execute-code-fallback

# Execute Code Fallback Pattern When `execute_code_sandbox` fails due to e2b unavailability, immediately fall back to `run_shell` with embedded Python code. This ensures task continuity when the sandbox service is down. ## When to Apply Use this pattern when you observe: - `execute_code_sandbox` returns errors mentioning "e2b", "sandbox unavailable", or connection timeouts - Repeated sandbox execution failures (2+ attempts) - Error messages indicating the code execution environment is unreachable ## Fallback Procedure ### Step 1: Detect the Failure Identify that `execute_code_sandbox` has failed. Common error indicators: - "e2b service unavailable" - "Sandbox connection failed" - "Execution environment not reachable" - Timeout errors during code execution ### Step 2: Switch to run_shell with Embedded Python Instead of: ```python execute_code_sandbox(code="...") ``` Use: ```bash run_shell(command="python3 -c '...your Python code...'") ``` ### Step 3: Install Dependencies First (If Needed) If your Python code requires external packages, install them first: ```bash run_shell(command="pip install pandas requests matplotlib") ``` Then execute your main code: ```bash run_shell(command="python3 << 'EOF' import pandas as pd import requests # Your code here print("Success") EOF ") ``` ### Step 4: Use Heredoc for Multi-line Code For complex Python scripts, use heredoc syntax for cleaner code: ```bash run_shell(command="python3 << 'PYTHON_SCRIPT' import json import os # Complex logic here data = {'key': 'value'} with open('output.json', 'w') as f: json.dump(data, f) print('File created successfully') PYTHON_SCRIPT ") ``` ## Complete Example **Scenario:** You need to process a CSV file and generate a report. **Original approach (sandbox):** ```python execute_code_sandbox(code=""" import pandas as pd df = pd.read_csv('data.csv') summary = df.describe() print(summary) """) ``` **Fallback approach (run_shell):** ```bash # First install dependencies if needed run_shell(command="pip install pandas --quiet") # Then execute the code run_shell(command="python3 << 'EOF' import pandas as pd df = pd.read_csv('data.csv') summary = df.describe() print(summary) EOF ") ``` ## Important Considerations 1. **State Persistence**: Unlike `execute_code_sandbox`, `run_shell` executions may not share state between calls. Save intermediate results to files if needed. 2. **Working Directory**: Ensure you're operating in the correct directory. Use `pwd` to verify or include `cd /path/to/workdir` in your commands. 3. **Python Version**: Use `python3` explicitly to avoid ambiguity. Verify with `python3 --version` if needed. 4. **Error Handling**: Check the stdout/stderr from `run_shell` to confirm success. Failed Python scripts will return non-zero exit codes. 5. **Security**: Be cautious when embedding user-provided data into shell commands. Escape appropriately or use file-based input. 6. **Performance**: For large computations, `run_shell` may be slower than sandbox. Consider breaking into smaller steps if timeouts occur. ## Quick Reference | Task | Sandbox Approach | Fallback Approach | |------|-----------------|-------------------| | Simple calculation | `execute_code_sandbox(code="print(2+2)")` | `run_shell(command="python3 -c 'print(2+2)'")` | | Install + run | `execute_code_sandbox(code="import pkg; ...")` | `run_shell(command="pip install pkg && python3 -c '...'")` | | Multi-line script | `execute_code_sandbox(code="...")` | `run_shell(command="python3 << 'EOF'...EOF")` | | File I/O | `execute_code_sandbox(code="...")` | `run_shell(command="python3 << 'EOF'...EOF")` | ## Recovery Checklist - [ ] Confirm `execute_code_sandbox` failure (not a code bug) - [ ] Switch to `run_shell` immediately - [ ] Install required packages with `pip install` - [ ] Use heredoc for multi-line Python - [ ] Verify output and handle errors - [ ] Save intermediate results to files if multi-step