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
)
- USER_ID
: Choose your own user ID with a character limit of 128.
For example,
user-123.
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
)
where USER_ID
is the user ID you defined. For example, user-123
.
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
-
USER_ID is the user ID you defined. For example,
user-123. -
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
)
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 )
