sandboxd Review: Self-Hosted Sandbox for AI Coding Agents

Ever looked at Lovable or Bolt and thought, “I could build something like this — if I had the infrastructure to run a thousand isolated dev environments?” Yeah, me too.

But here’s the thing — that multi-tenant sandbox layer with preview URLs, agent orchestration, and idle cost control is the hardest piece of the puzzle. Most teams either glue together Docker Compose stacks until they hit isolation limits, or resign themselves to $2000/month Kubernetes clusters.

Then I found sandboxd on GitHub. 632 stars in 13 days. MIT license. One command install. So I had to try it.

TL;DR

So here’s what it is: sandboxd is an open-source engine that turns a single Linux VPS into a fleet of isolated dev sandboxes — each one pre-loaded with an AI coding agent (OpenCode or Claude Code CLI) and a live preview URL. It handles multi-tenant isolation, auto-sleep/wake, and TLS routing out of the box. No Kubernetes required.

So I ran the install on a $12 DigitalOcean Droplet. Took me about 4 minutes to have my first sandbox running with an AI agent writing code inside it. And that’s not hype — that’s what ./install.sh gets you.

What Is sandboxd?

And I’d call it the infrastructure layer that’s missing from every “build your own AI app-builder” project. It’s a Go binary that acts as a control plane for Docker-based sandbox environments. And you POST to create a sandbox, POST tasks to send prompts, and the agent streams code back — all inside an isolated container with its own network, filesystem, and preview domain.

And it sits in the gap between Docker Compose DIY (which works but gives you zero multi-tenant isolation and no idle management) and Lovable/Bolt SaaS (which handles everything but costs a fortune and locks you in).

What Makes sandboxd Different

1. Real Multi-Tenant Isolation

So sandboxd doesn’t just fire up containers — it gives each sandbox its own network namespace, filesystem, and environment variables. And two sandboxes on the same VPS can’t see each other’s processes or ports. I tested this by spawning 5 sandboxes on a single $12 Droplet and running ps aux inside each one — zero cross-contamination.

And in practice, the README claims each sandbox is “an isolated machine from the network layer up.” After poking around for 20 minutes, I couldn’t find a leak.

2. Idle/Sleep That Actually Works

Actually, here’s the feature that makes this financially viable: sandboxd detects when a sandbox has no active connections or running tasks, sleeps the container (saving CPU/memory), and wakes it on the next inbound request.

I left a sandbox untouched for 30 minutes. docker stats showed it dropped from ~150 MB RAM to near zero. Then hit the preview URL and it was back online in under 3 seconds. So this is the difference between needing a single $20 VPS and a $200 cluster.

3. Built-In AI Agent Integration

So each sandbox comes pre-configured with OpenCode and Claude Code CLI — I’ve covered how Claude Code handles agent loops in my Claude Mem review, and sandboxd takes that same agent and containerizes it. So you submit a task — a natural language prompt — and the agent works inside the sandbox, creating files, running commands, installing dependencies. The output streams back in real time through sandboxd’s API.

I tested the simplest case: “Create a React counter app with TypeScript and Tailwind.” The agent picked OpenCode (default), scaffolded the project, ran npm install, and spun up a dev server — all inside the sandbox. The preview URL showed a working app. No manual intervention.

4. One-Command Deploy

And this matters more than most dev tools admit. sandboxd’s install is truly ./install.sh — no configuring Docker networks, no editing YAML files, no DNS setup. It detects Docker (requires Engine + Compose plugin), pulls the necessary images, and gives you a working API server.

And I ran this on a DigitalOcean Droplet with the Docker Marketplace image. The script completed in about 2 minutes. Then I was at a POST /sandbox prompt.

Quick Start: Getting sandboxd Running

Assuming you have a Linux VPS with Docker Engine and Docker Compose Plugin installed:

git clone https://github.com/tastyeffectco/sandboxd.git
cd sandboxd
./install.sh

And that’s it. The server starts on port 8080. Head to http://your-vps-ip:8080 and you’ll see the dashboard.

To create your first sandbox:

curl -X POST http://localhost:8080/sandbox \
  -H "Content-Type: application/json" \
  -d '{"name": "my-first-sandbox"}'

The response returns a sandbox ID and a preview domain. You can immediately send tasks:

curl -X POST http://localhost:8080/sandbox/{id}/tasks \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Create a Python Flask API with two endpoints"}'

After that, the agent streams code back. The preview URL updates live as files change.

One thing that caught me: you need Docker Compose Plugin specifically — not standalone docker-compose. The install script checks for this and errors out with a clear message, so you won’t waste time debugging.

sandboxd Cost Analysis: Why Self-Hosting Wins

Here’s the math that convinced me:

And the idle/sleep feature is what makes this possible. If you’re running a course, a hackathon, or a team of AI app builders, sandboxd turns a $12/month server into what would cost $500+ on SaaS platforms.

sandboxd Limitations to Watch Out For

I’m a fan, but sandboxd is beta — plain and simple.

• Linux only. No macOS or Windows Docker Desktop support yet. The install script bails early on non-Linux systems.
• Single node. This runs on one Docker host. No Kubernetes, no cluster mode. The README is explicit about this — it’s for single-machine multi-tenancy, not multi-machine scaling.
• TLS is manual. The Traefik reverse proxy routes traffic, but you need to bring your own TLS cert or use Cloudflare Tunnel / a reverse proxy for HTTPS.
• Beta roughness. My install had one edge case: the sandbox creation endpoint returned a 503 on my first try. Working sandbox. A quick retry fixed it. 3 open issues confirmed this is a known race condition being patched.
• No auth yet. sandboxd’s API doesn’t have authentication built in. On a public VPS, you’d want to front it with an auth reverse proxy or Tailscale/WireGuard.

Still, the project is 13 days old and sitting at 632 stars. The maintainers are pushing commits daily — 9 open issues, all active. The velocity is good, but production deployments need careful evaluation.

Who Should Use sandboxd

• AI product builders — You’re building a Lovable/Bolt/Replit competitor and need the sandbox layer without the infrastructure headaches.
• Course creators — Running coding workshops where students need isolated dev environments with AI agents.
• Indie hackers — You want a personal AI coding sandbox service for your own projects without paying $200/mo to a SaaS.
• Teams prototyping — You need multi-tenant dev environments for rapid AI feature experiments and don’t want to provision full clusters.

Who shouldn’t use it yet: production SaaS, regulated environments, anyone needing horizontal scale or multi-region deployment. sandboxd needs more runway before it’s ready for those use cases.

sandboxd Review: Bottom Line

So here’s the bottom line: sandboxd does one thing and does it well — it turns a single VPS into a multi-tenant AI coding sandbox fleet. The ./install.sh simplicity, the built-in agent support, and the idle/sleep cost control make it a no-brainer for anyone building AI app infrastructure.

And at 632 stars and 13 days old, it’s early — but the architecture is right. If you’re working on an AI app-builder product, sandboxd saves you months of Docker networking and container lifecycle plumbing. Pair it with something like PilotDeck for the orchestrator/agent layer and you have a solid open-source stack.

I’d start with a $12 DigitalOcean Droplet (Docker marketplace image) and see how far you get. For most small teams, that’s all you’ll need.