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本指南将展示如何在 LangSmith 中追踪 Google Agent Development Kit (ADK) 智能体。您将为 ADK 应用配置自动追踪功能，以捕获智能体调用、工具调用和 LLM 交互。

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安装

使用您偏好的包管理器安装所需包： 
uv add langsmith[google-adk]

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设置

设置您的 API 密钥： 
export LANGSMITH_TRACING=true
export LANGSMITH_ENDPOINT=https://api.smith.langchain.com
export LANGSMITH_API_KEY=<your_langsmith_api_key>
export LANGSMITH_PROJECT=<your_langsmith_project>

export GOOGLE_API_KEY=<your_google_api_key>
要创建 Google API 密钥，请参考 Google AI Studio。

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配置追踪

要追踪 ADK 智能体，请使用 LangSmith SDK 中的 configure_google_adk() 函数。在创建任何 ADK 智能体之前，在应用启动时调用此函数一次： 
from langsmith.integrations.google_adk import configure_google_adk

configure_google_adk(
    project_name="my-adk-project",  # 可选：默认为 LANGSMITH_PROJECT 环境变量
)
该函数接受以下可选参数：
  • project_name：发送追踪数据的 LangSmith 项目。默认为 LANGSMITH_PROJECT 环境变量。
  • name：根追踪的名称。默认为 "google_adk.session"。
  • metadata：用于附加上下文的键值对字典。
  • tags：用于分类追踪的字符串列表。

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示例

此示例创建一个带有工具的天气智能体，然后在启用追踪的情况下运行它： 
import asyncio

from dotenv import load_dotenv  # Optional
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from langsmith.integrations.google_adk import configure_google_adk

load_dotenv()  # Optional


async def main():
    # Configure LangSmith tracing
    configure_google_adk()

    # Define a tool
    def get_weather(city: str) -> dict:
        """Get weather for a city."""
        return {"city": city, "temperature": "72°F", "conditions": "Sunny"}

    # Create the agent
    agent = Agent(
        name="weather_agent",
        model="gemini-2.0-flash",
        description="Provides weather information.",
        instruction="Use the get_weather tool to answer weather questions.",
        tools=[get_weather],
    )

    # Set up session and runner
    session_service = InMemorySessionService()
    session = await session_service.create_session(
        app_name="weather_app",
        user_id="user_123",
        session_id="session_456",
    )

    runner = Runner(
        agent=agent,
        app_name="weather_app",
        session_service=session_service,
    )

    # Run the agent
    async for event in runner.run_async(
        user_id="user_123",
        session_id=session.id,
        new_message=types.Content(
            role="user",
            parts=[types.Part(text="What's the weather in San Francisco?")],
        ),
    ):
        if event.is_final_response():
            print(event.content.parts[0].text)


if __name__ == "__main__":
    asyncio.run(main())

​
在 LangSmith 中查看追踪

运行应用后，您可以在 LangSmith UI 中查看追踪数据，包括：
  • 智能体调用：通过 ADK 智能体的完整流程
  • 工具调用：智能体进行的单个函数调用
  • LLM 交互：与 Gemini 模型的请求和响应
  • 多智能体工作流：来自顺序和并行智能体组合的追踪
显示 Google ADK 智能体执行的 LangSmith 追踪视图

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自定义元数据和标签

在配置追踪时添加元数据和标签，以分类和筛选追踪： 
from langsmith.integrations.google_adk import configure_google_adk

configure_google_adk(
    project_name="production-agents",
    metadata={
        "environment": "production",
        "team": "ml-platform",
    },
    tags=["adk", "weather", "v2"],
)

​
多智能体工作流

该集成会自动追踪多智能体工作流，包括顺序和并行智能体组合： 
import asyncio

from dotenv import load_dotenv  # Optional
from google.adk.agents import Agent, SequentialAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from langsmith.integrations.google_adk import configure_google_adk

load_dotenv()  # Optional


async def main():
    # Configure LangSmith tracing
    # Traces go to LANGSMITH_PROJECT env var by default.
    # Pass project_name="my-project" to override.
    configure_google_adk()

    # Create sub-agents
    translator = Agent(
        name="translator",
        model="gemini-2.0-flash",
        description="Translates text to English.",
    )

    summarizer = Agent(
        name="summarizer",
        model="gemini-2.0-flash",
        description="Summarizes text concisely.",
    )

    # Create a sequential agent that runs sub-agents in order
    pipeline = SequentialAgent(
        name="translate_and_summarize",
        sub_agents=[translator, summarizer],
        description="Translates text then summarizes it.",
    )

    # Set up and run
    session_service = InMemorySessionService()
    session = await session_service.create_session(
        app_name="pipeline_app",
        user_id="user_123",
        session_id="session_456",
    )

    runner = Runner(
        agent=pipeline,
        app_name="pipeline_app",
        session_service=session_service,
    )

    events = runner.run_async(
        user_id="user_123",
        session_id=session.id,
        new_message=types.Content(
            role="user",
            parts=[types.Part(text="Quelle est la plus haute tour de Paris?")],
        ),
    )

    async for event in events:
        if event.is_final_response():
            print(event.content.parts[0].text)


if __name__ == "__main__":
    asyncio.run(main())
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