Sandbox Execution Fallback

When to Use

Apply this pattern when execute_code_sandbox fails repeatedly or exhibits instability. Common triggers include:

• Sandbox returns errors after 2-3 retry attempts
• Error messages indicate provider/system issues rather than code bugs
• Execution times out or hangs inconsistently
• Complex multi-step code with file I/O is needed
• Better error visibility and debugging is required

Procedure

Step 1: Detect When to Switch

Recognize the failure pattern:

• execute_code_sandbox produces repeated errors despite code corrections
• Error output suggests infrastructure issues (not syntax/logic errors)
• The sandbox environment appears unstable or limited

Step 2: Write Python Script to File

Use write_file to save your script:

write_file(
    path="script.py",
    content="#!/usr/bin/env python3

# Your Python code here
import sys
print('Executing via file-based approach')
# ... rest of your code
"
)

For multi-file projects, write each file separately:

write_file(path="utils.py", content="# Utility functions\ndef helper(): ...")
write_file(path="main.py", content="from utils import helper\nhelper()")

Step 3: Execute via Shell

Run the script using run_shell:

run_shell(command="python3 script.py")

For scripts in subdirectories:

run_shell(command="cd mydir && python3 script.py")

Step 4: Handle Output and Clean Up

• Parse stdout/stderr from run_shell output
• Inspect created files directly using read_file if needed
• Remove temporary scripts after successful execution:

  run_shell(command="rm script.py")
  

Advantages Over Sandbox Execution

Benefit	Explanation
Bypasses provider limitations	No sandbox resource constraints
Better error visibility	Full stack traces and system errors
Environment control	Direct access to system Python and packages
Multi-file support	Easy imports and module structure
Persistence	Files remain for inspection and debugging
Reliability	More consistent execution behavior

Complete Example

# Scenario: execute_code_sandbox failing on data processing task

# Step 1: Write the script
write_file(
    path="process_data.py",
    content="#!/usr/bin/env python3
import json
import csv

# Load and process data
with open('input.json', 'r') as f:
    data = json.load(f)

# Transform data
results = []
for item in data:
    results.append({'processed': item['value'] * 2})

# Write output
with open('output.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['processed'])
    writer.writeheader()
    writer.writerows(results)

print('Processing complete')
"
)

# Step 2: Execute via shell
run_shell(command="python3 process_data.py")

# Step 3: Read results
read_file(filetype="csv", file_path="output.csv")

# Step 4: Clean up (optional)
run_shell(command="rm process_data.py")

Best Practices

1. Use absolute or clear relative paths - Avoid ambiguity in file locations
2. Add error handling in scripts - Catch exceptions and print meaningful messages
3. Validate script content before writing - Ensure proper Python syntax
4. Log execution steps - Track what was written and executed for debugging
5. Clean up temporary files - Remove scripts after use unless needed for later inspection

Troubleshooting

Issue	Solution
python3 not found	Try python or specify full path /usr/bin/python3
Import errors	Use run_shell(command="pip3 install package") first
Permission denied	Add execute permission: run_shell(command="chmod +x script.py")
Working directory issues	Use absolute paths or cd in the command