Update more product names (Agent Runtime, Knowledge Engine, Agent Retrieval) (#1701)

* Rename RAG Engine to Knowledge Engine across docs

* Fix remaining Agent Engine / Reasoning Engine product names

* Rename Vector Search 2.0 to Agent Retrieval in grounding docs

Author: koverholt

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)

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)

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)
 

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.
 
 

docs/integrations/rag-engine.md docs/integrations/knowledge-engine.mddocs/integrations/rag-engine.md renamed to docs/integrations/knowledge-engine.md

@@ -1,20 +1,25 @@
 ---
-catalog_title: RAG Engine
-catalog_description: Perform private data retrieval using RAG Engine
+catalog_title: Knowledge Engine
+catalog_description: Perform private data retrieval using Knowledge Engine
 catalog_icon: /integrations/assets/agent-platform.svg
 catalog_tags: ["data","google"]
 ---
 
-# RAG Engine tool for ADK
+# Knowledge Engine tool for ADK
 
 <div class="language-support-tag">
   <span class="lst-supported">Supported in ADK</span><span class="lst-python">Python v0.1.0</span><span class="lst-java">Java v0.2.0</span>
 </div>
 
-The `vertex_ai_rag_retrieval` tool allows the agent to perform private data retrieval using RAG Engine.
+The `vertex_ai_rag_retrieval` tool allows the agent to perform private data
+retrieval using Knowledge Engine.
 
-When you use grounding with RAG Engine, you need to prepare a RAG corpus beforehand.
-Please refer to the [RAG ADK agent sample](https://github.com/google/adk-samples/blob/main/python/agents/RAG/rag/shared_libraries/prepare_corpus_and_data.py) or [RAG Engine page](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-quickstart) for setting it up.
+When you use grounding with Knowledge Engine, you need to prepare a RAG corpus
+beforehand. Please refer to the [RAG ADK agent
+sample](https://github.com/google/adk-samples/blob/main/python/agents/RAG/rag/shared_libraries/prepare_corpus_and_data.py)
+or [Knowledge Engine
+page](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-quickstart)
+for setting it up.
 
 !!! warning "Warning: Single tool per agent limitation"
 

docs/sessions/memory.md

@@ -28,12 +28,12 @@ The Python ADK ships three `MemoryService` implementations. Use the table below
 
 | **Feature** | **InMemoryMemoryService** | **VertexAiMemoryBankService** | **VertexAiRagMemoryService** |
 | :--- | :--- | :--- | :--- |
-| **Persistence** | None (data is lost on restart) | Yes (Managed by Agent Platform) | Yes (stored in RAG Engine) |
+| **Persistence** | None (data is lost on restart) | Yes (Managed by Agent Platform) | Yes (stored in Knowledge Engine) |
 | **Primary Use Case** | Prototyping, local development, and simple testing. | Building meaningful, evolving memories from user conversations. | Vector-search retrieval over the full conversation corpus, or alongside other RAG-indexed content. |
-| **Memory Extraction** | Stores full conversation | Extracts [meaningful information](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/generate-memories) from conversations and consolidates it with existing memories (powered by LLM) | Stores full conversation, indexed by [RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview). |
-| **Search Capability** | Basic keyword matching. | Advanced semantic search. | Vector similarity search over RAG Engine. |
-| **Setup Complexity** | None. It's the default. | Low. Requires an [Agent Runtime](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/overview) instance on Agent Platform. | Medium. Requires [RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/manage-your-rag-corpus). |
-| **Dependencies** | None. | Google Cloud Project, Agent Platform API | Google Cloud Project, RAG Engine, the Agent Platform SDK (optional install). |
+| **Memory Extraction** | Stores full conversation | Extracts [meaningful information](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/generate-memories) from conversations and consolidates it with existing memories (powered by LLM) | Stores full conversation, indexed by [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview). |
+| **Search Capability** | Basic keyword matching. | Advanced semantic search. | Vector similarity search over Knowledge Engine. |
+| **Setup Complexity** | None. It's the default. | Low. Requires an [Agent Runtime](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/overview) instance on Agent Platform. | Medium. Requires [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/manage-your-rag-corpus). |
+| **Dependencies** | None. | Google Cloud Project, Agent Platform API | Google Cloud Project, Knowledge Engine, the Agent Platform SDK (optional install). |
 | **When to use it** | When you want to search across multiple sessions’ chat histories for prototyping. | When you want your agent to remember and learn from past interactions. | When you already have RAG infrastructure or want to retrieve over raw conversation transcripts. |
 
 `VertexAiRagMemoryService` is only exported from `google.adk.memory` when the Agent Platform SDK is installed. Memory Bank and RAG-backed memory are documented in [Memory Bank](#memory-bank) and [RAG Memory](#rag-memory) below.
@@ -397,7 +397,7 @@ Or, you can configure your agent to use the Memory Bank by manually instantiatin
 
 ## RAG Memory
 
-The `VertexAiRagMemoryService` stores conversations in [RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview) and retrieves them by vector similarity. Use it when you already have RAG infrastructure or want raw transcript retrieval rather than the LLM-extracted memories produced by Memory Bank. Requires the Agent Platform SDK.
+The `VertexAiRagMemoryService` stores conversations in [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview) and retrieves them by vector similarity. Use it when you already have RAG infrastructure or want raw transcript retrieval rather than the LLM-extracted memories produced by Memory Bank. Requires the Agent Platform SDK.
 
 === "Python"
 

docs/sessions/session/index.md

@@ -210,7 +210,7 @@ the storage backend that best suits your needs:
     *   A Google Cloud project (`pip install vertexai`)
     *   A Google Cloud storage bucket that can be configured by this
         [step](https://cloud.google.com/vertex-ai/docs/pipelines/configure-project#storage).
-    *   A Reasoning Engine resource name/ID that can setup following this
+    *   An Agent Runtime resource name/ID that can setup following this
         [tutorial](/deploy/agent-runtime/).
     *   If you do not have a Google Cloud project and you want to try the VertexAiSessionService, see [Agent Platform Express Mode](/integrations/express-mode/).
 *   **Best for:** Scalable production applications deployed on Google Cloud,

mkdocs.yml

@@ -199,8 +199,9 @@ plugins:
         'tools/third-party/qdrant.md': 'integrations/qdrant.md'
         'tools/google-cloud/spanner.md': 'integrations/spanner.md'
         'tools/third-party/stripe.md': 'integrations/stripe.md'
-        'tools/google-cloud/vertex-ai-rag-engine.md': 'integrations/rag-engine.md'
-        'integrations/vertex-ai-rag-engine.md': 'integrations/rag-engine.md'
+        'tools/google-cloud/vertex-ai-rag-engine.md': 'integrations/knowledge-engine.md'
+        'integrations/vertex-ai-rag-engine.md': 'integrations/knowledge-engine.md'
+        'integrations/rag-engine.md': 'integrations/knowledge-engine.md'
         'tools/google-cloud/vertex-ai-search.md': 'integrations/agent-search.md'
         'integrations/vertex-ai-search.md': 'integrations/agent-search.md'
         'grounding/vertex_ai_search_grounding.md': 'grounding/grounding_with_search.md'

tools/feature-matrix/start.md

@@ -22,7 +22,7 @@ The following matrix shows which ADK features are currently supported in each la
 | | **Built-in: Google Search** | [Supported](https://adk.dev/tools/built-in-tools/#google-search) | [Supported](https://adk.dev/tools/built-in-tools/#google-search) |
 | | **Built-in: Code Execution** | [Supported](https://adk.dev/tools/built-in-tools/#code-execution) | [Supported](https://adk.dev/tools/built-in-tools/#code-execution) |
 | | **Built-in: GKE Code Executor** | [Supported](https://adk.dev/tools/built-in-tools/#gke-code-executor) | Not Supported |
-| | **Built-in: RAG Engine** | [Supported](https://adk.dev/tools/built-in-tools/#rag-engine) | Not Supported |
+| | **Built-in: Knowledge Engine** | [Supported](https://adk.dev/integrations/knowledge-engine/) | Not Supported |
 | | **Built-in: Agent Search** | [Supported](https://adk.dev/tools/built-in-tools/#agent-search) | Not Supported |
 | | **Built-in: BigQuery, Spanner, Bigtable** | [Supported](https://adk.dev/tools/built-in-tools/#bigquery) | Not Supported |
 | | **Google Cloud Tools (API Hub, etc.)** | [Supported](https://adk.dev/tools/google-cloud-tools/#apigee-api-hub-tools) | Not Supported |