OpenSandbox: A Safe Harbor for Your AI Agents
Why This Matters
If you're building AI coding agents, you've probably faced this dilemma: how do you safely execute code that an LLM generates? Running arbitrary AI-generated code directly on your machine is a security nightmare waiting to happen. One hallucinated rm -rf / or a malicious prompt injection, and you're in trouble.
That's where OpenSandbox comes in. OpenSourced by Alibaba, this general-purpose sandbox platform provides isolated environments for AI agents to execute code, interact with GUIs, and run evaluation pipelines—all without risking your infrastructure. With over 7,400 GitHub stars and 2,300+ stars gained just this week, it's clearly striking a chord with developers.
In this article, we'll explore what OpenSandbox offers, how to set it up, and practical use cases for your AI projects.
What OpenSandbox Provides
OpenSandbox addresses several key challenges in AI agent development:
- Safe Code Execution: Run untrusted AI-generated code in isolated containers
- Multi-Language SDKs: Python, JavaScript, Go, and more
- Unified APIs: Consistent interface across different runtime environments
- Docker & Kubernetes Support: Deploy anywhere from local development to production clusters
- Evaluation Pipelines: Built-in support for agent benchmarking
- RL Training Integration: Safe environments for reinforcement learning
Getting Started with OpenSandbox
Prerequisites
Before diving in, make sure you have:
- Docker installed and running
- Python 3.8+ (for the Python SDK)
- Git
Installation
Clone the repository and install the SDK:
git clone https://github.com/alibaba/OpenSandbox.git
cd OpenSandbox
pip install -e ./sdks/python
Your First Sandboxed Execution
Let's start with a simple example—running Python code inside a sandbox:
from opensandbox import Sandbox, ExecutionConfig
# Create a sandbox instance
sandbox = Sandbox(
runtime="python:3.11",
timeout=30, # seconds
memory_limit="512M"
)
# Define the code to execute
code = """
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
result = fibonacci(20)
print(f"Fibonacci(20) = {result}")
"""
# Execute in the sandbox
result = sandbox.execute(code)
print(result.stdout) # Output: Fibonacci(20) = 6765
print(result.exit_code) # 0 for success
# Always clean up
sandbox.destroy()
That's it! The code ran in an isolated container, and your host system was never at risk.
Configuration Options
OpenSandbox offers extensive configuration for different scenarios:
from opensandbox import Sandbox, ExecutionConfig, ResourceLimits
config = ExecutionConfig(
# Resource limits
resources=ResourceLimits(
cpu="1", # 1 CPU core
memory="1G", # 1GB RAM
disk="5G", # 5GB disk space
network=False # No network access
),
# Execution settings
timeout=60, # 60 second timeout
workdir="/workspace", # Working directory
# Security settings
allow_internet=False,
allow_filesystem=False, # Read-only filesystem
# Output options
capture_stdout=True,
capture_stderr=True
)
sandbox = Sandbox(config=config)
Real-World Use Cases
1. AI Coding Agents
The most common use case—letting AI agents write and run code safely:
from opensandbox import Sandbox
import anthropic
client = anthropic.Anthropic()
sandbox = Sandbox(runtime="python:3.11")
def run_ai_code(user_prompt: str) -> str:
"""Let Claude write and execute code safely."""
# Ask the AI to write code
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=2048,
messages=[{
"role": "user",
"content": f"Write Python code to: {user_prompt}. "
f"Output only the code, no explanations."
}]
)
code = message.content[0].text
# Execute in sandbox
result = sandbox.execute(code)
return {
"code": code,
"output": result.stdout,
"error": result.stderr if result.exit_code != 0 else None
}
# Example usage
result = run_ai_code("calculate the sum of squares from 1 to 100")
print(result["output"]) # 338350
2. Evaluation Pipelines
Benchmark your AI agents against test suites:
from opensandbox import Sandbox, EvaluationSuite
# Define test cases
test_cases = [
{
"name": "sort_list",
"prompt": "sort this list: [3, 1, 4, 1, 5, 9, 2, 6]",
"expected_output": "[1, 1, 2, 3, 4, 5, 6, 9]"
},
{
"name": "reverse_string",
"prompt": "reverse this string: 'hello world'",
"expected_output": "dlrow olleh"
}
]
# Run evaluation
suite = EvaluationSuite(sandbox_config={"runtime": "python:3.11"})
results = suite.run(test_cases, agent_function=run_ai_code)
print(f"Accuracy: {results.accuracy}%")
print(f"Passed: {results.passed}/{results.total}")
3. GUI Agent Testing
For agents that interact with graphical interfaces:
from opensandbox import GUISandbox
# Launch a GUI environment
gui_sandbox = GUISandbox(
display="xvfb", # Virtual display
resolution="1920x1080"
)
# Execute GUI interactions
gui_sandbox.execute("""
import pyautogui
# Click at coordinates
pyautogui.click(100, 200)
# Type text
pyautogui.write('Hello from sandbox!')
# Take screenshot
screenshot = pyautogui.screenshot()
screenshot.save('/output/screenshot.png')
""")
# Retrieve artifacts
gui_sandbox.get_file('/output/screenshot.png', './local_screenshot.png')
4. Kubernetes Deployment for Production
Scale your sandbox infrastructure:
# opensandbox-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: opensandbox
spec:
replicas: 5
selector:
matchLabels:
app: opensandbox
template:
metadata:
labels:
app: opensandbox
spec:
containers:
- name: sandbox
image: opensandbox/runtime:latest
resources:
limits:
cpu: "1"
memory: "2Gi"
requests:
cpu: "500m"
memory: "1Gi"
securityContext:
readOnlyRootFilesystem: true
runAsNonRoot: true
---
apiVersion: v1
kind: Service
metadata:
name: opensandbox-api
spec:
selector:
app: opensandbox
ports:
- port: 8080
targetPort: 8080
Deploy and use the API:
from opensandbox import RemoteSandbox
# Connect to Kubernetes deployment
sandbox = RemoteSandbox(
endpoint="http://opensandbox-api.default.svc.cluster.local:8080",
api_key="your-api-key"
)
result = sandbox.execute("print('Hello from K8s!')")
FAQ & Troubleshooting
Why is my sandbox timing out?
Problem: Code execution exceeds timeout limits.
Solution: Increase the timeout or optimize your code:
sandbox = Sandbox(timeout=300) # 5 minutes
Also check for infinite loops or blocking operations in your agent's generated code.
How do I persist files between executions?
Problem: Generated files disappear after sandbox termination.
Solution: Use volume mounts:
sandbox = Sandbox(
volumes={
"/host/data": "/sandbox/data" # Mount host directory
}
)
Can I run multiple sandboxes in parallel?
Problem: Need to process many requests concurrently.
Solution: Yes! OpenSandbox supports concurrent execution:
import asyncio
from opensandbox import AsyncSandbox
async def process_batch(prompts: list[str]):
sandbox = AsyncSandbox(runtime="python:3.11")
tasks = [
sandbox.execute(generate_code(p))
for p in prompts
]
results = await asyncio.gather(*tasks)
return results
My agent needs internet access. How?
Problem: Agent needs to fetch data from the web.
Solution: Enable network access (use with caution):
sandbox = Sandbox(
allow_internet=True,
network_whitelist=["api.openai.com", "api.anthropic.com"]
)
How do I debug failed executions?
Problem: Agent generates code that fails silently.
Solution: Capture both stdout and stderr:
result = sandbox.execute(code)
if result.exit_code != 0:
print(f"Error: {result.stderr}")
print(f"Exit code: {result.exit_code}")
# Get execution logs
logs = sandbox.get_logs()
Conclusion
OpenSandbox fills a critical gap in the AI agent ecosystem. As more developers build coding assistants, evaluation pipelines, and autonomous agents, the need for safe execution environments becomes non-negotiable.
Key takeaways:
- Security first: Never run AI-generated code directly on your machine
- Flexibility: Docker for development, Kubernetes for production
- SDK support: Python, JavaScript, Go SDKs available
- Evaluation ready: Built-in support for agent benchmarking
The project is actively maintained and gaining traction rapidly. If you're building AI agents that execute code, OpenSandbox deserves a spot in your toolkit.
Resources:
- GitHub: github.com/alibaba/OpenSandbox
- Documentation: Available in the repo's
/docsfolder
What's your experience with sandboxing AI agents? Have you tried OpenSandbox or similar tools? Drop a comment below!
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