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Sandboxes

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Daytona provides full composable computerssandboxes — for AI agents. Sandboxes are isolated runtime environments you can manage programmatically to run code. Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk. Agents get access to a full composable computer environment where they can install packages, run servers, compile code, and manage processes.

Sandboxes have 1 vCPU, 1GB RAM, and 3GiB disk by default. Organizations get a maximum sandbox resource limit of 4 vCPUs, 8GB RAM, and 10GB disk. For more power, see resources or contact support@daytona.io.

Sandboxes use snapshots to capture a fully configured environment (base OS, installed packages, dependencies, and configuration) to create new sandboxes.

Each sandbox has its own network stack with per-sandbox firewall rules. By default, sandboxes follow standard network policies, but you can restrict egress to a specific set of allowed destinations or block all outbound traffic entirely. For more details, see network limits.

Daytona provides methods to create sandboxes.

  1. Navigate to Daytona Sandboxes ↗
  2. Click Create Sandbox
  3. Click Create to create a sandbox
from daytona import Daytona
daytona = Daytona()
sandbox = daytona.create()

Daytona provides methods to create GPU sandboxes.

Daytona supports NVIDIA GPU devices for sandbox creation. This allows you to run GPU workloads such as model inference, fine-tuning, and CUDA-accelerated compute inside a sandbox.

Each GPU sandbox is ephemeral and supports up to 16 vCPUs, 192GB RAM, and 512GB disk.

  1. Navigate to Daytona Sandboxes ↗
  2. Click Create Sandbox
  3. Select a GPU snapshot
  4. Click Create to create a GPU sandbox

To create a GPU sandbox programmatically, use the pre-built daytona-gpu snapshot, target us-east-1 region, and set auto_delete_interval to 0 (ephemeral).

from daytona import Daytona, DaytonaConfig, CreateSandboxFromSnapshotParams
daytona = Daytona(DaytonaConfig(target="us-east-1"))
sandbox = daytona.create(
CreateSandboxFromSnapshotParams(
snapshot="daytona-gpu",
auto_delete_interval=0
),
)

Daytona provides methods to create ephemeral sandboxes.

Ephemeral sandboxes are automatically deleted once they are stopped. They are useful for short-lived tasks or testing purposes.

To create an ephemeral sandbox, set the ephemeral parameter to True when creating a sandbox. Setting autoDeleteInterval: 0 (ephemeral) has the same effect.

from daytona import Daytona, CreateSandboxFromSnapshotParams
daytona = Daytona()
params = CreateSandboxFromSnapshotParams(
ephemeral=True,
auto_stop_interval=5, # delete after 5 minutes of inactivity
)
sandbox = daytona.create(params)

Sandboxes have 1 vCPU, 1GB RAM, and 3GiB disk by default. Organizations get a maximum sandbox resource limit of 4 vCPUs, 8GB RAM, and 10GB disk.

ResourceUnitDefaultMinimumMaximum
CPUvCPU114
MemoryGiB118
DiskGiB3110

To set custom sandbox resources, use the Resources class. All resource parameters are optional and must be integers. If not specified, Daytona will use the default values. Maximum values are per-sandbox limits set at the organization level.

from daytona import Daytona, CreateSandboxFromImageParams, Image, Resources
daytona = Daytona()
sandbox = daytona.create(
CreateSandboxFromImageParams(
image=Image.debian_slim("3.12"),
resources=Resources(cpu=2, memory=4, disk=8),
)
)

Daytona provides methods to start sandboxes.

  1. Navigate to Daytona Sandboxes ↗
  2. Click the start icon () next to the sandbox you want to start
sandbox.start()

Daytona provides methods to get a sandbox by ID or name.

sandbox = daytona.get("my-sandbox-id-or-name")

Daytona provides methods to list sandboxes.

for sandbox in daytona.list():
print(sandbox.id)

Daytona Dashboard ↗ provides a sandbox details page to view detailed information about a sandbox and interact with it directly.

  1. Navigate to Daytona Sandboxes ↗
  2. Click on a sandbox you want to view the details of
  3. Click View to open the sandbox details page

The sandbox details page provides a summary of the sandbox information and actions to perform on the sandbox:

  • Name: the name of the sandbox
  • UUID: the unique identifier of the sandbox
  • State: the sandbox state with a visual indicator
  • Actions: start, stop, recover, archive, delete, refresh, SSH access, screen recordings
  • Region: the target region where the sandbox is running
  • Snapshot: the snapshot used to create the sandbox
  • Resources: allocated sandbox CPU, memory, and disk
  • Lifecycle: auto-stop, auto-archive, and auto-delete intervals
  • Labels: key-value pairs assigned to the sandbox
  • Timestamps: when the sandbox was created and when the last event occurred
  • Web terminal: an embedded web terminal session directly in the browser
  • Filesystem: sandbox filesystem tree for viewing and managing files and directories: create, upload, download, copy, refresh, collapse, search, and delete capabilities
  • VNC: a graphical desktop session for sandboxes that have a desktop environment
  • Logs: a detailed record of user and system activity for the sandbox
  • Metrics: sandbox metrics data displayed as charts
  • Traces: distributed traces and spans collected from the sandbox
  • Spending: usage and cost over time

Daytona provides methods to stop sandboxes.

Stopped sandboxes maintain filesystem persistence while their memory state is cleared. They incur only disk usage costs and can be started again when needed.

The stopped state should be used when a sandbox is expected to be started again. Otherwise, it is recommended to stop and then archive the sandbox to eliminate disk usage costs.

  1. Navigate to Daytona Sandboxes ↗
  2. Click the stop icon () next to the sandbox you want to stop
sandbox.stop()

If you need a faster shutdown, use force stop (force=true / --force) to terminate the sandbox immediately. Force stop is ungraceful and should be used when quick termination is more important than process cleanup. Avoid force stop for normal shutdowns where the process should flush buffers, write final state, or run cleanup hooks.

Common use cases for force stop include:

  • you need to reduce stop time and can accept immediate termination
  • the entrypoint ignores termination signals or hangs during shutdown

Daytona provides methods to pause sandboxes.

Pausing a sandbox keeps both filesystem state and memory persistence, so sandboxes can resume from in-memory runtime state. Compared to regular stop behavior, pause is useful for workloads with active in-memory context and state continuity.

Daytona supports pause functionality through VM-based runners. Pause is handled through the existing stop action. This means stop behaves as pause and preserves memory state, while force stop performs a full shutdown without preserving memory state.

Daytona provides methods to archive sandboxes.

A sandbox must be stopped before it can be archived. When a sandbox is archived, the entire filesystem state is moved to a cost-effective object storage, making it available for an extended period.

Starting an archived sandbox takes more time than starting a stopped sandbox, depending on its size. It can be started again in the same way as a stopped sandbox.

sandbox.archive()

Daytona provides methods to recover sandboxes.

sandbox.recover()

When a sandbox enters an error state, it can sometimes be recovered using the recover method, depending on the underlying error reason. The recoverable flag indicates whether the error state can be resolved through an automated recovery procedure.

Recovery actions are not performed automatically because they address errors that require further user intervention, such as freeing up storage space.

# Check if the sandbox is recoverable
if sandbox.recoverable:
sandbox.recover()

Daytona provides methods to resize sandbox resources after creation.

On a running sandbox, you can increase CPU and memory without interruption. To decrease CPU or memory, or to increase disk capacity, stop the sandbox first. Disk size can only be increased and cannot be decreased.

Resizing updates the sandbox resource allocation (cpu, memory, and disk) for that sandbox only. CPU and memory control compute capacity for running workloads, while disk controls persistent filesystem capacity. Values must be integers and stay within your organization’s per-sandbox resource limits.

# Resize a started sandbox (CPU and memory can be increased)
sandbox.resize(Resources(cpu=2, memory=4))
# Resize a stopped sandbox (CPU and memory can change, disk can only increase)
sandbox.stop()
sandbox.resize(Resources(cpu=4, memory=8, disk=20))
sandbox.start()

To verify CPU and memory limits inside the sandbox after resizing, read cgroup values directly. Tools such as nproc, free, top, htop, /proc/cpuinfo, and /proc/meminfo read host-level values and do not reflect sandbox resource limits.

Terminal window
cat /sys/fs/cgroup/cpu.max # "<quota> <period>" (cores = quota / period)
cat /sys/fs/cgroup/memory.max # bytes
df -h / # disk

Daytona provides methods to fork sandboxes.

Forking creates a duplicate of your sandbox’s filesystem and memory, and copies it into a new sandbox. The new sandbox is fully independent: it can be started, stopped, and deleted without affecting the original. The sandbox must be in started state before forking.

Daytona tracks the parent-child relationship in a fork tree, so you can always trace a fork’s lineage back to the sandbox it was created from. You can fork a fork, building out branches as needed. The parent sandbox cannot be deleted while it has active fork children.

  1. Navigate to Daytona Sandboxes ↗
  2. Click the three-dot menu () next to the sandbox you want to fork
  3. Select Fork
# Fork sandbox through the Sandbox instance
forked = sandbox._experimental_fork(name="my-forked-sandbox")

To view the fork tree for a sandbox and all its related sandboxes:

  1. Navigate to Daytona Sandboxes ↗
  2. Click the three-dot menu () next to a forked sandbox
  3. Select View Forks

The fork tree displays each sandbox in the hierarchy along with its current state and creation time, allowing you to trace the lineage of any fork back to its origin.

Daytona provides methods to set sandbox labels.

Setting labels replaces the full label set for the sandbox. Include all labels you want to keep in the request. If you omit an existing label, it will be removed.

sandbox.set_labels({
"team": "platform",
"env": "staging",
})

Daytona provides methods to create snapshots from sandboxes.

A snapshot captures an immutable, point-in-time copy of a sandbox’s filesystem and memory that you can use as a base to create new sandboxes, effectively templating a known-good environment for reuse. You can think of it as a checkpoint you can restore from whenever you need a clean, identical starting point.

# Create snapshot from sandbox
sandbox._experimental_create_snapshot("my-sandbox-snapshot")

Daytona provides methods to delete sandboxes.

  1. Navigate to Daytona Sandboxes ↗
  2. Click the Delete button next to the sandbox you want to delete.
sandbox.delete()

A sandbox can have several different states. Each state reflects the status of your sandbox.

  • Creating: the sandbox is provisioning and will be ready to use
  • Starting: the sandbox is starting and will be ready to use
  • Started: the sandbox has started and is ready to use
  • Stopping: the sandbox is stopping and will no longer accept requests
  • Stopped: the sandbox has stopped and is no longer running
  • Deleting: the sandbox is deleting and will be removed
  • Deleted: the sandbox has been deleted and no longer exists
  • Archiving: the sandbox is archiving and its state will be preserved
  • Archived: the sandbox has been archived and its state is preserved
  • Resizing: the sandbox is being resized to a new set of resources
  • Error: the sandbox is in an error state and needs to be recovered
  • Restoring: the sandbox is being restored from archive and will be ready to use shortly
  • Unknown: the default sandbox state before it is created
  • Pulling Snapshot: the sandbox is pulling a snapshot to provide a base environment
  • Building Snapshot: the sandbox is building a snapshot to provide a base environment
  • Build Pending: the sandbox build is pending and will start shortly
  • Build Failed: the sandbox build failed and needs to be retried

The diagram demonstrates the states and possible transitions between them.

Sandbox lifecycle diagram

Daytona sandboxes support Python, TypeScript, and JavaScript programming language runtimes for direct code execution inside the sandbox. The language parameter controls which programming language runtime is used for the sandbox:

  • python
  • typescript
  • javascript

If omitted, the Daytona SDK will default to python. To override this, explicitly set the language value when creating the sandbox.

Daytona sandboxes can be automatically stopped, archived, and deleted based on user-defined intervals. You can also refresh the last activity timestamp to explicitly signal activity when lifecycle behavior depends on inactivity windows.

Daytona provides methods to update a sandbox’s last activity timestamp.

This updates the sandbox’s recorded activity time without changing its runtime state. It is useful when your workflow is driven by external systems or background orchestration that may not reset inactivity tracking.

For example, if you run long-lived automation around a sandbox and want to avoid unintended auto-stop behavior, call this operation periodically to indicate that the sandbox is still actively used.

sandbox.refresh_activity()

Daytona provides methods to set the auto-stop interval.

The auto-stop interval sets the amount of time after which a running sandbox will be automatically stopped. The auto-stop triggers even if there are internal processes running in the sandbox.

The system differentiates between “internal processes” and “active user interaction”. Merely having a script or background task running is not sufficient to keep the sandbox alive.

The parameter can either be set to:

  • a time interval in minutes
  • 0: disables the auto-stop functionality, allowing the sandbox to run indefinitely

If the parameter is not set, the default interval of 15 minutes will be used.

sandbox = daytona.create(CreateSandboxFromSnapshotParams(
snapshot="my-snapshot-name",
# Disables the auto-stop feature - default is 15 minutes
auto_stop_interval=0,
))

The inactivity timer resets only for specific external interactions:

The following do not reset the timer:

  • SDK requests that are not toolbox actions
  • Background scripts (e.g., npm run dev run as a fire-and-forget command)
  • Long-running tasks without external interaction
  • Processes that don’t involve active monitoring

If you run a long-running task like LLM inference that takes more than 15 minutes to complete without any external interaction, the sandbox may auto-stop mid-process because the process itself doesn’t count as “activity”, therefore the timer is not reset.

Daytona provides methods to set the auto-archive interval.

The auto-archive interval sets the amount of time after which a continuously stopped sandbox will be automatically archived. The parameter can either be set to:

  • a time interval in minutes
  • 0: the maximum interval of 30 days will be used

If the parameter is not set, the default interval of 7 days will be used.

sandbox = daytona.create(CreateSandboxFromSnapshotParams(
snapshot="my-snapshot-name",
# Auto-archive after a sandbox has been stopped for 1 hour
auto_archive_interval=60,
))

Daytona provides methods to set the auto-delete interval.

The auto-delete interval sets the amount of time after which a continuously stopped sandbox will be automatically deleted. By default, sandboxes will never be automatically deleted. The parameter can either be set to:

  • a time interval in minutes
  • -1: disables the auto-delete functionality
  • 0: the sandbox will be deleted immediately after stopping

If the parameter is not set, the sandbox will not be deleted automatically.

sandbox = daytona.create(CreateSandboxFromSnapshotParams(
snapshot="my-snapshot-name",
# Auto-delete after a sandbox has been stopped for 1 hour
auto_delete_interval=60,
))
# Delete the sandbox immediately after it has been stopped
sandbox.set_auto_delete_interval(0)
# Disable auto-deletion
sandbox.set_auto_delete_interval(-1)

Daytona provides methods to run sandboxes indefinitely.

By default, Daytona sandboxes auto-stop after 15 minutes of inactivity. To keep a sandbox running without interruption, set the auto-stop interval to 0 when creating a new sandbox:

sandbox = daytona.create(CreateSandboxFromSnapshotParams(
snapshot="my_awesome_snapshot",
# Disables the auto-stop feature - default is 15 minutes
auto_stop_interval=0,
))