Claude Code实战教程(14)：Agent SDK 构建 AI 应用——从工具使用者到产品创造者

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-sonnet-5",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "你好，请介绍一下你自己"}
    ]
)

print(message.content)

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "get_weather",
        "description": "获取指定城市的天气",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"}
            },
            "required": ["city"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-5",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "北京天气怎么样？"}
    ]
)

# 处理工具调用
if message.stop_reason == "tool_use":
    tool_result = call_tool(message.content[0].input)
    
    message = client.messages.create(
        model="claude-sonnet-5",
        max_tokens=1024,
        tools=tools,
        messages=[
            {"role": "user", "content": "北京天气怎么样？"},
            {"role": "assistant", "content": message.content},
            {"role": "user", "content": tool_result}
        ]
    )
    
    print(message.content[0].text)

import anthropic
import subprocess

def review_code(pr_diff):
    client = anthropic.Anthropic()
    
    message = client.messages.create(
        model="claude-opus-5",
        max_tokens=4096,
        messages=[
            {"role": "user", "content": f"""
                请审查以下 PR 的代码：
                
                {pr_diff}
                
                重点关注：
                1. 安全性问题
                2. 性能问题
                3. 代码规范
                4. 潜在 Bug
                
                输出格式化的审查报告
            """}
        ]
    )
    
    return message.content[0].text

# 使用示例
diff = subprocess.run(["git", "diff", "origin/main...HEAD"], capture_output=True).stdout
review = review_code(diff)
print(review)

from concurrent.futures import ThreadPoolExecutor
import anthropic

def analyze_security(diff):
    client = anthropic.Anthropic()
    # 安全审查逻辑
    ...

def analyze_performance(diff):
    client = anthropic.Anthropic()
    # 性能审查逻辑
    ...

def analyze_quality(diff):
    client = anthropic.Anthropic()
    # 代码质量审查逻辑
    ...

# 并行执行
with ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(
        lambda fn: fn(diff),
        [analyze_security, analyze_performance, analyze_quality]
    ))

ai-test-generator/
├── main.py
├── requirements.txt
└── templates/
    ├── jest.json
    └── pytest.json

import anthropic

class TestGenerator:
    def __init__(self, code):
        self.code = code
        self.client = anthropic.Anthropic()
    
    def generate_tests(self, framework="jest"):
        prompt = f"""
        分析以下代码并生成 {framework} 测试：
        
        ```
        {self.code}
        ```
        
        要求：
        1. 覆盖所有边界情况
        2. 包含正向和负向测试
        3. 使用 {framework} 最佳实践
        """
        
        message = self.client.messages.create(
            model="claude-sonnet-5",
            max_tokens=2048,
            messages=[{"role": "user", "content": prompt}]
        )
        
        return message.content[0].text

# 使用
code = """
def add(a, b):
    return a + b
"""

generator = TestGenerator(code)
tests = generator.generate_tests("pytest")
print(tests)

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "main.py"]

from fastapi import FastAPI
import anthropic

app = FastAPI()
client = anthropic.Anthropic()

@app.post("/analyze")
async def analyze_code(code: str):
    message = client.messages.create(
        model="claude-opus-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": f"分析以下代码：\n{code}"}]
    )
    return {"analysis": message.content[0].text}