Merge pull request #473 from microsoft/bruno-renameToMicrosoftFoundry

Update documentation for Microsoft Foundry and clean up code

Author: elbruno

02-SetupDevEnvironment/getting-started-azure-openai.md

@@ -1,19 +1,19 @@
 # Setting Up the Development Environment for Azure OpenAI
 
-If you want to use Azure AI Foundry models for your .NET AI apps in this course, follow the steps in this guide.
+If you want to use Microsoft Foundry models for your .NET AI apps in this course, follow the steps in this guide.
 
 Don't want to use Azure OpenAI?
 
 👉 [To use GitHub Models this is the guide for you](README.md)
 👉 [Here are the steps for Ollama](getting-started-ollama.md)
 
-## Create the Azure AI Foundry resources
+## Create the Microsoft Foundry resources
 
-To use Azure AI Foundry models, you need to create a hub and project in the Azure AI Foundry portal. Then you'll need to deploy a model. This section will show you how to do that.
+To use Microsoft Foundry models, you need to create a hub and project in the Microsoft Foundry portal. Then you'll need to deploy a model. This section will show you how to do that.
 
-### Create a Hub and Project in Azure AI Foundry
+### Create a Hub and Project in Microsoft Foundry
 
-1. Go to the [Azure AI Foundry Portal](https://ai.azure.com/).
+1. Go to the [Microsoft Foundry Portal](https://ai.azure.com/).
 1. Sign in with your Azure account.
 1. Select **All hubs + projects** from the left-hand menu and then click the **+ New hub** from the dropdown. (Note: You may have to click on **+ New project** first to see the **+ New hub** option).
     ![Create a new hub](./images/ai-foundry-hub-selection.png)
@@ -28,13 +28,13 @@ To use Azure AI Foundry models, you need to create a hub and project in the Azur
     - Give your project a name (e.g., "GenAINET") or accept the default.
     - Click **Create**.
 
-🎉 **Done!** You’ve just created your first project in Azure AI Foundry.
+🎉 **Done!** You've just created your first project in Microsoft Foundry.
 
-### Deploy a Language Model in Azure AI Foundry
+### Deploy a Language Model in Microsoft Foundry
 
 Now, let’s deploy a **gpt-4o-mini** model to your project:
 
-1. In the Azure AI Foundry portal, navigate to your project (it should automatically open after creating it).
+1. In the Microsoft Foundry portal, navigate to your project (it should automatically open after creating it).
 1. Click on **Models and Endpoints** from the left-hand menu and then the **Deploy Model** button.
 1. Select **Deploy base model** from the dropdown.
 1. Search for **gpt-4o-mini** in the model catalog.
@@ -43,7 +43,7 @@ Now, let’s deploy a **gpt-4o-mini** model to your project:
 1. Click **Deploy** and wait for the model to be provisioned.
 1. Once deployed, note the **Model Name**, **Target URI**, and **API Key** from the model details page.
 
-🎉 **Done!** You’ve deployed your first Large Language Model in Azure AI Foundry.
+🎉 **Done!** You've deployed your first Large Language Model in Microsoft Foundry.
 
 ![Model deployed, copy model name, endpoint url and apikey](./images/deploytoazure-20-copymodelinfo.png)
 
@@ -58,7 +58,7 @@ To be secure, let's add the API key you just created to your Codespace's secrets
 
     ![Adding a new Codespace secret](./images/codespaces-secret.jpeg)
 1. Name your secret **AZURE_AI_SECRET**.
-1. Paste the API key you copied from the Azure AI Foundry portal into the **Secret** field.
+1. Paste the API key you copied from the Microsoft Foundry portal into the **Secret** field.
 
 ## Creating a GitHub Codespace
 
@@ -90,15 +90,17 @@ Now let’s update the code to use the newly deployed model. First we'll need to
 1. Open the terminal and switch to the project directory:
 
    If you're using Windows Command Prompt (CMD) or PowerShell:
+
    ```bash
    cd samples\CoreSamples\BasicChat-01MEAI
    ```
-   
+
    If you're using Linux, macOS, Git Bash, WSL, or the VS Code terminal:
+
    ```bash
    cd samples/CoreSamples/BasicChat-01MEAI
    ```
-   
+
    > **Note**: GitHub Codespaces runs a Linux environment, so always use forward slashes (`/`) in paths when working in Codespaces, regardless of your local operating system.
 
 1. Run the following commands to add the required package:
@@ -114,7 +116,6 @@ Now let’s update the code to use the newly deployed model. First we'll need to
 
     Add the following using statements at the top of the file:
 
-
     ```csharp
     using System.ClientModel;
     using Azure.AI.OpenAI;
@@ -160,13 +161,13 @@ Now let’s update the code to use the newly deployed model. First we'll need to
 
 ## Summary
 
-In this lesson, you learned how to set up your development environment for the rest of the course. You created a GitHub Codespace and configured it to use Azure OpenAI. You also updated the sample code to use the newly deployed model in Azure AI Foundry.
+In this lesson, you learned how to set up your development environment for the rest of the course. You created a GitHub Codespace and configured it to use Azure OpenAI. You also updated the sample code to use the newly deployed model in Microsoft Foundry.
 
 ### Additional Resources
 
-- [Azure AI Foundry Documentation](https://learn.microsoft.com/azure/ai-services/)
+- [Microsoft Foundry Documentation](https://learn.microsoft.com/azure/ai-services/)
 - [Working with GitHub Codespaces](https://docs.github.com/en/codespaces/getting-started)
-- [How to Deploy Models in Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/deploy-models-openai)
+- [How to Deploy Models in Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/deploy-models-openai)
 - [Azure.AI.OpenAI NuGet Package](https://www.nuget.org/packages/Azure.AI.OpenAI)
 
 ## Next Steps

03-CoreGenerativeAITechniques/01-lm-completions-functions.md

@@ -16,7 +16,7 @@ A text completion itself is not a chat application, it is a one and done interac
 
 ### Text completions
 
-Let's see how you would use text completions using the **Microsoft.Extensions.AI** library in .NET. 
+Let's see how you would use text completions using the **Microsoft.Extensions.AI** library in .NET.
 
 > 🧑‍💻**Sample code**: [Here is a working example of this application](../samples/CoreSamples/BasicChat-01MEAI/) you can follow along with.
 
@@ -26,22 +26,25 @@ To run the sample code, you'll need to:
 
 1. Make sure you have set up a GitHub Codespace with the appropriate environment as described in the [Setup guide](../02-SetupDevEnvironment/readme.md)
 2. Ensure you have configured your GitHub Token as described in the [Pre-flight check section](../02-SetupDevEnvironment/readme.md#pre-flight-check-setting-up-github-access-tokens)
-3. Open a terminal in your codespace (Ctrl+` or Cmd+`)
+3. Open a terminal in your codespace (Ctrl+`or Cmd+`)
 4. Navigate to the sample code directory:
-   
+
    If you're using Windows Command Prompt (CMD) or PowerShell:
+
    ```bash
    cd samples\CoreSamples\BasicChat-01MEAI
    ```
-   
+
    If you're using Linux, macOS, Git Bash, WSL, or the VS Code terminal:
+
    ```bash
    cd samples/CoreSamples/BasicChat-01MEAI
    ```
-   
+
    > **Note**: GitHub Codespaces runs a Linux environment, so always use forward slashes (`/`) in paths when working in Codespaces, regardless of your local operating system.
 
 5. Run the application:
+
    ```bash
    dotnet run
    ```
@@ -69,13 +72,13 @@ Console.WriteLine(response.Text);
 ```
 
 > 🗒️**Note:** This example showed GitHub Models as the hosting service. If you want to use Ollama, [check out this example](../samples/CoreSamples/BasicChat-03Ollama/) (it instantiates a different `IChatClient`).
-> 
-> If you want to use Azure AI Foundry you can use the same code, but you will need to change the endpoint and the credentials.
+> If you want to use Microsoft Foundry you can use the same code, but you will need to change the endpoint and the credentials.
+>
 >
 > **GitHub Models Endpoint:** The endpoint `https://models.github.ai/inference` is the new dedicated GitHub Models endpoint as announced in the [GitHub Models deprecation notice](https://github.blog/changelog/2025-07-17-deprecation-of-azure-endpoint-for-github-models/), replacing the previous Azure-based endpoint.
 >
 > If you want to use both Ollama and Semantic Kernel together, [check out the BasicChat-04OllamaSK example](../samples/CoreSamples/BasicChat-04OllamaSK/).
-> 
+>
 > For instructions on how to set up Ollama, refer to [Getting Started with Ollama](../02-SetupDevEnvironment/getting-started-ollama.md).
 
 > 🙋 **Need help?**: If you encounter any issues running this example, [open an issue in the repository](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/issues/new?template=Blank+issue) and we'll help you troubleshoot.
@@ -210,9 +213,9 @@ In the next lesson you'll see how to start chatting with data and build what's k
 
 ## Additional resources
 
-- [Build an AI chat app with .NET](https://learn.microsoft.com/dotnet/ai/quickstarts/get-started-openai?tabs=azd&pivots=openai)
-- [Execute a local .NET function](https://learn.microsoft.com/dotnet/ai/quickstarts/quickstart-azure-openai-tool?tabs=azd&pivots=openai)
-- [Chat with a local AI model](https://learn.microsoft.com/dotnet/ai/quickstarts/quickstart-local-ai)
+* [Build an AI chat app with .NET](https://learn.microsoft.com/dotnet/ai/quickstarts/get-started-openai?tabs=azd&pivots=openai)
+* [Execute a local .NET function](https://learn.microsoft.com/dotnet/ai/quickstarts/quickstart-azure-openai-tool?tabs=azd&pivots=openai)
+* [Chat with a local AI model](https://learn.microsoft.com/dotnet/ai/quickstarts/quickstart-local-ai)
 
 ## Up next
 

03-CoreGenerativeAITechniques/04-agents.md

@@ -16,11 +16,12 @@ One key thing to keep in mind when building agents is that they are focused on d
 
 ## Creating an AI Agent
 
-We'll be working with a couple of new concepts in order to build an AI agent in .NET. We'll be using a new SDK and will have to do some additional setup in Azure AI Foundry to get things started.
+We'll be working with a couple of new concepts in order to build an AI agent in .NET. We'll be using a new SDK and will have to do some additional setup in Microsoft Foundry to get things started.
 
 > 🧑‍💻**Sample code**: We'll be working from the [AgentLabs-01-Simple sample](../samples/CoreSamples/AgentLabs-01-Simple/) for this lesson.
 >
 > We also have more advanced agent examples:
+>
 > - [AgentLabs-02-Functions](../samples/CoreSamples/AgentLabs-02-Functions/) - Showing how to create custom functions for your agents
 > - [AgentLabs-03-OpenAPIs](../samples/CoreSamples/AgentLabs-03-OpenAPIs/) - Demonstrating how agents can use OpenAPI specifications
 > - [AgentLabs-03-PythonParksInformationServer](../samples/CoreSamples/AgentLabs-03-PythonParksInformationServer/) - A Python server that works with the OpenAPI example
@@ -29,7 +30,7 @@ We'll be working with a couple of new concepts in order to build an AI agent in
 
 We're going to introduce a new Azure Service that will help us build agents, the appropriately named [Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/overview).
 
-To run the code samples included in this lesson, you'll need to perform some additional setup in Azure AI Foundry. You can follow [these instructions to setup a **Basic Agent**](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-csharp).
+To run the code samples included in this lesson, you'll need to perform some additional setup in Microsoft Foundry. You can follow [these instructions to setup a **Basic Agent**](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-csharp).
 
 ### Azure AI Projects library
 
@@ -65,12 +66,12 @@ We'll be building a single purpose agent that acts as a tutor to math students.
     AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential(options));
     ```
 
-    You can find the project connection string in AI Foundry by opening up the Hub you created, then the project. It will be on the right-hand side.
+    You can find the project connection string in Microsoft Foundry by opening up the Hub you created, then the project. It will be on the right-hand side.
 
     ![Screenshot of the project homepage in AI Foundry with the project connection string highlighted in red](./images/project-connection-string.png)
 
 1. Next we want to create the tutor agent. Remember, it should be focused only on one thing.
-   
+
     ```csharp
     // create Agent
     Response<Agent> agentResponse = await client.CreateAgentAsync(
@@ -87,7 +88,7 @@ We'll be building a single purpose agent that acts as a tutor to math students.
 
     > 🗒️**Note**: You can create your own tools too. See the [Functions](./src/AgentLabs-02-Functions/) to learn more.
 
-    Second note the `instructions` that are being sent along. It's a prompt and we're limiting it to answering math questions. Then last creating the agent is an async operation. That's because it's creating an object within Azure AI Foundry Agents service. So we both `await` the `CreateAgentAsync` function and then grab the `Value` of its return to get at the actual `Agent` object. You'll see this pattern occur over and over again when creating objects with the **Azure.AI.Projects** SDK.
+    Second note the `instructions` that are being sent along. It's a prompt and we're limiting it to answering math questions. Then last creating the agent is an async operation. That's because it's creating an object within Microsoft Foundry Agents service. So we both `await` the `CreateAgentAsync` function and then grab the `Value` of its return to get at the actual `Agent` object. You'll see this pattern occur over and over again when creating objects with the **Azure.AI.Projects** SDK.
 
 1. An `AgentThread` is an object that handles the communication between individual agents and the user and so on. We'll need to create that so we can add a `ThreadMessage` on to it. And in this case it's the user's first question.
 
@@ -120,6 +121,7 @@ We'll be building a single purpose agent that acts as a tutor to math students.
         , additionalInstructions: "You are working in FREE TIER EXPERIENCE mode, every user has premium account for a short period of time. Explain detailed the steps to answer the user questions"
         );
     ThreadRun run = runResponse.Value;
+
     ```
 
 1. All that's left then is to check the status of the run

06-AgentFx/readme.md

@@ -8,7 +8,7 @@ Build powerful, orchestrated AI agents with Microsoft Agent Framework - the next
 
 - 🤖 Understanding the Microsoft Agent Framework and its architecture
 - 🔗 Creating and orchestrating multiple AI agents
-- 🌐 Working with different AI providers (GitHub Models, Azure AI Foundry, Ollama)
+- 🌐 Working with different AI providers (GitHub Models, Microsoft Foundry, Ollama)
 - 🔄 Building sequential and concurrent agent workflows
 - 🛠️ Integrating with Model Context Protocol (MCP) for enhanced capabilities
 
@@ -79,7 +79,7 @@ Before starting with AgentFx, ensure you have:
 - **.NET 9 SDK** or later
 - **AI Provider Access**: At least one of the following:
   - GitHub Token for GitHub Models
-  - Azure AI Foundry endpoint using managed identities o using an API key
+  - Microsoft Foundry endpoint using managed identities o using an API key
   - Ollama installed locally
 
 ### Installation
@@ -106,7 +106,7 @@ This lesson includes multiple code samples demonstrating different aspects of th
 |--------|-------------|-------------|----------|
 | [AgentFx01](../samples/AgentFx/AgentFx01/) | Single agent that writes creative stories | Basic agent setup, ChatClientAgent | GitHub Models |
 | [AgentFx02](../samples/AgentFx/AgentFx02/) | Two-agent workflow: Writer + Editor | Sequential workflows, agent chaining | GitHub Models |
-| [AgentFx-AIFoundry-01](../samples/AgentFx/AgentFx-AIFoundry-01/) | Single agent using Azure AI Foundry | Azure CLI authentication, managed identity | Azure AI Foundry |
+| [AgentFx-AIFoundry-01](../samples/AgentFx/AgentFx-AIFoundry-01/) | Single agent using Microsoft Foundry | Azure CLI authentication, managed identity | Microsoft Foundry |
 | [AgentFx-Ollama-01](../samples/AgentFx/AgentFx-Ollama-01/) | Single agent using local Ollama models | Local AI inference, privacy-focused | Ollama (local) |
 | [AgentFx-BackgroundResponses-01-Simple](../samples/AgentFx/AgentFx-BackgroundResponses-01-Simple/) | Background responses with continuation tokens | Streaming interruption, response continuation | Configurable |
 | [AgentFx-BackgroundResponses-02-Tools](../samples/AgentFx/AgentFx-BackgroundResponses-02-Tools/) | Background responses with tool integration | Tool usage during background processing | Configurable |
@@ -119,7 +119,7 @@ This lesson includes multiple code samples demonstrating different aspects of th
 | Sample | Description | Key Concepts | Providers |
 |--------|-------------|-------------|-----------|
 | [AgentFx-MultiModel](../samples/AgentFx/AgentFx-MultiModel/) | Three-agent workflow across multiple providers | Multi-provider orchestration, OpenTelemetry tracing | GitHub Models or Azure OpenAI + Ollama |
-| [AgentFx-MultiAgents](../samples/AgentFx/AgentFx-MultiAgents/) | Researcher-Writer-Reviewer workflow with persistent agents | Azure AI Foundry persistent agents, flexible configuration | Azure AI Foundry + Azure OpenAI/GitHub Models + Ollama |
+| [AgentFx-MultiAgents](../samples/AgentFx/AgentFx-MultiAgents/) | Researcher-Writer-Reviewer workflow with persistent agents | Microsoft Foundry persistent agents, flexible configuration | Microsoft Foundry + Azure OpenAI/GitHub Models + Ollama |
 
 ### Integration & Web Samples
 
@@ -176,7 +176,7 @@ dotnet user-secrets set "GITHUB_TOKEN" "your-github-token"
 dotnet run
 ```
 
-**Azure AI Foundry** (requires Azure CLI login):
+**Microsoft Foundry** (requires Azure CLI login):
 
 Windows (CMD/PowerShell):
 
@@ -328,7 +328,7 @@ var nextResponse = await agent.RunAsync("What is my name?", resumedThread);
 ### Key Considerations
 
 - **Agent consistency**: You must use the same agent type to deserialize threads, as different agents may have unique internal implementations
-- **Storage flexibility**: The framework abstracts storage details - some agents (like Azure AI Foundry) persist history in the service, while others (like OpenAI chat completion) manage state in-memory
+- **Storage flexibility**: The framework abstracts storage details - some agents (like Microsoft Foundry) persist history in the service, while others (like OpenAI chat completion) manage state in-memory
 - **Production storage**: For production scenarios, use reliable storage like cloud databases or blob storage instead of local files
 
 > 🧑‍💻 **Sample code**: Explore the persisted conversation samples:
@@ -380,7 +380,7 @@ Name = "Agent1"           // Avoid, please no!
 Choose AI providers based on your needs:
 
 - **GitHub Models**: Quick prototyping and development
-- **Azure AI Foundry**: Production workloads with enterprise features
+- **Microsoft Foundry**: Production workloads with enterprise features
 - **Ollama**: Local development, privacy-sensitive data
 
 ### 4. Error Handling
@@ -477,7 +477,7 @@ The Microsoft Agent Framework represents the next evolution in .NET AI developme
 - [Microsoft Agent Framework Overview](https://learn.microsoft.com/agent-framework/overview/agent-framework-overview)
 - [Model Context Protocol C# SDK](https://github.com/modelcontextprotocol/csharp-sdk)
 - [Microsoft.Extensions.AI Documentation](https://learn.microsoft.com/dotnet/ai/ai-extensions)
-- [Azure AI Foundry](https://ai.azure.com/)
+- [Microsoft Foundry](https://ai.azure.com/)
 - [GitHub Models](https://github.com/marketplace/models)
 - [Ollama Documentation](https://ollama.ai/docs)