---
sidebar_position: 13
slug: /code_component
sidebar_custom_props: {
  categoryIcon: LucideCodeXml
}
---
# Code component

A component that enables users to integrate Python or JavaScript codes into their Agent for dynamic data processing.

---

## Scenarios

A **Code** component is essential when you need to integrate complex code logic (Python or JavaScript) into your Agent for dynamic data processing.

## Prerequisites

### 1. Ensure gVisor is properly installed

We use gVisor to isolate code execution from the host system. Please follow [the official installation guide](https://gvisor.dev/docs/user_guide/install/) to install gVisor, ensuring your operating system is compatible before proceeding.

### 2. Ensure Sandbox is properly installed

RAGFlow Sandbox is a secure, pluggable code execution backend. It serves as the code executor for the **Code** component. Please follow the [instructions here](https://github.com/infiniflow/ragflow/tree/main/agent/sandbox) to install RAGFlow Sandbox.

:::note Docker client version
The executor manager image now bundles Docker CLI `29.1.0` (API 1.44+). Older images shipped Docker 24.x and will fail against newer Docker daemons with `client version 1.43 is too old`. Pull the latest `infiniflow/sandbox-executor-manager:latest` or rebuild it in `./sandbox/executor_manager` if you encounter this error.
:::

:::tip NOTE
If your RAGFlow Sandbox is not working, please be sure to consult the [Troubleshooting](#troubleshooting) section in this document. We assure you that it addresses 99.99% of the issues!
:::

### 3. (Optional) Install necessary dependencies

If you need to import your own Python or JavaScript packages into Sandbox, please follow the commands provided in the [How to import my own Python or JavaScript packages into Sandbox?](#how-to-import-my-own-python-or-javascript-packages-into-sandbox) section to install the additional dependencies. 

### 4. Enable Sandbox-specific settings in RAGFlow

Ensure all Sandbox-specific settings are enabled in **ragflow/docker/.env**.

### 5. Restart the service after making changes

Any changes to the configuration or environment *require* a full service restart to take effect.

## Configurations 

### Input

You can specify multiple input sources for the **Code** component. Click **+ Add variable** in the **Input variables** section to include the desired input variables. 

### Code

This field allows you to enter and edit your source code.

:::danger IMPORTANT
If your code implementation includes defined variables, whether input or output variables, ensure they are also specified in the corresponding **Input** or **Output** sections.
:::

#### A Python code example

```Python 
    def main(arg1: str, arg2: str) -> dict:
        return {
            "result": arg1 + arg2,
        }
```

#### A JavaScript code example

```JavaScript

    const axios = require('axios');
    async function main(args) {
      try {
        const response = await axios.get('https://github.com/infiniflow/ragflow');
        console.log('Body:', response.data);
      } catch (error) {
        console.error('Error:', error.message);
      }
    }
```

### Return values

You define the output variable(s) of the **Code** component here.

:::danger IMPORTANT
If you define output variables here, ensure they are also defined in your code implementation; otherwise, their values will be `null`. The following are two examples:


![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/set_object_output.jpg)

![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/set_nested_object_output.png)
:::

### Output

The output is split into two parts:

- **Business**: the business output defined in **Return Value**
- **System**: runtime fields that are populated automatically, such as `content`, `actual_type`, and `attachments`

For example, the following code generates a simple line chart:

```Python
def main() -> dict:
    from pathlib import Path

    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    artifacts_dir = Path("artifacts")
    artifacts_dir.mkdir(parents=True, exist_ok=True)

    x = [1, 2, 3, 4, 5]
    y = [2, 4, 6, 8, 10]

    output_path = artifacts_dir / "simple_plot.png"

    plt.figure(figsize=(6, 4))
    plt.plot(x, y, marker="o")
    plt.title("Simple Line Chart")
    plt.xlabel("X")
    plt.ylabel("Y")
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(output_path)
    plt.close()

    return {
        "result": "plot generated successfully",
        "file_path": str(output_path),
    }
```
![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/codeexec_output1.jpg)

Business Output shows the return value you defined, while System Output shows the generated `content`, the inferred `actual_type`, and the collected `attachments`.

![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/codeexec_output2.jpg)

## Troubleshooting

### `HTTPConnectionPool(host='sandbox-executor-manager', port=9385): Read timed out.`

**Root cause**  

- You did not properly install gVisor and `runsc` was not recognized as a valid Docker runtime.
- You did not pull the required base images for the runners and no runner was started.

**Solution**

For the gVisor issue:

1. Install [gVisor](https://gvisor.dev/docs/user_guide/install/).
2. Restart Docker.
3. Run the following to double check:

   ```bash
   docker run --rm --runtime=runsc hello-world
   ```

For the base image issue, pull the required base images:

```bash
docker pull infiniflow/sandbox-base-nodejs:latest
docker pull infiniflow/sandbox-base-python:latest
```

### `docker: Error response from daemon: client version 1.43 is too old. Minimum supported API version is 1.44`

**Root cause**

Your executor manager image includes Docker CLI 24.x (API 1.43), but the host Docker daemon, e.g., Docker 25+ / 29.x, now requires API 1.44+.

**Solution**

Pull the latest executor manager image or rebuild it in `./sandbox/executor_manager` to upgrade the built-in Docker client:

```bash
docker pull infiniflow/sandbox-executor-manager:latest
# or
docker build -t sandbox-executor-manager:latest ./sandbox/executor_manager
```

### `HTTPConnectionPool(host='none', port=9385): Max retries exceeded.`

**Root cause**  

`sandbox-executor-manager` is not mapped in `/etc/hosts`.

**Solution**  

Add a new entry to `/etc/hosts`:

`127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager`

### `Container pool is busy`

**Root cause**  

All runners are currently in use, executing tasks. 

**Solution**  

Please try again shortly or increase the pool size in the configuration to improve availability and reduce waiting times.


## Frequently asked questions

### How to import my own Python or JavaScript packages into Sandbox?

To import your Python packages, update **sandbox_base_image/python/requirements.txt** to install the required dependencies. For example, to add the `openpyxl` package, proceed with the following command lines:

```bash {4,6}
(ragflow) ➜ ragflow/sandbox main ✓ pwd # make sure you are in the right directory
/home/infiniflow/workspace/ragflow/sandbox

(ragflow) ➜ ragflow/sandbox main ✓ echo "openpyxl" >> sandbox_base_image/python/requirements.txt # add the package to the requirements.txt file

(ragflow) ➜ ragflow/sandbox main ✗ cat sandbox_base_image/python/requirements.txt # make sure the package is added
numpy
pandas
requests
openpyxl # here it is

(ragflow) ➜ ragflow/sandbox main ✗ make # rebuild the docker image, this command will rebuild the image and start the service immediately. To build image only, using `make build` instead.

(ragflow) ➜ ragflow/sandbox main ✗ docker exec -it sandbox_python_0 /bin/bash # entering container to check if the package is installed


# in the container
nobody@ffd8a7dd19da:/workspace$ python # launch python shell
Python 3.11.13 (main, Aug 12 2025, 22:46:03) [GCC 12.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import openpyxl # import the package to verify installation
>>>
# That's okay!
```

To import your JavaScript packages, navigate to `sandbox_base_image/nodejs` and use `npm` to install the required packages. For example, to add the `lodash` package, run the following commands:

```bash
(ragflow) ➜ ragflow/sandbox main ✓ pwd
/home/infiniflow/workspace/ragflow/sandbox

(ragflow) ➜ ragflow/sandbox main ✓ cd sandbox_base_image/nodejs

(ragflow) ➜ ragflow/sandbox/sandbox_base_image/nodejs main ✓ npm install lodash 

(ragflow) ➜ ragflow/sandbox/sandbox_base_image/nodejs main ✓ cd ../.. # go back to sandbox root directory

(ragflow) ➜ ragflow/sandbox main ✗ make # rebuild the docker image, this command will rebuild the image and start the service immediately. To build image only, using `make build` instead.

(ragflow) ➜ ragflow/sandbox main ✗ docker exec -it sandbox_nodejs_0 /bin/bash # entering container to check if the package is installed

# in the container
nobody@dd4bbcabef63:/workspace$ npm list lodash # verify via npm list
/workspace
`-- lodash@4.17.21 extraneous

nobody@dd4bbcabef63:/workspace$ ls node_modules | grep lodash # or verify via listing node_modules
lodash

# That's okay!
```