Sandbox Agent

Sandbox Agent is a community skill for building AI agents that execute code in isolated sandbox environments, covering sandbox provisioning, code execution management, output capture, resource limits, and secure runtime configurations for safe agent operations.

What Is This?

Overview

Sandbox Agent provides patterns for creating AI agents that run user or generated code in secure, isolated environments. It covers sandbox provisioning with configurable language runtimes, code execution with timeout and memory limits, stdout and stderr capture for returning results, file system isolation that prevents access to host resources, and cleanup procedures that reset sandbox state between executions. The skill enables developers to build agents that safely execute code without risking the host system.

Who Should Use This

This skill serves developers building AI coding assistants that need to run generated code, teams creating educational platforms where students execute code through AI tutors, and engineers designing agent systems that use code execution as a problem-solving tool.

Why Use It?

Problems It Solves

Running AI-generated code directly on host machines risks system damage from malicious or buggy output. Without resource limits, code execution can consume all available memory or CPU indefinitely. Capturing execution output requires careful stream handling to return results to the agent. Leftover files and processes from previous executions can interfere with subsequent runs.

Core Highlights

Sandbox provisioning creates isolated environments with configured language runtimes on demand. Resource limits enforce maximum CPU time, memory usage, and disk space per execution. Output capture collects stdout, stderr, and return codes for structured result delivery. Cleanup automation resets sandbox state between runs to prevent cross-execution interference.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
import subprocess
import tempfile
from pathlib import Path

@dataclass
class SandboxConfig:
    language: str = "python"
    timeout_seconds: int = 30
    max_memory_mb: int = 256
    allowed_imports: list[str] = field(
        default_factory=lambda: ["json", "math", "re"])

@dataclass
class ExecutionResult:
    stdout: str = ""
    stderr: str = ""
    exit_code: int = 0
    timed_out: bool = False

class CodeSandbox:
    def __init__(self, config: SandboxConfig):
        self.config = config
        self.work_dir = tempfile.mkdtemp()

    def execute(self, code: str) -> ExecutionResult:
        file_path = Path(self.work_dir) / "main.py"
        file_path.write_text(code, encoding="utf-8")
        try:
            proc = subprocess.run(
                ["python3", str(file_path)],
                capture_output=True, text=True,
                timeout=self.config.timeout_seconds,
                cwd=self.work_dir)
            return ExecutionResult(
                stdout=proc.stdout,
                stderr=proc.stderr,
                exit_code=proc.returncode)
        except subprocess.TimeoutExpired:
            return ExecutionResult(
                stderr="Execution timed out",
                exit_code=1, timed_out=True)

Real-World Examples

from dataclasses import dataclass, field
import shutil

class SandboxPool:
    def __init__(self, pool_size: int = 3):
        self.pool_size = pool_size
        self.sandboxes: list[CodeSandbox] = []
        self.available: list[int] = []

    def initialize(self, config: SandboxConfig):
        for i in range(self.pool_size):
            sb = CodeSandbox(config)
            self.sandboxes.append(sb)
            self.available.append(i)

    def acquire(self) -> CodeSandbox | None:
        if not self.available:
            return None
        idx = self.available.pop(0)
        return self.sandboxes[idx]

    def release(self, sandbox: CodeSandbox):
        idx = self.sandboxes.index(sandbox)
        self._cleanup(sandbox)
        self.available.append(idx)

    def _cleanup(self, sandbox: CodeSandbox):
        work = Path(sandbox.work_dir)
        for item in work.iterdir():
            if item.is_file():
                item.unlink()

    def status(self) -> dict:
        return {"total": self.pool_size,
                "available": len(self.available),
                "in_use": self.pool_size - len(
                    self.available)}

Advanced Tips

Pre-warm sandbox pools during application startup to eliminate provisioning latency when agents need to execute code. Scan generated code for dangerous patterns like file system access or network calls before execution. Log all execution inputs and outputs for audit trails and debugging agent behavior.

When to Use It?

Use Cases

Build a coding assistant that writes and runs Python code to answer data analysis questions. Create a code review agent that executes test suites in a sandbox to verify proposed changes. Deploy an educational platform where students submit code through an AI tutor that runs it safely.

Related Topics

Container isolation, secure code execution, process sandboxing, agent tool design, and resource management for multi-tenant systems.

Important Notes

Requirements

Language runtime installed in the sandbox environment. Process isolation mechanism such as containers or restricted user permissions. Temporary storage for code files and execution artifacts.

Usage Recommendations

Do: set strict timeout and memory limits to prevent runaway code from consuming resources. Clean up sandbox state after every execution to prevent data leakage between runs. Log execution inputs for security auditing and debugging.

Don't: allow sandbox code to access the host network or file system outside the designated work directory. Run untrusted code without resource limits that could cause denial of service. Trust that AI-generated code is safe without scanning for dangerous patterns.

Limitations

Process-level isolation is weaker than container-based sandboxing for untrusted code. Sandbox provisioning adds latency to each code execution request. Language runtime availability in the sandbox limits which programming languages agents can use.