fix: update gemini 2.0 flash to latest ver for Live API

docs/agents/custom-agents.md

@@ -111,7 +111,7 @@ This method orchestrates the sub-agents using standard Python async/await and co
 These are standard `LlmAgent` definitions, responsible for specific tasks. Their `output_key` parameter is crucial for placing results into the `session.state` where other agents or the custom orchestrator can access them.
 
 ```python
-GEMINI_FLASH = "gemini-2.0-flash-exp" # Define model constant
+GEMINI_FLASH = "gemini-2.0-flash" # Define model constant
 --8<-- "examples/python/snippets/agents/custom-agent/storyflow_agent.py:llmagents"
 ```
 

docs/agents/llm-agents.md

@@ -14,12 +14,12 @@ First, you need to establish what the agent *is* and what it's *for*.
 
 * **`description` (Optional, Recommended for Multi-Agent):** Provide a concise summary of the agent's capabilities. This description is primarily used by *other* LLM agents to determine if they should route a task to this agent. Make it specific enough to differentiate it from peers (e.g., "Handles inquiries about current billing statements," not just "Billing agent").
 
-* **`model` (Required):** Specify the underlying LLM that will power this agent's reasoning. This is a string identifier like `"gemini-2.0-flash-exp"`. The choice of model impacts the agent's capabilities, cost, and performance. See the [Models](models.md) page for available options and considerations.
+* **`model` (Required):** Specify the underlying LLM that will power this agent's reasoning. This is a string identifier like `"gemini-2.0-flash"`. The choice of model impacts the agent's capabilities, cost, and performance. See the [Models](models.md) page for available options and considerations.
 
 ```python
 # Example: Defining the basic identity
 capital_agent = LlmAgent(
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country."
     # instruction and tools will be added next
@@ -46,7 +46,7 @@ The `instruction` parameter is arguably the most critical for shaping an `LlmAge
 ```python
 # Example: Adding instructions
 capital_agent = LlmAgent(
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country.",
     instruction="""You are an agent that provides the capital city of a country.
@@ -84,7 +84,7 @@ def get_capital_city(country: str) -> str:
 
 # Add the tool to the agent
 capital_agent = LlmAgent(
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country.",
     instruction="""You are an agent that provides the capital city of a country... (previous instruction text)""",

docs/agents/models.md

@@ -13,46 +13,55 @@ The following sections guide you through using these methods based on your needs
 
 This is the most direct way to use Google's flagship models within ADK.
 
-**Integration Method:** Pass the model's identifier string directly to the `model` parameter of `LlmAgent`.
+**Integration Method:** Pass the model's identifier string directly to the `model` parameter of `LlmAgent` (or its alias, `Agent`).
 
 **Backend Options & Setup:**
 
-The `google-genai` library, used internally by ADK for Gemini, can connect through two backends:
+The `google-genai` library, used internally by ADK for Gemini, can connect through either Google AI Studio or Vertex AI.
 
-1. **Google AI Studio:**
-    * **Use Case:** Best for rapid prototyping and development.
-    * **Setup:** Typically requires an API key set as an environment variable:
+!!!note "Model support for voice/video streaming"
 
-    ```shell
-    export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
-    export GOOGLE_GENAI_USE_VERTEXAI=FALSE
-    ```
+    In order to use voice/video streaming in ADK, you will need to use Gemini models that support the Live API. You can find the **model ID(s)** that supports the Gemini Live API in the documentation:
+
+    - [Google AI Studio: Gemini Live API](https://ai.google.dev/gemini-api/docs/models#live-api)
+    - [Vertex AI: Gemini Live API](https://cloud.google.com/vertex-ai/generative-ai/docs/live-api)
+
+### Google AI Studio
+
+* **Use Case:** Google AI Studio is the easiest way to started with Gemini. All you need is the [API key](https://aistudio.google.com/app/apikey). Best for rapid prototyping and development.
+* **Setup:** Typically requires an API key set as an environment variable:
+
+```shell
+export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
+export GOOGLE_GENAI_USE_VERTEXAI=FALSE
+```
 
-    * **Models:** Find available models on the [Google AI for Developers site](https://ai.google.dev/gemini-api/docs/models).
+* **Models:** Find all available models on the [Google AI for Developers site](https://ai.google.dev/gemini-api/docs/models).
 
-2. **Vertex AI:**
-    * **Use Case:** Recommended for production applications, leveraging Google Cloud infrastructure.
-    * **Setup:**
-        * Authenticate using Application Default Credentials (ADC):
+### Vertex AI
 
-            ```shell
-            gcloud auth application-default login
-            ```
+* **Use Case:** Recommended for production applications, leveraging Google Cloud infrastructure. Gemini on Vertex AI supports enterprise-grade features, security, and compliance controls.
+* **Setup:**
+    * Authenticate using Application Default Credentials (ADC):
 
-        * Set your Google Cloud project and location:
+        ```shell
+        gcloud auth application-default login
+        ```
 
-            ```shell
-            export GOOGLE_CLOUD_PROJECT="YOUR_PROJECT_ID"
-            export GOOGLE_CLOUD_LOCATION="YOUR_VERTEX_AI_LOCATION" # e.g., us-central1
-            ```
+    * Set your Google Cloud project and location:
 
-        * Explicitly tell the library to use Vertex AI:
+        ```shell
+        export GOOGLE_CLOUD_PROJECT="YOUR_PROJECT_ID"
+        export GOOGLE_CLOUD_LOCATION="YOUR_VERTEX_AI_LOCATION" # e.g., us-central1
+        ```
 
-            ```shell
-            export GOOGLE_GENAI_USE_VERTEXAI=TRUE
-            ```
+    * Explicitly tell the library to use Vertex AI:
+
+        ```shell
+        export GOOGLE_GENAI_USE_VERTEXAI=TRUE
+        ```
 
-    * **Models:** Find available model IDs in the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models).
+* **Models:** Find available model IDs in the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models).
 
 **Example:**
 
@@ -62,7 +71,7 @@ from google.adk.agents import LlmAgent
 # --- Example using a stable Gemini Flash model ---
 agent_gemini_flash = LlmAgent(
     # Use the latest stable Flash model identifier
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     name="gemini_flash_agent",
     instruction="You are a fast and helpful Gemini assistant.",
     # ... other agent parameters

docs/agents/multi-agents.md

@@ -29,13 +29,13 @@ The foundation for structuring multi-agent systems is the parent-child relations
 from google.adk.agents import LlmAgent, BaseAgent
 
 # Define individual agents
-greeter = LlmAgent(name="Greeter", model="gemini-2.0-flash-exp")
+greeter = LlmAgent(name="Greeter", model="gemini-2.0-flash")
 task_doer = BaseAgent(name="TaskExecutor") # Custom non-LLM agent
 
 # Create parent agent and assign children via sub_agents
 coordinator = LlmAgent(
     name="Coordinator",
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     description="I coordinate greetings and tasks.",
     sub_agents=[ # Assign sub_agents here
         greeter,
@@ -196,7 +196,7 @@ image_tool = AgentTool(agent=image_agent) # Wrap the agent
 # Parent agent uses the AgentTool
 artist_agent = LlmAgent(
     name="Artist",
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     instruction="Create a prompt and use the ImageGen tool to generate the image.",
     tools=[image_tool] # Include the AgentTool
 )
@@ -229,7 +229,7 @@ support_agent = LlmAgent(name="Support", description="Handles technical support
 
 coordinator = LlmAgent(
     name="HelpDeskCoordinator",
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     instruction="Route user requests: Use Billing agent for payment issues, Support agent for technical problems.",
     description="Main help desk router.",
     # allow_transfer=True is often implicit with sub_agents in AutoFlow
@@ -317,15 +317,15 @@ summarizer = LlmAgent(name="Summarizer", description="Summarizes text.")
 # Mid-level agent combining tools
 research_assistant = LlmAgent(
     name="ResearchAssistant",
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     description="Finds and summarizes information on a topic.",
     tools=[AgentTool(agent=web_searcher), AgentTool(agent=summarizer)]
 )
 
 # High-level agent delegating research
 report_writer = LlmAgent(
     name="ReportWriter",
-    model="gemini-2.0-flash-exp",
+    model="gemini-2.0-flash",
     instruction="Write a report on topic X. Use the ResearchAssistant to gather information.",
     tools=[AgentTool(agent=research_assistant)]
     # Alternatively, could use LLM Transfer if research_assistant is a sub_agent

docs/get-started/quickstart-streaming.md

@@ -1,9 +1,16 @@
 # ADK Streaming Quickstart {#adk-streaming-quickstart}
 
-With this quickstart, you'll learn to create a simple agent and use ADK Streaming to enable audio and video communication with it. 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](https://fastapi.tiangolo.com/).
+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](https://fastapi.tiangolo.com/).
 
 **Note:** This guide assumes you have experience using a terminal in Windows, Mac, and Linux environments.
 
+## Supported models for voice/video streaming {#supported-models}
+
+In order to use voice/video streaming in ADK, you will need to use Gemini models that support the Live API. You can find the **model ID(s)** that supports the Gemini Live API in the documentation:
+
+- [Google AI Studio: Gemini Live API](https://ai.google.dev/gemini-api/docs/models#live-api)
+- [Vertex AI: Gemini Live API](https://cloud.google.com/vertex-ai/generative-ai/docs/live-api)
+
 ## 1. Setup Environment & Install ADK {#1.-setup-installation}
 
 Create & Activate Virtual Environment (Recommended):
@@ -38,7 +45,9 @@ adk-streaming/  # Project folder
 
 ### agent.py
 
-Copy-paste the following code block to the [`agent.py`](http://agent.py). Please note that ADK Streaming works with `gemini-2.0-flash-exp` model only.
+Copy-paste the following code block to the [`agent.py`](http://agent.py).
+
+For `model`, please double check the model ID as described earlier in the [Models section](#supported-models).
 
 ```py
 from google.adk.agents import Agent
@@ -48,7 +57,8 @@ root_agent = Agent(
    # A unique name for the agent.
    name="basic_search_agent",
    # The Large Language Model (LLM) that agent will use.
-   model="gemini-2.0-flash-exp",
+   model="gemini-2.0-flash-live-001", # Google AI Studio
+   #model="gemini-2.0-flash-live-preview-04-09" # Vertex AI Studio
    # A short description of the agent's purpose.
    description="Agent to answer questions using Google Search.",
    # Instructions to set the agent's behavior.

docs/get-started/quickstart.md

@@ -182,16 +182,25 @@ There are multiple ways to interact with your agent:
     ![adk-web-dev-ui-function-call.png](../assets/adk-web-dev-ui-function-call.png)
 
     **Step 5.** You can also enable your microphone and talk to your agent:
+
+    !!!note "Model support for voice/video streaming"
+
+        In order to use voice/video streaming in ADK, you will need to use Gemini models that support the Live API. You can find the **model ID(s)** that supports the Gemini Live API in the documentation:
+
+        - [Google AI Studio: Gemini Live API](https://ai.google.dev/gemini-api/docs/models#live-api)
+        - [Vertex AI: Gemini Live API](https://cloud.google.com/vertex-ai/generative-ai/docs/live-api)
+
+        You can then replace the `model` string in `root_agent` in the `agent.py` file you created earlier ([jump to section](#agentpy)). Your code should look something like:
+
+        ```py
+        root_agent = Agent(
+            name="weather_time_agent",
+            model="replace-me-with-model-id", #e.g. gemini-2.0-flash-live-001
+            ...
+        ```
 
     ![adk-web-dev-ui-audio.png](../assets/adk-web-dev-ui-audio.png)
 
-    !!!note "Model support"
-
-        Currently only `gemini-2.0-flash-exp` supports talking to your agent via
-        audio/video, and can be used either with your API key from Google AI
-        Studio or via
-        [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal-live-api).
-
 === "Terminal (adk run)"
 
     Run the following command, to chat with your Google Search agent.

docs/get-started/tutorial.md

@@ -142,10 +142,9 @@ os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "False"
 ### Define Model Constants for easier use
 
 ```
+MODEL_GEMINI_2_0_FLASH = "gemini-2.0-flash"
 
-MODEL_GEMINI_2_0_FLASH = "gemini-2.0-flash-exp"
-
-# Note: Specific model names might change. Refer to LiteLLM/Provider documentation.
+# Note: Specific model names might change. Refer to LiteLLM or the model provider's documentation.
 MODEL_GPT_4O = "openai/gpt-4o"
 MODEL_CLAUDE_SONNET = "anthropic/claude-3-sonnet-20240229"
 

docs/guides/responsible-agents.md

@@ -156,7 +156,7 @@ def validate_tool_params(
 
 # Hypothetical Agent setup
 root_agent = LlmAgent( # Use specific agent type
-    model='gemini-2.0-flash-exp',
+    model='gemini-2.0-flash',
     name='root_agent',
     instruction="...",
     before_tool_callback=validate_tool_params, # Assign the callback

docs/sessions/memory.md

@@ -85,7 +85,7 @@ This example demonstrates the basic flow using the `InMemory` services for simpl
     # --- Constants ---
     APP_NAME = "memory_example_app"
     USER_ID = "mem_user"
-    MODEL = "gemini-2.0-flash-exp" # Use a valid model
+    MODEL = "gemini-2.0-flash" # Use a valid model
 
     # --- Agent Definitions ---
     # Agent 1: Simple agent to capture information

docs/sessions/state.md

@@ -81,7 +81,7 @@ from google.genai.types import Content, Part
 # Define agent with output_key
 greeting_agent = LlmAgent(
     name="Greeter",
-    model="gemini-2.0-flash-exp", # Use a valid model
+    model="gemini-2.0-flash", # Use a valid model
     instruction="Generate a short, friendly greeting.",
     output_key="last_greeting" # Save response to state['last_greeting']
 )

docs/tools/google-cloud-tools.md

@@ -83,7 +83,7 @@ Note: this tutorial includes an agent creation. If you already have an agent, yo
     from .tools import sample_toolset
 
     root_agent = LlmAgent(
-        model='gemini-2.0-flash-exp',
+        model='gemini-2.0-flash',
         name='enterprise_assistant',
         instruction='Help user, leverage the tools you have access to',
         tools=sample_toolset.get_tools(),)
@@ -169,7 +169,7 @@ When running the agent, make sure to run adk web in project\_root\_folder
     from .tools import connector_tool
 
     root_agent = LlmAgent(
-    model='gemini-2.0-flash-exp',
+    model='gemini-2.0-flash',
     name='connector_agent',
     instruction="Help user, leverage the tools you have access to",
     tools=connector_tool.get_tools(),
@@ -217,7 +217,7 @@ When running the agent, make sure to run adk web in project\_root\_folder
     from .tools import integration_tool, connector_tool
 
     root_agent = LlmAgent(
-    model='gemini-2.0-flash-exp',
+    model='gemini-2.0-flash',
     name='integration_agent',
     instruction="Help user, leverage the tools you have access to",
     tools=integration_tool.get_tools(),

docs/tools/mcp-tools.md

@@ -234,7 +234,7 @@ async def get_agent_async():
   tools, exit_stack = await get_tools_async()
   print(f"Fetched {len(tools)} tools from MCP server.")
   root_agent = LlmAgent(
-      model='gemini-2.0-flash-exp', # Adjust if needed
+      model='gemini-2.0-flash', # Adjust if needed
       name='maps_assistant',
       instruction='Help user with mapping and directions using available tools.',
       tools=tools,

docs/tools/openapi-tools.md

@@ -70,7 +70,7 @@ Follow these steps to integrate an OpenAPI spec into your agent:
 
     my_agent = LlmAgent(
         name="api_interacting_agent",
-        model="gemini-2.0-flash-exp", # Or your preferred model
+        model="gemini-2.0-flash", # Or your preferred model
         tools=api_tools, # Pass the list of generated tools
         # ... other agent config ...
     )

docs/tools/third-party-tools.md

@@ -55,7 +55,7 @@ ADK provides the `LangchainTool` wrapper to integrate tools from the LangChain e
     # Define the ADK agent, including the wrapped tool
     my_agent = Agent(
         name="langchain_tool_agent",
-        model="gemini-2.0-flash-exp",
+        model="gemini-2.0-flash",
         description="Agent to answer questions using TavilySearch.",
         instruction="I can answer your questions by searching the internet. Just ask me anything!",
         tools=[adk_tavily_tool] # Add the wrapped tool here
@@ -125,7 +125,7 @@ ADK provides the `CrewaiTool` wrapper to integrate tools from the CrewAI library
     # Define the ADK agent
     my_agent = Agent(
         name="crewai_search_agent",
-        model="gemini-2.0-flash-exp",
+        model="gemini-2.0-flash",
         description="Agent to find recent news using the Serper search tool.",
         instruction="I can find the latest news for you. What topic are you interested in?",
         tools=[adk_serper_tool] # Add the wrapped tool here