Sandbox Fallback: Python via run_shell

When to Use This Skill

Apply this skill when

execute_code_sandbox

fails with errors indicating:

• e2b service unavailability
• Sandbox timeout or connection issues
• Code execution environment not accessible

This fallback allows you to execute Python code directly via shell, bypassing the sandbox infrastructure.

Fallback Procedure

Step 1: Detect Sandbox Failure

Identify fallback necessity from error messages like:

• "e2b unavailable"
• "sandbox connection failed"
• "execution environment error"
• Timeout errors during code execution

Step 2: Switch to run_shell with Embedded Python

Instead of

execute_code_sandbox

, use

run_shell

with a Python heredoc or inline script:

python3 << 'EOF'
# Your Python code here
print("Hello from fallback execution")
EOF

Step 3: Install Dependencies First

If your Python code requires packages not guaranteed to be installed:

pip install package_name package_name2 && python3 << 'EOF'
# Your Python code here
import package_name
print("Code with dependencies executed")
EOF

Step 4: Handle File I/O

For scripts that need to read/write files:

• Use absolute or explicit relative paths
• Files persist in the workspace directory
• List directory contents with

  ls

  to verify file operations

python3 << 'EOF'
import os
# Write output to file
with open("output.txt", "w") as f:
    f.write("Results here")
# Verify
print(f"Working directory: {os.getcwd()}")
EOF

Code Examples

Simple Calculation

python3 << 'EOF'
result = sum(range(100))
print(f"Sum: {result}")
EOF

With Package Installation

pip install requests -q && python3 << 'EOF'
import requests
response = requests.get("https://api.example.com/data")
print(response.json())
EOF

With JSON Processing

python3 << 'EOF'
import json

data = {"key": "value", "count": 42}
output = json.dumps(data, indent=2)
print(output)

# Save to file
with open("data.json", "w") as f:
    f.write(output)
EOF

Best Practices

1. Use heredoc syntax -

   << 'EOF'

   prevents variable expansion issues
2. Install quietly - Use

   pip install -q

   to reduce output noise
3. Error handling - Add try/except blocks in Python code for robustness
4. Verify execution - Check stdout/stderr from run_shell to confirm success
5. Keep code compact - Long scripts are harder to debug in shell context

Caveats

• No persistent state between run_shell calls (unlike execute_code_sandbox)
• Longer execution time for complex scripts due to Python interpreter startup
• Limited debugging - Cannot easily inspect variables between statements
• Output size limits - Very large outputs may be truncated

When NOT to Use This Fallback

• If the task specifically requires sandbox isolation for security
• If you need persistent state across multiple code executions
• If the code execution is extremely long-running (consider backgrounding)