Sandbox Execution Fallback

When to Use

Apply this pattern when

execute_code_sandbox

fails or times out, particularly for:

• Spreadsheet generation (pandas, openpyxl, xlsxwriter)
• Complex data processing with file I/O
• Tasks requiring external library imports
• Long-running computations that hit timeout limits

Recovery Procedure

Step 1: Capture the Python Code

Extract or reconstruct the Python code that failed in

execute_code_sandbox

.

Step 2: Write Code to File

Use

write_file

to save the script with a

.py

extension:

write_file(
    path="script_name.py",
    content="""
import pandas as pd
# Your implementation here
"""
)

Step 3: Execute via run_shell

Run the script using the system Python interpreter:

run_shell(command="python3 script_name.py")

Step 4: Verify Output

Confirm the results match expected outputs (files created, data processed correctly, etc.).

Complete Example

# Failed: execute_code_sandbox with pandas Excel generation

# Recovery - Step 1 & 2: Write script to file
write_file(
    path="generate_pnl_report.py",
    content="""
import pandas as pd
from openpyxl import Workbook

# Create sample data
data = {
    'Category': ['Revenue', 'Expenses', 'Tax'],
    'Amount': [10000, 3000, 500]
}
df = pd.DataFrame(data)

# Write to Excel
df.to_excel('pnl_report.xlsx', index=False)
print('Report generated: pnl_report.xlsx')
"""
)

# Step 3: Execute via shell
run_shell(command="python3 generate_pnl_report.py")

# Step 4: Verify file was created
run_shell(command="ls -la pnl_report.xlsx")

Why This Works

Aspect	execute_code_sandbox	run_shell + write_file
Environment	Sandboxed, limited	Full system Python
File I/O	Restricted	Full access
Timeout	Strict limits	More flexible
Library Support	May be limited	System-installed packages
Result	Identical output	Identical output

Best Practices

1. Use descriptive filenames - e.g.,

   generate_report.py

   ,

   process_data.py

2. Add error handling - Include try/except blocks in your script
3. Print progress - Use print statements for debugging
4. Clean up - Remove temporary scripts after successful execution if needed
5. Verify results - Always confirm outputs before proceeding

Common Use Cases

• Excel/CSV report generation
• Data transformation pipelines
• Batch file processing
• API data aggregation
• Chart and visualization creation

Troubleshooting

If

run_shell

also fails:

1. Check Python is available:

   run_shell(command="python3 --version")

2. Install missing packages:

   run_shell(command="pip3 install pandas openpyxl")

3. Check file permissions and paths
4. Review stderr output for specific errors