三大 SDK 横向对比：怎么选

# OpenAI Agents SDK
from agents import Agent, Runner

agent = Agent(name="助手", instructions="你是一个助手", model="gpt-4o-mini")
result = Runner.run_sync(agent, "你好")
print(result.final_output)

# Google Gemini SDK
from google import genai
client = genai.Client(api_key="...")
response = client.models.generate_content(model="gemini-2.0-flash", contents="你好")
print(response.text)

# Anthropic Claude SDK
import anthropic
client = anthropic.Anthropic(api_key="...")
msg = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "你好"}]
)
print(msg.content[0].text)

# OpenAI
agent = Agent(instructions="系统提示内容", ...)

# Gemini
config = types.GenerateContentConfig(system_instruction="系统提示内容")
client.models.generate_content(..., config=config)

# Anthropic
client.messages.create(system="系统提示内容", ...)

from agents import function_tool

@function_tool
def get_weather(city: str) -> str:
    """获取城市天气"""
    return f"{city}: 晴天 25°C"

agent = Agent(tools=[get_weather], ...)
result = Runner.run_sync(agent, "北京天气")
# Runner 自动处理工具调用循环，无需手动干预

# 方式一：手动定义 schema + 手动循环
tools = types.Tool(function_declarations=[
    types.FunctionDeclaration(name="get_weather", description="...", parameters=...)
])
response = client.models.generate_content(config=types.GenerateContentConfig(tools=[tools]))
# 检查 response.candidates[0].content.parts[0].function_call
# 手动执行，手动回传 function_response

# 方式二：自动 Function Calling（新版）
def get_weather(city: str) -> str:
    """获取城市天气"""  # docstring 自动变成工具描述
    return f"{city}: 晴天"

response = client.models.generate_content(
    tools=[get_weather],  # 直接传函数
    config=types.GenerateContentConfig(
        automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=False)
    )
)

tools = [{
    "name": "get_weather",
    "description": "获取城市天气",
    "input_schema": {
        "type": "object",
        "properties": {"city": {"type": "string", "description": "城市名"}},
        "required": ["city"]
    }
}]

# 必须手动写循环
while True:
    response = client.messages.create(tools=tools, messages=messages)
    if response.stop_reason == "tool_use":
        # 手动执行工具，构造 tool_result，追加到 messages
        ...
    else:
        return response.content[0].text

# OpenAI
async with Runner.run_streamed(agent, "...") as stream:
    async for event in stream.stream_events():
        print(event.delta, end="")

# Gemini
for chunk in client.models.generate_content_stream(model="...", contents="..."):
    print(chunk.text, end="")

# Anthropic
with client.messages.stream(model="...", messages=[...]) as stream:
    for text in stream.text_stream:
        print(text, end="")