Inlined single use variables.

Address PR comments
Merge branch 'main' into peibekwe/declarative-bugfix-python-new
2026-06-16 21:04:09 +08:00 · 2026-06-12 08:55:36 -07:00 · 2026-06-11 22:02:58 -07:00 · 2026-06-11 17:08:12 -07:00 · 2026-06-11 22:35:57 +00:00 · 2026-06-11 22:34:15 +00:00
233 changed files with 8275 additions and 855 deletions
@@ -214,6 +214,7 @@ WARP.md
 **/memory-bank/
 **/projectBrief.md
 **/tmpclaude*
+.kiro/
 # Dependency-bound validation reports
 python/scripts/dependency-*-results.json
 python/scripts/dependencies/dependency-*-results.json
@@ -138,10 +138,15 @@
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" Version="2.1.0" />
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Mcp" Version="1.0.0" />
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.7" />
+    <!-- Valkey -->
    <!-- Redis -->
    <PackageVersion Include="StackExchange.Redis" Version="2.10.1" />
+    <!-- Valkey -->
+    <PackageVersion Include="Valkey.Glide" Version="1.1.0" />
    <!-- Console UX -->
    <PackageVersion Include="Spectre.Console" Version="0.49.1" />
+    <!-- AWS -->
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.6.10" />
    <!-- Test -->
    <PackageVersion Include="FluentAssertions" Version="8.8.0" />
    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net8.0'" Version="8.0.22" />
@@ -24,7 +24,6 @@
    <File Path="samples/02-agents/AgentProviders/README.md" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_Anthropic/Agent_With_Anthropic.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Agent_With_AzureAIAgentsPersistent.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
@@ -129,6 +128,7 @@
    <Project Path="samples/02-agents/Harness/Harness_Step02_Research_WithBackgroundAgents/Harness_Step02_Research_WithBackgroundAgents.csproj" />
    <Project Path="samples/02-agents/Harness/Harness_Step03_DataProcessing/Harness_Step03_DataProcessing.csproj" />
    <Project Path="samples/02-agents/Harness/Harness_Step04_CodeExecution/Harness_Step04_CodeExecution.csproj" />
+    <Project Path="samples/02-agents/Harness/Harness_Step05_Loop/Harness_Step05_Loop.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -194,6 +194,8 @@
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey/AgentWithMemory_Step03_MemoryUsingValkey.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AgentWithOpenAI/">
    <File Path="samples/02-agents/AgentWithOpenAI/README.md" />
@@ -625,6 +627,7 @@
    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
    <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj" />
  </Folder>
  <Folder Name="/Tests/" />
  <Folder Name="/Tests/IntegrationTests/">
@@ -678,5 +681,6 @@
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Valkey.UnitTests/Microsoft.Agents.AI.Valkey.UnitTests.csproj" />
  </Folder>
 </Solution>
@@ -50,19 +50,6 @@ internal static class AgentsSamples
            ],
        },

-        new SampleDefinition
-        {
-            Name = "Agent_With_AzureAIAgentsPersistent",
-            ProjectPath = "samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should contain a joke about a pirate.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
        new SampleDefinition
        {
            Name = "Agent_With_AzureAIProject",
@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-#pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
-
-// This sample shows how to create and use a simple AI agent with Microsoft Foundry Agents as the backend.
-
-using Azure.AI.Agents.Persistent;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-
-const string JokerName = "Joker";
-const string JokerInstructions = "You are good at telling jokes.";
-
-// Get a client to create/retrieve server side agents with.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var persistentAgentsClient = new PersistentAgentsClient(endpoint, new DefaultAzureCredential());
-
-// You can create a server side persistent agent with the Azure.AI.Agents.Persistent SDK.
-var agentMetadata = await persistentAgentsClient.Administration.CreateAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can retrieve an already created server side persistent agent as an AIAgent.
-AIAgent agent1 = await persistentAgentsClient.GetAIAgentAsync(agentMetadata.Value.Id);
-
-// You can also create a server side persistent agent and return it as an AIAgent directly.
-AIAgent agent2 = await persistentAgentsClient.CreateAIAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can then invoke the agent like any other AIAgent.
-AgentSession session = await agent1.CreateSessionAsync();
-Console.WriteLine(await agent1.RunAsync("Tell me a joke about a pirate.", session));
-
-// Cleanup for sample purposes.
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent1.Id);
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent2.Id);
@@ -1,26 +0,0 @@
-# Classic Foundry Agents 
-
-This sample demonstrates how to create an agent using the classic Foundry Agents experience.
-
-# Classic vs New Foundry Agents
-
-Below is a comparison between the classic and new Foundry Agents approaches:
-
-[Migration Guide](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/migrate?view=foundry)
-
-# Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
-```
@@ -8,8 +8,8 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string JokerName = "JokerAgent";

@@ -1,4 +1,4 @@
-# New Foundry Agents 
+# New Foundry Agents 

 This sample demonstrates how to create an agent using the new Foundry Agents experience. 

@@ -21,6 +21,6 @@ Before you begin, ensure you have the following prerequisites:
 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
@@ -13,7 +13,7 @@ using OpenAI.Chat;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
-var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";
+var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "Phi-4-mini-instruct";

 // Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Microsoft Foundry.
 var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };
@@ -1,4 +1,4 @@
-## Overview
+## Overview

 This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.

@@ -13,7 +13,7 @@ Before you begin, ensure you have the following prerequisites:
 - .NET 10 SDK or later
 - Microsoft Foundry resource
 - A model deployment in your Microsoft Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
-so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
+so if you want to use a different model, ensure that you set your `FOUNDRY_MODEL` environment
 variable to the name of your deployed model.
 - An API key or role based authentication to access the Microsoft Foundry resource

@@ -30,5 +30,5 @@ $env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azur
 $env:AZURE_OPENAI_API_KEY="************"

 # Optional, defaults to Phi-4-mini-instruct
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="Phi-4-mini-instruct"
+$env:FOUNDRY_MODEL="Phi-4-mini-instruct"
 ```
@@ -16,7 +16,6 @@ See the README.md for each sample for the prerequisites for that sample.
 |---|---|
 |[Creating an AIAgent with A2A](./Agent_With_A2A/)|This sample demonstrates how to create AIAgent for an existing A2A agent.|
 |[Creating an AIAgent with Anthropic](./Agent_With_Anthropic/)|This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service|
-|[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
 |[Creating an AIAgent with Foundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Microsoft Foundry to create an AIAgent|
 |[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
@@ -26,6 +26,9 @@ var skillsProvider = new AgentSkillsProvider(
    SubprocessScriptRunner.RunAsync);

 // --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -67,6 +67,9 @@ var unitConverterSkill = new AgentInlineSkill(
 var skillsProvider = new AgentSkillsProvider(unitConverterSkill);

 // --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -22,6 +22,9 @@ var unitConverter = new UnitConverterSkill();
 var skillsProvider = new AgentSkillsProvider(unitConverter);

 // --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -64,6 +64,9 @@ var skillsProvider = new AgentSkillsProviderBuilder()
    .Build();

 // --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -80,6 +80,9 @@ var weightSkill = new WeightConverterSkill();
 var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);

 // --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(
@@ -16,6 +16,9 @@ var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH

 using var codeAct = new HyperlightCodeActProvider(HyperlightCodeActProviderOptions.CreateForWasm(guestPath));

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -39,6 +39,9 @@ options.Tools = [fetchDocs, queryData, sendEmail];

 using var codeAct = new HyperlightCodeActProvider(options);

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -31,6 +31,9 @@ var instructions =
    + "and calling `execute_code` instead of computing values yourself.\n\n"
    + executeCode.BuildInstructions(toolsVisibleToModel: false);

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Valkey\Microsoft.Agents.AI.Valkey.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,55 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates using Valkey for persistent chat history with the Agent Framework.
+// ValkeyChatHistoryProvider persists conversation history across sessions using Valkey lists.
+//
+// Prerequisites:
+//   - A running Valkey server (any version):
+//       docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+//   - Azure OpenAI endpoint and deployment configured via environment variables
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Valkey;
+using Microsoft.Extensions.AI;
+using OpenAI.Chat;
+using Valkey.Glide;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var valkeyConnection = Environment.GetEnvironmentVariable("VALKEY_CONNECTION") ?? "localhost:6379";
+
+var connection = await ConnectionMultiplexer.ConnectAsync(valkeyConnection);
+
+Console.WriteLine("=== ValkeyChatHistoryProvider — Persistent Chat History ===\n");
+
+var historyProvider = new ValkeyChatHistoryProvider(
+    connection,
+    _ => new ValkeyChatHistoryProvider.State($"sample-{Guid.NewGuid():N}"),
+    new ValkeyChatHistoryProviderOptions
+    {
+        KeyPrefix = "sample_chat",
+        MaxMessages = 20
+    });
+
+AIAgent historyAgent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions()
+    {
+        ChatOptions = new() { Instructions = "You are a helpful assistant that remembers our conversation." },
+        ChatHistoryProvider = historyProvider
+    });
+
+AgentSession session1 = await historyAgent.CreateSessionAsync();
+Console.WriteLine(await historyAgent.RunAsync("Hello! My name is Alex and I'm a software engineer.", session1));
+Console.WriteLine(await historyAgent.RunAsync("I'm working on a project using Valkey for caching.", session1));
+Console.WriteLine(await historyAgent.RunAsync("What do you remember about me?", session1));
+
+var messageCount = await historyProvider.GetMessageCountAsync(session1);
+Console.WriteLine($"\n  Stored {messageCount} messages in Valkey.\n");
+
+// Clean up
+connection.Dispose();
+
+Console.WriteLine("Done!");
@@ -0,0 +1,30 @@
+# Agent with Memory Using Valkey
+
+This sample demonstrates using Valkey for persistent chat history with the Agent Framework.
+
+## Components
+
+- **ValkeyChatHistoryProvider** — Persists conversation history across sessions using Valkey lists. Works with any Valkey or Redis OSS server (no search module required).
+
+## Prerequisites
+
+- Azure OpenAI endpoint and deployment
+- A running Valkey server (any version):
+
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+```
+
+## Environment Variables
+
+| Variable | Description | Default |
+|---|---|---|
+| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint URL | (required) |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Model deployment name | `gpt-5.4-mini` |
+| `VALKEY_CONNECTION` | Valkey connection string | `localhost:6379` |
+
+## Running
+
+```bash
+dotnet run
+```
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="AWSSDK.Extensions.Bedrock.MEAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Valkey\Microsoft.Agents.AI.Valkey.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,57 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates using Valkey for persistent chat history with the Agent Framework,
+// powered by Amazon Bedrock.
+//
+// Prerequisites:
+//   - A running Valkey server (any version):
+//       docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+//   - AWS credentials configured (environment variables, AWS profile, or IAM role)
+//   - Access to an Amazon Bedrock model (e.g., Anthropic Claude)
+
+using Amazon;
+using Amazon.BedrockRuntime;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Valkey;
+using Microsoft.Extensions.AI;
+using Valkey.Glide;
+
+var awsRegion = Environment.GetEnvironmentVariable("AWS_REGION") ?? "us-east-1";
+var modelId = Environment.GetEnvironmentVariable("BEDROCK_MODEL_ID") ?? "anthropic.claude-3-5-sonnet-20241022-v2:0";
+var valkeyConnection = Environment.GetEnvironmentVariable("VALKEY_CONNECTION") ?? "localhost:6379";
+
+// Create the Bedrock runtime client.
+var bedrockRuntime = new AmazonBedrockRuntimeClient(RegionEndpoint.GetBySystemName(awsRegion));
+IChatClient chatClient = bedrockRuntime.AsIChatClient(modelId);
+
+var connection = await ConnectionMultiplexer.ConnectAsync(valkeyConnection);
+
+Console.WriteLine("=== ValkeyChatHistoryProvider — Persistent Chat History (Bedrock) ===\n");
+
+var historyProvider = new ValkeyChatHistoryProvider(
+    connection,
+    _ => new ValkeyChatHistoryProvider.State($"bedrock-sample-{Guid.NewGuid():N}"),
+    new ValkeyChatHistoryProviderOptions
+    {
+        KeyPrefix = "bedrock_chat",
+        MaxMessages = 20
+    });
+
+AIAgent historyAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
+{
+    ChatOptions = new() { Instructions = "You are a helpful assistant that remembers our conversation." },
+    ChatHistoryProvider = historyProvider
+});
+
+AgentSession session1 = await historyAgent.CreateSessionAsync();
+Console.WriteLine(await historyAgent.RunAsync("Hello! My name is Alex and I'm a software engineer.", session1));
+Console.WriteLine(await historyAgent.RunAsync("I'm working on a project using Valkey for caching.", session1));
+Console.WriteLine(await historyAgent.RunAsync("What do you remember about me?", session1));
+
+var messageCount = await historyProvider.GetMessageCountAsync(session1);
+Console.WriteLine($"\n  Stored {messageCount} messages in Valkey.\n");
+
+// Clean up
+connection.Dispose();
+
+Console.WriteLine("Done!");
@@ -0,0 +1,41 @@
+# Agent with Memory Using Valkey + Amazon Bedrock
+
+This sample demonstrates using Valkey for persistent chat history with the Agent Framework, powered by Amazon Bedrock via the `AWSSDK.Extensions.Bedrock.MEAI` adapter.
+
+## Components
+
+- **ValkeyChatHistoryProvider** — Persists conversation history across sessions using Valkey lists. Works with any Valkey or Redis OSS server (no search module required).
+- **Amazon Bedrock** — Provides the LLM via `AWSSDK.Extensions.Bedrock.MEAI`, which implements `IChatClient` from `Microsoft.Extensions.AI`.
+
+## Prerequisites
+
+- AWS credentials configured (environment variables, AWS CLI profile, or IAM role)
+- Access to an Amazon Bedrock model (e.g., Anthropic Claude 3.5 Sonnet)
+- A running Valkey server (any version):
+
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+```
+
+## Environment Variables
+
+| Variable | Description | Default |
+|---|---|---|
+| `AWS_REGION` | AWS region for Bedrock | `us-east-1` |
+| `BEDROCK_MODEL_ID` | Bedrock model identifier | `anthropic.claude-3-5-sonnet-20241022-v2:0` |
+| `VALKEY_CONNECTION` | Valkey connection string | `localhost:6379` |
+| `AWS_ACCESS_KEY_ID` | AWS access key (if not using profile/role) | — |
+| `AWS_SECRET_ACCESS_KEY` | AWS secret key (if not using profile/role) | — |
+
+## Running
+
+```bash
+# Using default AWS credential chain (profile, env vars, or IAM role)
+dotnet run
+
+# Or with explicit credentials
+export AWS_ACCESS_KEY_ID="your-access-key"
+export AWS_SECRET_ACCESS_KEY="your-secret-key"
+export AWS_REGION="us-east-1"
+dotnet run
+```
@@ -13,9 +13,9 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;

-string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string foundryEndpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? "memory-store-sample";
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";

 // Create an AIProjectClient for Foundry with Azure Identity authentication.
@@ -1,4 +1,4 @@
-# Agent with Memory Using Microsoft Foundry
+# Agent with Memory Using Microsoft Foundry

 This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories across sessions.

@@ -22,11 +22,11 @@ This sample demonstrates how to create and run an agent that uses Microsoft Foun

 ```bash
 # Microsoft Foundry project endpoint and memory store name
-export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
+export FOUNDRY_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
 export AZURE_AI_MEMORY_STORE_ID="my_memory_store"

 # Model deployment names (models deployed in your Foundry project)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+export FOUNDRY_MODEL="gpt-5.4-mini"
 export AZURE_AI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002"
 ```

@@ -13,8 +13,8 @@ using OpenAI.Files;
 using OpenAI.Responses;
 using OpenAI.VectorStores;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Create an AI Project client and get an OpenAI client that works with the foundry service.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
@@ -10,8 +10,8 @@ using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Hosting;
 using ModelContextProtocol.Server;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -1,4 +1,4 @@
-This sample demonstrates how to expose an existing AI agent as an MCP tool.
+This sample demonstrates how to expose an existing AI agent as an MCP tool.

 ## Run the sample

@@ -21,9 +21,9 @@ To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector)
   ```
 1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
 1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Microsoft Foundry Project to create and run the agent:
-    - AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
-    - AZURE_AI_MODEL_DEPLOYMENT_NAME = gpt-5.4-mini # Replace with your model deployment name
+    - FOUNDRY_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
+    - FOUNDRY_MODEL = gpt-5.4-mini # Replace with your model deployment name
 1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
 1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
 1. Specify your prompt as a value for the `query` argument, for example: `Tell me a joke about a pirate` and click the `Run Tool` button to run the tool.
-1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.
+1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.
@@ -8,9 +8,9 @@ using Azure.AI.Agents.Persistent;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 var deepResearchDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_REASONING_DEPLOYMENT_NAME") ?? "o3-deep-research";
-var modelDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var modelDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECTION_ID") ?? throw new InvalidOperationException("AZURE_AI_BING_CONNECTION_ID is not set.");

 // Configure extended network timeout for long-running Deep Research tasks.
@@ -1,4 +1,4 @@
-# What this sample demonstrates
+# What this sample demonstrates

 This sample demonstrates how to create an Azure AI Agent with the Deep Research Tool, which leverages the o3-deep-research reasoning model to perform comprehensive research on complex topics.

@@ -37,7 +37,7 @@ Set the following environment variables:

 ```powershell
 # Replace with your Microsoft Foundry project endpoint
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"

 # Replace with your Bing Grounding connection ID (full ARM resource URI)
 $env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"
@@ -46,4 +46,4 @@ $env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/pr
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"

 # Optional, defaults to gpt-5.4-mini
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
@@ -40,6 +40,9 @@ using OpenAI.Chat;
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 var chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetChatClient(deploymentName);

@@ -9,13 +9,16 @@ using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI.Foundry;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string JokerName = "JokerAgent";

 // Create the AIProjectClient to manage server-side agents.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

 // Create a server-side agent version using the native SDK.
 ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
@@ -1,4 +1,4 @@
-# Agent Step 00 - FoundryAgent Lifecycle
+# Agent Step 00 - FoundryAgent Lifecycle

 This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a server-side versioned agent in Microsoft Foundry: create → run → delete.

@@ -6,14 +6,14 @@ This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a serv

 - A Microsoft Foundry project endpoint
 - A model deployment name (defaults to `gpt-5.4-mini`)
- Azure CLI installed and authenticated
+- An authenticated Azure identity (for example, sign in with `az login`)

 ## Environment Variables

 | Variable | Description | Required |
 | --- | --- | --- |
-| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
-| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Model deployment name | No (defaults to `gpt-5.4-mini`) |
+| `FOUNDRY_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
+| `FOUNDRY_MODEL` | Model deployment name | No (defaults to `gpt-5.4-mini`) |

 ## Running the sample

@@ -6,8 +6,8 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -14,15 +14,15 @@ Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -15,15 +15,15 @@ Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -15,15 +15,15 @@ Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -15,8 +15,8 @@ static string GetWeather([Description("The location to get the weather for.")] s
 // Define the function tool.
 AITool tool = AIFunctionFactory.Create(GetWeather);

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -16,15 +16,15 @@ Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Extensions.AI;
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use function tools that require human-in-the-loo

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using SampleApp;

 #pragma warning disable CA5399

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample demonstrates how to configure an agent to produce structured output

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to persist and resume agent conversations using ses

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -10,8 +10,8 @@ using OpenTelemetry;
 using OpenTelemetry.Trace;

 string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Create TracerProvider with console exporter.
 string sourceName = Guid.NewGuid().ToString("N");
@@ -13,13 +13,13 @@ This sample demonstrates how to add OpenTelemetry observability to an agent usin

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:APPLICATIONINSIGHTS_CONNECTION_STRING="..."  # Optional
 ```

@@ -9,8 +9,8 @@ using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Hosting;
 using SampleApp;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to register a `ChatClientAgent` in a dependency inj

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using ModelContextProtocol.Client;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Connect to the Microsoft Learn MCP server via HTTP (Streamable HTTP transport).
 Console.WriteLine("Connecting to MCP server at https://learn.microsoft.com/api/mcp ...");
@@ -12,14 +12,14 @@ This sample shows how to use MCP (Model Context Protocol) client tools with a `C

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)
 - Node.js installed (for npx/MCP server)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use image multi-modality with an agent.

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and a vision-capable model deployment (e.g., `gpt-5.4-mini`)
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Extensions.AI;
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use one agent as a function tool for another age

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -20,8 +20,8 @@ static string GetWeather([Description("The location to get the weather for.")] s
 static string GetDateTime()
    => DateTimeOffset.Now.ToString();

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -14,13 +14,13 @@ This sample demonstrates multiple middleware layers working together: PII filter

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -16,8 +16,8 @@ using Microsoft.Extensions.AI;
 using Microsoft.Extensions.DependencyInjection;
 using SampleApp;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string AssistantInstructions = "You are a helpful assistant that helps people find information.";
 const string AssistantName = "PluginAssistant";
@@ -13,13 +13,13 @@ This sample shows how to use plugins with a `ChatClientAgent` using the Response

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using OpenAI.Assistants;
 const string AgentInstructions = "You are a personal math tutor. When asked a math question, write and run code using the python tool to answer the question.";
 const string AgentName = "CoderAgent-RAPI";

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample shows how to use the Code Interpreter tool with a `ChatClientAgent`

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -10,9 +10,12 @@ using Microsoft.Agents.AI.Foundry;
 using Microsoft.Extensions.AI;
 using OpenAI.Responses;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_COMPUTER_USE_DEPLOYMENT_NAME") ?? "computer-use-preview";

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 using IHostedFileClient fileClient = projectClient.GetProjectOpenAIClient().AsIHostedFileClient();

@@ -1,4 +1,4 @@
-# Computer Use with the Responses API
+# Computer Use with the Responses API

 This sample shows how to use the Computer Use tool with `AIProjectClient.AsAIAgent(...)`.

@@ -39,12 +39,12 @@ The model receives a screenshot as input, analyzes it, and responds with a compu

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
 $env:AZURE_AI_COMPUTER_USE_DEPLOYMENT_NAME="computer-use-preview"
 ```

@@ -9,8 +9,8 @@ using Microsoft.Extensions.AI;
 using OpenAI.Assistants;
 using OpenAI.Files;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string AgentInstructions = "You are a helpful assistant that can search through uploaded files to answer questions.";

@@ -13,13 +13,13 @@ This sample shows how to use the File Search tool with a `ChatClientAgent` using

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string AgentInstructions = "You are a helpful assistant that can use the countries API to retrieve information about countries by their currency code.";
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
@@ -13,13 +13,13 @@ This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Re

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -21,8 +21,8 @@ BingCustomSearchToolOptions bingCustomSearchToolParameters = new([
    new BingCustomSearchConfiguration(connectionId, instanceName)
 ]);

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)
 - Bing Custom Search resource configured with a connection ID

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID="your-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/your-bing-connection"
 $env:AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME="your-instance-name"  # The Bing Custom Search configuration name (from Azure portal)
 ```
@@ -19,8 +19,8 @@ const string AgentInstructions = """
 var sharepointOptions = new SharePointGroundingToolOptions();
 sharepointOptions.ProjectConnections.Add(new ToolProjectConnection(sharepointConnectionId));

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the SharePoint Grounding tool with a `ChatClientAge

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)
 - SharePoint connection configured in your Microsoft Foundry project

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/SharepointTestTool"
 ```

@@ -16,8 +16,8 @@ const string AgentInstructions = "You are a helpful assistant with access to Mic
 var fabricToolOptions = new FabricDataAgentToolOptions();
 fabricToolOptions.ProjectConnections.Add(new ToolProjectConnection(fabricConnectionId));

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent`

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)
 - Microsoft Fabric connection configured in your Microsoft Foundry project

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/FabricTestTool"
 ```

@@ -11,8 +11,8 @@ using OpenAI.Responses;
 const string AgentInstructions = "You are a helpful assistant that can search the web to find current information and answer questions accurately.";
 const string AgentName = "WebSearchAgent-RAPI";

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample shows how to use the Web Search tool with a `ChatClientAgent` using

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -14,8 +14,8 @@ using Microsoft.Agents.AI.Foundry;
 using Microsoft.Extensions.AI;
 using OpenAI.Responses;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? $"foundry-memory-sample-{Guid.NewGuid():N}";

@@ -13,14 +13,14 @@ This sample demonstrates how to use the Memory Search tool with a `ChatClientAge

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)
 - A memory store created beforehand via Azure Portal or Python SDK

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
 ```

@@ -30,8 +30,8 @@ Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t =>
 // Wrap each MCP tool with a DelegatingAIFunction to log local invocations.
 List<AITool> wrappedTools = mcpTools.Select(tool => (AITool)new LoggingMcpTool(tool)).ToList();

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use a local MCP (Model Context Protocol) client

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using OpenAI.Responses;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -35,13 +35,13 @@ The container ID and file ID are available from the `ContainerFileCitationMessag

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
 ```

 ## Run the sample
@@ -24,9 +24,9 @@ using OpenAI.Responses;
 const string ToolboxName = "research_toolbox";
 const string Query = "What tools do you have access to?";

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 TokenCredential credential = new DefaultAzureCredential();

@@ -12,18 +12,18 @@ This sample shows how to use a Foundry Toolbox by pointing an `McpClient` at the
 ## Prerequisites

 - A Microsoft Foundry project with a toolbox configured (or let the sample create one for you)
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 The sample creates a toolbox named `research_toolbox` in your Foundry project on
 startup, then connects to its MCP endpoint at
-`{AZURE_AI_PROJECT_ENDPOINT}/toolboxes/research_toolbox/mcp?api-version=v{version}`.
+`{FOUNDRY_PROJECT_ENDPOINT}/toolboxes/research_toolbox/mcp?api-version=v{version}`.

 ## Run the sample

@@ -14,9 +14,9 @@ using Microsoft.Agents.AI;
 using ModelContextProtocol.Client;

 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string toolboxMcpServerUrl = Environment.GetEnvironmentVariable("FOUNDRY_TOOLBOX_MCP_SERVER_URL")
    ?? throw new InvalidOperationException("FOUNDRY_TOOLBOX_MCP_SERVER_URL is not set.");

@@ -15,13 +15,13 @@ and inject them as `AIContextProviders` so the agent can discover and use them a

 - A Microsoft Foundry project with a toolbox already configured
 - The toolbox MCP endpoint must expose `skill://index.json` with `skill-md` entries (SEP-2640). If the resource is absent, the sample runs but the skills provider will be empty.
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:FOUNDRY_TOOLBOX_MCP_SERVER_URL="https://your-foundry-service.services.ai.azure.com/api/projects/your-project/toolboxes/your-toolbox/mcp?api-version=v1"
 ```

@@ -4,12 +4,12 @@ These samples demonstrate how to use Microsoft Foundry with Agent Framework.

 ## Quick start

-The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
+You can create a Foundry agent directly with the `FoundryAgent` type:

 ```csharp
 FoundryAgent agent = new(
    new Uri(endpoint),
-    new AzureCliCredential(),
+    new DefaultAzureCredential(),
    model: "gpt-5.4-mini",
    instructions: "You are good at telling jokes.",
    name: "JokerAgent");
@@ -32,13 +32,13 @@ FoundryAgent agent = aiProjectClient.AsAIAgent(

 - .NET 10 SDK or later
 - Foundry project endpoint
- Azure CLI installed and authenticated
+- An authenticated Azure identity (for example, sign in with `az login`)

 Set:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 Some samples require extra tool-specific environment variables. See each sample for details.
@@ -78,7 +78,11 @@ Some samples require extra tool-specific environment variables. See each sample

 ## Running the samples

+Use the basics sample for a quick smoke test:
+
 ```powershell
 cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\FoundryAgent_Step01
-```
+dotnet run --project .\Agent_Step01_Basics
+```
+
+If you want to exercise the full create-run-delete lifecycle, run `Agent_Step00_FoundryAgentLifecycle`.
@@ -8,8 +8,8 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -1,4 +1,4 @@
-# Evaluation - Custom Evals
+# Evaluation - Custom Evals

 This sample demonstrates writing custom domain-specific evaluation functions using `FunctionEvaluator.Create`. Custom evaluators run locally with no cloud evaluator service needed — useful for enforcing business rules, format requirements, or safety guardrails.

@@ -13,13 +13,13 @@ This sample demonstrates writing custom domain-specific evaluation functions usi
 ## Prerequisites

 - .NET 10 SDK or later
- Azure CLI installed and authenticated (`az login`)
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -6,10 +6,13 @@ using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

 // Create a math tutor agent.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
    .AsAIAgent(
        model: deploymentName,
@@ -1,4 +1,4 @@
-# Evaluation - Expected Outputs
+# Evaluation - Expected Outputs

 This sample demonstrates evaluating agent responses against expected outputs using built-in checks.

@@ -16,8 +16,8 @@ This sample demonstrates evaluating agent responses against expected outputs usi
 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -10,8 +10,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI.Evaluation;
 using FoundryEvals = Microsoft.Agents.AI.Foundry.FoundryEvals;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -1,4 +1,4 @@
-# Evaluation - Simple Eval
+# Evaluation - Simple Eval

 The simplest agent evaluation: create a Foundry agent, run it against test questions, and use Foundry quality evaluators (Relevance, Coherence) to score the responses.

@@ -11,14 +11,14 @@ The simplest agent evaluation: create a Foundry agent, run it against test quest
 ## Prerequisites

 - .NET 10 SDK or later
- Azure CLI installed and authenticated (`az login`)
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)
 - A deployed model in your Azure AI Foundry project

 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -25,8 +25,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using SampleApp;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -1,4 +1,4 @@
-# What this sample demonstrates
+# What this sample demonstrates

 This sample demonstrates how to use a `HarnessAgent` with the Harness `AIContextProviders` (`TodoProvider` and `AgentModeProvider`) for interactive research tasks with web search capabilities powered by Azure AI Foundry. The `HarnessAgent` pre-configures function invocation, per-service-call chat history persistence, and context-window compaction.

@@ -30,7 +30,7 @@ Set the following environment variables:
 export AZURE_FOUNDRY_OPENAI_ENDPOINT="https://your-project.services.ai.azure.com/openai/v1/"

 # Optional: Model deployment name (defaults to gpt-5.4)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4"
+export FOUNDRY_MODEL="gpt-5.4"
 ```

 ## Running the Sample
@@ -20,8 +20,8 @@ using Harness.Shared.Console.OpenAI;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -30,6 +30,9 @@ const string TracingSourceName = "Harness.SubAgents";
 // Set up OpenTelemetry tracing that writes spans to a text file.
 using var tracerProvider = HarnessTracing.CreateFileTracerProvider(TracingSourceName);

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 // Create the AIProjectClient for communicating with the Foundry responses service.
 var projectClient = new AIProjectClient(
    new Uri(endpoint),
@@ -1,4 +1,4 @@
-# Harness Step 02 — BackgroundAgents (Stock Price Research)
+# Harness Step 02 — BackgroundAgents (Stock Price Research)

 This sample demonstrates how to use the **BackgroundAgentsProvider** to delegate work from a parent agent to background agents. Both agents use `HarnessAgent` for pre-configured function invocation, per-service-call persistence, and context-window compaction.

@@ -35,7 +35,7 @@ A parent agent receives a list of stock tickers and uses a web-search background
 - An Azure AI Foundry endpoint with an OpenAI model deployment
 - Set the following environment variables:
  - `AZURE_FOUNDRY_OPENAI_ENDPOINT` — Your Foundry OpenAI endpoint URL
-  - `AZURE_AI_MODEL_DEPLOYMENT_NAME` — Model deployment name (defaults to `gpt-5.4`)
+  - `FOUNDRY_MODEL` — Model deployment name (defaults to `gpt-5.4`)

 ## Running the Sample

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Peter Ibekwe	81ea2f6703	Inlined single use variables.	2026-06-12 08:55:36 -07:00
Peter Ibekwe	56ccbc86a8	Address PR comments	2026-06-11 22:02:58 -07:00
Peter Ibekwe	dd980534f3	Merge branch 'main' into peibekwe/declarative-bugfix-python-new	2026-06-11 17:08:12 -07:00
Yufeng HeandGitHub	4c1b9efa8c	.NET: fix: filter filesystem checkpoint index by session (#6132 ) * fix: filter filesystem checkpoint index by session * fix: filter checkpoint index by parent * .NET: preserve legacy checkpoint index discovery	2026-06-11 22:35:57 +00:00
Peter IbekweandGitHub	e7937947d9	Python: Bug fix for declarative workflows (#6468 ) * Fix declarative object parsing bug * Remove unnecessary comment * Address PR comments * Address PR comments. * Fix CI failures.	2026-06-11 22:34:15 +00:00
westey GitHub Copilot	3d5421edc1	Python: Integrate shell tool into harness agent (#6451 ) * Integrate shell tool into AgentHarness * Validate shell_executor exposes as_function() with a clear TypeError Addresses PR review feedback: a public factory should fail fast with an actionable error rather than a cryptic AttributeError when an incompatible shell_executor is supplied. Validation happens upfront, regardless of whether the client supports shell tools. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Type shell harness params via TYPE_CHECKING import Addresses PR review feedback: type shell_executor and shell_environment_provider_options instead of Any, using a TYPE_CHECKING import from agent_framework_tools.shell. The import never executes at runtime, so there is no circular dependency, and the lazy runtime import of ShellEnvironmentProvider is retained. Since ShellExecutor is a protocol without as_function(), the validated getattr result is invoked directly. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 20:51:59 +00:00
Peter Ibekwe	0ade298cc4	declarative action approval bugfix	2026-06-11 13:16:36 -07:00
Giles Odigwe GitHub Copilot Copilot	8b0405de1b	.NET: Fix CopySessionConfig() and CopyResumeSessionConfig() to preserve SessionConfig.Streaming value (#6463 ) * Fix CopySessionConfig and CopyResumeSessionConfig ignoring Streaming value (#4732) CopySessionConfig() and CopyResumeSessionConfig() hardcoded Streaming = true, ignoring the caller's explicitly set SessionConfig.Streaming value. This made it impossible to disable streaming when using AsAIAgent() with the GitHub Copilot SDK. Changed both methods to use source.Streaming ?? true (and source?.Streaming ?? true for the nullable overload), preserving the caller's value when set while maintaining backward compatibility by defaulting to true when unset. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix non-streaming response path for SessionConfig.Streaming=false (#4732) The config-copy fix (preserving Streaming=false via null-coalescing) was already in place, but ConvertToAgentResponseUpdate(AssistantMessageEvent) always emitted raw AIContent without text—assuming delta events had already delivered it. When streaming is disabled there are no delta events, so the assistant's final text was silently dropped. Changes: - Add isStreaming parameter to ConvertToAgentResponseUpdate for AssistantMessageEvent so it emits TextContent in non-streaming mode. - Capture the resolved streaming flag in RunCoreStreamingAsync and pass it through the event subscription closure. - Add/update unit tests for both streaming and non-streaming paths. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add test for null Data path in ConvertToAgentResponseUpdate (#4732) Add a regression test covering the null-propagation path where AssistantMessageEvent.Data is null. The production code already handles this via ?. operators, but no test previously verified the behavior. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 18:18:05 +00:00
Peter Ibekwe	db2c576f56	Merge branch 'main' into peibekwe/declarative-bugfix-python-new	2026-06-11 11:11:00 -07:00
Eduard van Valkenburg GitHub Copilot	df29af611c	Python: Add tool approval middleware (#6414 ) * Add Python tool approval middleware Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix tool approval restored state handling Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Gate hidden approvals on explicit approval responses Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Handle string inputs in approval replay scan Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Cover argument-scoped approval rules Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Refine tool approval state and budgets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix tool approval PR CI failures Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Revert DevUI Aspire README link change Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 17:35:44 +00:00
Ben Thomas GitHub Copilot	c79f886dc3	.NET: Align Foundry sample environment variables and credentials. (#6422 ) * dotnet: refresh Foundry sample guidance Carry forward the still-relevant sample guidance and Foundry-specific documentation fixes from the old stacked sample migration work, adapted to the current repo layout and policy. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: rename Foundry sample env vars Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: remove persistent provider sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: drop SAMPLE_GUIDELINES.md from this PR Defer the guidelines doc and its cross-link to a follow-on PR to avoid broken-link failures in CI. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: add DefaultAzureCredential warning to remaining samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: address PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 17:26:00 +00:00
Giles Odigwe GitHub Copilot	c9e2a490be	Fix AzureFunctions integration tests — set FUNCTIONS_WORKER_RUNTIME (#6425 ) Azure Functions Core Tools v4 can no longer auto-detect the worker runtime when local.settings.json is absent. Add the required FUNCTIONS_WORKER_RUNTIME=dotnet-isolated environment variable to both StartFunctionApp helpers and re-enable the skipped tests. Fixes: https://github.com/microsoft/agent-framework/issues/6402 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 15:09:55 +00:00
westeyandGitHub	12ce099165	.NET: Add LoopAgent capability for Harnesses (#6384 ) * Add LoopAgent capability for Harnesses * Address PR comments. * Add support for returning user messages and response aggregation * Support fresh context per iteration with input sessions via cloning * Add ability to receive newly created sessions via callback * Address PR comments * Add judge criteria * Address PR comments	2026-06-11 15:00:01 +00:00
Matthias Howell GitHub Roger Barreto Matthias Howell westey	8e1998ddcb	.NET: Adds Valkey to chat message history - issue 5445 (#5542 ) * Adds Valkey to chat message history * Address review: switch to Valkey.Glide, add options class, remove context provider - Switch from StackExchange.Redis to Valkey.Glide 1.1.0 (official Valkey .NET client) - Extract optional params into ValkeyChatHistoryProviderOptions - Add JsonSerializerOptions support, remove [RequiresUnreferencedCode] - Make MaxMessages/MaxMessagesToRetrieve readonly via options - Remove ValkeyContextProvider (overlaps with ChatHistoryMemoryProvider + MEVD) - Remove ValkeyProviderScope (only used by context provider) - Remove connection string constructors (caller manages IConnectionMultiplexer) - Update samples to use new API and gpt-5.4-mini * Use type-safe JsonSerializer overloads, remove suppress attributes Use JsonSerializerOptions.GetTypeInfo() for Serialize/Deserialize calls to enable NativeAOT/trimming compatibility without suppress attributes. Default to AgentAbstractionsJsonUtilities.DefaultOptions when no options provided. Signed-off-by: Matthias Howell <matthias.howell@improving.com> * Update READMEs: remove context provider references Remove ValkeyContextProvider and long-term memory references from sample READMEs since the context provider was removed from this PR. Simplify Valkey server requirements (no search module needed for chat history). Signed-off-by: Matthias Howell <matthias.howell@improving.com> * Apply suggestion from @westey-m * Fix formatting (dotnet format) Signed-off-by: Matthias Howell <matthias.howell@improving.com> * Update dotnet/src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> --------- Signed-off-by: Matthias Howell <matthias.howell@improving.com> Co-authored-by: Matthias Howell <matthias.howell@yoppworks.com> Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>	2026-06-11 13:18:00 +00:00
chetantoshniwal GitHub Azure SRE Agent Copilot Giles Odigwe	4149f24791	Python: [Generated by SRE Agent] Fix MCP allowed_tools empty list handling (#6296 ) * Fix MCP allowed_tools empty list handling When allowed_tools is set to an empty list [], the falsy check 'if not self.allowed_tools' incorrectly treats it as unconfigured (same as None), causing all tools to be exposed. Change to an explicit 'is None' check so that an empty list correctly results in no tools being allowed. Co-authored-by: Azure SRE Agent <noreply@microsoft.com> * Clarify allowed_tools docstring: None vs [] semantics Per Eduard's review on PR #6296: explicitly document that None exposes all tools and [] exposes none, across all four MCPTool / MCPStdioTool / MCPStreamableHTTPTool / MCPWebsocketTool docstrings. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * allowed_tools docstring: recommend load_tools=False for full disable Per Eduard's follow-up on PR #6296: `load_tools=False` is the cleaner idiom when you don't want to expose any tools. Reframe `allowed_tools=[]` in the docstring as a runtime guard / inspection-only path and cross-reference `load_tools`. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Azure SRE Agent <noreply@microsoft.com> Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-06-11 06:46:46 +00:00