When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.
Fallback pattern for executing Python code when execute_code_sandbox fails
Review the instructions the agent will receive before you install.
Use this pattern when execute_code_sandbox fails repeatedly (typically 2+ attempts) due to environment limitations, timeouts, dependency issues, or sandbox restrictions.
Instead of executing code directly in the sandbox, write the Python script to a file and execute it via shell:
write_filerun_shell with python3 script.pyUse write_file to save your Python code:
- Path: Choose a descriptive name (e.g., "process_data.py", "analyze.py")
- Content: Your complete Python script with all imports and logic
Use run_shell to execute:
- Command: "python3 <script_name>.py"
- Timeout: Set appropriately for your task (default 30s, increase if needed)
- Capture stdout/stderr from run_shell
- Parse results as needed
- Optionally delete the script file after execution
# Instead of this (which may fail):
execute_code_sandbox(code="import pandas as pd; df = pd.read_csv('data.csv')...")
# Do this:
write_file(path="analyze.py", content="""
import pandas as pd
import json
df = pd.read_csv('data.csv')
result = df.groupby('category').sum()
print(json.dumps(result.to_dict()))
""")
run_shell(command="python3 analyze.py", timeout=60)
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