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:
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
}
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
template, 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'
,
# ...
}
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 agent locally, the following instructions use in-memory sessions:
Create a session for your agent:
session
=
await
app
.
async_create_session
(
user_id
=
" USER_ID
"
)
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 agent 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 within the session (for example, "SEK"
) by specifying
the session_id
:
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'
,
# ...
}
