Create a developer folder and add samples.

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Hangfei Lin
2025-05-07 09:26:19 -07:00
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
name: Check Pyink Formatting
on:
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
name: Python Unit Tests
on:
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# Application Integration Agent Sample
## Introduction
This sample demonstrates how to use the `ApplicationIntegrationToolset` within an ADK agent to interact with external applications, specifically Jira in this case. The agent (`agent.py`) is configured to manage Jira issues using a pre-configured Application Integration connection.
## Prerequisites
1. **Set up Integration Connection:**
* You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create an JIRA connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection.
*
2. **Configure Environment Variables:**
* Create a `.env` file in the same directory as `agent.py` (or add to your existing one).
* Add the following variables to the `.env` file, replacing the placeholder values with your actual connection details:
```dotenv
CONNECTION_NAME=<YOUR_JIRA_CONNECTION_NAME>
CONNECTION_PROJECT=<YOUR_GOOGLE_CLOUD_PROJECT_ID>
CONNECTION_LOCATION=<YOUR_CONNECTION_LOCATION>
```
## How to Use
1. **Install Dependencies:** Ensure you have the necessary libraries installed (e.g., `google-adk`, `python-dotenv`).
2. **Run the Agent:** Execute the agent script from your terminal:
```bash
python agent.py
```
3. **Interact:** Once the agent starts, you can interact with it by typing prompts related to Jira issue management.
## Sample Prompts
Here are some examples of how you can interact with the agent:
* `Can you list me all the issues ?`
* `Can you list me all the projects ?`
* `Can you create an issue: "Bug in product XYZ" in project ABC ?`
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Sample agent using Application Integration toolset."""
import os
from dotenv import load_dotenv
from google.adk.agents.llm_agent import LlmAgent
from google.adk.tools.application_integration_tool import ApplicationIntegrationToolset
# Load environment variables from .env file
load_dotenv()
connection_name = os.getenv("CONNECTION_NAME")
connection_project = os.getenv("CONNECTION_PROJECT")
connection_location = os.getenv("CONNECTION_LOCATION")
jira_tool = ApplicationIntegrationToolset(
project=connection_project,
location=connection_location,
connection=connection_name,
entity_operations={"Issues": [], "Projects": []},
tool_name="jira_issue_manager",
)
root_agent = LlmAgent(
model="gemini-2.0-flash",
name="Issue_Management_Agent",
instruction="""
You are an agent that helps manage issues in a JIRA instance.
Be accurate in your responses based on the tool response. You can perform any formatting in the response that is appropriate or if asked by the user.
If there is an error in the tool response, understand the error and try and see if you can fix the error and then and execute the tool again. For example if a variable or parameter is missing, try and see if you can find it in the request or user query or default it and then execute the tool again or check for other tools that could give you the details.
If there are any math operations like count or max, min in the user request, call the tool to get the data and perform the math operations and then return the result in the response. For example for maximum, fetch the list and then do the math operation.
""",
tools=jira_tool.get_tools(),
)
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from google.adk import Agent
from google.adk.tools.tool_context import ToolContext
from google.genai import types
async def log_query(tool_context: ToolContext, query: str):
"""Roll a die with the specified number of sides."""
await tool_context.save_artifact('query', types.Part(text=query))
root_agent = Agent(
model='gemini-2.0-flash-exp',
name='log_agent',
description='Log user query.',
instruction="""Always log the user query and reploy "kk, I've logged."
""",
tools=[log_query],
generate_content_config=types.GenerateContentConfig(
safety_settings=[
types.SafetySetting( # avoid false alarm about rolling dice.
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold=types.HarmBlockThreshold.OFF,
),
]
),
)
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# OAuth Sample
## Introduction
This sample tests and demos the OAuth support in ADK via two tools:
* 1. bigquery_datasets_list:
List user's datasets.
* 2. bigquery_datasets_get:
Get a dataset's details.
* 3. bigquery_datasets_insert:
Create a new dataset.
* 4. bigquery_tables_list:
List all tables in a dataset.
* 5. bigquery_tables_get:
Get a table's details.
* 6. bigquery_tables_insert:
Insert a new table into a dataset.
## How to use
* 1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret.
Be sure to choose "web" as your client type.
* 2. Configure your .env file to add two variables:
* GOOGLE_CLIENT_ID={your client id}
* GOOGLE_CLIENT_SECRET={your client secret}
Note: done't create a separate .env , instead put it to the same .env file that stores your Vertex AI or Dev ML credentials
* 3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui to "Authorized redirect URIs".
Note: localhost here is just a hostname that you use to access the dev ui, replace it with the actual hostname you use to access the dev ui.
* 4. For 1st run, allow popup for localhost in Chrome.
## Sample prompt
* `Do I have any datasets in project sean-dev-agent ?`
* `Do I have any tables under it ?`
* `could you get me the details of this table ?`
* `Can you help to create a new dataset in the same project? id : sean_test , location: us`
* `could you show me the details of this new dataset ?`
* `could you create a new table under this dataset ? table name : sean_test_table. column1 : name is id , type is integer, required. column2 : name is info , type is string, required. column3 : name is backup , type is string, optional.`
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dotenv import load_dotenv
from google.adk import Agent
from google.adk.tools.google_api_tool import bigquery_tool_set
# Load environment variables from .env file
load_dotenv()
# Access the variable
oauth_client_id = os.getenv("OAUTH_CLIENT_ID")
oauth_client_secret = os.getenv("OAUTH_CLIENT_SECRET")
bigquery_tool_set.configure_auth(oauth_client_id, oauth_client_secret)
bigquery_datasets_list = bigquery_tool_set.get_tool("bigquery_datasets_list")
bigquery_datasets_get = bigquery_tool_set.get_tool("bigquery_datasets_get")
bigquery_datasets_insert = bigquery_tool_set.get_tool(
"bigquery_datasets_insert"
)
bigquery_tables_list = bigquery_tool_set.get_tool("bigquery_tables_list")
bigquery_tables_get = bigquery_tool_set.get_tool("bigquery_tables_get")
bigquery_tables_insert = bigquery_tool_set.get_tool("bigquery_tables_insert")
root_agent = Agent(
model="gemini-2.0-flash",
name="bigquery_agent",
instruction="""
You are a helpful Google BigQuery agent that help to manage users' data on Goolge BigQuery.
Use the provided tools to conduct various operations on users' data in Google BigQuery.
Scenario 1:
The user wants to query their biguqery datasets
Use bigquery_datasets_list to query user's datasets
Scenario 2:
The user wants to query the details of a specific dataset
Use bigquery_datasets_get to get a dataset's details
Scenario 3:
The user wants to create a new dataset
Use bigquery_datasets_insert to create a new dataset
Scenario 4:
The user wants to query their tables in a specific dataset
Use bigquery_tables_list to list all tables in a dataset
Scenario 5:
The user wants to query the details of a specific table
Use bigquery_tables_get to get a table's details
Scenario 6:
The user wants to insert a new table into a dataset
Use bigquery_tables_insert to insert a new table into a dataset
Current user:
<User>
{userInfo?}
</User>
""",
tools=[
bigquery_datasets_list,
bigquery_datasets_get,
bigquery_datasets_insert,
bigquery_tables_list,
bigquery_tables_get,
bigquery_tables_insert,
],
)
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data science agent."""
from google.adk.agents.llm_agent import Agent
from google.adk.tools import built_in_code_execution
def base_system_instruction():
"""Returns: data science agent system instruction."""
return """
# Guidelines
**Objective:** Assist the user in achieving their data analysis goals within the context of a Python Colab notebook, **with emphasis on avoiding assumptions and ensuring accuracy.** Reaching that goal can involve multiple steps. When you need to generate code, you **don't** need to solve the goal in one go. Only generate the next step at a time.
**Code Execution:** All code snippets provided will be executed within the Colab environment.
**Statefulness:** All code snippets are executed and the variables stays in the environment. You NEVER need to re-initialize variables. You NEVER need to reload files. You NEVER need to re-import libraries.
**Imported Libraries:** The following libraries are ALREADY imported and should NEVER be imported again:
```tool_code
import io
import math
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
```
**Output Visibility:** Always print the output of code execution to visualize results, especially for data exploration and analysis. For example:
- To look a the shape of a pandas.DataFrame do:
```tool_code
print(df.shape)
```
The output will be presented to you as:
```tool_outputs
(49, 7)
```
- To display the result of a numerical computation:
```tool_code
x = 10 ** 9 - 12 ** 5
print(f'{{x=}}')
```
The output will be presented to you as:
```tool_outputs
x=999751168
```
- You **never** generate ```tool_outputs yourself.
- You can then use this output to decide on next steps.
- Print just variables (e.g., `print(f'{{variable=}}')`.
**No Assumptions:** **Crucially, avoid making assumptions about the nature of the data or column names.** Base findings solely on the data itself. Always use the information obtained from `explore_df` to guide your analysis.
**Available files:** Only use the files that are available as specified in the list of available files.
**Data in prompt:** Some queries contain the input data directly in the prompt. You have to parse that data into a pandas DataFrame. ALWAYS parse all the data. NEVER edit the data that are given to you.
**Answerability:** Some queries may not be answerable with the available data. In those cases, inform the user why you cannot process their query and suggest what type of data would be needed to fulfill their request.
"""
root_agent = Agent(
model="gemini-2.0-flash-001",
name="data_science_agent",
instruction=base_system_instruction() + """
You need to assist the user with their queries by looking at the data and the context in the conversation.
You final answer should summarize the code and code execution relavant to the user query.
You should include all pieces of data to answer the user query, such as the table from code execution results.
If you cannot answer the question directly, you should follow the guidelines above to generate the next step.
If the question can be answered directly with writing any code, you should do that.
If you doesn't have enough data to answer the question, you should ask for clarification from the user.
You should NEVER install any package on your own like `pip install ...`.
When plotting trends, you should make sure to sort and order the data by the x-axis.
""",
tools=[built_in_code_execution],
)
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from google.adk import Agent
from pydantic import BaseModel
class WeahterData(BaseModel):
temperature: str
humidity: str
wind_speed: str
root_agent = Agent(
name='root_agent',
model='gemini-2.0-flash',
instruction="""\
Answer user's questions based on the data you have.
If you don't have the data, you can just say you don't know.
Here are the data you have for San Jose
* temperature: 26 C
* humidity: 20%
* wind_speed: 29 mph
Here are the data you have for Cupertino
* temperature: 16 C
* humidity: 10%
* wind_speed: 13 mph
""",
output_schema=WeahterData,
output_key='weather_data',
)
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
from google.adk import Agent
from google.adk.planners import BuiltInPlanner
from google.adk.planners import PlanReActPlanner
from google.adk.tools.tool_context import ToolContext
from google.genai import types
def roll_die(sides: int, tool_context: ToolContext) -> int:
"""Roll a die and return the rolled result.
Args:
sides: The integer number of sides the die has.
Returns:
An integer of the result of rolling the die.
"""
result = random.randint(1, sides)
if not 'rolls' in tool_context.state:
tool_context.state['rolls'] = []
tool_context.state['rolls'] = tool_context.state['rolls'] + [result]
return result
async def check_prime(nums: list[int]) -> str:
"""Check if a given list of numbers are prime.
Args:
nums: The list of numbers to check.
Returns:
A str indicating which number is prime.
"""
primes = set()
for number in nums:
number = int(number)
if number <= 1:
continue
is_prime = True
for i in range(2, int(number**0.5) + 1):
if number % i == 0:
is_prime = False
break
if is_prime:
primes.add(number)
return (
'No prime numbers found.'
if not primes
else f"{', '.join(str(num) for num in primes)} are prime numbers."
)
root_agent = Agent(
model='gemini-2.5-pro-preview-03-25',
# model='gemini-2.0-flash',
name='data_processing_agent',
description=(
'hello world agent that can roll a dice of 8 sides and check prime'
' numbers.'
),
instruction="""
You roll dice and answer questions about the outcome of the dice rolls.
You can roll dice of different sizes.
You can use multiple tools in parallel by calling functions in parallel(in one request and in one round).
It is ok to discuss previous dice roles, and comment on the dice rolls.
When you are asked to roll a die, you must call the roll_die tool with the number of sides. Be sure to pass in an integer. Do not pass in a string.
You should never roll a die on your own.
When checking prime numbers, call the check_prime tool with a list of integers. Be sure to pass in a list of integers. You should never pass in a string.
You should not check prime numbers before calling the tool.
When you are asked to roll a die and check prime numbers, you should always make the following two function calls:
1. You should first call the roll_die tool to get a roll. Wait for the function response before calling the check_prime tool.
2. After you get the function response from roll_die tool, you should call the check_prime tool with the roll_die result.
2.1 If user asks you to check primes based on previous rolls, make sure you include the previous rolls in the list.
3. When you respond, you must include the roll_die result from step 1.
You should always perform the previous 3 steps when asking for a roll and checking prime numbers.
You should not rely on the previous history on prime results.
""",
tools=[
roll_die,
check_prime,
],
planner=BuiltInPlanner(
thinking_config=types.ThinkingConfig(
include_thoughts=True,
),
),
# planner=PlanReActPlanner(),
generate_content_config=types.GenerateContentConfig(
safety_settings=[
types.SafetySetting( # avoid false alarm about rolling dice.
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold=types.HarmBlockThreshold.OFF,
),
]
),
)
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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import time
import warnings
import agent
from dotenv import load_dotenv
from google.adk import Runner
from google.adk.artifacts import InMemoryArtifactService
from google.adk.cli.utils import logs
from google.adk.sessions import InMemorySessionService
from google.adk.sessions import Session
from google.genai import types
load_dotenv(override=True)
warnings.filterwarnings('ignore', category=UserWarning)
logs.log_to_tmp_folder()
async def main():
app_name = 'my_app'
user_id_1 = 'user1'
session_service = InMemorySessionService()
artifact_service = InMemoryArtifactService()
runner = Runner(
app_name=app_name,
agent=agent.root_agent,
artifact_service=artifact_service,
session_service=session_service,
)
session_11 = session_service.create_session(app_name, user_id_1)
async def run_prompt(session: Session, new_message: str):
content = types.Content(
role='user', parts=[types.Part.from_text(text=new_message)]
)
print('** User says:', content.model_dump(exclude_none=True))
async for event in runner.run_async(
user_id=user_id_1,
session_id=session.id,
new_message=content,
):
if event.content.parts and event.content.parts[0].text:
print(f'** {event.author}: {event.content.parts[0].text}')
start_time = time.time()
print('Start time:', start_time)
print('------------------------------------')
await run_prompt(session_11, 'Hi')
await run_prompt(session_11, 'Roll a die.')
await run_prompt(session_11, 'Roll a die again.')
await run_prompt(session_11, 'What numbers did I got?')
end_time = time.time()
print('------------------------------------')
print('End time:', end_time)
print('Total time:', end_time - start_time)
def main_sync():
app_name = 'my_app'
user_id_1 = 'user1'
session_service = InMemorySessionService()
artifact_service = InMemoryArtifactService()
runner = Runner(
app_name=app_name,
agent=agent.root_agent,
artifact_service=artifact_service,
session_service=session_service,
)
session_11 = session_service.create_session(app_name, user_id_1)
def run_prompt(session: Session, new_message: str):
content = types.Content(
role='user', parts=[types.Part.from_text(text=new_message)]
)
print('** User says:', content.model_dump(exclude_none=True))
for event in runner.run_sync(
session=session,
new_message=content,
):
if event.content.parts and event.content.parts[0].text:
print(f'** {event.author}: {event.content.parts[0].text}')
start_time = time.time()
print('Start time:', start_time)
print('------------------------------------')
run_prompt(session_11, 'Hi')
run_prompt(session_11, 'Roll a die.')
run_prompt(session_11, 'Roll a die again.')
run_prompt(session_11, 'What numbers did I got?')
end_time = time.time()
print('------------------------------------')
print('End time:', end_time)
print('Total time:', end_time - start_time)
if __name__ == '__main__':
asyncio.run(main())
main_sync()
@@ -0,0 +1,15 @@
# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
@@ -0,0 +1,54 @@
# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from google.genai import Client
from google.genai import types
from google.adk import Agent
from google.adk.tools import load_artifacts
from google.adk.tools import ToolContext
# Only Vertex AI supports image generation for now.
client = Client()
async def generate_image(prompt: str, tool_context: 'ToolContext'):
"""Generates an image based on the prompt."""
response = client.models.generate_images(
model='imagen-3.0-generate-002',
prompt=prompt,
config={'number_of_images': 1},
)
if not response.generated_images:
return {'status': 'failed'}
image_bytes = response.generated_images[0].image.image_bytes
await tool_context.save_artifact(
'image.png',
types.Part.from_bytes(data=image_bytes, mime_type='image/png'),
)
return {
'status': 'success',
'detail': 'Image generated successfully and stored in artifacts.',
'filename': 'image.png',
}
root_agent = Agent(
model='gemini-2.0-flash-001',
name='root_agent',
description="""An agent that generates images and answer questions about the images.""",
instruction="""You are an agent whose job is to generate or edit an image based on the user's prompt.
""",
tools=[generate_image, load_artifacts],
)
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