Android-studio61 · pull · Apr 29, 2026 · Apr 28, 2026 · Apr 28, 2026 · Apr 28, 2026
diff --git a/docs/deploy/agent-runtime/deploy.md b/docs/deploy/agent-runtime/deploy.md
@@ -9,13 +9,13 @@ ADK agent code to Google Cloud
 [Agent Runtime](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview).
 You should follow this deployment path if you have an existing Google Cloud
 project and if you want to carefully manage deploying an ADK agent to Agent
-Engine runtime environment. These instructions use Cloud Console, the gcloud
+Runtime environment. These instructions use Cloud Console, the gcloud
 command line interface, and the ADK command line interface (ADK CLI). This path
 is recommended for users who are already familiar with configuring Google Cloud
 projects, and users preparing for production deployments.
 
 These instructions describe how to deploy an ADK project to Google Cloud Agent
-Engine runtime environment, which includes the following stages:
+Runtime environment, which includes the following stages:
 
 *   [Setup Google Cloud project](#setup-cloud-project)
 *   [Prepare agent project folder](#define-your-agent)

diff --git a/docs/deploy/agent-runtime/test.md b/docs/deploy/agent-runtime/test.md
@@ -289,7 +289,7 @@ async for event in remote_app.async_stream_query(
 
 If you have performed deployments as tests, it is a good practice to clean up
 your cloud resources after you have finished. You can delete the deployed Agent
-Engine instance to avoid any unexpected charges on your Google Cloud account.
+Runtime instance to avoid any unexpected charges on your Google Cloud account.
 
 ```python
 remote_app.delete(force=True)

diff --git a/docs/grounding/index.md b/docs/grounding/index.md
@@ -6,7 +6,7 @@ ADK supports multiple grounding approaches:
 
 - **Google Search Grounding**: Connect agents to real-time web information for queries requiring current data like news, weather, or facts that may have changed since the model's training.
 - **Grounding with Search**: Connect agents to your organization's private documents and enterprise data for queries requiring proprietary information.
-- **Agentic RAG**: Build agents that reason about how to search, constructing queries and filters dynamically using Vector Search 2.0, RAG Engine, or other retrieval systems.
+- **Agentic RAG**: Build agents that reason about how to search, constructing queries and filters dynamically using Agent Retrieval, Knowledge Engine, or other retrieval systems.
 
 <div class="grid cards" markdown>
 
@@ -54,7 +54,7 @@ ADK supports multiple grounding approaches:
 
     ---
 
-    A document Q&A agent powered by RAG Engine. Upload documents and ask questions to receive accurate answers with citations formatted as URLs pointing to source materials.
+    A document Q&A agent powered by Knowledge Engine. Upload documents and ask questions to receive accurate answers with citations formatted as URLs pointing to source materials.
 
     - [RAG Agent](https://github.com/google/adk-samples/tree/main/python/agents/RAG)
 

diff --git a/docs/integrations/adspirer.md b/docs/integrations/adspirer.md
@@ -88,7 +88,7 @@ agent can operate autonomously with built-in protections.
         from mcp import StdioServerParameters
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="advertising_agent",
             instruction=(
                 "You are an advertising agent that helps users create, manage, "
@@ -125,7 +125,7 @@ agent can operate autonomously with built-in protections.
         ADSPIRER_ACCESS_TOKEN = "YOUR_ADSPIRER_ACCESS_TOKEN"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="advertising_agent",
             instruction=(
                 "You are an advertising agent that helps users create, manage, "
@@ -157,7 +157,7 @@ agent can operate autonomously with built-in protections.
         import { LlmAgent, MCPToolset } from "@google/adk";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "advertising_agent",
             instruction:
                 "You are an advertising agent that helps users create, manage, " +
@@ -192,7 +192,7 @@ agent can operate autonomously with built-in protections.
         const ADSPIRER_ACCESS_TOKEN = "YOUR_ADSPIRER_ACCESS_TOKEN";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "advertising_agent",
             instruction:
                 "You are an advertising agent that helps users create, manage, " +

diff --git a/docs/integrations/agentmail.md b/docs/integrations/agentmail.md
@@ -52,7 +52,7 @@ language.
         AGENTMAIL_API_KEY = "YOUR_AGENTMAIL_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="agentmail_agent",
             instruction="Help users manage email inboxes and send messages",
             tools=[
@@ -85,7 +85,7 @@ language.
         const AGENTMAIL_API_KEY = "YOUR_AGENTMAIL_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "agentmail_agent",
             instruction: "Help users manage email inboxes and send messages",
             tools: [

diff --git a/docs/integrations/agentphone.md b/docs/integrations/agentphone.md
@@ -59,7 +59,7 @@ create autonomous AI voice agents using natural language.
         AGENTPHONE_API_KEY = "YOUR_AGENTPHONE_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="agentphone_agent",
             instruction="Help users make phone calls, send SMS, and manage phone numbers",
             tools=[
@@ -92,7 +92,7 @@ create autonomous AI voice agents using natural language.
         AGENTPHONE_API_KEY = "YOUR_AGENTPHONE_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="agentphone_agent",
             instruction="Help users make phone calls, send SMS, and manage phone numbers",
             tools=[
@@ -118,7 +118,7 @@ create autonomous AI voice agents using natural language.
         const AGENTPHONE_API_KEY = "YOUR_AGENTPHONE_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "agentphone_agent",
             instruction: "Help users make phone calls, send SMS, and manage phone numbers",
             tools: [
@@ -146,7 +146,7 @@ create autonomous AI voice agents using natural language.
         const AGENTPHONE_API_KEY = "YOUR_AGENTPHONE_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "agentphone_agent",
             instruction: "Help users make phone calls, send SMS, and manage phone numbers",
             tools: [

diff --git a/docs/integrations/asana.md b/docs/integrations/asana.md
@@ -44,7 +44,7 @@ tasks, goals, and team collaboration using natural language.
         from mcp import StdioServerParameters
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="asana_agent",
             instruction="Help users manage projects, tasks, and goals in Asana",
             tools=[
@@ -73,7 +73,7 @@ tasks, goals, and team collaboration using natural language.
         import { LlmAgent, MCPToolset } from "@google/adk";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "asana_agent",
             instruction: "Help users manage projects, tasks, and goals in Asana",
             tools: [

diff --git a/docs/integrations/atlassian.md b/docs/integrations/atlassian.md
@@ -48,7 +48,7 @@ collaboration workflows using natural language.
 
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="atlassian_agent",
             instruction="Help users work with data in Atlassian products",
             tools=[
@@ -77,7 +77,7 @@ collaboration workflows using natural language.
         import { LlmAgent, MCPToolset } from "@google/adk";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "atlassian_agent",
             instruction: "Help users work with data in Atlassian products",
             tools: [

diff --git a/docs/integrations/cartesia.md b/docs/integrations/cartesia.md
@@ -53,7 +53,7 @@ across languages, and create audio content using natural language.
         CARTESIA_API_KEY = "YOUR_CARTESIA_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="cartesia_agent",
             instruction="Help users generate speech and work with audio content",
             tools=[
@@ -84,7 +84,7 @@ across languages, and create audio content using natural language.
         const CARTESIA_API_KEY = "YOUR_CARTESIA_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "cartesia_agent",
             instruction: "Help users generate speech and work with audio content",
             tools: [

diff --git a/docs/integrations/chroma.md b/docs/integrations/chroma.md
@@ -56,7 +56,7 @@ search, and metadata filtering.
         # CHROMA_API_KEY = "your-api-key"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="chroma_agent",
             instruction="Help users store and retrieve information using semantic search",
             tools=[
@@ -105,7 +105,7 @@ search, and metadata filtering.
         // const CHROMA_API_KEY = "your-api-key";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "chroma_agent",
             instruction: "Help users store and retrieve information using semantic search",
             tools: [

diff --git a/docs/integrations/code-exec-agent-runtime.md b/docs/integrations/code-exec-agent-runtime.md
@@ -13,7 +13,7 @@ catalog_tags: ["code", "google"]
 
 The Agent Runtime Code Execution ADK Tool provides a low-latency, highly
 efficient method for running AI-generated code using the
-[Google Cloud Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview)
+[Google Cloud Agent Runtime](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview)
 service. This tool is designed for fast execution, tailored for agentic workflows,
 and uses sandboxed environments for improved security. The Code Execution tool
 allows code and data to persist over multiple requests, enabling complex,
@@ -28,7 +28,7 @@ This code execution tool is part of the Agent Runtime suite, however you do not
 have to deploy your agent to Agent Runtime to use it. You can run your agent
 locally or with other services and use this tool. For more information about the
 Code Execution feature in Agent Runtime, see the
-[Agent Engine Code Execution](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview)
+[Agent Runtime Code Execution](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview)
 documentation.
 
 

diff --git a/docs/integrations/couchbase.md b/docs/integrations/couchbase.md
@@ -56,7 +56,7 @@ issues.
         CB_PASSWORD = "password"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="couchbase_agent",
             instruction="Help users explore and query Couchbase databases",
             tools=[
@@ -91,7 +91,7 @@ issues.
         const CB_PASSWORD = "password";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "couchbase_agent",
             instruction: "Help users explore and query Couchbase databases",
             tools: [

diff --git a/docs/integrations/daytona.md b/docs/integrations/daytona.md
@@ -50,7 +50,7 @@ plugin = DaytonaPlugin(
 )
 
 root_agent = Agent(
-    model="gemini-2.5-pro",
+    model="gemini-flash-latest",
     name="sandbox_agent",
     instruction="Help users execute code and commands in a secure sandbox",
     tools=plugin.get_tools(),

diff --git a/docs/integrations/elevenlabs.md b/docs/integrations/elevenlabs.md
@@ -57,7 +57,7 @@ AI experiences using natural language.
         ELEVENLABS_API_KEY = "YOUR_ELEVENLABS_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="elevenlabs_agent",
             instruction="Help users generate speech, clone voices, and process audio",
             tools=[
@@ -87,7 +87,7 @@ AI experiences using natural language.
         const ELEVENLABS_API_KEY = "YOUR_ELEVENLABS_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "elevenlabs_agent",
             instruction: "Help users generate speech, clone voices, and process audio",
             tools: [

diff --git a/docs/integrations/firestore-session-service.md b/docs/integrations/firestore-session-service.md
@@ -154,7 +154,7 @@ public class YourAgentApplication {
                 You are a helpful assistant that tells the current time in a city.
                 Use the 'getCurrentTime' tool for this purpose.
                 \""")
-            .model("gemini-3.1-pro-preview")
+            .model("gemini-flash-latest")
             .tools(FunctionTool.create(YourAgentApplication.class, "getCurrentTime"))
             .build();
     }

diff --git a/docs/integrations/galileo.md b/docs/integrations/galileo.md
@@ -94,7 +94,7 @@ def get_current_time(city: str) -> dict:
 
 
 root_agent = Agent(
-    model="gemini-3-flash-preview",
+    model="gemini-flash-latest",
     name="root_agent",
     description="Tells the current time in a specified city.",
     instruction=(

diff --git a/docs/integrations/github.md b/docs/integrations/github.md
@@ -46,7 +46,7 @@ automate workflows using natural language.
         GITHUB_TOKEN = "YOUR_GITHUB_TOKEN"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="github_agent",
             instruction="Help users get information from GitHub",
             tools=[
@@ -74,7 +74,7 @@ automate workflows using natural language.
         const GITHUB_TOKEN = "YOUR_GITHUB_TOKEN";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "github_agent",
             instruction: "Help users get information from GitHub",
             tools: [

diff --git a/docs/integrations/gitlab.md b/docs/integrations/gitlab.md
@@ -56,7 +56,7 @@ searches, and automate development workflows using natural language.
         GITLAB_INSTANCE_URL = "gitlab.com"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="gitlab_agent",
             instruction="Help users get information from GitLab",
             tools=[
@@ -90,7 +90,7 @@ searches, and automate development workflows using natural language.
         const GITLAB_INSTANCE_URL = "gitlab.com";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "gitlab_agent",
             instruction: "Help users get information from GitLab",
             tools: [

diff --git a/docs/integrations/google-developer-knowledge.md b/docs/integrations/google-developer-knowledge.md
@@ -52,7 +52,7 @@ Guide](https://developers.google.com/knowledge/mcp#installation) for the precise
         DEVELOPER_KNOWLEDGE_API_KEY = "YOUR_DEVELOPER_KNOWLEDGE_API_KEY"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="google_knowledge_agent",
             instruction="Search Google developer documentation for implementation guidance.",
             tools=[
@@ -76,7 +76,7 @@ Guide](https://developers.google.com/knowledge/mcp#installation) for the precise
         const DEVELOPER_KNOWLEDGE_API_KEY = "YOUR_DEVELOPER_KNOWLEDGE_API_KEY";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "google_knowledge_agent",
             instruction: "Search Google developer documentation for implementation guidance.",
             tools: [

diff --git a/docs/integrations/hugging-face.md b/docs/integrations/hugging-face.md
@@ -45,7 +45,7 @@ your ADK agent to the Hugging Face Hub and thousands of Gradio AI Applications.
         HUGGING_FACE_TOKEN = "YOUR_HUGGING_FACE_TOKEN"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="hugging_face_agent",
             instruction="Help users get information from Hugging Face",
             tools=[
@@ -78,7 +78,7 @@ your ADK agent to the Hugging Face Hub and thousands of Gradio AI Applications.
         HUGGING_FACE_TOKEN = "YOUR_HUGGING_FACE_TOKEN"
 
         root_agent = Agent(
-            model="gemini-2.5-pro",
+            model="gemini-flash-latest",
             name="hugging_face_agent",
             instruction="Help users get information from Hugging Face",
             tools=[
@@ -104,7 +104,7 @@ your ADK agent to the Hugging Face Hub and thousands of Gradio AI Applications.
         const HUGGING_FACE_TOKEN = "YOUR_HUGGING_FACE_TOKEN";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "hugging_face_agent",
             instruction: "Help users get information from Hugging Face",
             tools: [
@@ -132,7 +132,7 @@ your ADK agent to the Hugging Face Hub and thousands of Gradio AI Applications.
         const HUGGING_FACE_TOKEN = "YOUR_HUGGING_FACE_TOKEN";
 
         const rootAgent = new LlmAgent({
-            model: "gemini-2.5-pro",
+            model: "gemini-flash-latest",
             name: "hugging_face_agent",
             instruction: "Help users get information from Hugging Face",
             tools: [