> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dify.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Code

> Execute custom Python or JavaScript for data processing

The Code node executes custom Python or JavaScript to handle complex data transformations, calculations, and logic within your workflow. Use it when preset nodes aren't sufficient for your specific processing needs.

<Frame caption="Code Node Configuration Interface">
  ![Code Node Configuration Interface](https://assets-docs.dify.ai/dify-enterprise-mintlify/en/guides/workflow/node/9969aa1bc1912aebe366f5d8f5dde296.png)
</Frame>

## Configuration

Define **Input Variables** to access data from other nodes in your workflow, then reference these variables in your code. Your function must return a dictionary containing the **Output Variables** you've declared.

```python theme={null}
def main(input_variable: str) -> dict:
    # Process the input
    result = input_variable.upper()
    return {
        'output_variable': result
    }
```

## Language Support

Choose between **Python** and **JavaScript** based on your needs and familiarity. Both languages run in secure sandboxes with access to common libraries for data processing.

<Tabs>
  <Tab title="Python">
    Python includes standard libraries like `json`, `math`, `datetime`, and `re`. Ideal for data analysis, mathematical operations, and text processing.

    ```python theme={null}
    def main(data: list) -> dict:
        import json
        import math
        
        average = sum(data) / len(data)
        return {'result': math.ceil(average)}
    ```
  </Tab>

  <Tab title="JavaScript">
    JavaScript provides standard built-in objects and methods. Good for JSON manipulation and string operations.

    ```javascript theme={null}
    function main(data) {
        const processed = data.map(item => item.toUpperCase());
        return { result: processed };
    }
    ```
  </Tab>
</Tabs>

## Error Handling and Retries

Configure automatic retry behavior for failed code executions and define fallback strategies when code encounters errors.

<Frame caption="Error Handling Configuration Options">
  ![Error Handling Configuration Options](https://assets-docs.dify.ai/2024/12/58f392734ce44b22cd8c160faf28cd14.png)
</Frame>

**Retry Settings** allow up to 10 automatic retries with configurable intervals (maximum 5000ms). Enable this for handling temporary processing issues.

**Error Handling** lets you define fallback paths when code execution fails, allowing your workflow to continue running even when the code encounters problems.

<Frame caption="Retry Configuration Interface">
  ![Retry Configuration Interface](https://assets-docs.dify.ai/2024/12/9fdd5525a91dc925b79b89272893becf.png)
</Frame>

## Output Validation and Limits

Code outputs are automatically validated with strict limits:

* **Strings**: Maximum length of 80,000 characters, null bytes are removed
* **Numbers**: Range from -999999999 to 999999999, floats limited to 10 decimal places
* **Objects/Arrays**: Maximum depth of 5 levels to prevent complex nested structures

These limits ensure performance and prevent memory issues in workflows.

## Security Considerations

Code executes in a strict sandbox that prevents file system access, network requests, and system commands. This maintains security while providing programming flexibility.

Some operations are automatically blocked for security reasons. Avoid attempting to access system files or execute potentially dangerous operations:

<Frame caption="Security Filtering by Cloudflare WAF">
  ![Security Filtering by Cloudflare WAF](https://assets-docs.dify.ai/2024/12/ad4dc065c4c567c150ab7fa7bfd123a3.png)
</Frame>

If your code isn't saving, check your browser's Network tab - security filters may be blocking potentially dangerous operations.

## Dependencies Support

Code nodes support external dependencies for both Python and JavaScript:

```python theme={null}
# Python: Import numpy, pandas, requests, etc.
import numpy as np
import pandas as pd

def main(data: list) -> dict:
    df = pd.DataFrame(data)
    return {'mean': float(np.mean(df['values']))}
```

```javascript theme={null}
// JavaScript: Import lodash, moment, etc.
const _ = require('lodash');

function main(data) {
    return { unique: _.uniq(data) };
}
```

Dependencies are pre-installed in the sandbox environment. Check the available packages list in your Dify installation.

## Self-Hosted Setup

For self-hosted Dify installations, start the sandbox service for secure code execution:

```bash theme={null}
docker-compose -f docker-compose.middleware.yaml up -d
```

The sandbox service requires Docker and isolates code execution from your main system for security.