Run Code Tool

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What it does

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Key features

• Execute Python code in isolated Jupyter notebook cells
• Support for popular libraries (pandas, numpy, matplotlib, etc.)
• Enhanced output display with separate sections for different output types
• Smart output categorization - stdout, stderr, results, and text outputs are displayed distinctly
• Automatic error handling with detailed tracebacks
• Persistent session state across multiple executions

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Parameters

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Output types & display

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📤 Output (stdout)

print("Processing data...")
print("Results calculated successfully!")

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📊 Result (expression results)

# This result will be prominently displayed
42 + 8  # Shows: 50

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⚠️ Warnings/Errors (stderr)

import warnings
warnings.warn("This is a warning message")

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🎨 Rich Media (charts, images, etc.)

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()  # Chart appears as rich visual content

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Common use cases

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Data analysis with clear output

import pandas as pd
import numpy as np

# Create sample data
data = {'sales': [100, 150, 200, 175], 'month': ['Jan', 'Feb', 'Mar', 'Apr']}
df = pd.DataFrame(data)

print("Dataset created successfully!")  # Appears in Output section
print(f"Shape: {df.shape}")            # Appears in Output section

df.describe()  # Appears in Result section with rich formatting

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Create visualizations with status updates

import matplotlib.pyplot as plt

print("Creating visualization...")  # Shows progress in Output

# Create a simple chart
plt.figure(figsize=(8, 6))
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.title('Sample Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

print("Chart generated!")  # Confirms completion
plt.show()                 # Chart appears as rich visual

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Mathematical calculations with explanations

import math

print("Calculating complex formula...")  # Process indicator

# Complex calculations
result = math.sqrt(144) + math.pow(2, 8)
print(f"Square root of 144: {math.sqrt(144)}")  # Step-by-step output
print(f"2 to the power of 8: {math.pow(2, 8)}")

result  # Final result prominently displayed

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Error handling demonstration

import warnings

print("Starting calculation...")

# This generates a warning (appears in orange section)
warnings.warn("Using deprecated function")

# This works fine
result = 10 * 5
print(f"Calculation complete: {result}")

result  # Final result in blue section

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Knowledge base file access

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What’s available in the sandbox

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When to use local files vs KB tools

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Example: bulk processing with local KB files

import json

# Step 1: Load the structured index (extracted_data.json is available for accessible KBs
# without an explicit request)
with open('/home/user/kb/{kb_id}/extracted_data.json') as f:
    index = json.load(f)

# Step 2: Filter to the files you need
target_files = [f for f in index if f.get('status') == 'APPROVED']

# Step 3: Request those files via kb_files on THIS execute_python call, e.g.
#   kb_files=[f"kb/{kb_id}/{entry['fileName']}" for entry in target_files]
# then read their .md companions directly — no per-file tool calls needed
for entry in target_files:
    md_path = f"/home/user/kb/{kb_id}/{entry['fileName']}.md"
    with open(md_path) as f:
        content = f.read()
    # Process content...

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Available libraries

• Data Science: pandas, numpy, scipy, scikit-learn
• Visualization: matplotlib, seaborn, plotly
• Web: requests, beautifulsoup4
• Utilities: json, csv, datetime, os, sys

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Output formats

• Text: Standard print output and string representations
• Images: PNG, JPEG, SVG graphics from matplotlib, plotly, etc.
• HTML: Rich HTML content and tables
• Charts: Interactive visualizations
• Markdown: Formatted text with markdown syntax

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Visual improvements

• Color-coded output sections for easy identification
• Collapsible code view to focus on results
• Success indicators for code that runs without visible output
• Enhanced error display with structured error information
• Rich media rendering for charts, images, and formatted data

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Best practices

• Use print() statements to show progress and intermediate results
• Structure your code with clear steps and explanations
• Handle errors gracefully with try/except blocks
• Break complex operations into smaller, trackable steps
• Import libraries at the beginning of your code

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Troubleshooting

• Check if the library is in the available libraries list
• Try importing alternative libraries with similar functionality
• Use built-in Python modules when possible

• Simplify complex operations
• Avoid infinite loops or very long-running processes
• Break large datasets into smaller chunks

• Reduce dataset size or use sampling
• Clear variables you no longer need with del variable
• Use more memory-efficient data structures

• Use print() statements to generate stdout output
• Ensure your final line is an expression (not assignment) to see results
• Check that your code doesn’t have syntax errors

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Related tools

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