Quickstart with Agent Development Kit

After you configure your Agent Development Kit (ADK) agent to use Vertex AI Agent Engine Sessions and Memory Bank, your agent automatically reads and writes memories and sessions for you.

For the quickstart using the REST API, see Quickstart with REST API.

This tutorial demonstrates how you can use Vertex AI Agent Engine Sessions and Memory Bank with the ADK to create and use sessions and long-term memories:

  1. Create your local ADK agent and runner.

  2. Interact with your agent to dynamically generate long-term memories that are accessible across sessions.

  3. Clean up.

Before you begin

To complete the steps demonstrated in this tutorial, you must first follow the steps in Set up for Memory Bank.

Set environment variables

To use the ADK, set your environment variables:

import os

os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "TRUE"
os.environ["GOOGLE_CLOUD_PROJECT"] = "PROJECT_ID"
os.environ["GOOGLE_CLOUD_LOCATION"] = "LOCATION"

Replace the following:

  • PROJECT_ID: Your project ID.
  • LOCATION: Your region. Only us-central1 is supported for Vertex AI Agent Engine Memory Bank.

Create your ADK agent

  1. When developing your ADK agent, include a Memory tool, which controls when the memory service is used and how memories are included in the prompt. The example agent uses the PreloadMemoryTool, which always retrieves memories at the start of each turn and includes the memories in the system instruction:

    from google import adk

agent = adk.Agent(
    model="gemini-2.0-flash",
    name='stateful_agent',
    instruction="""You are a Vehicle Voice Agent, designed to assist users with information and in-vehicle actions.

1.  **Direct Action:** If a user requests a specific vehicle function (e.g., "turn on the AC"), execute it immediately using the corresponding tool. You don't have the outcome of the actual tool execution, so provide a hypothetical tool execution outcome.
2.  **Information Retrieval:** Respond concisely to general information requests with your own knowledge (e.g., restaurant recommendation).
3.  **Clarity:** When necessary, try to seek clarification to better understand the user's needs and preference before taking an action.
4.  **Brevity:** Limit responses to under 30 words.
""",
    tools=[adk.tools.preload_memory_tool.PreloadMemoryTool()]
)

  2. Create a VertexAiMemoryBankService memory service, which the ADK runner uses for retrieving memories.

    from google.adk.memory import VertexAiMemoryBankService

agent_engine_id = agent_engine.api_resource.name.split("/")[-1]

memory_service = VertexAiMemoryBankService(
    project="PROJECT_ID",
    ___location="LOCATION",
    agent_engine_id=agent_engine_id
)

  3. Create an ADK runner, which orchestrates the execution of your agents, tools, and callbacks.

    from google.adk.sessions import VertexAiSessionService
from google.genai import types

# You can use any ADK session service.
session_service = VertexAiSessionService(
    project="PROJECT_ID",
    ___location="LOCATION",
    agent_engine_id=agent_engine_id
)

app_name="APP_NAME"
runner = adk.Runner(
    agent=agent,
    app_name=app_name,
    session_service=session_service,
    memory_service=memory_service
)

def call_agent(query, session, user_id):
  content = types.Content(role='user', parts=[types.Part(text=query)])
  events = runner.run(user_id=user_id, session_id=session, new_message=content)

  for event in events:
      if event.is_final_response():
          final_response = event.content.parts[0].text
          print("Agent Response: ", final_response)

    Replace the following:

    • APP_NAME: The name of your ADK app.

Interact with your agent

After defining your agent and setting up Sessions and Memory Bank, you can interact with your agent.

  1. Create your first session. Since there are no available memories during the first session with a user, the agent doesn't know any user preferences, such as their preferred temperature:

    session = await session_service.create_session(
    app_name=app_name,
    user_id="USER_ID"
)

call_agent(
    "Can you update the temperature to my preferred temperature?",
    session.id,
    "USER_ID"
)

# Agent response: "What is your preferred temperature?"
call_agent("I like it at 71 degrees", session.id, "USER_ID")
# Agent Response:  Setting the temperature to 71 degrees Fahrenheit.
# Temperature successfully changed.

    Replace the following:

    • USER_ID: An identifier for your user. Memories generated from this session are keyed by this opaque identifier. The generated memories' scope is stored as {"user_id": "USER_ID"}.

  2. Generate memories for your current session. If Memory Bank extracts memories from the conversation, they are stored under the scope {"user_id": USER_ID, "app_name": APP_NAME}.

    session = await session_service.get_session(
    app_name=app_name,
    user_id="USER_ID",
    session_id=session.id
)
await memory_service.add_session_to_memory(session)

  3. Create your second session. If you used the PreloadMemoryTool, the agent retrieves memories at the beginning of each turn to access preferences the user previously communicated to the agent.

    session = await session_service.create_session(
    app_name=app_name,
    user_id="USER_ID"
)

call_agent("Fix the temperature!", session.id, "USER_ID")
# Agent Response:  Setting temperature to 71 degrees.  Is that correct?

Clean up

To clean up all resources used in this project, you can delete the Google Cloud project you used for the quickstart.

Otherwise, you can delete the individual resources you created in this tutorial, as follows:

  1. Use the following code sample to delete the Vertex AI Agent Engine instance, which also deletes any Sessions or Memories belonging to that Vertex AI Agent Engine.

    agent_engine.delete(force=True)

  2. Delete any locally created files.

What's next