Code execution tool 使 Grok 能够实时编写并执行 Python 代码，显著扩展文本生成之外的能力。借助该功能，Grok 可以进行精确计算、复杂数据分析、统计计算，并解决仅通过文本无法处理的数学问题。

主要能力

数学计算：求解复杂方程、执行统计分析并精确处理数值计算
数据分析：处理 dataset，并从 prompt 中提取洞察
金融建模：构建金融 model、计算风险指标并执行量化分析
科学计算：处理科学计算、simulation 和数据转换
代码生成与测试：实时编写、测试和调试 Python code snippet

何时使用 Code Execution

Code execution tool 特别适用于：

数值问题：需要精确计算而非近似值时
数据处理：分析 prompt 中的复杂数据
复杂逻辑：需要中间结果的多步计算
验证：复核数学结果或验证假设

SDK 支持

Code execution tool 可通过多个 SDK 和 API 使用，其命名约定有所不同：

SDK/API	Tool 名称	说明
xAI SDK	`code_execution`	xAI SDK native implementation
OpenAI Responses API	`code_interpreter`	兼容 OpenAI API 格式
Vercel AI SDK	`xai.tools.codeExecution()`	Vercel AI SDK 集成

所有与 Responses API 兼容的 SDK 也支持该 tool。

实现示例

以下完整示例展示如何在不同平台和使用场景中集成 code execution tool。

基础计算

import os

from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import code_execution

client = Client(api_key=os.getenv("XAI_API_KEY"))
chat = client.chat.create(
    model="grok-4.5",  # reasoning model
    tools=[code_execution()],
    include=["verbose_streaming"],
)

# Ask for a mathematical calculation
chat.append(user("Calculate the compound interest for $10,000 at 5% annually for 10 years"))

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nFinal Response:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)
print("\\n\\nServer Side Tool Calls:")
print(response.tool_calls)

import os
from openai import OpenAI

api_key = os.getenv("XAI_API_KEY")
client = OpenAI(
    api_key=api_key,
    base_url="https://api.x.ai/v1",
)

response = client.responses.create(
    model="grok-4.5",
    input=[
        {
            "role": "user",
            "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years",
        },
    ],
    tools=[
        {
            "type": "code_interpreter",
        },
    ],
)

print(response)

import os
import requests

url = "https://api.x.ai/v1/responses"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('XAI_API_KEY')}"
}
payload = {
    "model": "grok-4.5",
    "input": [
        {
            "role": "user",
            "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years"
        }
    ],
    "tools": [
        {
            "type": "code_interpreter",
        }
    ]
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())

curl https://api.x.ai/v1/responses \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer $XAI_API_KEY" \\
  -d '{
  "model": "grok-4.5",
  "input": [
    {
      "role": "user",
      "content": "Calculate the compound interest for $10,000 at 5% annually for 10 years"
    }
  ],
  "tools": [
    {
      "type": "code_interpreter"
    }
  ]
}'

import { xai } from '@ai-sdk/xai';
import { generateText } from 'ai';

const { text } = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: 'Calculate the compound interest for $10,000 at 5% annually for 10 years',
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
});

console.log(text);

数据分析

import os
from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import code_execution

client = Client(api_key=os.getenv("XAI_API_KEY"))

# Multi-turn conversation with data analysis
chat = client.chat.create(
    model="grok-4.5",  # reasoning model
    tools=[code_execution()],
    include=["verbose_streaming"],
)

# Step 1: Load and analyze data
chat.append(user("""
I have sales data for Q1-Q4: [120000, 135000, 98000, 156000].
Please analyze this data and create a visualization showing:
1. Quarterly trends
2. Growth rates
3. Statistical summary
"""))

print("##### Step 1: Data Analysis #####\\n")

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nAnalysis Results:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)

chat.append(response)

# Step 2: Follow-up analysis
chat.append(user("Now predict Q1 next year using linear regression"))

print("\\n\\n##### Step 2: Prediction Analysis #####\\n")

is_thinking = True
for response, chunk in chat.stream():
    # View the server-side tool calls as they are being made in real-time
    for tool_call in chunk.tool_calls:
        print(f"\\nCalling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
    if response.usage.reasoning_tokens and is_thinking:
        print(f"\\rThinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
    if chunk.content and is_thinking:
        print("\\n\\nPrediction Results:")
        is_thinking = False
    if chunk.content and not is_thinking:
        print(chunk.content, end="", flush=True)

print("\\n\\nCitations:")
print(response.citations)
print("\\n\\nUsage:")
print(response.usage)
print(response.server_side_tool_usage)
print("\\n\\nServer Side Tool Calls:")
print(response.tool_calls)

import { xai } from '@ai-sdk/xai';
import { generateText } from 'ai';

// Step 1: Load and analyze data
const step1 = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: \`I have sales data for Q1-Q4: [120000, 135000, 98000, 156000].
Please analyze this data and create a visualization showing:
1. Quarterly trends
2. Growth rates
3. Statistical summary\`,
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
});

console.log('##### Step 1: Data Analysis #####');
console.log(step1.text);

// Step 2: Follow-up analysis using previousResponseId
const step2 = await generateText({
  model: xai.responses('grok-4.5'),
  prompt: 'Now predict Q1 next year using linear regression',
  tools: {
    code_execution: xai.tools.codeExecution(),
  },
  providerOptions: {
    xai: {
      previousResponseId: step1.response.id,
    },
  },
});

console.log('##### Step 2: Prediction Analysis #####');
console.log(step2.text);

最佳实践

1. 在请求中明确说明

清晰、详细地说明希望代码完成的操作：

# Good: Specific and clear
"Calculate the correlation matrix for these variables and highlight correlations above 0.7"

# Avoid: Vague requests  
"Analyze this data"

2. 提供 Context 和数据格式

始终指定数据格式和所有数据约束，并尽可能提供充分的 context：

# Good: Includes data format and requirements
"""
Here's my CSV data with columns: date, revenue, costs
Please calculate monthly profit margins and identify the best-performing month.
Data: [['2024-01', 50000, 35000], ['2024-02', 55000, 38000], ...]
"""

3. 使用合适的 Model 设置

Temperature：数学计算请使用较低值（0.0-0.3）
Model：使用 grok-4.5 等 reasoning model，以获得更好的代码生成效果

常见使用场景

金融分析

# Portfolio optimization, risk calculations, option pricing
"Calculate the Sharpe ratio for a portfolio with returns [0.12, 0.08, -0.03, 0.15] and risk-free rate 0.02"

统计分析

# Hypothesis testing, regression analysis, probability distributions
"Perform a t-test to compare these two groups and interpret the p-value: Group A: [23, 25, 28, 30], Group B: [20, 22, 24, 26]"

科学计算

# Simulations, numerical methods, equation solving
"Solve this differential equation using numerical methods: dy/dx = x^2 + y, with initial condition y(0) = 1"

限制与注意事项

执行环境：代码在预装常用 library 的 sandboxed Python environment 中运行
时间限制：复杂计算可能受执行时间限制
内存使用：大型 dataset 可能达到内存限制
Package 可用性：支持大多数主流 Python package（NumPy、Pandas、Matplotlib、SciPy）
File I/O：出于安全原因，file system 访问受限

安全说明

代码在安全、隔离的环境中执行
无法访问外部网络或 file system
临时 execution context，不会在请求之间持久保存
所有计算均为 stateless 且安全

工具

Code Execution Tool