Sandboxes for AI agents
An agent that writes code needs somewhere to run it that isn't your laptop or your production server. A 639 Shell sandbox is a full Linux machine made for that: its own kernel, root, a shell and files, created in seconds and deleted on time with everything in it.
What makes it a sandbox
- It deletes itself. A new sandbox gets one hour; the agent or you can change that, or keep it.
- Nothing on the internet can connect to it. No public IPv4 or IPv6, no open ports (web links you make for it still work: they go through us).
- It's on no private network with your other machines.
- Outbound internet can be cut to just what the code needs:
set_networkwith the internet off and a short allow list (GitHub, package registries, model APIs, or named hosts).
Everything else is an ordinary machine: files in and out, commands with live output.
From an agent
Over MCP, an agent calls create_machine with sandbox: true, runs code with exec,
and lets the lifetime clean up. From Python:
from cloud639 import Client
c = Client() # reads CLOUD639_API_KEY
with c.create("s1", "ubuntu", sandbox=True, delete_on_exit=True) as m:
print(m.exec("python3 untrusted.py").stdout)
Try it with no sign-up
ssh try@shell.639cloud.com github:<your-github-username> 'python3 -c "print(6*7)"'
A trial machine for an hour, reaching only GitHub and package registries. Connect with an SSH key that's on your GitHub account. 639 Shell is an alpha; compute is free for testers.
More: Sandboxes · Agents and MCP · Benchmarks
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