# Source: https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool.md

# Code execution tool

---

Claude can analyze data, create visualizations, perform complex calculations, run system commands, create and edit files, and process uploaded
files directly within the API conversation.
The code execution tool allows Claude to run Bash commands and manipulate files, including writing code, in a secure, sandboxed environment.

<Note>
The code execution tool is currently in public beta.

To use this feature, add the `"code-execution-2025-08-25"` [beta header](/docs/en/api/beta-headers) to your API requests.

Please reach out through our [feedback form](https://forms.gle/LTAU6Xn2puCJMi1n6) to share your feedback on this feature.
</Note>

## Model compatibility

The code execution tool is available on the following models:

| Model | Tool Version |
|-------|--------------|
| Claude Opus 4.5 (`claude-opus-4-5-20251101`) | `code_execution_20250825` |
| Claude Opus 4.1 (`claude-opus-4-1-20250805`) | `code_execution_20250825` |
| Claude Opus 4 (`claude-opus-4-20250514`) | `code_execution_20250825` |
| Claude Sonnet 4.5 (`claude-sonnet-4-5-20250929`) | `code_execution_20250825` |
| Claude Sonnet 4 (`claude-sonnet-4-20250514`) | `code_execution_20250825` |
| Claude Sonnet 3.7 (`claude-3-7-sonnet-20250219`) ([deprecated](/docs/en/about-claude/model-deprecations)) | `code_execution_20250825` |
| Claude Haiku 4.5 (`claude-haiku-4-5-20251001`) | `code_execution_20250825` |
| Claude Haiku 3.5 (`claude-3-5-haiku-latest`) ([deprecated](/docs/en/about-claude/model-deprecations)) | `code_execution_20250825` |

<Note>
The current version `code_execution_20250825` supports Bash commands and file operations. A legacy version `code_execution_20250522` (Python only) is also available. See [Upgrade to latest tool version](#upgrade-to-latest-tool-version) for migration details.
</Note>

<Warning>
Older tool versions are not guaranteed to be backwards-compatible with newer models. Always use the tool version that corresponds to your model version.
</Warning>

## Quick start

Here's a simple example that asks Claude to perform a calculation:

<CodeGroup>
```bash Shell
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
            }
        ],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```

```python Python
import anthropic

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)

print(response)
```

```typescript TypeScript
import { Anthropic } from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

async function main() {
  const response = await anthropic.beta.messages.create({
    model: "claude-sonnet-4-5",
    betas: ["code-execution-2025-08-25"],
    max_tokens: 4096,
    messages: [
      {
        role: "user",
        content: "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      }
    ],
    tools: [{
      type: "code_execution_20250825",
      name: "code_execution"
    }]
  });

  console.log(response);
}

main().catch(console.error);
```
</CodeGroup>

## How code execution works

When you add the code execution tool to your API request:

1. Claude evaluates whether code execution would help answer your question
2. The tool automatically provides Claude with the following capabilities:
   - **Bash commands**: Execute shell commands for system operations and package management
   - **File operations**: Create, view, and edit files directly, including writing code
3. Claude can use any combination of these capabilities in a single request
4. All operations run in a secure sandbox environment
5. Claude provides results with any generated charts, calculations, or analysis

## How to use the tool

### Execute Bash commands

Ask Claude to check system information and install packages:

<CodeGroup>
```bash Shell
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": "Check the Python version and list installed packages"
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```

```python Python
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Check the Python version and list installed packages"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)
```

```typescript TypeScript
const response = await anthropic.beta.messages.create({
  model: "claude-sonnet-4-5",
  betas: ["code-execution-2025-08-25"],
  max_tokens: 4096,
  messages: [{
    role: "user",
    content: "Check the Python version and list installed packages"
  }],
  tools: [{
    type: "code_execution_20250825",
    name: "code_execution"
  }]
});
```
</CodeGroup>

### Create and edit files directly

Claude can create, view, and edit files directly in the sandbox using the file manipulation capabilities:

<CodeGroup>
```bash Shell
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": "Create a config.yaml file with database settings, then update the port from 5432 to 3306"
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```

```python Python
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Create a config.yaml file with database settings, then update the port from 5432 to 3306"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)
```

```typescript TypeScript
const response = await anthropic.beta.messages.create({
  model: "claude-sonnet-4-5",
  betas: ["code-execution-2025-08-25"],
  max_tokens: 4096,
  messages: [{
    role: "user",
    content: "Create a config.yaml file with database settings, then update the port from 5432 to 3306"
  }],
  tools: [{
    type: "code_execution_20250825",
    name: "code_execution"
  }]
});
```
</CodeGroup>

### Upload and analyze your own files

To analyze your own data files (CSV, Excel, images, etc.), upload them via the Files API and reference them in your request:

<Note>
Using the Files API with Code Execution requires two beta headers: `"anthropic-beta": "code-execution-2025-08-25,files-api-2025-04-14"`
</Note>

The Python environment can process various file types uploaded via the Files API, including:

- CSV
- Excel (.xlsx, .xls)
- JSON
- XML
- Images (JPEG, PNG, GIF, WebP)
- Text files (.txt, .md, .py, etc)

#### Upload and analyze files

1. **Upload your file** using the [Files API](/docs/en/build-with-claude/files)
2. **Reference the file** in your message using a `container_upload` content block
3. **Include the code execution tool** in your API request

<CodeGroup>
```bash Shell
# First, upload a file
curl https://api.anthropic.com/v1/files \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: files-api-2025-04-14" \
    --form 'file=@"data.csv"' \

# Then use the file_id with code execution
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25,files-api-2025-04-14" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze this CSV data"},
                {"type": "container_upload", "file_id": "file_abc123"}
            ]
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```

```python Python
import anthropic

client = anthropic.Anthropic()

# Upload a file
file_object = client.beta.files.upload(
    file=open("data.csv", "rb"),
)

# Use the file_id with code execution
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25", "files-api-2025-04-14"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Analyze this CSV data"},
            {"type": "container_upload", "file_id": file_object.id}
        ]
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)
```

```typescript TypeScript
import { Anthropic } from '@anthropic-ai/sdk';
import { createReadStream } from 'fs';

const anthropic = new Anthropic();

async function main() {
  // Upload a file
  const fileObject = await anthropic.beta.files.create({
    file: createReadStream("data.csv"),
  });

  // Use the file_id with code execution
  const response = await anthropic.beta.messages.create({
    model: "claude-sonnet-4-5",
    betas: ["code-execution-2025-08-25", "files-api-2025-04-14"],
    max_tokens: 4096,
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Analyze this CSV data" },
        { type: "container_upload", file_id: fileObject.id }
      ]
    }],
    tools: [{
      type: "code_execution_20250825",
      name: "code_execution"
    }]
  });

  console.log(response);
}

main().catch(console.error);
```
</CodeGroup>

#### Retrieve generated files

When Claude creates files during code execution, you can retrieve these files using the Files API:

<CodeGroup>
```python Python
from anthropic import Anthropic

# Initialize the client
client = Anthropic()

# Request code execution that creates files
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25", "files-api-2025-04-14"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Create a matplotlib visualization and save it as output.png"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)

# Extract file IDs from the response
def extract_file_ids(response):
    file_ids = []
    for item in response.content:
        if item.type == 'bash_code_execution_tool_result':
            content_item = item.content
            if content_item.type == 'bash_code_execution_result':
                for file in content_item.content:
                    if hasattr(file, 'file_id'):
                        file_ids.append(file.file_id)
    return file_ids

# Download the created files
for file_id in extract_file_ids(response):
    file_metadata = client.beta.files.retrieve_metadata(file_id)
    file_content = client.beta.files.download(file_id)
    file_content.write_to_file(file_metadata.filename)
    print(f"Downloaded: {file_metadata.filename}")
```

```typescript TypeScript
import { Anthropic } from '@anthropic-ai/sdk';
import { writeFileSync } from 'fs';

// Initialize the client
const anthropic = new Anthropic();

async function main() {
  // Request code execution that creates files
  const response = await anthropic.beta.messages.create({
    model: "claude-sonnet-4-5",
    betas: ["code-execution-2025-08-25", "files-api-2025-04-14"],
    max_tokens: 4096,
    messages: [{
      role: "user",
      content: "Create a matplotlib visualization and save it as output.png"
    }],
    tools: [{
      type: "code_execution_20250825",
      name: "code_execution"
    }]
  });

  // Extract file IDs from the response
  function extractFileIds(response: any): string[] {
    const fileIds: string[] = [];
    for (const item of response.content) {
      if (item.type === 'bash_code_execution_tool_result') {
        const contentItem = item.content;
        if (contentItem.type === 'bash_code_execution_result' && contentItem.content) {
          for (const file of contentItem.content) {
            fileIds.push(file.file_id);
          }
        }
      }
    }
    return fileIds;
  }

  // Download the created files
  const fileIds = extractFileIds(response);
  for (const fileId of fileIds) {
    const fileMetadata = await anthropic.beta.files.retrieveMetadata(fileId);
    const fileContent = await anthropic.beta.files.download(fileId);

    // Convert ReadableStream to Buffer and save
    const chunks: Uint8Array[] = [];
    for await (const chunk of fileContent) {
      chunks.push(chunk);
    }
    const buffer = Buffer.concat(chunks);
    writeFileSync(fileMetadata.filename, buffer);
    console.log(`Downloaded: ${fileMetadata.filename}`);
  }
}

main().catch(console.error);
```
</CodeGroup>

### Combine operations

A complex workflow using all capabilities:

<CodeGroup>
```bash Shell
# First, upload a file
curl https://api.anthropic.com/v1/files \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: files-api-2025-04-14" \
    --form 'file=@"data.csv"' \
    > file_response.json

# Extract file_id (using jq)
FILE_ID=$(jq -r '.id' file_response.json)

# Then use it with code execution
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25,files-api-2025-04-14" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": [
                {
                    "type": "text", 
                    "text": "Analyze this CSV data: create a summary report, save visualizations, and create a README with the findings"
                },
                {
                    "type": "container_upload", 
                    "file_id": "'$FILE_ID'"
                }
            ]
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```

```python Python
# Upload a file
file_object = client.beta.files.upload(
    file=open("data.csv", "rb"),
)

# Use it with code execution
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25", "files-api-2025-04-14"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Analyze this CSV data: create a summary report, save visualizations, and create a README with the findings"},
            {"type": "container_upload", "file_id": file_object.id}
        ]
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)

# Claude might:
# 1. Use bash to check file size and preview data
# 2. Use text_editor to write Python code to analyze the CSV and create visualizations
# 3. Use bash to run the Python code
# 4. Use text_editor to create a README.md with findings
# 5. Use bash to organize files into a report directory
```

```typescript TypeScript
// Upload a file
const fileObject = await anthropic.beta.files.create({
  file: createReadStream("data.csv"),
});

// Use it with code execution
const response = await anthropic.beta.messages.create({
  model: "claude-sonnet-4-5",
  betas: ["code-execution-2025-08-25", "files-api-2025-04-14"],
  max_tokens: 4096,
  messages: [{
    role: "user",
    content: [
      {type: "text", text: "Analyze this CSV data: create a summary report, save visualizations, and create a README with the findings"},
      {type: "container_upload", file_id: fileObject.id}
    ]
  }],
  tools: [{
    type: "code_execution_20250825",
    name: "code_execution"
  }]
});

// Claude might:
// 1. Use bash to check file size and preview data
// 2. Use text_editor to write Python code to analyze the CSV and create visualizations
// 3. Use bash to run the Python code
// 4. Use text_editor to create a README.md with findings
// 5. Use bash to organize files into a report directory
```
</CodeGroup>

## Tool definition

The code execution tool requires no additional parameters:

```json JSON
{
  "type": "code_execution_20250825",
  "name": "code_execution"
}
```

When this tool is provided, Claude automatically gains access to two sub-tools:
- `bash_code_execution`: Run shell commands
- `text_editor_code_execution`: View, create, and edit files, including writing code

## Response format

The code execution tool can return two types of results depending on the operation:

### Bash command response

```json
{
  "type": "server_tool_use",
  "id": "srvtoolu_01B3C4D5E6F7G8H9I0J1K2L3",
  "name": "bash_code_execution",
  "input": {
    "command": "ls -la | head -5"
  }
},
{
  "type": "bash_code_execution_tool_result",
  "tool_use_id": "srvtoolu_01B3C4D5E6F7G8H9I0J1K2L3",
  "content": {
    "type": "bash_code_execution_result",
    "stdout": "total 24\ndrwxr-xr-x 2 user user 4096 Jan 1 12:00 .\ndrwxr-xr-x 3 user user 4096 Jan 1 11:00 ..\n-rw-r--r-- 1 user user  220 Jan 1 12:00 data.csv\n-rw-r--r-- 1 user user  180 Jan 1 12:00 config.json",
    "stderr": "",
    "return_code": 0
  }
}
```

### File operation responses

**View file:**
```json
{
  "type": "server_tool_use",
  "id": "srvtoolu_01C4D5E6F7G8H9I0J1K2L3M4",
  "name": "text_editor_code_execution",
  "input": {
    "command": "view",
    "path": "config.json"
  }
},
{
  "type": "text_editor_code_execution_tool_result",
  "tool_use_id": "srvtoolu_01C4D5E6F7G8H9I0J1K2L3M4",
  "content": {
    "type": "text_editor_code_execution_result",
    "file_type": "text",
    "content": "{\n  \"setting\": \"value\",\n  \"debug\": true\n}",
    "numLines": 4,
    "startLine": 1,
    "totalLines": 4
  }
}
```

**Create file:**
```json
{
  "type": "server_tool_use",
  "id": "srvtoolu_01D5E6F7G8H9I0J1K2L3M4N5",
  "name": "text_editor_code_execution",
  "input": {
    "command": "create",
    "path": "new_file.txt",
    "file_text": "Hello, World!"
  }
},
{
  "type": "text_editor_code_execution_tool_result",
  "tool_use_id": "srvtoolu_01D5E6F7G8H9I0J1K2L3M4N5",
  "content": {
    "type": "text_editor_code_execution_result",
    "is_file_update": false
  }
}
```

**Edit file (str_replace):**
```json
{
  "type": "server_tool_use",
  "id": "srvtoolu_01E6F7G8H9I0J1K2L3M4N5O6",
  "name": "text_editor_code_execution",
  "input": {
    "command": "str_replace",
    "path": "config.json",
    "old_str": "\"debug\": true",
    "new_str": "\"debug\": false"
  }
},
{
  "type": "text_editor_code_execution_tool_result",
  "tool_use_id": "srvtoolu_01E6F7G8H9I0J1K2L3M4N5O6",
  "content": {
    "type": "text_editor_code_execution_result",
    "oldStart": 3,
    "oldLines": 1,
    "newStart": 3,
    "newLines": 1,
    "lines": ["-  \"debug\": true", "+  \"debug\": false"]
  }
}
```

### Results

All execution results include:
- `stdout`: Output from successful execution
- `stderr`: Error messages if execution fails
- `return_code`: 0 for success, non-zero for failure

Additional fields for file operations:
- **View**: `file_type`, `content`, `numLines`, `startLine`, `totalLines`
- **Create**: `is_file_update` (whether file already existed)
- **Edit**: `oldStart`, `oldLines`, `newStart`, `newLines`, `lines` (diff format)

### Errors

Each tool type can return specific errors:

**Common errors (all tools):**
```json
{
  "type": "bash_code_execution_tool_result",
  "tool_use_id": "srvtoolu_01VfmxgZ46TiHbmXgy928hQR",
  "content": {
    "type": "bash_code_execution_tool_result_error",
    "error_code": "unavailable"
  }
}
```

**Error codes by tool type:**

| Tool | Error Code | Description |
|------|-----------|-------------|
| All tools | `unavailable` | The tool is temporarily unavailable |
| All tools | `execution_time_exceeded` | Execution exceeded maximum time limit |
| All tools | `container_expired` | Container expired and is no longer available |
| All tools | `invalid_tool_input` | Invalid parameters provided to the tool |
| All tools | `too_many_requests` | Rate limit exceeded for tool usage |
| text_editor | `file_not_found` | File doesn't exist (for view/edit operations) |
| text_editor | `string_not_found` | The `old_str` not found in file (for str_replace) |

#### `pause_turn` stop reason

The response may include a `pause_turn` stop reason, which indicates that the API paused a long-running turn. You may
provide the response back as-is in a subsequent request to let Claude continue its turn, or modify the content if you
wish to interrupt the conversation.

## Containers

The code execution tool runs in a secure, containerized environment designed specifically for code execution, with a higher focus on Python.

### Runtime environment
- **Python version**: 3.11.12
- **Operating system**: Linux-based container
- **Architecture**: x86_64 (AMD64)

### Resource limits
- **Memory**: 5GiB RAM
- **Disk space**: 5GiB workspace storage
- **CPU**: 1 CPU

### Networking and security
- **Internet access**: Completely disabled for security
- **External connections**: No outbound network requests permitted
- **Sandbox isolation**: Full isolation from host system and other containers
- **File access**: Limited to workspace directory only
- **Workspace scoping**: Like [Files](/docs/en/build-with-claude/files), containers are scoped to the workspace of the API key
- **Expiration**: Containers expire 30 days after creation

### Pre-installed libraries
The sandboxed Python environment includes these commonly used libraries:
- **Data Science**: pandas, numpy, scipy, scikit-learn, statsmodels
- **Visualization**: matplotlib, seaborn
- **File Processing**: pyarrow, openpyxl, xlsxwriter, xlrd, pillow, python-pptx, python-docx, pypdf, pdfplumber, pypdfium2, pdf2image, pdfkit, tabula-py, reportlab[pycairo], Img2pdf
- **Math & Computing**: sympy, mpmath
- **Utilities**: tqdm, python-dateutil, pytz, joblib, unzip, unrar, 7zip, bc, rg (ripgrep), fd, sqlite

## Container reuse

You can reuse an existing container across multiple API requests by providing the container ID from a previous response.
This allows you to maintain created files between requests.

### Example

<CodeGroup>
```python Python
import os
from anthropic import Anthropic

# Initialize the client
client = Anthropic(
    api_key=os.getenv("ANTHROPIC_API_KEY")
)

# First request: Create a file with a random number
response1 = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Write a file with a random number and save it to '/tmp/number.txt'"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)

# Extract the container ID from the first response
container_id = response1.container.id

# Second request: Reuse the container to read the file
response2 = client.beta.messages.create(
    container=container_id,  # Reuse the same container
    model="claude-sonnet-4-5",
    betas=["code-execution-2025-08-25"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Read the number from '/tmp/number.txt' and calculate its square"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)
```

```typescript TypeScript
import { Anthropic } from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

async function main() {
  // First request: Create a file with a random number
  const response1 = await anthropic.beta.messages.create({
    model: "claude-sonnet-4-5",
    betas: ["code-execution-2025-08-25"],
    max_tokens: 4096,
    messages: [{
      role: "user",
      content: "Write a file with a random number and save it to '/tmp/number.txt'"
    }],
    tools: [{
      type: "code_execution_20250825",
      name: "code_execution"
    }]
  });

  // Extract the container ID from the first response
  const containerId = response1.container.id;

  // Second request: Reuse the container to read the file
  const response2 = await anthropic.beta.messages.create({
    container: containerId,  // Reuse the same container
    model: "claude-sonnet-4-5",
    betas: ["code-execution-2025-08-25"],
    max_tokens: 4096,
    messages: [{
      role: "user",
      content: "Read the number from '/tmp/number.txt' and calculate its square"
    }],
    tools: [{
      type: "code_execution_20250825",
      name: "code_execution"
    }]
  });

  console.log(response2.content);
}

main().catch(console.error);
```

```bash Shell
# First request: Create a file with a random number
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25" \
    --header "content-type: application/json" \
    --data '{
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": "Write a file with a random number and save it to \"/tmp/number.txt\""
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }' > response1.json

# Extract container ID from the response (using jq)
CONTAINER_ID=$(jq -r '.container.id' response1.json)

# Second request: Reuse the container to read the file
curl https://api.anthropic.com/v1/messages \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01" \
    --header "anthropic-beta: code-execution-2025-08-25" \
    --header "content-type: application/json" \
    --data '{
        "container": "'$CONTAINER_ID'",
        "model": "claude-sonnet-4-5",
        "max_tokens": 4096,
        "messages": [{
            "role": "user",
            "content": "Read the number from \"/tmp/number.txt\" and calculate its square"
        }],
        "tools": [{
            "type": "code_execution_20250825",
            "name": "code_execution"
        }]
    }'
```
</CodeGroup>

## Streaming

With streaming enabled, you'll receive code execution events as they occur:

```javascript
event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "server_tool_use", "id": "srvtoolu_xyz789", "name": "code_execution"}}

// Code execution streamed
event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "input_json_delta", "partial_json": "{\"code\":\"import pandas as pd\\ndf = pd.read_csv('data.csv')\\nprint(df.head())\"}"}}

// Pause while code executes

// Execution results streamed
event: content_block_start
data: {"type": "content_block_start", "index": 2, "content_block": {"type": "code_execution_tool_result", "tool_use_id": "srvtoolu_xyz789", "content": {"stdout": "   A  B  C\n0  1  2  3\n1  4  5  6", "stderr": ""}}}
```

## Batch requests

You can include the code execution tool in the [Messages Batches API](/docs/en/build-with-claude/batch-processing). Code execution tool calls through the Messages Batches API are priced the same as those in regular Messages API requests.

## Usage and pricing

Code execution tool usage is tracked separately from token usage. Execution time has a minimum of 5 minutes.
If files are included in the request, execution time is billed even if the tool is not used due to files being preloaded onto the container.

Each organization receives 1,550 free hours of usage with the code execution tool per month. Additional usage beyond the first 1,550 hours is billed at $0.05 per hour, per container.

## Upgrade to latest tool version

By upgrading to `code-execution-2025-08-25`, you get access to file manipulation and Bash capabilities, including code in multiple languages. There is no price difference.

### What's changed

| Component | Legacy | Current |
|-----------|------------------|----------------------------|
| Beta header | `code-execution-2025-05-22` | `code-execution-2025-08-25` |
| Tool type | `code_execution_20250522` | `code_execution_20250825` |
| Capabilities | Python only | Bash commands, file operations |
| Response types | `code_execution_result` | `bash_code_execution_result`, `text_editor_code_execution_result` |

### Backward compatibility

- All existing Python code execution continues to work exactly as before
- No changes required to existing Python-only workflows

### Upgrade steps

To upgrade, you need to make the following changes in your API requests:

1. **Update the beta header**:
   ```diff
   - "anthropic-beta": "code-execution-2025-05-22"
   + "anthropic-beta": "code-execution-2025-08-25"
   ```

2. **Update the tool type**:
   ```diff
   - "type": "code_execution_20250522"
   + "type": "code_execution_20250825"
   ```

3. **Review response handling** (if parsing responses programmatically):
   - The previous blocks for Python execution responses will no longer be sent
   - Instead, new response types for Bash and file operations will be sent (see Response Format section)

## Programmatic tool calling

The code execution tool powers [programmatic tool calling](/docs/en/agents-and-tools/tool-use/programmatic-tool-calling), which allows Claude to write code that calls your custom tools programmatically within the execution container. This enables efficient multi-tool workflows, data filtering before reaching Claude's context, and complex conditional logic.

<CodeGroup>
```python Python
# Enable programmatic calling for your tools
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["advanced-tool-use-2025-11-20"],
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": "Get weather for 5 cities and find the warmest"
    }],
    tools=[
        {
            "type": "code_execution_20250825",
            "name": "code_execution"
        },
        {
            "name": "get_weather",
            "description": "Get weather for a city",
            "input_schema": {...},
            "allowed_callers": ["code_execution_20250825"]  # Enable programmatic calling
        }
    ]
)
```
</CodeGroup>

Learn more in the [Programmatic tool calling documentation](/docs/en/agents-and-tools/tool-use/programmatic-tool-calling).

## Using code execution with Agent Skills

The code execution tool enables Claude to use [Agent Skills](/docs/en/agents-and-tools/agent-skills/overview). Skills are modular capabilities consisting of instructions, scripts, and resources that extend Claude's functionality.

Learn more in the [Agent Skills documentation](/docs/en/agents-and-tools/agent-skills/overview) and [Agent Skills API guide](/docs/en/build-with-claude/skills-guide).