Develop and deploy agents on Vertex AI Agent Engine

Develop and deploy agents on Vertex AI Agent Engine

This page demonstrates how to create and deploy an agent to Vertex AI Agent Engine Runtime using the following agent frameworks:

• LangGraph

• LangChain

• AG2

• LlamaIndex Query Pipeline (preview)

This quickstart guides you through the following steps:

• Set up your Google Cloud project.

• Install the Vertex AI SDK for Python and your chosen framework.

• Develop a currency exchange agent.

• Deploy the agent to Vertex AI Agent Engine Runtime.

• Test the deployed agent.

For the quickstart using Agent Development Kit, see Develop and deploy agents on Vertex AI Agent Engine with Agent Development Kit.

Before you begin

Note: To enable APIs, you need the serviceusage.services.enable permission. If you don't have this permission, ask your administrator to grant you the Service Usage Admin (roles/serviceusage.serviceUsageAdmin) role.

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

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

To get the permissions that you need to use Vertex AI Agent Engine, ask your administrator to grant you the following IAM roles on your project:

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

For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Install and initialize the Vertex AI SDK for Python

1. Run the following command to install the Vertex AI SDK for Python and other required packages:

   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

   Preview

   This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

   pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]>=1.112

2. Authenticate as a user

   Colab

   Run the following code:

   from google.colab import auth
   
   auth.authenticate_user(project_id="PROJECT_ID")
   

   Cloud Shell

   No action required.

   Local Shell

   Run the following command:

   gcloud auth application-default login

3. Run the following code to import Vertex AI Agent Engine and initialize the SDK:

   import vertexai
   
   client = vertexai.Client(
       project="PROJECT_ID",               # Your project ID.
       location="LOCATION",                # Your cloud region.
   )
   

   Where:

   • PROJECT_ID is the Google Cloud project ID under which you develop and deploy agents

   • LOCATION is one of the supported regions.

Develop an agent

1. Develop a currency exchange tool for your agent:

   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()
   

2. Instantiate an agent:

   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

   Preview

   This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

   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,
   )
   

3. Test the agent locally:

   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

   Preview

   This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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

Deploy an agent

1. Deploy the agent by creating a reasoningEngine resource in Vertex AI:

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

from vertexai import agent_engines

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

LlamaIndex

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

from vertexai import agent_engines

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

Use an agent

Test the deployed agent by sending a query:

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

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

remote_agent.delete(force=True)

What's next

• Explore the notebooks.
• Set up the environment.
• Develop an agent.
• Deploy an agent.
• Use an agent.
• Manage an agent.
• Get support.