google__adk-docs/docs/live/get-started/streaming-python.md

Build live streaming agent with Python

With this quickstart, you'll learn to create a simple agent and use ADK Streaming to enable voice and video communication with it that is low-latency and bidirectional. We will install ADK, set up a basic "Google Search" agent, try running the agent with Streaming with adk web tool, and then explain how to build a simple asynchronous web app by yourself using ADK Streaming and FastAPI.

Note: This guide assumes you have experience using a terminal in Windows, Mac, and Linux environments.

Supported models for voice/video streaming

Voice and video streaming requires a Gemini model that supports the Live API. You can find the model ID(s) that support it in the documentation:

• Google AI Studio: Gemini Live API
• Agent Platform: Gemini Live API

1. Setup Environment & Install ADK

Create & Activate Virtual Environment (Recommended):

# Create
python3 -m venv .venv
# Activate (each new terminal)
# macOS/Linux: source .venv/bin/activate
# Windows CMD: .venv\Scripts\activate.bat
# Windows PowerShell: .venv\Scripts\Activate.ps1

Install ADK:

pip install google-adk

2. Project Structure

Create the following folder structure with empty files:

adk-streaming/  # Project folder
└── app/ # the web app folder
    ├── .env # Gemini API key
    └── google_search_agent/ # Agent folder
        ├── __init__.py # Python package
        └── agent.py # Agent definition

agent.py

Copy-paste the following code block into the agent.py file.

For model, please double-check the model ID as described earlier in the Models section.

from google.adk.agents import Agent
from google.adk.tools import google_search  # Import the tool

root_agent = Agent(
   # A unique name for the agent.
   name="basic_search_agent",
   # The Large Language Model (LLM) that agent will use.
   # Please fill in the latest model id that supports live from
   # https://adk.dev/live/get-started/streaming-python/#supported-models
   model="...",
   # A short description of the agent's purpose.
   description="Agent to answer questions using Google Search.",
   # Instructions to set the agent's behavior.
   instruction="You are an expert researcher. You always stick to the facts.",
   # Add google_search tool to perform grounding with Google search.
   tools=[google_search]
)

agent.py is where all your agent(s)' logic will be stored, and you must have a root_agent defined.

Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.

Copy-paste the following code block to __init__.py file.

from . import agent

3. Set up the platform

To run the agent, choose a platform from either Google AI Studio or Google Cloud Agent Platform:

=== "Gemini - Google AI Studio" 1. Get an API key from Google AI Studio. 2. Open the .env file located inside (app/) and copy-paste the following code.

    ```env title=".env"
    GOOGLE_GENAI_USE_ENTERPRISE=FALSE
    GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
    ```

3. Replace `PASTE_YOUR_ACTUAL_API_KEY_HERE` with your actual `API KEY`.

=== "Gemini - Google Cloud Agent Platform" 1. You need an existing Google Cloud account and a project. * Set up a Google Cloud project * Set up the gcloud CLI * Authenticate to Google Cloud, from the terminal by running gcloud auth login. * Enable the Agent Platform API. 2. Open the .env file located inside (app/). Copy-paste the following code and update the project ID and location.

    ```env title=".env"
    GOOGLE_GENAI_USE_ENTERPRISE=TRUE
    GOOGLE_CLOUD_PROJECT=PASTE_YOUR_ACTUAL_PROJECT_ID
    GOOGLE_CLOUD_LOCATION=us-central1
    ```

For more information on connecting to Google Cloud from ADK agents, see Connect to Google Cloud and Agent Platform.

4. Try the agent with adk web

Now it's ready to try the agent. Run the following command to launch the dev UI. First, make sure to set the current directory to app:

cd app

Also, set SSL_CERT_FILE variable with the following command. This is required for the voice and video tests later.

=== "OS X & Linux" bash export SSL_CERT_FILE=$(python3 -m certifi)

=== "Windows" powershell $env:SSL_CERT_FILE = (python3 -m certifi)

Then, run the dev UI:

adk web

!!!info "Note for Windows users"

When hitting the `_make_subprocess_transport NotImplementedError`, consider using `adk web --no-reload` instead.

!!! warning "Caution: ADK Web for development only"

ADK Web is ***not meant for use in production deployments***. You should
use ADK Web for development and debugging purposes only.

Open the URL provided (usually http://localhost:8000 or http://127.0.0.1:8000) directly in your browser. This connection stays entirely on your local machine. Select google_search_agent.

Try with voice and video

To try with voice, reload the web browser, click the microphone button to enable the voice input, and ask the the following questions in voice. The agent will use the google_search tool to get the latest information to answer those questions. You will hear the answer in voice in real-time.

• What is the weather in New York?
• What is the time in New York?
• What is the weather in Paris?
• What is the time in Paris?

To try with video, reload the web browser, click the camera button to enable the video input, and ask questions like "What do you see?". The agent will answer what they see in the video input.

Caveat

• You can not use text chat with the native-audio models. You will see errors when entering text messages on adk web.

Stop the tool

Stop adk web by pressing Ctrl-C on the console.

Note on ADK Streaming

Model callbacks (before_model_callback and after_model_callback) are not invoked on the streaming path; ADK only runs them on the run_async path. Agent callbacks (before_agent_callback, after_agent_callback) and tool callbacks (before_tool_callback, after_tool_callback) do run while streaming, as do LongRunningFunctionTool and ExampleTool. Of the workflow agents, only SequentialAgent supports streaming: LoopAgent and ParallelAgent raise NotImplementedError.

Congratulations! You've successfully created and interacted with your first Streaming agent using ADK!

Next steps: build custom streaming app

Build a custom server walks through the server and client code for a custom asynchronous web app built with ADK, enabling real-time, bidirectional audio and text communication. From there, Sessions and the streaming loop covers the application lifecycle in depth, and Events covers everything run_live() hands back to you.