This page shows you how to develop an agent using the Agent Development Kit template (the AdkApp class in the Vertex AI SDK for Python). The agent returns the exchange rate between two currencies on a specified date.
Use the following steps:
- Define and configure a model
- (Optional) Define and use a tool
- (Optional) Manage sessions
- (Optional) Manage memories
Before you begin
Make sure your environment is set up by following the steps in Set up your environment.
Define and configure a model
Define the model version:
model = "gemini-2.0-flash"
(Optional) Configure the safety settings of the model. To learn more about the options available for safety settings in Gemini, see Configure safety attributes. The following is an example of how you can configure the safety settings:
from google.genai import types
safety_settings = [
types.SafetySetting(
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold=types.HarmBlockThreshold.OFF,
),
]
(Optional) Specify content generation parameters:
from google.genai import types
generate_content_config = types.GenerateContentConfig(
safety_settings=safety_settings,
temperature=0.28,
max_output_tokens=1000,
top_p=0.95,
)
Create an AdkApp using the model configurations:
from google.adk.agents import Agent
from vertexai.agent_engines import AdkApp
agent = Agent(
model=model, # Required.
name='currency_exchange_agent', # Required.
generate_content_config=generate_content_config, # Optional.
)
app = AdkApp(agent=agent)
If you are running in an interactive environment, such as the terminal or a Colab notebook, you can run a query as an intermediate testing step:
async for event in app.async_stream_query(
user_id="USER_ID", # Required
message="What is the exchange rate from US dollars to Swedish currency?",
):
print(event)
where USER_ID is a user-defined ID with a character limit of 128.
The response is a Python dictionary similar to the following example:
{'actions': {'artifact_delta': {},
'requested_auth_configs': {},
'state_delta': {}},
'author': 'currency_exchange_agent',
'content': {'parts': [{'text': 'To provide you with the most accurate '
'exchange rate, I need to know the specific '
'currencies you\'re asking about. "Swedish '
'currency" could refer to:\n'
'\n'
'* **Swedish Krona (SEK):** This is the '
'official currency of Sweden.\n'
'\n'
"Please confirm if you're interested in the "
'exchange rate between USD and SEK. Once you '
'confirm, I can fetch the latest exchange rate '
'for you.\n'}],
'role': 'model'},
'id': 'LYg7wg8G',
'invocation_id': 'e-113ca547-0f19-4d50-9dde-f76cbc001dce',
'timestamp': 1744166956.925927}
(Optional) Define and use a tool
After you define your model, define the tools that your model uses for reasoning.
When you define your function, it's important to include comments that fully and clearly describe the function's parameters, what the function does, and what the function returns. This information is used by the model to determine which function to use. You must also test your function locally to confirm that it works.
Use the following code to define a function that returns an exchange rate:
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.
Uses the Frankfurter API (https://api.frankfurter.app/) to obtain
exchange rate data.
Args:
currency_from: The base currency (3-letter currency code).
Defaults to "USD" (US Dollar).
currency_to: The target currency (3-letter currency code).
Defaults to "EUR" (Euro).
currency_date: The date for which to retrieve the exchange rate.
Defaults to "latest" for the most recent exchange rate data.
Can be specified in YYYY-MM-DD format for historical rates.
Returns:
dict: A dictionary containing the exchange rate information.
Example: {"amount": 1.0, "base": "USD", "date": "2023-11-24",
"rates": {"EUR": 0.95534}}
"""
import requests
response = requests.get(
f"https://api.frankfurter.app/{currency_date}",
params={"from": currency_from, "to": currency_to},
)
return response.json()
To test the function before you use it in your agent, run the following:
get_exchange_rate(currency_from="USD", currency_to="SEK")
The response should be similar to the following:
{'amount': 1.0, 'base': 'USD', 'date': '2025-04-03', 'rates': {'SEK': 9.6607}}
To use the tool inside the AdkApp, add it to the list of tools under
the tools= argument:
from google.adk.agents import Agent
agent = Agent(
model=model, # Required.
name='currency_exchange_agent', # Required.
tools=[get_exchange_rate], # Optional.
)
You can test the agent locally by performing test queries against it. Run the following command to test the agent locally using US dollars and Swedish Krona:
from vertexai.agent_engines import AdkApp
app = AdkApp(agent=agent)
async for event in app.async_stream_query(
user_id="USER_ID",
message="What is the exchange rate from US dollars to SEK on 2025-04-03?",
):
print(event)
The response is a sequence of dictionaries that's similar to the following:
{'author': 'currency_exchange_agent',
'content': {'parts': [{'function_call': {'args': {'currency_date': '2025-04-03',
'currency_from': 'USD',
'currency_to': 'SEK'},
'id': 'adk-e39f3ba2-fa8c-4169-a63a-8e4c62b89818',
'name': 'get_exchange_rate'}}],
'role': 'model'},
'id': 'zFyIaaif',
# ...
}
{'author': 'currency_exchange_agent',
'content': {'parts': [{'function_response': {'id': 'adk-e39f3ba2-fa8c-4169-a63a-8e4c62b89818',
'name': 'get_exchange_rate',
'response': {'amount': 1.0,
'base': 'USD',
'date': '2025-04-03',
'rates': {'SEK': 9.6607}}}}],
'role': 'user'},
'id': 'u2YR4Uom',
# ...
}
{'author': 'currency_exchange_agent',
'content': {'parts': [{'text': 'The exchange rate from USD to SEK on '
'2025-04-03 is 9.6607.'}],
'role': 'model'},
'id': 'q3jWA3wl',
# ...
}
(Optional) Manage sessions
AdkApp uses in-memory sessions when running locally and uses cloud-based managed sessions after you deploy the agent to Vertex AI Agent Engine. This section describes how to configure your ADK agent to work with managed sessions.
(Optional) Customize your sessions database
If you want to override the default managed session service with your own database, you can define a session_service_builder function as follows:
def session_service_builder():
from google.adk.sessions import InMemorySessionService
return InMemorySessionService()
Pass your database to AdkApp as session_service_builder=:
from vertexai.agent_engines import AdkApp
app = AdkApp(
agent=agent, # Required.
session_service_builder=session_service_builder, # Optional.
)
Use the agent with sessions
When you run the AdkApp locally, the following instructions use in-memory sessions:
Create a session for your agent:
session = await app.async_create_session(user_id="USER_ID")
print(session)
The session is created as the dictionary representation of an ADK session object.
List sessions associated with your agent:
await app.async_list_sessions(user_id="USER_ID")
Get a particular session:
session = await app.async_get_session(user_id="USER_ID", session_id="SESSION_ID")
where SESSION_ID is the ID for the particular session you want to retrieve.
Query the AdkApp using sessions:
async for event in app.async_stream_query(
user_id="USER_ID",
session_id=SESSION_ID, # Optional. you can pass in the session_id when querying the agent
message="What is the exchange rate from US dollars to Swedish currency on 2025-04-03?",
):
print(event)
where USER_ID is a user-defined ID with a character limit of
128. The agent might respond with a request for information like the following:
{'author': 'currency_exchange_agent',
'content': {'parts': [{'text': 'I need to know the Swedish currency code to '
'provide you with the exchange rate.'}],
'role': 'model'},
'id': 'wIgZAtQ4',
#...
}
You can send a response (for example, "SEK") on behalf of
USER_ID within the session corresponding to session
by specifying:
async for event in app.async_stream_query(
user_id="USER_ID",
session_id=session.id, # Optional. you can pass in the session_id when querying the agent
message="SEK",
):
print(event)
You should receive a continuation of the conversation like the following sequence of dictionaries:
{'author': 'currency_exchange_agent',
'content': {'parts': [{'function_call': {'args': {'currency_date': '2025-04-03',
'currency_from': 'USD',
'currency_to': 'SEK'},
'id': 'adk-2b9230a6-4b92-4a1b-9a65-b708ff6c68b6',
'name': 'get_exchange_rate'}}],
'role': 'model'},
'id': 'bOPHtzji',
# ...
}
{'author': 'currency_exchange_agent',
'content': {'parts': [{'function_response': {'id': 'adk-2b9230a6-4b92-4a1b-9a65-b708ff6c68b6',
'name': 'get_exchange_rate',
'response': {'amount': 1.0,
'base': 'USD',
'date': '2025-04-03',
'rates': {'SEK': 9.6607}}}}],
'role': 'user'},
'id': '9AoDFmiL',
# ...
}
{'author': 'currency_exchange_agent',
'content': {'parts': [{'text': 'The exchange rate from USD to SEK on '
'2025-04-03 is 1 USD to 9.6607 SEK.'}],
'role': 'model'},
'id': 'hmle7trT',
# ...
}
(Optional) Manage memories
By default, AdkApp
uses an in-memory implementation of agentic memory when running locally and uses
Vertex AI Agent Engine Memory Bank
after you deploy the agent to Vertex AI Agent Engine.
When developing your ADK agent, you can include a PreloadMemoryTool that
controls when the agent retrieves memories and how memories are included in the
prompt. The following example agent always retrieves memories at the start of each turn
and includes the memories in the system instruction:
from google import adk
from vertexai.agent_engines import AdkApp
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()],
)
app = AdkApp(agent=agent)
(Optional) Customize your memory service
If you want to override the default memory service, you can define a
memory_service_builder function that returns a BaseMemoryService as follows:
def memory_service_builder():
from google.adk.memory import InMemoryMemoryService
return InMemoryMemoryService()
Pass your database to AdkApp as memory_service_builder=:
from vertexai.agent_engines import AdkApp
app = AdkApp(
agent=agent, # Required.
memory_service_builder=memory_service_builder, # Optional.
)
Use the agent with memories
Test your ADK agent with memories:
Create a session and interact with the agent:
initial_session = await app.async_create_session(user_id="USER_ID") async for event in app.async_stream_query( user_id="USER_ID", session_id=initial_session.id, message="Can you update the temperature to my preferred temperature?", ): print(event)Since there are no available memories during the first session and the agent does not know any user preferences, the agent may reply with a response such as "What is your preferred temperature?" You can respond with the following command:
async for event in app.async_stream_query( user_id="USER_ID", session_id=initial_session.id, message="I like it at 71 degrees", ): print(event)The agent might return with a response such as "Setting the temperature to 71 degrees Fahrenheit. Temperature successfully changed." The agent's response may vary depending on the model you used.
Generate memories from the session. To store information from the session for use in future sessions, use the
async_add_session_to_memorymethod:await app.async_add_session_to_memory(session=initial_session)Test that the agent has retained memory of the session (using
PreloadMemoryTool) by creating a new session and prompting the agent:new_session = await app.async_create_session(user_id="USER_ID") async for event in app.async_stream_query( user_id="USER_ID", session_id=initial_session.id, message="Fix the temperature!", ): print(event)The agent might return a response such as "setting temperature to 71 degrees. Is that correct?" The agent's response may vary depending on the model and memory service provider you used.
Use the
async_search_memorymethod to display the agent's memories:response = await app.async_search_memory( user_id="USER_ID", query="Fix the temperature!", ) print(response)
What's next
- Sessions overview
- Memory Bank overview
- Use an Agent Development Kit agent.
- Evaluate an agent.
- Deploy an agent.
- Troubleshoot developing an agent.
- Get support.