在 Vertex AI Agent Engine 上开发和部署智能体

本页面演示了如何使用以下智能体框架创建智能体并将其部署到 Vertex AI Agent Engine 运行时：

本快速入门将引导您完成以下步骤：

设置 Google Cloud 项目。
安装 Vertex AI SDK for Python 和您选择的框架。
开发货币兑换代理。
将代理部署到 Vertex AI Agent Engine 运行时。
测试已部署的智能体。

如需了解使用智能体开发套件的快速入门，请参阅使用智能体开发套件在 Vertex AI Agent Engine 上开发和部署智能体。

准备工作

登录您的 Google Cloud 账号。如果您是 Google Cloud新手，请创建一个账号来评估我们的产品在实际场景中的表现。新客户还可获享 $300 赠金，用于运行、测试和部署工作负载。

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI and Cloud Storage APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the APIs

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI and Cloud Storage APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the APIs

如需获得使用 Vertex AI Agent Engine 所需的权限，请让您的管理员为您授予项目的以下 IAM 角色：

Vertex AI User (roles/aiplatform.user)
Storage Admin (roles/storage.admin)

如需详细了解如何授予角色，请参阅管理对项目、文件夹和组织的访问权限。

您也可以通过自定义角色或其他预定义角色来获取所需的权限。

安装并初始化 Python 版 Vertex AI SDK

运行以下命令以安装 Vertex AI SDK for Python 和其他所需的软件包：
LangGraph
```
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112
```
LangChain
```
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112
```
AG2
```
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]>=1.112
```
LlamaIndex

预览版 - Private Service Connect 接口

此功能受服务专用条款的“通用服务条款”部分中的“非正式版产品条款”的约束。非正式版功能“按原样”提供，可能只能获得有限的支持。如需了解详情，请参阅发布阶段说明。
```
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]>=1.112
```
以用户身份进行身份验证
Colab
运行以下代码：
```
from google.colab import auth

auth.authenticate_user(project_id="PROJECT_ID")
```
Cloud Shell
您无需执行任何操作。
本地 Shell
运行以下命令：
```
gcloud auth application-default login
```
运行以下代码以导入 Vertex AI Agent Engine 并初始化 SDK：
1. （可选）在测试您开发的代理之前，您需要导入 Vertex AI Agent Engine 并按如下方式初始化 SDK：
  Google Cloud 项目
  import vertexai vertexai.init( project="PROJECT_ID", # Your project ID. location="LOCATION", # Your cloud region. )
  其中：
  - PROJECT_ID 是您将在其中开发和部署代理的 Google Cloud 项目 ID
  - LOCATION 是支持的区域之一。
2. 在部署代理之前，您需要导入 Vertex AI Agent Engine 并按如下方式初始化 SDK：
  Google Cloud 项目
  import vertexai client = vertexai.Client( project="PROJECT_ID", # Your project ID. location="LOCATION", # Your cloud region. )
  其中：
  - PROJECT_ID 是您将在其中开发和部署代理的 Google Cloud 项目 ID
  - LOCATION 是支持的区域之一。

开发代理

为智能体开发货币兑换工具：

def get_exchange_rate(
    currency_from: str = "USD",
    currency_to: str = "EUR",
    currency_date: str = "latest",
):
    """Retrieves the exchange rate between two currencies on a specified date."""
    import requests

    response = requests.get(
        f"https://api.frankfurter.app/{currency_date}",
        params={"from": currency_from, "to": currency_to},
    )
    return response.json()

实例化代理：

LangGraph

from vertexai import agent_engines

agent = agent_engines.LanggraphAgent(
    model="gemini-2.0-flash",
    tools=[get_exchange_rate],
    model_kwargs={
        "temperature": 0.28,
        "max_output_tokens": 1000,
        "top_p": 0.95,
    },
)

LangChain

from vertexai import agent_engines

agent = agent_engines.LangchainAgent(
    model="gemini-2.0-flash",
    tools=[get_exchange_rate],
    model_kwargs={
        "temperature": 0.28,
        "max_output_tokens": 1000,
        "top_p": 0.95,
    },
)

AG2

from vertexai import agent_engines

agent = agent_engines.AG2Agent(
    model="gemini-2.0-flash",
    runnable_name="Get Exchange Rate Agent",
    tools=[get_exchange_rate],
)

LlamaIndex

from vertexai.preview import reasoning_engines

def runnable_with_tools_builder(model, runnable_kwargs=None, **kwargs):
    from llama_index.core.query_pipeline import QueryPipeline
    from llama_index.core.tools import FunctionTool
    from llama_index.core.agent import ReActAgent

    llama_index_tools = []
    for tool in runnable_kwargs.get("tools"):
        llama_index_tools.append(FunctionTool.from_defaults(tool))
    agent = ReActAgent.from_tools(llama_index_tools, llm=model, verbose=True)
    return QueryPipeline(modules = {"agent": agent})

agent = reasoning_engines.LlamaIndexQueryPipelineAgent(
    model="gemini-2.0-flash",
    runnable_kwargs={"tools": [get_exchange_rate]},
    runnable_builder=runnable_with_tools_builder,
)

在本地测试代理：

LangGraph

agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to SEK today?"),
]})

LangChain

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

AG2

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

LlamaIndex

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

部署代理

通过在 Vertex AI 中创建 reasoningEngine 资源来部署代理：

LangGraph

remote_agent = client.agent_engines.create(
    agent,
    config={
        "requirements": ["google-cloud-aiplatform[agent_engines,langchain]"],
    },
)

LangChain

remote_agent = client.agent_engines.create(
    agent,
    config={
        "requirements": ["google-cloud-aiplatform[agent_engines,langchain]"],
    },
)

AG2

remote_agent = client.agent_engines.create(
    agent,
    config={
        "requirements": ["google-cloud-aiplatform[agent_engines,ag2]"],
    },
)

LlamaIndex

remote_agent = client.agent_engines.create(
    agent,
    config={
        "requirements": ["google-cloud-aiplatform[agent_engines,llama_index]"],
    },
)

使用代理

通过发送查询来测试部署的智能体：

LangGraph

remote_agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to SEK today?"),
]})

LangChain

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

AG2

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

LlamaIndex

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

清理

为避免因本页中使用的资源导致您的 Google Cloud 账号产生费用，请按照以下步骤操作。

remote_agent.delete(force=True)

在 Vertex AI Agent Engine 上开发和部署智能体

准备工作

安装并初始化 Python 版 Vertex AI SDK

LangGraph

LangChain

AG2

LlamaIndex

Colab

Cloud Shell

本地 Shell

Google Cloud 项目

Google Cloud 项目

开发代理

LangGraph

LangChain

AG2

LlamaIndex

LangGraph

LangChain

AG2

LlamaIndex

部署代理

LangGraph

LangChain

AG2

LlamaIndex

使用代理

LangGraph

LangChain

AG2

LlamaIndex

清理

后续步骤