Develop and deploy agents on Agent Engine
This page demonstrates how to create and deploy an agent that returns the exchange rate between two currencies on a specified date.
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.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
To get the permissions that you need to use 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:
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]
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
Run the following code to import 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:
LangGraph
from vertexai.preview.reasoning_engines import LanggraphAgent
agent = LanggraphAgent(
model="gemini-1.5-flash-001",
tools=[get_exchange_rate],
model_kwargs={
"temperature": 0.28,
"max_output_tokens": 1000,
"top_p": 0.95,
},
)
LangChain
from vertexai.preview.reasoning_engines import LangchainAgent
agent = LangchainAgent(
model="gemini-1.5-flash-001",
tools=[get_exchange_rate],
model_kwargs={
"temperature": 0.28,
"max_output_tokens": 1000,
"top_p": 0.95,
},
)
AG2
from vertexai.preview.reasoning_engines import AG2Agent
agent = AG2Agent(
model="gemini-1.5-flash-001",
runnable_name="Get Exchange Rate Agent",
tools=[get_exchange_rate],
)
Finally, test the agent locally:
LangGraph
agent.query(input={"messages": [
("user", "What's the exchange rate from US dollars to Swedish currency?"),
]})
LangChain
agent.query(
input="What's the exchange rate from US dollars to Swedish currency?"
)
AG2
agent.query(
input="What's the exchange rate from US dollars to Swedish currency?"
)
Deploy an agent
To deploy the agent:
LangGraph
remote_agent = agent_engines.create(
agent,
requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)
LangChain
remote_agent = agent_engines.create(
agent,
requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)
AG2
remote_agent = agent_engines.create(
agent,
requirements=["google-cloud-aiplatform[agent_engines,ag2]"],
)
This creates a reasoningEngine
resource in Vertex AI.
Use an agent
Test the deployed agent by sending a query:
LangGraph
remote_agent.query(input={"messages": [
("user", "What's the exchange rate from US dollars to Swedish currency?"),
]})
LangChain
remote_agent.query(
input="What's the exchange rate from US dollars to Swedish currency?"
)
AG2
remote_agent.query(
input="What's the exchange rate from US dollars to Swedish currency?"
)
Clean up
To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.
remote_agent.delete()