This guide covers best practices for integrating BoxLite as a sandboxed execution environment for AI agents. It builds on the quick-start patterns in the Guides with deeper coverage of configuration, concurrency, timeouts, security, and file transfer.
Recommended Configuration
Workload-Type Reference
| Workload | Image | CPUs | Memory | Disk | Notes |
|---|
| Code execution | python:slim | 1 | 512 MiB | None | Ephemeral, fast startup |
| Data analysis | python:slim | 2 | 2048 MiB | None | More memory for pandas/numpy |
| Web browsing | Use BrowserBox | 2 | 2048 MiB | None | Chromium needs resources |
| Multi-tool agent | python:slim | 2 | 1024 MiB | None | Balance cost vs. capability |
| Persistent env | python:slim | 1 | 512 MiB | 10 GB | State survives restarts |
Starter Configuration
import boxlite
options = boxlite.BoxOptions(
image="python:slim",
cpus=2,
memory_mib=1024,
working_dir="/workspace",
security=boxlite.SecurityOptions.maximum(),
)
Security Presets
SecurityOptions has three presets:
| Preset | Jailer | Seccomp | Resource Limits | Use Case |
|---|
development() | Off | Off | None | Debugging sandbox issues |
standard() | On | On (Linux) | None | General workloads |
maximum() | On | On (Linux) | max_open_files=1024, max_file_size=1GiB, max_processes=100 | Untrusted AI code |
For AI agents running untrusted code, use SecurityOptions.maximum():
security = boxlite.SecurityOptions.maximum()
# Customize if needed
security.max_open_files = 2048
security.network_enabled = False # Disable network for strict isolation
Concurrency Model
One Box, Multiple Executions (Recommended)
A single box can run many exec() calls. Each call spawns a new process inside the same VM. This avoids repeated VM boot overhead and is safe because the VM provides hardware isolation from the host.
import asyncio
import boxlite
async def main():
runtime = boxlite.Boxlite.default()
box = await runtime.create(boxlite.BoxOptions(
image="python:slim",
cpus=2,
memory_mib=1024,
security=boxlite.SecurityOptions.maximum(),
))
try:
# Run agent tools concurrently in the same box
results = await asyncio.gather(
box.exec("python", ["-c", "print('task A')"]),
box.exec("python", ["-c", "print('task B')"]),
box.exec("python", ["-c", "print('task C')"]),
)
for execution in results:
result = await execution.wait()
print(f"Exit code: {result.exit_code}")
finally:
await box.stop()
await runtime.remove(box.id)
When to use: Most AI agent scenarios. Keeps VM boot cost to one-time.
One Box Per Agent
Use separate boxes when you need strict isolation between agents, different images, or independent resource limits.
async def run_isolated_agent(code: str, image: str = "python:slim"):
"""Each agent gets its own box."""
async with boxlite.SimpleBox(image=image, memory_mib=512) as box:
result = await box.exec("python", "-c", code)
return result.stdout
async def main():
agents = [
run_isolated_agent("print('agent 1')"),
run_isolated_agent("print('agent 2')", image="node:alpine"),
run_isolated_agent("print('agent 3')"),
]
results = await asyncio.gather(*agents)
When to use: Multi-tenant isolation, different language runtimes, or strict resource separation.
Timeout Handling and Zombie Prevention
The Problem
asyncio.wait_for() cancels the Python coroutine but does not kill the guest process. Without explicit cleanup, the process continues running inside the VM indefinitely.
The following pattern leaves a zombie process running inside the box:# BAD: process keeps running inside the box after timeout
try:
execution = await box.exec("python", ["-c", "import time; time.sleep(9999)"])
result = await asyncio.wait_for(execution.wait(), timeout=5)
except asyncio.TimeoutError:
print("Timed out") # Process is still running in the VM!
Correct Pattern
Always kill the execution in the timeout handler:
async def exec_with_timeout(box, cmd, args=None, timeout=30):
"""Execute a command with proper timeout and cleanup."""
execution = await box.exec(cmd, args or [])
try:
result = await asyncio.wait_for(execution.wait(), timeout=timeout)
return result
except asyncio.TimeoutError:
await execution.kill()
raise
Defensive Helper
For maximum safety, combine timeout handling with a try/finally block:
async def safe_exec(box, cmd, args=None, timeout=30):
"""Execute with timeout, guaranteed process cleanup."""
execution = await box.exec(cmd, args or [])
try:
result = await asyncio.wait_for(execution.wait(), timeout=timeout)
return result
except asyncio.TimeoutError:
try:
await execution.kill()
except Exception:
pass # Best-effort kill
raise
except Exception:
try:
await execution.kill()
except Exception:
pass # Best-effort kill on any failure
raise
Security Boundaries
Read-Only Volume Mounts
Use read-only volumes to provide data to the sandbox without risk of modification:
options = boxlite.BoxOptions(
image="python:slim",
volumes=[
("/host/datasets", "/mnt/data", "ro"), # Agent can read but not write
("/host/config", "/etc/app/config", "ro"), # Configuration files
],
)
SecurityOptions Fields
| Field | Type | Description |
|---|
jailer_enabled | bool | OS-level sandbox (seccomp on Linux, sandbox-exec on macOS) |
seccomp_enabled | bool | Syscall filtering (Linux only) |
max_open_files | int | None | Limit open file descriptors |
max_file_size | int | None | Maximum file size in bytes |
max_processes | int | None | Maximum number of processes |
max_memory | int | None | Maximum virtual memory in bytes |
max_cpu_time | int | None | Maximum CPU time in seconds |
network_enabled | bool | Allow network access from sandbox (macOS only) |
close_fds | bool | Close inherited file descriptors |
Network Isolation
To prevent an agent from accessing the network:
security = boxlite.SecurityOptions.maximum()
security.network_enabled = False
options = boxlite.BoxOptions(
image="python:slim",
security=security,
# No ports= means no incoming connections either
)
In the Python bindings, network_enabled is currently a macOS-only control. On Linux and other platforms, network isolation is typically enforced by the container/runtime networking configuration (for example, running in an isolated network namespace and not publishing ports), and network_enabled may not itself hard-disable all outbound connectivity.
Resource Limits as Security Boundaries
Resource limits prevent a rogue agent from consuming all host resources:
options = boxlite.BoxOptions(
image="python:slim",
cpus=1, # Cap CPU usage
memory_mib=512, # Hard memory limit (OOM kills the box)
security=boxlite.SecurityOptions.maximum(),
)
File Transfer Patterns
Comparison
| Method | Direction | Best For | Size Limit |
|---|
box.copy_in() | Host -> Guest | Files and directories | Large files |
box.copy_out() | Guest -> Host | Extracting results | Large files |
exec + base64 | Either | Small inline data | ~1 MB (shell limit) |
| Volume mounts | Both | Shared datasets, config | No limit |
copy_in / copy_out
runtime = boxlite.Boxlite.default()
box = await runtime.create(boxlite.BoxOptions(image="python:slim"))
# Copy file into box
await box.copy_in("/host/script.py", "/workspace/script.py")
# Run the script
execution = await box.exec("python", ["/workspace/script.py"])
result = await execution.wait()
# Copy results out
await box.copy_out("/workspace/output.json", "/host/output.json")
await box.stop()
await runtime.remove(box.id)
Inline Data via exec
For small payloads, write data through a command:
import base64
# Send small file via base64
data = b"print('hello from transferred script')"
encoded = base64.b64encode(data).decode()
execution = await box.exec("sh", [
"-c",
f"echo {encoded} | base64 -d > /workspace/script.py && python /workspace/script.py",
])
result = await execution.wait()
Volume Mounts
For datasets or configuration that should be available immediately:
options = boxlite.BoxOptions(
image="python:slim",
volumes=[
("/host/datasets", "/mnt/data", "ro"), # Input data
("/host/results", "/mnt/results", "rw"), # Output directory
],
)
Use copy_in/copy_out for dynamic per-request files. Use volume mounts for shared datasets. Use inline base64 only for trivially small payloads.
Terminal Resizing
When running interactive TTY sessions (e.g., an AI agent controlling a shell), use resize_tty() to set the terminal dimensions. This ensures proper line wrapping and avoids garbled output from programs that query terminal size.
runtime = boxlite.Boxlite.default()
box = await runtime.create(boxlite.BoxOptions(image="alpine:latest"))
# Start a shell with TTY
execution = await box.exec("sh", tty=True)
# Set terminal size to 40 rows x 120 columns
await execution.resize_tty(40, 120)
# Send commands via stdin
stdin = execution.stdin()
await stdin.send_input(b"ls -la\n")
# Read output
stdout = execution.stdout()
async for line in stdout:
print(line)
resize_tty() only works on executions started with tty=True. Calling it on a non-TTY execution returns an error.
Complete Example
Putting it all together: proper configuration, security, concurrent execution with timeouts, TTY resizing, and cleanup.
import asyncio
import boxlite
async def safe_exec(box, cmd, args=None, timeout=30):
"""Execute with timeout and guaranteed process cleanup."""
execution = await box.exec(cmd, args or [])
try:
result = await asyncio.wait_for(execution.wait(), timeout=timeout)
return result
except asyncio.TimeoutError:
try:
await execution.kill()
except Exception:
pass
raise
async def main():
runtime = boxlite.Boxlite.default()
# Configure box with security and resource limits
box = await runtime.create(boxlite.BoxOptions(
image="python:slim",
cpus=2,
memory_mib=1024,
working_dir="/workspace",
volumes=[
("/host/datasets", "/mnt/data", "ro"),
],
security=boxlite.SecurityOptions.maximum(),
))
try:
# Copy a script into the box
await box.copy_in("/host/analysis.py", "/workspace/analysis.py")
# Run with timeout protection
result = await safe_exec(
box,
"python",
["/workspace/analysis.py"],
timeout=60,
)
print(f"Exit code: {result.exit_code}")
# Run concurrent tasks safely
tasks = [
safe_exec(box, "python", ["-c", "print('task 1')"], timeout=10),
safe_exec(box, "python", ["-c", "print('task 2')"], timeout=10),
]
results = await asyncio.gather(*tasks, return_exceptions=True)
for i, r in enumerate(results):
if isinstance(r, Exception):
print(f"Task {i} failed: {r}")
else:
print(f"Task {i} exit code: {r.exit_code}")
# Copy results out
await box.copy_out("/workspace/results.json", "/host/results.json")
# Interactive TTY session with resize
execution = await box.exec("sh", tty=True)
await execution.resize_tty(40, 120)
stdin = execution.stdin()
await stdin.send_input(b"echo 'interactive session'\n")
await stdin.send_input(b"exit\n")
await execution.wait()
finally:
await box.stop()
await runtime.remove(box.id)
asyncio.run(main())
See Also