Give your AI agents a safe place to run code

Run agent-generated code in isolated sandboxes that boot in under a second, with filesystem access, exposed ports, and snapshots.

sb = sandbox.create()

Sandbox created: sb-9f2c (0.8s)

sb.exec("python train.py")

Training on B200... loss=0.42

Checkpoint saved to /outputs/step_500

sb.snapshot()

Snapshot saved: snap-b41e

https://sb-9f2c.sandbox.beam.cloud

Compute primitives built for agents

Everything an agent needs. Process management, file system operations, and access to stdout and stderr.

Sub-second boots

Sandboxes cold boot in under a second, dependencies included, so agent loops aren't stuck waiting on infrastructure.

Isolated by default

Untrusted, generated code runs in isolated, non-root containers — away from your app and other workloads.

Run anything

Execute Python with run_code, shell out to any binary with exec, and stream logs back as processes run.

Files in, files out

Upload inputs, let the agent work, and download artifacts through a simple filesystem API.

Snapshots you can fork

Capture a sandbox's filesystem as an image and boot new sandboxes from that exact state — perfect for branching agent runs.

Preview URLs

Expose any port behind an SSL-terminated, authenticated URL so you (or the agent) can see what it built.

Give your agents a computer

01

Create a sandbox

Define the image — Python version, packages, even non-Python runtimes — and call create(). It's up in seconds.

02

Let the agent work

Run generated code, execute processes, read logs, and move files through the SDK from your agent loop.

03

Snapshot, expose, or terminate

Fork state with filesystem snapshots, expose ports for previews, set a TTL for long sessions, or tear it down.

Example Code

# Process management
process = sandbox.process.exec("python3", "train.py")
for line in process.logs:
    print(line, end="")

# Filesystem in and out
sandbox.fs.upload_file("data.csv", "/workspace/data.csv")
sandbox.fs.download_file("/workspace/output.csv", "results.csv")

# Keep the session alive for an hour
sandbox.update_ttl(3600)

# Snapshot the filesystem to fork or resume later
image_id = sandbox.create_image_from_filesystem()

Frequently asked questions

How isolated are sandboxes?+

Each sandbox runs in its own isolated, non-root container, separated from your other workloads. For stricter requirements, Beam also offers a self-hosted deployment that keeps everything inside your own VPC.

Can agents run things other than Python?+

Yes. Sandboxes run arbitrary processes — start from any base image (Node, for example) and exec whatever the agent needs.

How long can a sandbox stay alive?+

As long as you want. Sandboxes shut down automatically by default, but update_ttl lets you extend sessions or keep them running indefinitely.

Can I resume or branch an agent's environment?+

Yes. Snapshot the filesystem to an image, then boot any number of new sandboxes from that state — ideal for retries and exploring branches.

Which agent frameworks does this work with?+

Any of them. The Sandbox SDK is plain Python, so LangChain tools, custom loops, or anything that can call a function can drive it.