name: sandbox-execution-fallback-717e65 description: Fallback from execute_code_sandbox to file-based run_shell execution when sandbox fails

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

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