Develop and deploy agents on Agent Engine

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

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.

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.

   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

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

   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

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

7. Enable the Vertex AI and Cloud Storage APIs.

   Enable the 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

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]

   LangChain

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

   AG2

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

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 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()

What's next

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