https://github.com/batou9150/adk_quickstart
A basic template and example to get started with building and deploying your own AI agents using Agent Development Kit (ADK).
https://github.com/batou9150/adk_quickstart
adk agents
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A basic template and example to get started with building and deploying your own AI agents using Agent Development Kit (ADK).
- Host: GitHub
- URL: https://github.com/batou9150/adk_quickstart
- Owner: batou9150
- Created: 2025-05-18T05:59:00.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-05T13:23:29.000Z (4 months ago)
- Last Synced: 2025-06-05T14:28:31.818Z (4 months ago)
- Topics: adk, agents
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ADK Quickstart 🚀
Welcome to ADK Quickstart ! This project provides a basic template and example to help you get started with building and deploying your own AI agents using Agent Development Kit (ADK).
ADK is a **flexible and modular framework** designed to simplify the development and deployment of AI agents. While optimized for Gemini and the Google ecosystem, ADK is **model-agnostic**, **deployment-agnostic**, and built for **compatibility with other frameworks**. It aims to make agent development feel more like traditional software development, enabling you to create, deploy, and orchestrate everything from simple agentic tasks to complex workflows.
-----
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
* Python (version 3.9+)
* Access to a Google Cloud project with Vertex AI APIs enabled.
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/batou9150/adk_quickstart.git
cd adk_quickstart
```
2. **Install dependencies using Poetry:**
```bash
poetry install
```
3. **Set up Google Cloud credentials:**
* At the top directory `multi_tool_agent/`, make a .env by copying .env.example
```bash
cp .env.example .env
# Open .env and add your configurations
```
* Set the following environment variables.
```
# Choose Model Backend: FALSE -> AI Studio, TRUE -> Vertex AI
GOOGLE_GENAI_USE_VERTEXAI=TRUE
# Vertex backend config
GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID
GOOGLE_CLOUD_LOCATION=LOCATION
```
4. **Activate the virtual environment set up by Poetry, run:**
```bash
eval $(poetry env activate)
```
### Running the Example Agent
To run the example agent, execute the main script:
```bash
adk web
```
Try the following inputs :
* `What time is it in New York?`
* `Is it raining in New York?`
## Deploy to Agent Engine
```python
from multi_tool_agent.agent import root_agent
import vertexai
from vertexai import agent_engines
from vertexai.preview.reasoning_engines import AdkApp
vertexai.init(
project="YOUR_PROJECT_ID",
location="LOCATION",
staging_bucket=f"gs://BUCKET_NAME",
)
app = AdkApp(
agent=root_agent,
enable_tracing=True,
)
remote_app = agent_engines.create(
app,
display_name="Weather & Time Agent",
description="A basic multi-tool agent",
requirements=[
"google-adk (>=1.2.0,<2.0.0)",
"google-cloud-aiplatform[adk,agent_engines] (>=1.96.0,<2.0.0)",
"google-genai (>=1.5.0,<2.0.0)",
"pydantic (>=2.10.6,<3.0.0)",
"cloudpickle (>=3.1.1)",
],
extra_packages=[
"./multi_tool_agent", # The main package
],
)
```
Test the remote agent :
```python
# remote_app = agent_engines.get('RESSOURCE_ID')
remote_session = remote_app.create_session(user_id='u_123')
remote_session
for event in remote_app.stream_query(
user_id="u_123",
session_id=remote_session["id"],
message="whats the weather in new york",
):
print(event)
```