Develop and deploy agents on Vertex AI Agent Engine

This page demonstrates how to create and deploy an agent that returns the exchange rate between two currencies on a specified date, using the following agent frameworks:

Before you begin

  1. 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.

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

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI and Cloud Storage APIs.

    Enable the APIs

    5.

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

    Go to project selector

  6. Make sure that billing is enabled for your Google Cloud project.

  7. Enable the Vertex AI and Cloud Storage APIs.

    Enable the APIs

    8.

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:

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:

    ADK 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,adk]

    LangGraph 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]

    LangChain 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]

    AG2 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]

    LlamaIndex 

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

  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
from vertexai import agent_engines

vertexai.init(
    project="PROJECT_ID",               # Your project ID.
    location="LOCATION",                # Your cloud region.
    staging_bucket="gs://BUCKET_NAME",  # Your staging bucket.
)

Develop an agent

First, develop a tool:

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()

Next, instantiate an agent:

ADK 

from google.adk.agents import Agent
from vertexai.preview.reasoning_engines import AdkApp

agent = Agent(
    model="gemini-2.0-flash",
    name='currency_exchange_agent',
    tools=[get_exchange_rate],
)

app = AdkApp(agent=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 

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,
)

Finally, test the agent locally:

ADK 

for event in app.stream_query(
    user_id="USER_ID",
    message="What is the exchange rate from US dollars to SEK today?",
):
    print(event)

where USER_ID is a user-defined ID with a character limit of 128.

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?"
)

Deploy an agent

To deploy the agent:

ADK

First, deploy the agent by running the following command:

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,adk]"],
)

Next, grant the required permissions to the deployed agent by following the instructions in Grant the deployed agent permissions.

LangGraph 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)

LangChain 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)

AG2 

from vertexai import agent_engines

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

LlamaIndex 

from vertexai import agent_engines

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

This creates a reasoningEngine resource in Vertex AI.

Use an agent

Test the deployed agent by sending a query:

ADK 

for event in remote_app.stream_query(
    user_id="USER_ID",
    message="What is the exchange rate from US dollars to SEK today?",
):

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?"
)

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