Update ADK doc according to issue #1350 - 3

docs/agents/models/apigee.md

@@ -20,15 +20,17 @@ Apigee proxy, you immediately gain enterprise-grade capabilities:
 
 !!! note
 
-    The `ApigeeLLM` wrapper is currently designed for use with Vertex AI
-    and the Gemini API (generateContent). We are continually expanding support for
-    other models and interfaces.
+    The `ApigeeLLM` wrapper is designed for use with Vertex AI, the Gemini API (`generateContent`), and any OpenAI-compatible API (such as the `/chat/completions` endpoint).
 
 ## Example implementation
 
 Integrate Apigee's governance into your agent's workflow by instantiating the
 `ApigeeLlm` wrapper object and pass it to an `LlmAgent` or other agent type.
 
+### Using Vertex AI and Gemini APIs
+
+When using `ApigeeLlm` with Vertex AI or the Gemini API, the configuration is optimized for their `generateContent` endpoints.
+
 === "Python"
 
     ```python
@@ -79,8 +81,46 @@ Integrate Apigee's governance into your agent's workflow by instantiating the
             .build();
     ```
 
+### Using OpenAI-compatible APIs
+
+The `ApigeeLlm` wrapper can also connect to any OpenAI-compatible API that uses the `/chat/completions` endpoint. This allows you to apply Apigee's governance features to a wider range of models.
+
+You can configure the wrapper for an OpenAI-compatible API in two ways:
+
+1.  **Using the `model` string:** Set the model provider to `openai`.
+2.  **Using the `api_type` parameter:** Explicitly set the `api_type` to `chat_completions`.
+
+=== "Python"
+
+    ```python
+    from google.adk.agents import LlmAgent
+    from google.adk.models.apigee_llm import ApigeeLlm
+
+    # Option 1: Configure using the model string
+    model_from_string = ApigeeLlm(
+        model="apigee/openai/gpt-4",
+        proxy_url=f"https://{APIGEE_PROXY_URL}",
+        custom_headers={"Authorization": f"Bearer {OPENAI_API_KEY}"}
+    )
+
+    # Option 2: Configure using the api_type parameter
+    model_from_api_type = ApigeeLlm(
+        model="apigee/gpt-4",
+        api_type="chat_completions",
+        proxy_url=f"https://{APIGEE_PROXY_URL}",
+        custom_headers={"Authorization": f"Bearer {OPENAI_API_KEY}"}
+    )
+
+    # Pass the configured model to your agent
+    agent = LlmAgent(
+        model=model_from_string, # or model_from_api_type
+        name="my_openai_agent",
+        instruction="You are a helpful assistant powered by an OpenAI model and governed by Apigee.",
+    )
+    ```
+
 With this configuration, every API call from your agent will be routed through
 Apigee first, where all necessary policies (security, rate limiting, logging)
 are executed before the request is securely forwarded to the underlying AI model
 endpoint. For a full code example using the Apigee proxy, see
-[Hello World Apigee LLM](https://github.com/google/adk-python/tree/main/contributing/samples/hello_world_apigeellm).
+[Hello World Apigee LLM](https://github.com/google/adk-python/tree/main/contributing/samples/hello_world_apigeellm).

docs/integrations/agent-registry.md

@@ -0,0 +1,194 @@
+---
+catalog_title: Google Cloud Agent Registry
+catalog_description: Connect with Google Cloud services as MCP tools and other agents.
+catalog_icon: /adk-docs/integrations/assets/developer-tools-color.svg
+catalog_tags: ["google", "mcp", "connectors", "a2a"]
+---
+
+# Google Cloud Agent Registry tool for ADK
+
+<div class="language-support-tag">
+  <span class="lst-supported">Supported in ADK</span><span class="lst-python">Python v1.26.0</span><span class="lst-preview">Preview</span>
+</div>
+
+The Google Cloud Agent Registry connector tool for Agent Development Kit (ADK)
+lets you access a wide range of Google Cloud services for your agents. You can
+use it to retrieve Model-Conversation-Tools (MCP) toolsets and remote A2A agents
+from the [Google Cloud Agent Registry](https://cloud.google.com/agent-registry/docs/overview).
+You can configure this tool to connect your agent to your Google Cloud projects
+and dynamically access Cloud services enabled for that project.
+
+!!! example "Preview release"
+    The Google Cloud Agent Registry feature is a Preview release. For
+    more information, see the
+    [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).
+
+## `AgentRegistry` vs `ApiRegistry`
+
+The `google.adk.integrations.agent_registry.AgentRegistry` class is the recommended
+tool for interacting with the Google Cloud Agent Registry. It supports retrieving both
+MCP toolsets and remote A2A agents.
+
+An older class, `google.adk.tools.api_registry.ApiRegistry`, is still available for
+interacting with the previous version of the service, the API Registry. For new
+development, you should use `AgentRegistry`.
+
+## Prerequisites
+
+Before using the Agent Registry with your agent, you need to ensure the following:
+
+-   **Google Cloud project:** Configure your agent to access AI models using an
+    existing Google Cloud project.
+
+-   **Agent Registry access:** The environment where your agent runs needs Google
+    Cloud [Application Default Credentials](https://docs.cloud.google.com/docs/authentication/provide-credentials-adc)
+    with the `agentregistry.viewer` role to list available resources.
+
+-   **Cloud APIs:** In your Google Cloud project, enable the
+    `agentregistry.googleapis.com` Google Cloud API.
+
+-   **Resource Access:** The credentials used by the agent must have permissions to
+    access the underlying services used by the tools or agents. For example, to
+    use BigQuery tools, the service account needs BigQuery IAM roles like
+    `bigquery.dataViewer` and `bigquery.jobUser`. For more information about
+    required permissions, see [Authentication and access](#auth).
+
+## Use with agent
+
+When configuring the Agent Registry connector tool with an agent, you first
+initialize the `AgentRegistry` class to establish a connection with the service,
+and then use the `get_mcp_toolset()` or `get_remote_a2a_agent()` methods to
+retrieve the desired resource.
+
+### Retrieving an MCP Toolset
+
+The following code example demonstrates how to create an agent that uses tools
+from an MCP server listed in Agent Registry. This agent is designed to interact
+with BigQuery:
+
+```python
+import os
+from google.adk.agents.llm_agent import LlmAgent
+from google.adk.integrations.agent_registry import AgentRegistry
+
+# Configure with your Google Cloud Project ID, location, and registered MCP server name
+PROJECT_ID = "your-google-cloud-project-id"
+LOCATION = "your-google-cloud-location"
+MCP_SERVER_NAME = f"projects/{PROJECT_ID}/locations/{LOCATION}/mcpServers/your-mcp-server-name"
+
+# Example header provider for BigQuery, a project header is required.
+def header_provider(context):
+    return {"x-goog-user-project": PROJECT_ID}
+
+# Initialize AgentRegistry
+agent_registry = AgentRegistry(
+    project_id=PROJECT_ID,
+    location=LOCATION,
+    header_provider=header_provider
+)
+
+# Get the toolset for the specific MCP server
+registry_tools = agent_registry.get_mcp_toolset(
+    mcp_server_name=MCP_SERVER_NAME,
+)
+
+# Create an agent with the tools
+root_agent = LlmAgent(
+    model="gemini-1.5-flash", # Or your preferred model
+    name="bigquery_assistant",
+    instruction="""
+Help user access their BigQuery data using the available tools.
+    """,
+    tools=[registry_tools],
+)
+```
+
+### Retrieving a Remote A2A Agent
+
+Here is an example of how to retrieve a remote A2A agent from the Agent Registry:
+
+```python
+from google.adk.integrations.agent_registry import AgentRegistry
+
+# Configure with your Google Cloud Project ID, location, and registered agent name
+PROJECT_ID = "your-google-cloud-project-id"
+LOCATION = "your-google-cloud-location"
+AGENT_NAME = f"projects/{PROJECT_ID}/locations/{LOCATION}/agents/your-agent-name"
+
+# Initialize AgentRegistry
+agent_registry = AgentRegistry(
+    project_id=PROJECT_ID,
+    location=LOCATION
+)
+
+# Get the remote A2A agent
+remote_agent = agent_registry.get_remote_a2a_agent(
+    agent_name=AGENT_NAME
+)
+
+# Now you can use the remote_agent in your application
+# For example, you could add it to a router agent.
+```
+
+## Authentication and access {#auth}
+
+Using the Agent Registry with your agent requires authentication for the services
+the agent accesses. By default the tool uses Google Cloud
+[Application Default Credentials](https://docs.cloud.google.com/docs/authentication/provide-credentials-adc)
+for authentication. When using this tool make sure your agent has the following
+permissions and access:
+
+-   **Agent Registry access:** The `AgentRegistry` class uses Application Default
+    Credentials (`google.auth.default()`) to authenticate requests to the Google
+    Cloud Agent Registry. Ensure the environment where the agent runs has
+    credentials with the necessary permissions to view the Agent Registry
+    resources, such as `agentregistry.viewer`.
+
+-   **MCP Server and Tool access:** The `McpToolset` returned by `get_mcp_toolset`
+    also uses the Google Cloud Application Default Credentials by default to
+    authenticate calls to the actual MCP server endpoint. The credentials used
+    must have the necessary permissions for both:
+    1.  Accessing the MCP server itself.
+    2.  Utilizing the underlying services and resources that the tools interact
+        with.
+
+-   **Remote A2A Agent access:** Similarly, the `RemoteA2aAgent` returned by
+    `get_remote_a2a_agent` will use Application Default Credentials to communicate
+    with the remote agent. The credentials must have the necessary permissions
+    to invoke the remote agent's skills.
+
+For example, when using MCP server tools that interact with BigQuery, the
+account associated with the credentials, such as a service account, must be
+granted appropriate BigQuery IAM roles, such as `bigquery.dataViewer` or
+`bigquery.jobUser`, within your Google Cloud project to access datasets and run
+queries. Additional headers for authentication or project context can be
+injected via the `header_provider` argument in the `AgentRegistry` constructor.
+
+## Configuration {#configuration}
+
+The `AgentRegistry` object has the following configuration options:
+
+-   **`project_id`** (str): The Google Cloud Project ID where the
+    Agent Registry is located.
+-   **`location`** (str): The location of the Agent Registry resources.
+-   **`header_provider`** (Callable, optional): A function that takes the call
+    context and returns a dictionary of additional HTTP headers to be sent with
+    requests to the MCP server or remote agent. This is often used for dynamic
+    authentication or project-specific headers.
+
+The `get_mcp_toolset()` function has the following configuration options:
+
+-   **`mcp_server_name`** (str): The full name of the registered MCP server from
+    which to load tools, for example:
+    `projects/my-project/locations/global/mcpServers/my-server`.
+
+The `get_remote_a2a_agent()` function has the following configuration options:
+
+-   **`agent_name`** (str): The full name of the registered agent from which to
+    load the remote agent, for example:
+    `projects/my-project/locations/global/agents/my-agent`.
+
+## Additional resources
+
+-   [Google Cloud Agent Registry](https://cloud.google.com/agent-registry/docs/overview)
+    documentation