Code Execution Fallback Pattern

When to Use This Skill

Apply this pattern when you encounter repeated failures with execute_code_sandbox:

• 2+ consecutive failures with opaque or unknown errors
• Timeout errors that persist across retry attempts
• Environment-related errors that don't resolve with code fixes

The Fallback Workflow

Step 1: Detect Repeated Failures

Track execution failures. After 2 consecutive failures with execute_code_sandbox, switch to the fallback approach.

Step 2: Write Script to File

Use write_file to save your Python script:

write_file(
    path="/workspace/script_name.py",
    content="# Your Python code here\nimport sys\n..."
)

Step 3: Execute via Shell

Use run_shell to run the script:

run_shell(
    command="python /workspace/script_name.py",
    timeout=300
)

Step 4: Capture Output

Parse stdout/stderr from run_shell output to verify success or diagnose issues.

Complete Example

# Instead of this (which may fail):
result = execute_code_sandbox(code="import pandas as pd\n...")

# Use this fallback pattern:
script_content = """
import pandas as pd
import sys

try:
    # Your logic here
    df = pd.DataFrame({'col': [1, 2, 3]})
    print(df.to_csv())
    sys.exit(0)
except Exception as e:
    print(f"ERROR: {e}", file=sys.stderr)
    sys.exit(1)
"""

# Write the script
write_file(path="/workspace/my_script.py", content=script_content)

# Execute via shell
result = run_shell(command="python /workspace/my_script.py", timeout=300)

Best Practices

1. Add error handling in your script - use try/except with sys.exit() codes
2. Set appropriate timeouts - run_shell default is 30s, increase for heavy operations
3. Clean up temporary files after execution if needed
4. Log the fallback trigger - document why you switched approaches
5. Verify Python availability - Most sandboxes have Python 3.x by default

Why This Works