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sandbox-fallback-python-e5d7ae

Continue Python tasks when the sandbox cannot start by switching to run_shell. The workflow covers short scripts, saved script files, library checks, longer timeouts, and output verification for tasks such as PDF generation and data processing.

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Fallback to run_shell with Python heredoc when execute_code_sandbox fails

See how to install

Good for

  • Continue Python work after sandbox startup failures
  • Generate PDFs through shell execution
  • Run data processing with required libraries
  • Check output files after switching execution methods

Files included (2)

  • SKILL.md3.6 KB
  • .skill_id44 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Python Sandbox Fallback Pattern

When to Use

Use this pattern when execute_code_sandbox fails with e2b/sandbox initialization errors, especially when running Python code that requires external libraries like reportlab, pandas, numpy, etc.

Common Failure Indicators

Detect these error patterns to trigger the fallback:

  • e2b initialization errors
  • sandbox creation/creation timeout failures
  • container startup errors
  • Connection/refusal errors from sandbox service

The Fallback Pattern

Basic Heredoc Approach

For short scripts, use a Python heredoc directly in run_shell:

run_shell(
    command="""python3 << 'EOF'
import sys
print(f"Python version: {sys.version}")
# Your code here
EOF
""",
    timeout=60
)

File-Based Approach for Complex Scripts

For longer scripts or when you need to preserve state across multiple executions:

# Step 1: Write the script to a temporary file
script_content = '''
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
import os

# Your code here
'''

run_shell(command=f"cat > /tmp/script.py << 'SCRIPT_EOF'\n{script_content}\nSCRIPT_EOF")

# Step 2: Execute the script
result = run_shell(command="python3 /tmp/script.py", timeout=120)

Key Considerations

Timeouts

  • run_shell typically needs longer timeouts than execute_code_sandbox
  • Use timeout=60 for simple scripts
  • Use timeout=120 or higher for library-heavy operations (PDF generation, data processing)

Library Availability

  • Verify required libraries are installed in the shell environment
  • Install missing libraries first if needed:
    run_shell(command="pip install reportlab", timeout=60)
    

Error Handling

  • Capture and inspect run_shell output for Python errors
  • Check stderr for import failures or runtime exceptions

Working Directory

  • Files created via run_shell are in the current working directory by default
  • Use absolute paths or os.getcwd() to reference files reliably

Example: PDF Generation with reportlab

# FALLBACK VERSION (when execute_code_sandbox fails):

pdf_code = """
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch

def create_memo(filename):
    c = canvas.Canvas(filename, pagesize=letter)
    c.drawString(1*inch, 10*inch, "Legal Memorandum")
    c.save()
    print(f"Created {filename}")

create_memo('/workspace/output.pdf')
"""

# Write script to file
run_shell(command=f"cat > /tmp/create_pdf.py << 'PYEOF'\n{pdf_code}\nPYEOF")

# Execute and verify
run_shell(command="python3 /tmp/create_pdf.py", timeout=120)

# Verify output exists
run_shell(command="ls -la /workspace/output.pdf", timeout=30)

Comparison Table

Aspectexecute_code_sandboxrun_shell (fallback)
IsolationHigh (containerized)Low (host environment)
ReliabilityCan fail with e2b errorsMore reliable
Library AccessPre-configured sandboxDepends on host
TimeoutDefault 30sConfigurable
File AccessARTIFACT_PATH conventionDirect filesystem

Migration Checklist

When switching from execute_code_sandbox to run_shell:

  • Increase timeout values (typically 2-4x)
  • Convert ARTIFACT_PATH references to actual file paths
  • Add explicit file verification steps after execution
  • Handle potential library import errors
  • Test script execution in shell environment first

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