Sandbox Backends

from praisonaiagents import Agent

agent = Agent(
    name="Coder",
    instructions="Run shell commands safely",
    sandbox=True,
)
agent.start("List Python files in the current directory")

from praisonaiagents import SandboxConfig
from praisonai_sandbox import DockerSandbox
import asyncio

async def demo():
    sandbox = DockerSandbox(config=SandboxConfig.docker("python:3.11-slim"))
    await sandbox.start()
    await sandbox.run_command("echo hello > f.txt")
    r = await sandbox.run_command("cat f.txt")
    print(r.stdout)   # "hello"
    await sandbox.stop()

asyncio.run(demo())

docker ps --filter label=praisonai=sandbox-exec

await sandbox.write_file("/sandbox/report.txt", "done")
await sandbox.read_file("report.txt")            # -> "done"
await sandbox.run_command("cat /sandbox/report.txt")  # -> "done"

import os
from praisonaiagents import Agent, SandboxConfig

os.environ["NOVITA_API_KEY"] = "your-api-key"

agent = Agent(
    name="Cloud Runner",
    instructions="Run code in a Novita cloud sandbox.",
    sandbox=SandboxConfig(sandbox_type="novita"),
)
agent.start("Print the Python version")

from praisonai_sandbox import NovitaSandbox

sandbox = NovitaSandbox()
result = await sandbox.execute("print('Hello, World!')")
print(result.stdout)  # Hello, World!

praisonai sandbox run --code "import sys; print(sys.version)" --type novita

from praisonaiagents import Agent, SandboxConfig

agent = Agent(
    name="Cloud Runner",
    instructions="Run code in a Daytona cloud sandbox.",
    sandbox=SandboxConfig(sandbox_type="daytona"),
)
agent.start("Print the Python version")

praisonai sandbox run --code "import sys; print(sys.version)" --type daytona

from praisonaiagents import Agent, SandboxConfig

agent = Agent(
    name="Cloud Runner",
    instructions="Run code in a Novita cloud sandbox.",
    sandbox=SandboxConfig.novita(),
)
agent.start("Print the Python version")

praisonai sandbox run --code "import sys; print(sys.version)" --type novita

$ praisonai sandbox run --code "print('hi')" --type modal
Error: sandbox 'modal' is unavailable: <reason>
Available: ['subprocess', 'sandlock']
To install the optional backend:  pip install "praisonai-sandbox[modal]"
                                  # or (legacy):  pip install "praisonai[modal]"
Or explicitly choose another sandbox:  --sandbox-type subprocess
$ echo $?
2

ValueError: Unknown sandbox type: 'capsule'. Available: ['docker', 'subprocess', 'sandlock', 'ssh', 'modal', 'daytona', 'novita', 'e2b']

ValueError: Unknown sandbox type: 'capsule'. Supported built-ins: 'docker', 'subprocess', 'e2b', 'sandlock', 'ssh', 'modal', 'daytona', 'novita'. Install a plugin package that registers 'capsule' under the 'praisonai.sandbox' entry-point group (e.g. pip install praisonai-plugins[capsule]).

# pyproject.toml
[project.entry-points."praisonai.sandbox"]
my-sandbox = "my_pkg.sandbox:MySandbox"

pip install "praisonai-plugins[capsule]"

from praisonaiagents import Agent, SandboxConfig

agent = Agent(
    name="SecureRunner",
    instructions="Execute code inside the Capsule sandbox.",
    sandbox=SandboxConfig.capsule(),   # strict security policy applied automatically
)

agent.start("Run a Python script")

# New (recommended); shim: from praisonai.sandbox import DockerSandbox
from praisonai_sandbox import DockerSandbox

sandbox = DockerSandbox()

# Process user data safely
async def process_file(filename):
    # Safe: no shell injection possible
    result = await sandbox.run_command([
        "python", "process.py", "--input", filename
    ], shell=False)
    return result.stdout

# Process with shell features when controlled
async def count_errors(log_file):
    import shlex
    # Trusted input, need shell features  
    result = await sandbox.run_command(
        f"grep 'ERROR' {shlex.quote(log_file)} | wc -l",
        shell=True
    )
    return int(result.stdout.strip())

from praisonaiagents.sandbox import ResourceLimits
# New (recommended); shim: from praisonai.sandbox import SubprocessSandbox
from praisonai_sandbox import SubprocessSandbox

limits = ResourceLimits(
    timeout_seconds=30,
    memory_mb=512
)

sandbox = SubprocessSandbox()
result = await sandbox.run_command(
    "python heavy_task.py",
    limits=limits,
    shell=False
)