Daytona — Secure Sandboxes for AI Code Execution

What it is

Daytona is an infrastructure platform that provides secure, isolated sandbox environments for running AI-generated code. It offers SDKs for Python, TypeScript, and Go, letting AI agents create sandboxes on demand, execute code, run shell commands, and manage files without risking the host system. Each sandbox is an isolated container with its own filesystem and network.

It targets AI agent builders, coding assistant developers, and teams that need to execute untrusted or AI-generated code in production without security concerns.

How it saves time or tokens

Daytona eliminates the need to build your own sandboxing infrastructure. Instead of configuring Docker containers, managing security policies, and handling cleanup, you call a single SDK method to create a sandbox, run code, and tear it down. The elastic architecture spins up sandboxes in seconds and scales automatically. For AI agents that generate and test code, this means faster iteration with built-in safety.

How to use

1. Install the SDK:

pip install daytona-sdk

1. Create a sandbox and execute code:

from daytona_sdk import Daytona

daytona = Daytona()
sandbox = daytona.create()

# Execute AI-generated code safely
response = sandbox.process.code_run(
    'print(sum(range(1, 101)))'
)
print(response.result)  # 5050

1. Run shell commands and manage files:

# Run shell commands
result = sandbox.process.exec('ls -la /workspace')

# Write and read files
sandbox.fs.upload('/workspace/data.json', '{"key": "value"}')
content = sandbox.fs.download('/workspace/data.json')

# Clean up
daytona.delete(sandbox)

Example

from daytona_sdk import Daytona

daytona = Daytona()
sandbox = daytona.create()

# Let an AI agent install packages and run code
sandbox.process.exec('pip install pandas numpy')

analysis_code = '''
import pandas as pd
import numpy as np

data = pd.DataFrame({
    'model': ['gpt-4o', 'claude-sonnet', 'gemini-pro'],
    'latency_ms': [320, 280, 350],
    'cost_per_1k': [0.01, 0.003, 0.0035]
})
print(data.to_string(index=False))
'''

result = sandbox.process.code_run(analysis_code)
print(result.result)

daytona.delete(sandbox)

Related on TokRepo

• AI tools for coding -- Development tools with AI integration
• AI tools for DevOps -- Infrastructure and deployment tools

Common pitfalls

• Sandbox creation has a cold-start latency of a few seconds. For latency-sensitive applications, pre-warm sandboxes by creating a pool in advance.
• Network access from sandboxes is configurable. If your AI-generated code needs to fetch external resources, ensure network policies allow outbound connections.
• Resource limits (CPU, memory, disk) should be set explicitly for production use. Unbounded sandboxes can lead to runaway processes from poorly written AI-generated code.