Buddy Works is pitching Buddy Sandboxes as one isolated Linux machine per coding agent - dependencies, databases, builds and tests all run there, with the result reachable through a public URL. The listing sits on Product Hunt at a 5.0 rating with one review and roughly 306 followers. Source: https://www.producthunt.com/products/buddy
Key facts
| Fact | Detail |
|---|---|
| Question answered | AI Sandboxes by Buddy: an Isolated Linux Machine per Coding Agent |
| Short answer | Buddy Works, listed on Product Hunt, pitches Buddy Sandboxes as an isolated Linux machine per agent: dependencies, databases, builds and tests run there, and the result opens through a public URL. |
| Reading time | ~3 min |
| Last updated | 2026-10-16 |
What the makers say it does
The launch thread is unusually specific, and the details come from the makers rather than a marketing page:
- One machine per agent. “Buddy Sandboxes give each agent its own isolated Linux machine,” writes maker Rafal Sztwiorok. Inside it an agent can install dependencies, run databases, build apps, execute tests and open the result through a public URL.
- The environment persists. Files, packages and build caches survive between sessions - so an agent does not start from a blank box every time.
- Root, Docker, SSH, public URL. All four are named as part of the package.
- One sandbox per task, branch or PR. Managed through MCP, CLI, SDK or API.
- Configuration as infrastructure. A second maker, Paweł Kapała, describes writing the setup in YAML and keeping it in the repo like any other IaC file: setup commands, apps and environment in one place. If a sandbox gets messy, you recreate it and the setup runs again from zero.
- Snapshots. Install dependencies, seed the database and warm the caches once, snapshot it, then start new sandboxes from that point. In his words, the agent no longer spends “the first 5 minutes of every task watching npm install”.
The problem statement is worth quoting too: coding agents can write code, but running it is another story. Give an agent your laptop and it can interfere with your setup; put it in a disposable environment and it has to rebuild everything each time.
The two answers to “where should the agent run?”
This launch is the cloud answer. Desktop agents are the other one, and the difference is not quality - it is scope:
- Sandboxed cloud agent: an isolated Linux box, no access to your machine, ideal for untrusted code, parallel branches and CI-style work. Nothing it does can break your desktop.
- Local desktop agent: direct access to your files, installers, shells and builds, with approval per action and a log of what ran. MOOGH works this way - it runs on your machine precisely because the work is on your machine.
Most people running agents seriously will end up with both: a sandbox for “build and test this branch”, a local agent for “sort this folder and fix my build config”. If you want the local half, start here: [download MOOGH].
What to weigh before adopting
- Cost model. The comments describe the capability, not the pricing at scale - a persistent machine per agent is a different bill from a shared container.
- Data location. A public URL per sandbox is convenient and also a thing to review against whatever your code contains.
- Snapshot discipline. The snapshot advice is good practice, but it is on you to seed environments so snapshots are worth restoring.
- Isolation is not review. Not being able to break your laptop is not the same as code being safe to ship.
FAQ
Do I need Docker locally? No - the point is that the machine lives elsewhere, and Docker is available inside it.
Can an agent in a sandbox touch my Windows machine? Not by default. That is the design trade: isolation instead of local access.
Does this replace CI? The makers position it as per-task, per-branch and per-PR environments rather than a substitute for a pipeline.
Could I run the same workflow locally? Yes, with a local agent and per-action approvals - which is why the two approaches are complements rather than rivals.
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