---
title: "程式碼解譯器"
url: "/docs/guides/code_interpreter"
canonical_url: "https://docs.litellm.ai/docs/guides/code_interpreter"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/completion/computer_use"
  - "/docs/sandbox"
---
# 程式碼解譯器

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


使用 OpenAI 的 Code Interpreter 工具，在安全且隔離的環境中執行 Python 程式碼。

| 功能 | 支援 |
|---------|-----------|
| LiteLLM Python SDK | ✅ |
| LiteLLM AI Gateway | ✅ |
| 支援的提供者 | `openai` |

:::tip[將 code interpreter 路由到您自己的 sandbox]

proxy 可以在 `code_interpreter` 攔截 `/v1/responses`，並在已設定的 sandbox（目前為 e2b）中執行程式碼，而不必使用 OpenAI 的 container，且不需變更 client request。回應格式保持完全一致（`code_interpreter_call` 在 `message` 旁邊）。請參閱 [Code Interpreter Sandbox Interception](/docs/sandbox#code-interpreter-interceptor)。

:::

## LiteLLM AI Gateway {#litellm-ai-gateway}

### API（OpenAI SDK） {#api-openai-sdk}

將 OpenAI SDK 指向您的 LiteLLM Gateway：

```python showLineNumbers title="code_interpreter_gateway.py"
from openai import OpenAI

client = OpenAI(
    api_key="sk-<your-litellm-api-key>",  # Your LiteLLM API key
    base_url="http://localhost:4000"
)

response = client.responses.create(
    model="openai/gpt-5.6-terra",
    tools=[{"type": "code_interpreter"}],
    input="Calculate the first 20 fibonacci numbers and plot them"
)

print(response)
```

#### 串流 {#streaming}

```python showLineNumbers title="code_interpreter_streaming.py"
from openai import OpenAI

client = OpenAI(
    api_key="sk-<your-litellm-api-key>",
    base_url="http://localhost:4000"
)

stream = client.responses.create(
    model="openai/gpt-5.6-terra",
    tools=[{"type": "code_interpreter"}],
    input="Generate sample sales data CSV and create a visualization",
    stream=True
)

for event in stream:
    print(event)
```

#### 取得產生的檔案內容 {#get-generated-file-content}

```python showLineNumbers title="get_file_content_gateway.py"
from openai import OpenAI

client = OpenAI(
    api_key="sk-<your-litellm-api-key>",
    base_url="http://localhost:4000"
)

# 1. Run code interpreter
response = client.responses.create(
    model="openai/gpt-5.6-terra",
    tools=[{"type": "code_interpreter"}],
    input="Create a scatter plot and save as PNG"
)

# 2. Get container_id from response
container_id = response.output[0].container_id

# 3. List files
files = client.containers.files.list(container_id=container_id)

# 4. Download file content
for file in files.data:
    content = client.containers.files.content(
        container_id=container_id,
        file_id=file.id
    )
    
    with open(file.filename, "wb") as f:
        f.write(content.read())
    print(f"Downloaded: {file.filename}")
```

### AI Gateway UI {#ai-gateway-ui}

LiteLLM Admin UI 內建支援 Code Interpreter。

**步驟：**

1. 前往 LiteLLM UI 中的 **Playground**
2. 選取一個 **OpenAI model**（例如，`openai/gpt-5.6-terra`）
3. 在 **Endpoint Type** 下方，將 `/v1/responses` 選為 endpoint
4. 在左側面板切換 **Code Interpreter**
5. 傳送要求執行程式碼或產生檔案的 prompt

UI 會顯示：
- 執行的 Python 程式碼（可收合）
- 內嵌顯示產生的圖片
- 檔案（CSV 等）的下載連結

## LiteLLM Python SDK {#litellm-python-sdk}

### 執行 Code Interpreter {#run-code-interpreter}

```python showLineNumbers title="code_interpreter.py"
import litellm

response = litellm.responses(
    model="openai/gpt-5.6-terra",
    input="Generate a bar chart of quarterly sales and save as PNG",
    tools=[{"type": "code_interpreter"}]
)

print(response)
```

### 取得產生的檔案內容 {#get-generated-file-content-1}

在 Code Interpreter 執行後，擷取產生的檔案：

```python showLineNumbers title="get_file_content.py"
import litellm

# 1. Run code interpreter
response = litellm.responses(
    model="openai/gpt-5.6-terra",
    input="Create a pie chart of market share and save as PNG",
    tools=[{"type": "code_interpreter"}]
)

# 2. Extract container_id from response
container_id = response.output[0].container_id  # e.g. "cntr_abc123..."

# 3. List files in container
files = litellm.list_container_files(
    container_id=container_id,
    custom_llm_provider="openai"
)

# 4. Download each file
for file in files.data:
    content = litellm.containers.retrieve_container_file_content(
        container_id=container_id,
        file_id=file.id,
        custom_llm_provider="openai"
    )
    
    with open(file.filename, "wb") as f:
        f.write(content)
    print(f"Downloaded: {file.filename}")
```

## 相關 {#related}

- [Containers API](/docs/containers) - 管理 containers
- [Container Files API](/docs/container_files) - 管理 containers 內的檔案
- [OpenAI Code Interpreter Docs](https://platform.openai.com/docs/guides/tools-code-interpreter) - OpenAI 官方文件

## Related pages

- [電腦使用](https://docs.litellm.ai/docs/completion/computer_use.md)
- [Code Interpreter Sandbox Interception](https://docs.litellm.ai/docs/sandbox.md)