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
- 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 theresourcemanager.projects.createpermission. Learn how to grant roles.
-
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 theserviceusage.services.enablepermission. Learn how to grant roles. -
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 theresourcemanager.projects.createpermission. Learn how to grant roles.
-
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 theserviceusage.services.enablepermission. Learn how to grant roles.
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
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]>=1.112LangGraph
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112LangChain
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112AG2
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]>=1.112LlamaIndex
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]>=1.112Authenticate 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 loginRun 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. )
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 import agent_engines
agent = Agent(
model="gemini-2.0-flash",
name='currency_exchange_agent',
tools=[get_exchange_rate],
)
app = agent_engines.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
remote_agent = client.agent_engines.create(
agent=app,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,adk]"],
}
)
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
from vertexai import agent_engines
remote_agent = agent_engines.create(
agent,
config={
"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
async for event in remote_agent.async_stream_query(
user_id="USER_ID",
message="What is the exchange rate from US dollars to SEK today?",
):
print(event)
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)