---
title: Code Execution
description: Secure Python and JavaScript sandbox for running code
---
## Overview
Execute Python and JavaScript code in a secure sandboxed environment directly from chat. The `code-execution` tool uses shared sandbox infrastructure with a five-minute execution deadline. Select Vercel Sandbox or Daytona independently of the tool. Sandboxes are optional. An app using sandbox-backed tools must select exactly one provider. Other tools can reuse the same infrastructure. Ad-hoc and saved-document execution use the selected executor. Saved documents execute their authorized stored revision, not replacement source supplied by the caller.
## Installation
Install the tool with a compatible [sandbox provider](../deployment/sandboxes):
```bash
bunx chat-js@alpha add code-execution --sandbox-provider vercel
```
Configure provider credentials using the [sandbox setup guide](../deployment/sandboxes). Sandbox selection is shared infrastructure, including persistent EVE session files when the provider supports them.
For saved code documents, install the execution action and its dependencies:
```bash
bunx chat-js@alpha add saved-code-execution --sandbox-provider vercel
```
## Pre-installed Packages
Both built-in providers install these packages inside each Python sandbox before executing your code:
- `matplotlib` - Plotting and visualization
- `pandas` - Data analysis
- `numpy` - Numerical computing
- `sympy` - Symbolic mathematics
- `yfinance` - Yahoo Finance market data
## Installing Additional Packages
Add `!pip install` lines at the top of your code. They are automatically stripped before execution:
```python
!pip install requests beautifulsoup4
import requests
from bs4 import BeautifulSoup
response = requests.get("https://example.com")
print(response.status_code)
```
## Output
There are two ways to return output:
**1. Use `print()`**
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.head())
```
**2. Assign to `result` or `results`**
The sandbox automatically prints these variables if they exist:
```python
import numpy as np
result = np.mean([1, 2, 3, 4, 5])
```
> **Warning**
>
> Implicit REPL output (last expression value) is not captured. Always use
> `print()` or assign to `result`.
## Charts
The sandbox supports `matplotlib` charts. No need to call `plt.show()`. The chart is saved automatically:
```python
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.title("Sine Wave")
plt.xlabel("x")
plt.ylabel("sin(x)")
```
Interactive `chart` dictionaries support these chart types. Matplotlib output is returned as a PNG and is not limited to this list:
- Line charts
- Scatter plots
- Bar charts
> **Note**
>
> Matplotlib charts are returned as PNG images. Assign a `chart` dictionary for
> interactive line, scatter, and bar charts.
## Customize the tool
The generic tool, renderer, schemas and descriptor live in `chatjs/tools/code-execution/`. Shared Python and JavaScript command wrappers live under `chatjs/tools/_shared/code-execution/`. Generated `chatjs/generated/code-executor.ts` exports the typed executor for ad-hoc and saved-document execution.
See [Sandboxes](../deployment/sandboxes) for provider credentials, runtime images, custom provider contracts, resource cleanup, and provider validation.
## Accounting
The built-in executor records $0.05 of usage cost for a completed execution after output validation and confirmed cleanup. This recorded cost is independent of the provider invoice. When installed, [Credits](../plugins/credits) applies its balance and debit policy. Without a billing adapter, core records the usage without debiting a balance. Infrastructure failures record zero application cost, although provider compute costs can still accrue. See [sandbox lifecycle and accounting](../deployment/sandboxes#lifecycle-and-accounting).