't exist before, but because Docker putting their weight on this would imply a broader adoption and better integration in the industry. I am sad that they are asking for a login here though, which doesn't make any sense to me.

Can someone more versed in Docker explain to me how this is different than building my own docker container from a Dockerfile for using Pi agent harness? That's what I do currently. I use Docker Desktop in windows as the backend for that.

The sandboxing problem is perhaps the greatest justification for doing agent integration via existing human interfaces rather than low level shell access. Granting access to shell is a super obvious path (it's easy) so I can understand us wanting to fight for it. But we should consider the other paths as well before we make our final stand.

Automating browsers with LLM agents properly requires a lot more work than Process.Start into powershell, but the advantages can be immense once you have achieved integration this way. Incrementally maintaining this integration is generally easy because human users cannot tolerate rapid changes either.

It's a hell of a lot easier to convince management to adopt a robot that looks and acts like a human employee than one that looks like a combine harvester. The combine is far more efficient, but it is also totally indiscriminate. Nothing constrains its appetite except for the invisible fence imposed by GPS. The amount of infrastructure required to keep farm equipment from running astray is incredible. In the context of agriculture, the added complexity is definitely worth it. We don't want to have to recreate the same thing with our technology if it can be avoided. Sandboxes and security isolation boundaries are not things to aspire to. These are costs to be paid for admission to something more valuable.

Most interesting part to me is credentials injection at the sandbox boundary level: https://docs.docker.com/ai/sandboxes/security/credentials

If any AI company was doing serious engineering isolated containers would have been a prerequisite to using their tools.

Reminds me of sandboxy - https://github.com/apple/containerization/tree/main/examples...

Also if your thing doesn't work with `pi` out of the box, then low effort

Requires login. Garbage.

I know some people want to run their agents when their computer is off, but I imagine a solution like this will be much more common than paying for a remote sandbox (i.e on fly.io or exe.dev), especially because it'll be free.

Though, they need to remove the login requirement.

I tried Docker Sandboxes but last time I checked you could not configure custom volume mounts, making more complex setups impossible. For work I need two directories for context for the agent to have access to…

I’d rather use another open source solution that doesn’t require a signup, and less likely to get rugpulled.

There is no reason to require a login for creating local mini sandboxes.

If you’re on Apple, native solutions like “container-machine init” come built in and are pretty good, if you’ll only be on Apple hardware.

Open source alternative with podman support and local telemetry collection https://github.com/VibePod/vibepod-cli

So this is a VM by Docker?

For those who do not trust

    docker run --rm -it -v "$(pwd)":/work -w /work myaiimage /bin/bash

Like many people, I suspect, I used Claude to write my own agent sandbox that suits my needs very well. Investing my time in a propietary product has become a hard sell.

The one thing I wonder about is how you enforce the usage of Docker Sandboxes vs running the agent on the host directly, apart from scanning machines for binaries

I am not sure I understand, how is this different from a devcontainer or other similar techniques?

On another topic, can't help but notice that "leading coding agents" somehow does not include Pi.

So it's basically a container with a fancy name, innit?

been using this for a while - works great! Has also had a lot of updates over the past year so worth checking out again if you tried it a while ago

Here's another sandbox that I found interesting: https://github.com/ashishb/amazing-sandbox

Ah let's see, do they still want you to LOGIN, in order to use a local dev tool? Yes, yes they do. No thanks Docker. You can keep your buzzword reasoning as to why this is needed.

On Linux, you can skip Docker and use bubblewrap. Some inspiration: https://blog.gpkb.org/posts/ai-agent-sandbox/

TO me, that's the important distinction: sandboxing limits what the agent can do but it doesn't necessarily enforce that the agent must run inside the sandbox. You need a separate control layer to enforce that boundary.

The linked page implies there is no linux support, I wonder why. It's there in the docs if you hunt for it.

Docker management will fail their tech at every opportunity.