Bump actions/checkout from 5 to 6 (#2404 )

Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
Python: Updated package versions (#2784 )
2026-06-16 21:04:09 +08:00 · 2025-12-11 18:43:52 +00:00 · 2025-12-11 18:39:08 +00:00 · 2025-12-11 17:24:08 +00:00 · 2025-12-11 14:08:20 +00:00 · 2025-12-11 11:04:22 +00:00
74 changed files with 2339 additions and 357 deletions
@@ -32,7 +32,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
        with:
          persist-credentials: false

@@ -37,7 +37,7 @@ jobs:
    outputs:
      dotnetChanges: ${{ steps.filter.outputs.dotnet}}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -68,7 +68,7 @@ jobs:
    runs-on: ${{ matrix.os }}
    environment: ${{ matrix.environment }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
            persist-credentials: false
            sparse-checkout: |
@@ -30,7 +30,7 @@ jobs:

    steps:
      - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
        with:
          fetch-depth: 0
          persist-credentials: false
@@ -19,7 +19,7 @@ jobs:
    runs-on: ubuntu-22.04
    # check out the latest version of the code
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          persist-credentials: false

@@ -27,7 +27,7 @@ jobs:
    env:
      UV_PYTHON: ${{ matrix.python-version }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - name: Set up python and install the project
@@ -24,7 +24,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Set up uv
        uses: astral-sh/setup-uv@v7
        with:
@@ -24,7 +24,7 @@ jobs:
    outputs:
      pythonChanges: ${{ steps.filter.outputs.python}}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -59,7 +59,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: Set up python and install the project
        id: python-setup
@@ -28,7 +28,7 @@ jobs:
    outputs:
      pythonChanges: ${{ steps.filter.outputs.python}}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -75,7 +75,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -135,7 +135,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -23,7 +23,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -19,7 +19,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Download coverage report
        uses: actions/download-artifact@v6
        with:
@@ -20,7 +20,7 @@ jobs:
    env:
      UV_PYTHON: "3.10"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      # Save the PR number to a file since the workflow_run event
      # in the coverage report workflow does not have access to it
      - name: Save PR number
@@ -27,7 +27,7 @@ jobs:
      run:
        working-directory: python
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -23,7 +23,7 @@
    <PackageVersion Include="Azure.AI.Projects.OpenAI" Version="1.0.0-beta.4" />
    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.8" />
    <PackageVersion Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
-    <PackageVersion Include="Azure.Identity" Version="1.17.0" />
+    <PackageVersion Include="Azure.Identity" Version="1.17.1" />
    <PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
    <!-- Google Gemini  -->
    <PackageVersion Include="Google.GenAI" Version="0.6.0" />
@@ -102,7 +102,7 @@
    <PackageVersion Include="ModelContextProtocol" Version="0.4.0-preview.3" />
    <!-- Inference SDKs -->
    <PackageVersion Include="Anthropic.SDK" Version="5.8.0" />
-    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.4.7" />
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.4.11" />
    <PackageVersion Include="Microsoft.ML.OnnxRuntimeGenAI" Version="0.10.0" />
    <PackageVersion Include="OllamaSharp" Version="5.4.8" />
    <PackageVersion Include="OpenAI" Version="2.7.0" />
@@ -129,6 +129,7 @@
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step03_CreateFromChatClient/Agent_OpenAI_Step03_CreateFromChatClient.csproj" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/Agent_OpenAI_Step05_Conversation.csproj" />
  </Folder>
  <Folder Name="/Samples/Purview/" />
  <Folder Name="/Samples/Purview/AgentWithPurview/">
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,98 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to maintain conversation state using the OpenAIResponseClientAgent
+// and AgentThread. By passing the same thread to multiple agent invocations, the agent
+// automatically maintains the conversation history, allowing the AI model to understand
+// context from previous exchanges.
+
+using System.ClientModel;
+using System.ClientModel.Primitives;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI;
+using OpenAI.Chat;
+using OpenAI.Conversations;
+
+string apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
+string model = Environment.GetEnvironmentVariable("OPENAI_MODEL") ?? "gpt-4o-mini";
+
+// Create a ConversationClient directly from OpenAIClient
+OpenAIClient openAIClient = new(apiKey);
+ConversationClient conversationClient = openAIClient.GetConversationClient();
+
+// Create an agent directly from the OpenAIResponseClient using OpenAIResponseClientAgent
+ChatClientAgent agent = new(openAIClient.GetOpenAIResponseClient(model).AsIChatClient(), instructions: "You are a helpful assistant.", name: "ConversationAgent");
+
+ClientResult createConversationResult = await conversationClient.CreateConversationAsync(BinaryContent.Create(BinaryData.FromString("{}")));
+
+using JsonDocument createConversationResultAsJson = JsonDocument.Parse(createConversationResult.GetRawResponse().Content.ToString());
+string conversationId = createConversationResultAsJson.RootElement.GetProperty("id"u8)!.GetString()!;
+
+// Create a thread for the conversation - this enables conversation state management for subsequent turns
+AgentThread thread = agent.GetNewThread(conversationId);
+
+Console.WriteLine("=== Multi-turn Conversation Demo ===\n");
+
+// First turn: Ask about a topic
+Console.WriteLine("User: What is the capital of France?");
+UserChatMessage firstMessage = new("What is the capital of France?");
+
+// After this call, the conversation state associated in the options is stored in 'thread' and used in subsequent calls
+ChatCompletion firstResponse = await agent.RunAsync([firstMessage], thread);
+Console.WriteLine($"Assistant: {firstResponse.Content.Last().Text}\n");
+
+// Second turn: Follow-up question that relies on conversation context
+Console.WriteLine("User: What famous landmarks are located there?");
+UserChatMessage secondMessage = new("What famous landmarks are located there?");
+
+ChatCompletion secondResponse = await agent.RunAsync([secondMessage], thread);
+Console.WriteLine($"Assistant: {secondResponse.Content.Last().Text}\n");
+
+// Third turn: Another follow-up that demonstrates context continuity
+Console.WriteLine("User: How tall is the most famous one?");
+UserChatMessage thirdMessage = new("How tall is the most famous one?");
+
+ChatCompletion thirdResponse = await agent.RunAsync([thirdMessage], thread);
+Console.WriteLine($"Assistant: {thirdResponse.Content.Last().Text}\n");
+
+Console.WriteLine("=== End of Conversation ===");
+
+// Show full conversation history
+Console.WriteLine("Full Conversation History:");
+ClientResult getConversationResult = await conversationClient.GetConversationAsync(conversationId);
+
+Console.WriteLine("Conversation created.");
+Console.WriteLine($"    Conversation ID: {conversationId}");
+Console.WriteLine();
+
+CollectionResult getConversationItemsResults = conversationClient.GetConversationItems(conversationId);
+foreach (ClientResult result in getConversationItemsResults.GetRawPages())
+{
+    Console.WriteLine("Message contents retrieved. Order is most recent first by default.");
+    using JsonDocument getConversationItemsResultAsJson = JsonDocument.Parse(result.GetRawResponse().Content.ToString());
+    foreach (JsonElement element in getConversationItemsResultAsJson.RootElement.GetProperty("data").EnumerateArray())
+    {
+        string messageId = element.GetProperty("id"u8).ToString();
+        string messageRole = element.GetProperty("role"u8).ToString();
+        Console.WriteLine($"    Message ID: {messageId}");
+        Console.WriteLine($"    Message Role: {messageRole}");
+
+        foreach (var content in element.GetProperty("content").EnumerateArray())
+        {
+            string messageContentText = content.GetProperty("text"u8).ToString();
+            Console.WriteLine($"    Message Text: {messageContentText}");
+        }
+        Console.WriteLine();
+    }
+}
+
+ClientResult deleteConversationResult = conversationClient.DeleteConversation(conversationId);
+using JsonDocument deleteConversationResultAsJson = JsonDocument.Parse(deleteConversationResult.GetRawResponse().Content.ToString());
+bool deleted = deleteConversationResultAsJson.RootElement
+    .GetProperty("deleted"u8)
+    .GetBoolean();
+
+Console.WriteLine("Conversation deleted.");
+Console.WriteLine($"    Deleted: {deleted}");
+Console.WriteLine();
@@ -0,0 +1,90 @@
+# Managing Conversation State with OpenAI
+
+This sample demonstrates how to maintain conversation state across multiple turns using the Agent Framework with OpenAI's Conversation API.
+
+## What This Sample Shows
+
+- **Conversation State Management**: Shows how to use `ConversationClient` and `AgentThread` to maintain conversation context across multiple agent invocations
+- **Multi-turn Conversations**: Demonstrates follow-up questions that rely on context from previous messages in the conversation
+- **Server-Side Storage**: Uses OpenAI's Conversation API to manage conversation history server-side, allowing the model to access previous messages without resending them
+- **Conversation Lifecycle**: Demonstrates creating, retrieving, and deleting conversations
+
+## Key Concepts
+
+### ConversationClient for Server-Side Storage
+
+The `ConversationClient` manages conversations on OpenAI's servers:
+
+```csharp
+// Create a ConversationClient from OpenAIClient
+OpenAIClient openAIClient = new(apiKey);
+ConversationClient conversationClient = openAIClient.GetConversationClient();
+
+// Create a new conversation
+ClientResult createConversationResult = await conversationClient.CreateConversationAsync(BinaryContent.Create(BinaryData.FromString("{}")));
+```
+
+### AgentThread for Conversation State
+
+The `AgentThread` works with `ChatClientAgentRunOptions` to link the agent to a server-side conversation:
+
+```csharp
+// Set up agent run options with the conversation ID
+ChatClientAgentRunOptions agentRunOptions = new() { ChatOptions = new ChatOptions() { ConversationId = conversationId } };
+
+// Create a thread for the conversation
+AgentThread thread = agent.GetNewThread();
+
+// First call links the thread to the conversation
+ChatCompletion firstResponse = await agent.RunAsync([firstMessage], thread, agentRunOptions);
+
+// Subsequent calls use the thread without needing to pass options again
+ChatCompletion secondResponse = await agent.RunAsync([secondMessage], thread);
+```
+
+### Retrieving Conversation History
+
+You can retrieve the full conversation history from the server:
+
+```csharp
+CollectionResult getConversationItemsResults = conversationClient.GetConversationItems(conversationId);
+foreach (ClientResult result in getConversationItemsResults.GetRawPages())
+{
+    // Process conversation items
+}
+```
+
+### How It Works
+
+1. **Create an OpenAI Client**: Initialize an `OpenAIClient` with your API key
+2. **Create a Conversation**: Use `ConversationClient` to create a server-side conversation
+3. **Create an Agent**: Initialize an `OpenAIResponseClientAgent` with the desired model and instructions
+4. **Create a Thread**: Call `agent.GetNewThread()` to create a new conversation thread
+5. **Link Thread to Conversation**: Pass `ChatClientAgentRunOptions` with the `ConversationId` on the first call
+6. **Send Messages**: Subsequent calls to `agent.RunAsync()` only need the thread - context is maintained
+7. **Cleanup**: Delete the conversation when done using `conversationClient.DeleteConversation()`
+
+## Running the Sample
+
+1. Set the required environment variables:
+   ```powershell
+   $env:OPENAI_API_KEY = "your_api_key_here"
+   $env:OPENAI_MODEL = "gpt-4o-mini"
+   ```
+
+2. Run the sample:
+   ```powershell
+   dotnet run
+   ```
+
+## Expected Output
+
+The sample demonstrates a three-turn conversation where each follow-up question relies on context from previous messages:
+
+1. First question asks about the capital of France
+2. Second question asks about landmarks "there" - requiring understanding of the previous answer
+3. Third question asks about "the most famous one" - requiring context from both previous turns
+
+After the conversation, the sample retrieves and displays the full conversation history from the server, then cleans up by deleting the conversation.
+
+This demonstrates that the conversation state is properly maintained across multiple agent invocations using OpenAI's server-side conversation storage.
@@ -13,4 +13,5 @@ Agent Framework provides additional support to allow OpenAI developers to use th
 |[Creating an AIAgent](./Agent_OpenAI_Step01_Running/)|This sample demonstrates how to create and run a basic agent with native OpenAI SDK types. Shows both regular and streaming invocation of the agent.|
 |[Using Reasoning Capabilities](./Agent_OpenAI_Step02_Reasoning/)|This sample demonstrates how to create an AI agent with reasoning capabilities using OpenAI's reasoning models and response types.|
 |[Creating an Agent from a ChatClient](./Agent_OpenAI_Step03_CreateFromChatClient/)|This sample demonstrates how to create an AI agent directly from an OpenAI.Chat.ChatClient instance using OpenAIChatClientAgent.|
-|[Creating an Agent from an OpenAIResponseClient](./Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/)|This sample demonstrates how to create an AI agent directly from an OpenAI.Responses.OpenAIResponseClient instance using OpenAIResponseClientAgent.|
+|[Creating an Agent from an OpenAIResponseClient](./Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/)|This sample demonstrates how to create an AI agent directly from an OpenAI.Responses.OpenAIResponseClient instance using OpenAIResponseClientAgent.|
+|[Managing Conversation State](./Agent_OpenAI_Step05_Conversation/)|This sample demonstrates how to maintain conversation state across multiple turns using the AgentThread for context continuity.|
@@ -36,9 +36,9 @@
  </ItemGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.4" />
+    <PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.5" />
    <PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
-    <PackageReference Include="Azure.Identity" Version="1.17.0" />
+    <PackageReference Include="Azure.Identity" Version="1.17.1" />
    <PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-preview.251125.1" />
    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.0-preview.1.25608.1" />
  </ItemGroup>
@@ -37,7 +37,7 @@
  <ItemGroup>
    <PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.4" />
    <PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
-    <PackageReference Include="Azure.Identity" Version="1.17.0" />
+    <PackageReference Include="Azure.Identity" Version="1.17.1" />
    <PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-preview.251125.1" />
    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.0-preview.1.25608.1" />
  </ItemGroup>
@@ -37,7 +37,7 @@
  <ItemGroup>
    <PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.4" />
    <PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
-    <PackageReference Include="Azure.Identity" Version="1.17.0" />
+    <PackageReference Include="Azure.Identity" Version="1.17.1" />
    <PackageReference Include="Microsoft.Agents.AI.Workflows" Version="1.0.0-preview.251125.1" />
    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.0-preview.1.25608.1" />
  </ItemGroup>
@@ -198,7 +198,7 @@ internal sealed class A2AAgent : AIAgent
    }

    /// <inheritdoc/>
-    public override string Id => this._id ?? base.Id;
+    protected override string? IdCore => this._id;

    /// <inheritdoc/>
    public override string? Name => this._name ?? base.Name;
@@ -22,9 +22,6 @@ namespace Microsoft.Agents.AI;
 [DebuggerDisplay("{DisplayName,nq}")]
 public abstract class AIAgent
 {
-    /// <summary>Default ID of this agent instance.</summary>
-    private readonly string _id = Guid.NewGuid().ToString("N");
-
    /// <summary>
    /// Gets the unique identifier for this agent instance.
    /// </summary>
@@ -37,7 +34,19 @@ public abstract class AIAgent
    /// agent instances in multi-agent scenarios. They should remain stable for the lifetime
    /// of the agent instance.
    /// </remarks>
-    public virtual string Id => this._id;
+    public string Id { get => this.IdCore ?? field; } = Guid.NewGuid().ToString("N");
+
+    /// <summary>
+    /// Gets a custom identifier for the agent, which can be overridden by derived classes.
+    /// </summary>
+    /// <value>
+    /// A string representing the agent's identifier, or <see langword="null"/> if the default ID should be used.
+    /// </value>
+    /// <remarks>
+    /// Derived classes can override this property to provide a custom identifier.
+    /// When <see langword="null"/> is returned, the <see cref="Id"/> property will use the default randomly-generated identifier.
+    /// </remarks>
+    protected virtual string? IdCore => null;

    /// <summary>
    /// Gets the human-readable name of the agent.
@@ -61,7 +70,7 @@ public abstract class AIAgent
    /// This property provides a guaranteed non-null string suitable for display in user interfaces,
    /// logs, or other contexts where a readable identifier is needed.
    /// </remarks>
-    public virtual string DisplayName => this.Name ?? this.Id ?? this._id; // final fallback to _id in case Id override returns null
+    public virtual string DisplayName => this.Name ?? this.Id;

    /// <summary>
    /// Gets a description of the agent's purpose, capabilities, or behavior.
@@ -54,7 +54,7 @@ public class DelegatingAIAgent : AIAgent
    protected AIAgent InnerAgent { get; }

    /// <inheritdoc />
-    public override string Id => this.InnerAgent.Id;
+    protected override string? IdCore => this.InnerAgent.Id;

    /// <inheritdoc />
    public override string? Name => this.InnerAgent.Name;
@@ -81,7 +81,7 @@ internal sealed partial class DevUIMiddleware
            }

            context.Response.StatusCode = StatusCodes.Status301MovedPermanently;
-            context.Response.Headers.Location = redirectUrl;
+            context.Response.Headers.Location = redirectUrl; // CodeQL [SM04598] justification: The redirect URL is constructed from a server-configured base path (_basePath), not user input. The query string is only appended as parameters and cannot change the redirect destination since this is a relative URL.

            if (this._logger.IsEnabled(LogLevel.Debug))
            {
@@ -19,7 +19,7 @@ internal sealed class EntityAgentWrapper(
    private readonly IServiceProvider? _entityScopedServices = entityScopedServices;

    // The ID of the agent is always the entity ID.
-    public override string Id => this._entityContext.Id.ToString();
+    protected override string? IdCore => this._entityContext.Id.ToString();

    public override async Task<AgentRunResponse> RunAsync(
        IEnumerable<ChatMessage> messages,
@@ -39,7 +39,7 @@ internal sealed class WorkflowHostAgent : AIAgent
        this._describeTask = this._workflow.DescribeProtocolAsync().AsTask();
    }

-    public override string Id => this._id ?? base.Id;
+    protected override string? IdCore => this._id;
    public override string? Name { get; }
    public override string? Description { get; }

@@ -121,7 +121,7 @@ public sealed partial class ChatClientAgent : AIAgent
    public IChatClient ChatClient { get; }

    /// <inheritdoc/>
-    public override string Id => this._agentOptions?.Id ?? base.Id;
+    protected override string? IdCore => this._agentOptions?.Id;

    /// <inheritdoc/>
    public override string? Name => this._agentOptions?.Name;
@@ -214,13 +214,31 @@ public class AIAgentTests
    [Fact]
    public void ValidateAgentIDIsIdempotent()
    {
+        // Arrange
        var agent = new MockAgent();

+        // Act
        string id = agent.Id;
+
+        // Assert
        Assert.NotNull(id);
        Assert.Equal(id, agent.Id);
    }

+    [Fact]
+    public void ValidateAgentIDCanBeProvidedByDerivedAgentClass()
+    {
+        // Arrange
+        var agent = new MockAgent(id: "test-agent-id");
+
+        // Act
+        string id = agent.Id;
+
+        // Assert
+        Assert.NotNull(id);
+        Assert.Equal("test-agent-id", id);
+    }
+
    #region GetService Method Tests

    /// <summary>
@@ -344,6 +362,13 @@ public class AIAgentTests

    private sealed class MockAgent : AIAgent
    {
+        public MockAgent(string? id = null)
+        {
+            this.IdCore = id;
+        }
+
+        protected override string? IdCore { get; }
+
        public override AgentThread GetNewThread()
            => throw new NotImplementedException();

@@ -6,6 +6,7 @@ using System.Threading;
 using System.Threading.Tasks;
 using Microsoft.Extensions.AI;
 using Moq;
+using Moq.Protected;

 namespace Microsoft.Agents.AI.Abstractions.UnitTests;

@@ -31,7 +32,7 @@ public class DelegatingAIAgentTests
        this._testThread = new TestAgentThread();

        // Setup inner agent mock
-        this._innerAgentMock.Setup(x => x.Id).Returns("test-agent-id");
+        this._innerAgentMock.Protected().SetupGet<string>("IdCore").Returns("test-agent-id");
        this._innerAgentMock.Setup(x => x.Name).Returns("Test Agent");
        this._innerAgentMock.Setup(x => x.Description).Returns("Test Description");
        this._innerAgentMock.Setup(x => x.GetNewThread()).Returns(this._testThread);
@@ -93,7 +94,7 @@ public class DelegatingAIAgentTests

        // Assert
        Assert.Equal("test-agent-id", id);
-        this._innerAgentMock.Verify(x => x.Id, Times.Once);
+        this._innerAgentMock.Protected().VerifyGet<string>("IdCore", Times.Once());
    }

    /// <summary>
@@ -276,15 +276,9 @@ public sealed class BasicStreamingTests : IAsyncDisposable
 [SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated via dependency injection")]
 internal sealed class FakeChatClientAgent : AIAgent
 {
-    public FakeChatClientAgent()
-    {
-        this.Id = "fake-agent";
-        this.Description = "A fake agent for testing";
-    }
+    protected override string? IdCore => "fake-agent";

-    public override string Id { get; }
-
-    public override string? Description { get; }
+    public override string? Description => "A fake agent for testing";

    public override AgentThread GetNewThread()
    {
@@ -350,15 +344,9 @@ internal sealed class FakeChatClientAgent : AIAgent
 [SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated via dependency injection")]
 internal sealed class FakeMultiMessageAgent : AIAgent
 {
-    public FakeMultiMessageAgent()
-    {
-        this.Id = "fake-multi-message-agent";
-        this.Description = "A fake agent that sends multiple messages for testing";
-    }
+    protected override string? IdCore => "fake-multi-message-agent";

-    public override string Id { get; }
-
-    public override string? Description { get; }
+    public override string? Description => "A fake agent that sends multiple messages for testing";

    public override AgentThread GetNewThread()
    {
@@ -421,7 +421,7 @@ public sealed class AGUIEndpointRouteBuilderExtensionsTests

    private sealed class MultiResponseAgent : AIAgent
    {
-        public override string Id => "multi-response-agent";
+        protected override string? IdCore => "multi-response-agent";

        public override string? Description => "Agent that produces multiple text chunks";

@@ -510,7 +510,7 @@ public sealed class AGUIEndpointRouteBuilderExtensionsTests

    private sealed class TestAgent : AIAgent
    {
-        public override string Id => "test-agent";
+        protected override string? IdCore => "test-agent";

        public override string? Description => "Test agent";

@@ -57,7 +57,7 @@ internal sealed class HelloAgent(string id = nameof(HelloAgent)) : AIAgent
    public const string Greeting = "Hello World!";
    public const string DefaultId = nameof(HelloAgent);

-    public override string Id => id;
+    protected override string? IdCore => id;
    public override string? Name => id;

    public override AgentThread GetNewThread()
@@ -19,7 +19,7 @@ public class SpecializedExecutorSmokeTests
 {
    public class TestAIAgent(List<ChatMessage>? messages = null, string? id = null, string? name = null) : AIAgent
    {
-        public override string Id => id ?? base.Id;
+        protected override string? IdCore => id;
        public override string? Name => name;

        public static List<ChatMessage> ToChatMessages(params string[] messages)
@@ -13,7 +13,7 @@ namespace Microsoft.Agents.AI.Workflows.UnitTests;

 internal class TestEchoAgent(string? id = null, string? name = null, string? prefix = null) : AIAgent
 {
-    public override string Id => id ?? base.Id;
+    protected override string? IdCore => id;
    public override string? Name => name ?? base.Name;

    public override AgentThread DeserializeThread(JsonElement serializedThread, JsonSerializerOptions? jsonSerializerOptions = null)
@@ -7,6 +7,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ## [Unreleased]

+## [1.0.0b251211] - 2025-12-11
+
+### Added
+
+- **agent-framework-core**: Extend HITL support for all orchestration patterns (#2620)
+- **agent-framework-core**: Add factory pattern to concurrent orchestration builder (#2738)
+- **agent-framework-core**: Add factory pattern to sequential orchestration builder (#2710)
+- **agent-framework-azure-ai**: Capture file IDs from code interpreter in streaming responses (#2741)
+
+### Changed
+
+- **agent-framework-azurefunctions**: Change DurableAIAgent log level from warning to debug when invoked without thread (#2736)
+
+### Fixed
+
+- **agent-framework-core**: Added more complete parsing for mcp tool arguments (#2756)
+- **agent-framework-core**: Fix GroupChat ManagerSelectionResponse JSON Schema for OpenAI Structured Outputs (#2750)
+- **samples**: Standardize OpenAI API key environment variable naming (#2629)
+
 ## [1.0.0b251209] - 2025-12-09

 ### Added
@@ -347,7 +366,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 For more information, see the [announcement blog post](https://devblogs.microsoft.com/foundry/introducing-microsoft-agent-framework-the-open-source-engine-for-agentic-ai-apps/).

-[Unreleased]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251209...HEAD
+[Unreleased]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251211...HEAD
+[1.0.0b251211]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251209...python-1.0.0b251211
 [1.0.0b251209]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251204...python-1.0.0b251209
 [1.0.0b251204]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251120...python-1.0.0b251204
 [1.0.0b251120]: https://github.com/microsoft/agent-framework/compare/python-1.0.0b251117...python-1.0.0b251120
@@ -4,7 +4,7 @@ description = "A2A integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -1,6 +1,6 @@
 [project]
 name = "agent-framework-ag-ui"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 description = "AG-UI protocol integration for Agent Framework"
 readme = "README.md"
 license-files = ["LICENSE"]
@@ -4,7 +4,7 @@ description = "Anthropic integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Azure AI Search integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -63,6 +63,8 @@ from azure.ai.agents.models import (
    McpTool,
    MessageDeltaChunk,
    MessageDeltaTextContent,
+    MessageDeltaTextFileCitationAnnotation,
+    MessageDeltaTextFilePathAnnotation,
    MessageDeltaTextUrlCitationAnnotation,
    MessageImageUrlParam,
    MessageInputContentBlock,
@@ -471,6 +473,45 @@ class AzureAIAgentClient(BaseChatClient):

        return url_citations

+    def _extract_file_path_contents(self, message_delta_chunk: MessageDeltaChunk) -> list[HostedFileContent]:
+        """Extract file references from MessageDeltaChunk annotations.
+
+        Code interpreter generates files that are referenced via file path or file citation
+        annotations in the message content. This method extracts those file IDs and returns
+        them as HostedFileContent objects.
+
+        Handles two annotation types:
+        - MessageDeltaTextFilePathAnnotation: Contains file_path.file_id
+        - MessageDeltaTextFileCitationAnnotation: Contains file_citation.file_id
+
+        Args:
+            message_delta_chunk: The message delta chunk to process
+
+        Returns:
+            List of HostedFileContent objects for any files referenced in annotations
+        """
+        file_contents: list[HostedFileContent] = []
+
+        for content in message_delta_chunk.delta.content:
+            if isinstance(content, MessageDeltaTextContent) and content.text and content.text.annotations:
+                for annotation in content.text.annotations:
+                    if isinstance(annotation, MessageDeltaTextFilePathAnnotation):
+                        # Extract file_id from the file_path annotation
+                        file_path = getattr(annotation, "file_path", None)
+                        if file_path is not None:
+                            file_id = getattr(file_path, "file_id", None)
+                            if file_id:
+                                file_contents.append(HostedFileContent(file_id=file_id))
+                    elif isinstance(annotation, MessageDeltaTextFileCitationAnnotation):
+                        # Extract file_id from the file_citation annotation
+                        file_citation = getattr(annotation, "file_citation", None)
+                        if file_citation is not None:
+                            file_id = getattr(file_citation, "file_id", None)
+                            if file_id:
+                                file_contents.append(HostedFileContent(file_id=file_id))
+
+        return file_contents
+
    def _get_real_url_from_citation_reference(
        self, citation_url: str, azure_search_tool_calls: list[dict[str, Any]]
    ) -> str:
@@ -530,6 +571,9 @@ class AzureAIAgentClient(BaseChatClient):
                        # Extract URL citations from the delta chunk
                        url_citations = self._extract_url_citations(event_data, azure_search_tool_calls)

+                        # Extract file path contents from code interpreter outputs
+                        file_contents = self._extract_file_path_contents(event_data)
+
                        # Create contents with citations if any exist
                        citation_content: list[Contents] = []
                        if event_data.text or url_citations:
@@ -538,6 +582,9 @@ class AzureAIAgentClient(BaseChatClient):
                                text_content_obj.annotations = url_citations
                            citation_content.append(text_content_obj)

+                        # Add file contents from file path annotations
+                        citation_content.extend(file_contents)
+
                        yield ChatResponseUpdate(
                            role=role,
                            contents=citation_content if citation_content else None,
@@ -4,7 +4,7 @@ description = "Azure AI Foundry integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -24,6 +24,7 @@ from agent_framework import (
    FunctionCallContent,
    FunctionResultContent,
    HostedCodeInterpreterTool,
+    HostedFileContent,
    HostedFileSearchTool,
    HostedMCPTool,
    HostedVectorStoreContent,
@@ -42,6 +43,8 @@ from azure.ai.agents.models import (
    FileInfo,
    MessageDeltaChunk,
    MessageDeltaTextContent,
+    MessageDeltaTextFileCitationAnnotation,
+    MessageDeltaTextFilePathAnnotation,
    MessageDeltaTextUrlCitationAnnotation,
    RequiredFunctionToolCall,
    RequiredMcpToolCall,
@@ -1362,6 +1365,108 @@ def test_azure_ai_chat_client_extract_url_citations_with_citations(mock_agents_c
    assert citation.annotated_regions[0].end_index == 20


+def test_azure_ai_chat_client_extract_file_path_contents_with_file_path_annotation(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with MessageDeltaChunk containing file path annotation."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock file_path annotation
+    mock_file_path = MagicMock()
+    mock_file_path.file_id = "assistant-test-file-123"
+
+    mock_annotation = MagicMock(spec=MessageDeltaTextFilePathAnnotation)
+    mock_annotation.file_path = mock_file_path
+
+    # Create mock text content with annotations
+    mock_text = MagicMock()
+    mock_text.annotations = [mock_annotation]
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 1
+    assert isinstance(file_contents[0], HostedFileContent)
+    assert file_contents[0].file_id == "assistant-test-file-123"
+
+
+def test_azure_ai_chat_client_extract_file_path_contents_with_file_citation_annotation(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with MessageDeltaChunk containing file citation annotation."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock file_citation annotation
+    mock_file_citation = MagicMock()
+    mock_file_citation.file_id = "cfile_test-citation-456"
+
+    mock_annotation = MagicMock(spec=MessageDeltaTextFileCitationAnnotation)
+    mock_annotation.file_citation = mock_file_citation
+
+    # Create mock text content with annotations
+    mock_text = MagicMock()
+    mock_text.annotations = [mock_annotation]
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 1
+    assert isinstance(file_contents[0], HostedFileContent)
+    assert file_contents[0].file_id == "cfile_test-citation-456"
+
+
+def test_azure_ai_chat_client_extract_file_path_contents_empty_annotations(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with no annotations returns empty list."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock text content with no annotations
+    mock_text = MagicMock()
+    mock_text.annotations = []
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 0
+
+
 def get_weather(
    location: Annotated[str, Field(description="The location to get the weather for.")],
 ) -> str:
@@ -4,7 +4,7 @@ description = "Azure Functions integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "OpenAI ChatKit integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Copilot Studio integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -1,6 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.

-import json
 import logging
 import re
 import sys
@@ -19,9 +18,9 @@ from mcp.client.websocket import websocket_client
 from mcp.shared.context import RequestContext
 from mcp.shared.exceptions import McpError
 from mcp.shared.session import RequestResponder
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, create_model

-from ._tools import AIFunction, HostedMCPSpecificApproval
+from ._tools import AIFunction, HostedMCPSpecificApproval, _build_pydantic_model_from_json_schema
 from ._types import (
    ChatMessage,
    Contents,
@@ -274,95 +273,26 @@ def _get_input_model_from_mcp_prompt(prompt: types.Prompt) -> type[BaseModel]:
    if not prompt.arguments:
        return create_model(f"{prompt.name}_input")

-    field_definitions: dict[str, Any] = {}
+    # Convert prompt arguments to JSON schema format
+    properties: dict[str, Any] = {}
+    required: list[str] = []
+
    for prompt_argument in prompt.arguments:
-        # For prompts, all arguments are typically required and string type
-        # unless specified otherwise in the prompt argument
-        python_type = str  # Default type for prompt arguments
-
-        # Create field definition for create_model
+        # For prompts, all arguments are typically string type unless specified otherwise
+        properties[prompt_argument.name] = {
+            "type": "string",
+            "description": prompt_argument.description if hasattr(prompt_argument, "description") else "",
+        }
        if prompt_argument.required:
-            field_definitions[prompt_argument.name] = (python_type, ...)
-        else:
-            field_definitions[prompt_argument.name] = (python_type, None)
+            required.append(prompt_argument.name)

-    return create_model(f"{prompt.name}_input", **field_definitions)
+    schema = {"properties": properties, "required": required}
+    return _build_pydantic_model_from_json_schema(prompt.name, schema)


 def _get_input_model_from_mcp_tool(tool: types.Tool) -> type[BaseModel]:
    """Creates a Pydantic model from a tools parameters."""
-    properties = tool.inputSchema.get("properties", None)
-    required = tool.inputSchema.get("required", [])
-    definitions = tool.inputSchema.get("$defs", {})
-
-    # Check if 'properties' is missing or not a dictionary
-    if not properties:
-        return create_model(f"{tool.name}_input")
-
-    def resolve_type(prop_details: dict[str, Any]) -> type:
-        """Resolve JSON Schema type to Python type, handling $ref."""
-        # Handle $ref by resolving the reference
-        if "$ref" in prop_details:
-            ref = prop_details["$ref"]
-            # Extract the reference path (e.g., "#/$defs/CustomerIdParam" -> "CustomerIdParam")
-            if ref.startswith("#/$defs/"):
-                def_name = ref.split("/")[-1]
-                if def_name in definitions:
-                    # Resolve the reference and use its type
-                    resolved = definitions[def_name]
-                    return resolve_type(resolved)
-            # If we can't resolve the ref, default to dict for safety
-            return dict
-
-        # Map JSON Schema types to Python types
-        json_type = prop_details.get("type", "string")
-        match json_type:
-            case "integer":
-                return int
-            case "number":
-                return float
-            case "boolean":
-                return bool
-            case "array":
-                return list
-            case "object":
-                return dict
-            case _:
-                return str  # default
-
-    field_definitions: dict[str, Any] = {}
-    for prop_name, prop_details in properties.items():
-        prop_details = json.loads(prop_details) if isinstance(prop_details, str) else prop_details
-
-        python_type = resolve_type(prop_details)
-        description = prop_details.get("description", "")
-
-        # Build field kwargs (description, array items schema, etc.)
-        field_kwargs: dict[str, Any] = {}
-        if description:
-            field_kwargs["description"] = description
-
-        # Preserve array items schema if present
-        if prop_details.get("type") == "array" and "items" in prop_details:
-            items_schema = prop_details["items"]
-            if items_schema and items_schema != {}:
-                field_kwargs["json_schema_extra"] = {"items": items_schema}
-
-        # Create field definition for create_model
-        if prop_name in required:
-            if field_kwargs:
-                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
-            else:
-                field_definitions[prop_name] = (python_type, ...)
-        else:
-            default_value = prop_details.get("default", None)
-            field_kwargs["default"] = default_value
-            if field_kwargs and any(k != "default" for k in field_kwargs):
-                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
-            else:
-                field_definitions[prop_name] = (python_type, default_value)
-
-    return create_model(f"{tool.name}_input", **field_definitions)
+    return _build_pydantic_model_from_json_schema(tool.name, tool.inputSchema)


 def _normalize_mcp_name(name: str) -> str:
@@ -25,7 +25,6 @@ from typing import (

 from opentelemetry.metrics import Histogram
 from pydantic import AnyUrl, BaseModel, Field, ValidationError, create_model
-from pydantic.fields import FieldInfo

 from ._logging import get_logger
 from ._serialization import SerializationMixin
@@ -932,6 +931,151 @@ TYPE_MAPPING = {
 }


+def _build_pydantic_model_from_json_schema(
+    model_name: str,
+    schema: Mapping[str, Any],
+) -> type[BaseModel]:
+    """Creates a Pydantic model from JSON Schema with support for $refs, nested objects, and typed arrays.
+
+    Args:
+        model_name: The name of the model to be created.
+        schema: The JSON Schema definition (should contain 'properties', 'required', '$defs', etc.).
+
+    Returns:
+        The dynamically created Pydantic model class.
+    """
+    properties = schema.get("properties")
+    required = schema.get("required", [])
+    definitions = schema.get("$defs", {})
+
+    # Check if 'properties' is missing or not a dictionary
+    if not properties:
+        return create_model(f"{model_name}_input")
+
+    def _resolve_type(prop_details: dict[str, Any], parent_name: str = "") -> type:
+        """Resolve JSON Schema type to Python type, handling $ref, nested objects, and typed arrays.
+
+        Args:
+            prop_details: The JSON Schema property details
+            parent_name: Name to use for creating nested models (for uniqueness)
+
+        Returns:
+            Python type annotation (could be int, str, list[str], or a nested Pydantic model)
+        """
+        # Handle $ref by resolving the reference
+        if "$ref" in prop_details:
+            ref = prop_details["$ref"]
+            # Extract the reference path (e.g., "#/$defs/CustomerIdParam" -> "CustomerIdParam")
+            if ref.startswith("#/$defs/"):
+                def_name = ref.split("/")[-1]
+                if def_name in definitions:
+                    # Resolve the reference and use its type
+                    resolved = definitions[def_name]
+                    return _resolve_type(resolved, def_name)
+            # If we can't resolve the ref, default to dict for safety
+            return dict
+
+        # Map JSON Schema types to Python types
+        json_type = prop_details.get("type", "string")
+        match json_type:
+            case "integer":
+                return int
+            case "number":
+                return float
+            case "boolean":
+                return bool
+            case "array":
+                # Handle typed arrays
+                items_schema = prop_details.get("items")
+                if items_schema and isinstance(items_schema, dict):
+                    # Recursively resolve the item type
+                    item_type = _resolve_type(items_schema, f"{parent_name}_item")
+                    # Return list[ItemType] instead of bare list
+                    return list[item_type]  # type: ignore
+                # If no items schema or invalid, return bare list
+                return list
+            case "object":
+                # Handle nested objects by creating a nested Pydantic model
+                nested_properties = prop_details.get("properties")
+                nested_required = prop_details.get("required", [])
+
+                if nested_properties and isinstance(nested_properties, dict):
+                    # Create the name for the nested model
+                    nested_model_name = f"{parent_name}_nested" if parent_name else "NestedModel"
+
+                    # Recursively build field definitions for the nested model
+                    nested_field_definitions: dict[str, Any] = {}
+                    for nested_prop_name, nested_prop_details in nested_properties.items():
+                        nested_prop_details = (
+                            json.loads(nested_prop_details)
+                            if isinstance(nested_prop_details, str)
+                            else nested_prop_details
+                        )
+
+                        nested_python_type = _resolve_type(
+                            nested_prop_details, f"{nested_model_name}_{nested_prop_name}"
+                        )
+                        nested_description = nested_prop_details.get("description", "")
+
+                        # Build field kwargs for nested property
+                        nested_field_kwargs: dict[str, Any] = {}
+                        if nested_description:
+                            nested_field_kwargs["description"] = nested_description
+
+                        # Create field definition
+                        if nested_prop_name in nested_required:
+                            nested_field_definitions[nested_prop_name] = (
+                                (
+                                    nested_python_type,
+                                    Field(**nested_field_kwargs),
+                                )
+                                if nested_field_kwargs
+                                else (nested_python_type, ...)
+                            )
+                        else:
+                            nested_field_kwargs["default"] = nested_prop_details.get("default", None)
+                            nested_field_definitions[nested_prop_name] = (
+                                nested_python_type,
+                                Field(**nested_field_kwargs),
+                            )
+
+                    # Create and return the nested Pydantic model
+                    return create_model(nested_model_name, **nested_field_definitions)  # type: ignore
+
+                # If no properties defined, return bare dict
+                return dict
+            case _:
+                return str  # default
+
+    field_definitions: dict[str, Any] = {}
+    for prop_name, prop_details in properties.items():
+        prop_details = json.loads(prop_details) if isinstance(prop_details, str) else prop_details
+
+        python_type = _resolve_type(prop_details, f"{model_name}_{prop_name}")
+        description = prop_details.get("description", "")
+
+        # Build field kwargs (description, etc.)
+        field_kwargs: dict[str, Any] = {}
+        if description:
+            field_kwargs["description"] = description
+
+        # Create field definition for create_model
+        if prop_name in required:
+            if field_kwargs:
+                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
+            else:
+                field_definitions[prop_name] = (python_type, ...)
+        else:
+            default_value = prop_details.get("default", None)
+            field_kwargs["default"] = default_value
+            if field_kwargs and any(k != "default" for k in field_kwargs):
+                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
+            else:
+                field_definitions[prop_name] = (python_type, default_value)
+
+    return create_model(f"{model_name}_input", **field_definitions)
+
+
 def _create_model_from_json_schema(tool_name: str, schema_json: Mapping[str, Any]) -> type[BaseModel]:
    """Creates a Pydantic model from a given JSON Schema.

@@ -948,29 +1092,8 @@ def _create_model_from_json_schema(tool_name: str, schema_json: Mapping[str, Any
            f"JSON schema for tool '{tool_name}' must contain a 'properties' key of type dict. "
            f"Got: {schema_json.get('properties', None)}"
        )
-    # Extract field definitions with type annotations
-    field_definitions: dict[str, tuple[type, FieldInfo]] = {}
-    for field_name, field_schema in schema_json["properties"].items():
-        field_args: dict[str, Any] = {}
-        if (field_description := field_schema.get("description", None)) is not None:
-            field_args["description"] = field_description
-        if (field_default := field_schema.get("default", None)) is not None:
-            field_args["default"] = field_default
-        field_type = field_schema.get("type", None)
-        if field_type is None:
-            raise ValueError(
-                f"Missing 'type' for field '{field_name}' in JSON schema. "
-                f"Got: {field_schema}, Supported types: {list(TYPE_MAPPING.keys())}"
-            )
-        python_type = TYPE_MAPPING.get(field_type)
-        if python_type is None:
-            raise ValueError(
-                f"Unsupported type '{field_type}' for field '{field_name}' in JSON schema. "
-                f"Got: {field_schema}, Supported types: {list(TYPE_MAPPING.keys())}"
-            )
-        field_definitions[field_name] = (python_type, Field(**field_args))

-    return create_model(f"{tool_name}_input", **field_definitions)  # type: ignore[call-overload, no-any-return]
+    return _build_pydantic_model_from_json_schema(tool_name, schema_json)


@overload
@@ -3,6 +3,7 @@
 import asyncio
 import inspect
 import logging
+import uuid
 from collections.abc import Callable, Sequence
 from typing import Any

@@ -189,8 +190,11 @@ class ConcurrentBuilder:
    r"""High-level builder for concurrent agent workflows.

    - `participants([...])` accepts a list of AgentProtocol (recommended) or Executor.
+    - `register_participants([...])` accepts a list of factories for AgentProtocol (recommended)
+       or Executor factories
    - `build()` wires: dispatcher -> fan-out -> participants -> fan-in -> aggregator.
-    - `with_custom_aggregator(...)` overrides the default aggregator with an Executor or callback.
+    - `with_aggregator(...)` overrides the default aggregator with an Executor or callback.
+    - `register_aggregator(...)` accepts a factory for an Executor as custom aggregator.

    Usage:

@@ -201,14 +205,33 @@ class ConcurrentBuilder:
        # Minimal: use default aggregator (returns list[ChatMessage])
        workflow = ConcurrentBuilder().participants([agent1, agent2, agent3]).build()

+        # With agent factories
+        workflow = ConcurrentBuilder().register_participants([create_agent1, create_agent2, create_agent3]).build()
+

        # Custom aggregator via callback (sync or async). The callback receives
        # list[AgentExecutorResponse] and its return value becomes the workflow's output.
-        def summarize(results):
+        def summarize(results: list[AgentExecutorResponse]) -> str:
            return " | ".join(r.agent_run_response.messages[-1].text for r in results)


-        workflow = ConcurrentBuilder().participants([agent1, agent2, agent3]).with_custom_aggregator(summarize).build()
+        workflow = ConcurrentBuilder().participants([agent1, agent2, agent3]).with_aggregator(summarize).build()
+
+
+        # Custom aggregator via a factory
+        class MyAggregator(Executor):
+            @handler
+            async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+                await ctx.yield_output(" | ".join(r.agent_run_response.messages[-1].text for r in results))
+
+
+        workflow = (
+            ConcurrentBuilder()
+            .register_participants([create_agent1, create_agent2, create_agent3])
+            .register_aggregator(lambda: MyAggregator(id="my_aggregator"))
+            .build()
+        )
+

        # Enable checkpoint persistence so runs can resume
        workflow = ConcurrentBuilder().participants([agent1, agent2, agent3]).with_checkpointing(storage).build()
@@ -219,10 +242,67 @@ class ConcurrentBuilder:

    def __init__(self) -> None:
        self._participants: list[AgentProtocol | Executor] = []
+        self._participant_factories: list[Callable[[], AgentProtocol | Executor]] = []
        self._aggregator: Executor | None = None
+        self._aggregator_factory: Callable[[], Executor] | None = None
        self._checkpoint_storage: CheckpointStorage | None = None
        self._request_info_enabled: bool = False

+    def register_participants(
+        self,
+        participant_factories: Sequence[Callable[[], AgentProtocol | Executor]],
+    ) -> "ConcurrentBuilder":
+        r"""Define the parallel participants for this concurrent workflow.
+
+        Accepts factories (callables) that return AgentProtocol instances (e.g., created
+        by a chat client) or Executor instances. Each participant created by a factory
+        is wired as a parallel branch using fan-out edges from an internal dispatcher.
+
+        Args:
+            participant_factories: Sequence of callables returning AgentProtocol or Executor instances
+
+        Raises:
+            ValueError: if `participant_factories` is empty or `.participants()`
+                       or `.register_participants()` were already called
+
+        Example:
+
+        .. code-block:: python
+
+            def create_researcher() -> ChatAgent:
+                return ...
+
+
+            def create_marketer() -> ChatAgent:
+                return ...
+
+
+            def create_legal() -> ChatAgent:
+                return ...
+
+
+            class MyCustomExecutor(Executor): ...
+
+
+            wf = ConcurrentBuilder().register_participants([create_researcher, create_marketer, create_legal]).build()
+
+            # Mixing agent(s) and executor(s) is supported
+            wf2 = ConcurrentBuilder().register_participants([create_researcher, MyCustomExecutor]).build()
+        """
+        if self._participants:
+            raise ValueError(
+                "Cannot mix .participants([...]) and .register_participants() in the same builder instance."
+            )
+
+        if self._participant_factories:
+            raise ValueError("register_participants() has already been called on this builder instance.")
+
+        if not participant_factories:
+            raise ValueError("participant_factories cannot be empty")
+
+        self._participant_factories = list(participant_factories)
+        return self
+
    def participants(self, participants: Sequence[AgentProtocol | Executor]) -> "ConcurrentBuilder":
        r"""Define the parallel participants for this concurrent workflow.

@@ -230,8 +310,12 @@ class ConcurrentBuilder:
        instances. Each participant is wired as a parallel branch using fan-out edges
        from an internal dispatcher.

+        Args:
+            participants: Sequence of AgentProtocol or Executor instances
+
        Raises:
-            ValueError: if `participants` is empty or contains duplicates
+            ValueError: if `participants` is empty, contains duplicates, or `.register_participants()`
+                       or `.participants()` were already called
            TypeError: if any entry is not AgentProtocol or Executor

        Example:
@@ -243,6 +327,14 @@ class ConcurrentBuilder:
            # Mixing agent(s) and executor(s) is supported
            wf2 = ConcurrentBuilder().participants([researcher_agent, my_custom_executor]).build()
        """
+        if self._participant_factories:
+            raise ValueError(
+                "Cannot mix .participants([...]) and .register_participants() in the same builder instance."
+            )
+
+        if self._participants:
+            raise ValueError("participants() has already been called on this builder instance.")
+
        if not participants:
            raise ValueError("participants cannot be empty")

@@ -265,38 +357,107 @@ class ConcurrentBuilder:
        self._participants = list(participants)
        return self

-    def with_aggregator(self, aggregator: Executor | Callable[..., Any]) -> "ConcurrentBuilder":
-        r"""Override the default aggregator with an Executor or a callback.
+    def register_aggregator(self, aggregator_factory: Callable[[], Executor]) -> "ConcurrentBuilder":
+        r"""Define a custom aggregator for this concurrent workflow.

-        - Executor: must handle `list[AgentExecutorResponse]` and
-            yield output using `ctx.yield_output(...)` and add a
-          output and the workflow becomes idle.
+        Accepts a factory (callable) that returns an Executor instance. The executor
+        should handle `list[AgentExecutorResponse]` and yield output using `ctx.yield_output(...)`.
+
+        Args:
+            aggregator_factory: Callable that returns an Executor instance
+
+        Example:
+        .. code-block:: python
+
+            class MyCustomExecutor(Executor): ...
+
+
+            wf = (
+                ConcurrentBuilder()
+                .register_participants([create_researcher, create_marketer, create_legal])
+                .register_aggregator(lambda: MyCustomExecutor(id="my_aggregator"))
+                .build()
+            )
+        """
+        if self._aggregator is not None:
+            raise ValueError(
+                "Cannot mix .with_aggregator(...) and .register_aggregator(...) in the same builder instance."
+            )
+
+        if self._aggregator_factory is not None:
+            raise ValueError("register_aggregator() has already been called on this builder instance.")
+
+        self._aggregator_factory = aggregator_factory
+        return self
+
+    def with_aggregator(
+        self,
+        aggregator: Executor
+        | Callable[[list[AgentExecutorResponse]], Any]
+        | Callable[[list[AgentExecutorResponse], WorkflowContext[Never, Any]], Any],
+    ) -> "ConcurrentBuilder":
+        r"""Override the default aggregator with an executor, an executor factory, or a callback.
+
+        - Executor: must handle `list[AgentExecutorResponse]` and yield output using `ctx.yield_output(...)`
        - Callback: sync or async callable with one of the signatures:
          `(results: list[AgentExecutorResponse]) -> Any | None` or
          `(results: list[AgentExecutorResponse], ctx: WorkflowContext) -> Any | None`.
          If the callback returns a non-None value, it becomes the workflow's output.

+        Args:
+            aggregator: Executor instance, or callback function
+
        Example:

        .. code-block:: python
+            # Executor-based aggregator
+            class CustomAggregator(Executor):
+                @handler
+                async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext) -> None:
+                    await ctx.yield_output(" | ".join(r.agent_run_response.messages[-1].text for r in results))
+
+
+            wf = ConcurrentBuilder().participants([a1, a2, a3]).with_aggregator(CustomAggregator()).build()
+

            # Callback-based aggregator (string result)
-            async def summarize(results):
+            async def summarize(results: list[AgentExecutorResponse]) -> str:
                return " | ".join(r.agent_run_response.messages[-1].text for r in results)


-            wf = ConcurrentBuilder().participants([a1, a2, a3]).with_custom_aggregator(summarize).build()
+            wf = ConcurrentBuilder().participants([a1, a2, a3]).with_aggregator(summarize).build()
+
+
+            # Callback-based aggregator (yield result)
+            async def summarize(results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+                await ctx.yield_output(" | ".join(r.agent_run_response.messages[-1].text for r in results))
+
+
+            wf = ConcurrentBuilder().participants([a1, a2, a3]).with_aggregator(summarize).build()
        """
+        if self._aggregator_factory is not None:
+            raise ValueError(
+                "Cannot mix .with_aggregator(...) and .register_aggregator(...) in the same builder instance."
+            )
+
+        if self._aggregator is not None:
+            raise ValueError("with_aggregator() has already been called on this builder instance.")
+
        if isinstance(aggregator, Executor):
            self._aggregator = aggregator
        elif callable(aggregator):
            self._aggregator = _CallbackAggregator(aggregator)
        else:
            raise TypeError("aggregator must be an Executor or a callable")
+
        return self

    def with_checkpointing(self, checkpoint_storage: CheckpointStorage) -> "ConcurrentBuilder":
-        """Enable checkpoint persistence using the provided storage backend."""
+        """Enable checkpoint persistence using the provided storage backend.
+
+        Args:
+            checkpoint_storage: CheckpointStorage instance for persisting workflow state
+        """
        self._checkpoint_storage = checkpoint_storage
        return self

@@ -329,7 +490,7 @@ class ConcurrentBuilder:
            before sending the outputs to the aggregator
        - Aggregator yields output and the workflow becomes idle. The output is either:
          - list[ChatMessage] (default aggregator: one user + one assistant per agent)
-          - custom payload from the provided callback/executor
+          - custom payload from the provided aggregator

        Returns:
            Workflow: a ready-to-run workflow instance
@@ -343,26 +504,69 @@ class ConcurrentBuilder:

            workflow = ConcurrentBuilder().participants([agent1, agent2]).build()
        """
-        if not self._participants:
-            raise ValueError("No participants provided. Call .participants([...]) first.")
+        if not self._participants and not self._participant_factories:
+            raise ValueError(
+                "No participants provided. Call .participants([...]) or .register_participants([...]) first."
+            )

+        # Internal nodes
        dispatcher = _DispatchToAllParticipants(id="dispatcher")
-        aggregator = self._aggregator or _AggregateAgentConversations(id="aggregator")
+        aggregator = (
+            self._aggregator
+            if self._aggregator is not None
+            else (
+                self._aggregator_factory()
+                if self._aggregator_factory is not None
+                else _AggregateAgentConversations(id="aggregator")
+            )
+        )

        builder = WorkflowBuilder()
-        builder.set_start_executor(dispatcher)
-        builder.add_fan_out_edges(dispatcher, list(self._participants))
+        if self._participant_factories:
+            # Register executors/agents to avoid warnings from the workflow builder
+            # if factories are provided instead of direct instances. This doesn't
+            # break the factory pattern since the concurrent builder still creates
+            # new instances per workflow build.
+            factory_names: list[str] = []
+            for factory in self._participant_factories:
+                factory_name = uuid.uuid4().hex
+                factory_names.append(factory_name)
+                instance = factory()
+                if isinstance(instance, Executor):
+                    builder.register_executor(lambda executor=instance: executor, name=factory_name)  # type: ignore[misc]
+                else:
+                    builder.register_agent(lambda agent=instance: agent, name=factory_name)  # type: ignore[misc]
+            # Register the dispatcher and the aggregator
+            builder.register_executor(lambda: dispatcher, name="dispatcher")
+            builder.register_executor(lambda: aggregator, name="aggregator")

-        if self._request_info_enabled:
-            # Insert interceptor between fan-in and aggregator
-            # participants -> fan-in -> interceptor -> aggregator
-            request_info_interceptor = RequestInfoInterceptor(executor_id="request_info")
-            builder.add_fan_in_edges(list(self._participants), request_info_interceptor)
-            builder.add_edge(request_info_interceptor, aggregator)
+            builder.set_start_executor("dispatcher")
+            builder.add_fan_out_edges("dispatcher", factory_names)
+            if self._request_info_enabled:
+                # Insert interceptor between fan-in and aggregator
+                # participants -> fan-in -> interceptor -> aggregator
+                builder.register_executor(
+                    lambda: RequestInfoInterceptor(executor_id="request_info"),
+                    name="request_info_interceptor",
+                )
+                builder.add_fan_in_edges(factory_names, "request_info_interceptor")
+                builder.add_edge("request_info_interceptor", "aggregator")
+            else:
+                # Direct fan-in to aggregator
+                builder.add_fan_in_edges(factory_names, "aggregator")
        else:
-            # Direct fan-in to aggregator
-            builder.add_fan_in_edges(list(self._participants), aggregator)
+            builder.set_start_executor(dispatcher)
+            builder.add_fan_out_edges(dispatcher, self._participants)

+            if self._request_info_enabled:
+                # Insert interceptor between fan-in and aggregator
+                # participants -> fan-in -> interceptor -> aggregator
+                request_info_interceptor = RequestInfoInterceptor(executor_id="request_info")
+                builder.add_fan_in_edges(self._participants, request_info_interceptor)
+                builder.add_edge(request_info_interceptor, aggregator)
+            else:
+                # Direct fan-in to aggregator
+                builder.add_fan_in_edges(self._participants, aggregator)
        if self._checkpoint_storage is not None:
            builder = builder.with_checkpointing(self._checkpoint_storage)

@@ -132,7 +132,11 @@ class ManagerSelectionResponse(BaseModel):
        final_message: Optional final message string when finishing conversation (will be converted to ChatMessage)
    """

-    model_config = {"extra": "forbid"}
+    model_config = {
+        "extra": "forbid",
+        # OpenAI strict mode requires all properties to be in required array
+        "json_schema_extra": {"required": ["selected_participant", "instruction", "finish", "final_message"]},
+    }

    selected_participant: str | None = None
    instruction: str | None = None
@@ -374,7 +374,7 @@ class WorkflowBuilder:
                )
        """
        if name in self._executor_registry:
-            raise ValueError(f"An executor factory with the name '{name}' is already registered.")
+            raise ValueError(f"An agent factory with the name '{name}' is already registered.")

        def wrapped_factory() -> AgentExecutor:
            agent = factory_func()
@@ -1148,21 +1148,29 @@ class WorkflowBuilder:
        if isinstance(self._start_executor, Executor):
            start_executor = self._start_executor

-        executors: dict[str, Executor] = {}
+        # Maps registered factory names to created executor instances for edge resolution
+        factory_name_to_instance: dict[str, Executor] = {}
+        # Maps executor IDs to created executor instances to prevent duplicates
+        executor_id_to_instance: dict[str, Executor] = {}
        deferred_edge_groups: list[EdgeGroup] = []
        for name, exec_factory in self._executor_registry.items():
            instance = exec_factory()
+            if instance.id in executor_id_to_instance:
+                raise ValueError(f"Executor with ID '{instance.id}' has already been created.")
+            executor_id_to_instance[instance.id] = instance
+
            if isinstance(self._start_executor, str) and name == self._start_executor:
                start_executor = instance
+
            # All executors will get their own internal edge group for receiving system messages
            deferred_edge_groups.append(InternalEdgeGroup(instance.id))  # type: ignore[call-arg]
-            executors[name] = instance
+            factory_name_to_instance[name] = instance

        def _get_executor(name: str) -> Executor:
            """Helper to get executor by the registered name. Raises if not found."""
-            if name not in executors:
-                raise ValueError(f"Executor with name '{name}' has not been registered.")
-            return executors[name]
+            if name not in factory_name_to_instance:
+                raise ValueError(f"Factory '{name}' has not been registered.")
+            return factory_name_to_instance[name]

        for registration in self._edge_registry:
            match registration:
@@ -1179,7 +1187,7 @@ class WorkflowBuilder:
                    cases_converted: list[SwitchCaseEdgeGroupCase | SwitchCaseEdgeGroupDefault] = []
                    for case in cases:
                        if not isinstance(case.target, str):
-                            raise ValueError("Switch case target must be a registered executor name (str) if deferred.")
+                            raise ValueError("Switch case target must be a registered factory name (str) if deferred.")
                        target_exec = _get_executor(case.target)
                        if isinstance(case, Default):
                            cases_converted.append(SwitchCaseEdgeGroupDefault(target_id=target_exec.id))
@@ -1201,7 +1209,7 @@ class WorkflowBuilder:
        if start_executor is None:
            raise ValueError("Failed to resolve starting executor from registered factories.")

-        return start_executor, list(executors.values()), deferred_edge_groups
+        return start_executor, list(executor_id_to_instance.values()), deferred_edge_groups

    def build(self) -> Workflow:
        """Build and return the constructed workflow.
@@ -3,7 +3,7 @@
 from collections.abc import AsyncIterable, Awaitable, Callable, Mapping, MutableMapping, MutableSequence, Sequence
 from datetime import datetime, timezone
 from itertools import chain
-from typing import Any, TypeVar
+from typing import Any, TypeVar, cast

 from openai import AsyncOpenAI, BadRequestError
 from openai.types.responses.file_search_tool_param import FileSearchToolParam
@@ -199,7 +199,7 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
            return response_format, prepared_text

        if isinstance(response_format, Mapping):
-            format_config = self._convert_response_format(response_format)
+            format_config = self._convert_response_format(cast("Mapping[str, Any]", response_format))
            if prepared_text is None:
                prepared_text = {}
            elif "format" in prepared_text and prepared_text["format"] != format_config:
@@ -212,20 +212,21 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
    def _convert_response_format(self, response_format: Mapping[str, Any]) -> dict[str, Any]:
        """Convert Chat style response_format into Responses text format config."""
        if "format" in response_format and isinstance(response_format["format"], Mapping):
-            return dict(response_format["format"])
+            return dict(cast("Mapping[str, Any]", response_format["format"]))

        format_type = response_format.get("type")
        if format_type == "json_schema":
            schema_section = response_format.get("json_schema", response_format)
            if not isinstance(schema_section, Mapping):
                raise ServiceInvalidRequestError("json_schema response_format must be a mapping.")
-            schema = schema_section.get("schema")
+            schema_section_typed = cast("Mapping[str, Any]", schema_section)
+            schema: Any = schema_section_typed.get("schema")
            if schema is None:
                raise ServiceInvalidRequestError("json_schema response_format requires a schema.")
-            name = (
-                schema_section.get("name")
-                or schema_section.get("title")
-                or (schema.get("title") if isinstance(schema, Mapping) else None)
+            name: str = str(
+                schema_section_typed.get("name")
+                or schema_section_typed.get("title")
+                or (cast("Mapping[str, Any]", schema).get("title") if isinstance(schema, Mapping) else None)
                or "response"
            )
            format_config: dict[str, Any] = {
@@ -532,12 +533,13 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
                        "text": content.text,
                    },
                }
-                if content.additional_properties is not None:
-                    if status := content.additional_properties.get("status"):
+                props: dict[str, Any] | None = getattr(content, "additional_properties", None)
+                if props:
+                    if status := props.get("status"):
                        ret["status"] = status
-                    if reasoning_text := content.additional_properties.get("reasoning_text"):
+                    if reasoning_text := props.get("reasoning_text"):
                        ret["content"] = {"type": "reasoning_text", "text": reasoning_text}
-                    if encrypted_content := content.additional_properties.get("encrypted_content"):
+                    if encrypted_content := props.get("encrypted_content"):
                        ret["encrypted_content"] = encrypted_content
                return ret
            case DataContent() | UriContent():
@@ -824,7 +826,7 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
            "raw_representation": response,
        }

-        conversation_id = self.get_conversation_id(response, chat_options.store)
+        conversation_id = self.get_conversation_id(response, chat_options.store)  # type: ignore[reportArgumentType]

        if conversation_id:
            args["conversation_id"] = conversation_id
@@ -911,6 +913,8 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
                        metadata.update(self._get_metadata_from_response(event_part))
                    case "refusal":
                        contents.append(TextContent(text=event_part.refusal, raw_representation=event))
+                    case _:
+                        pass
            case "response.output_text.delta":
                contents.append(TextContent(text=event.delta, raw_representation=event))
                metadata.update(self._get_metadata_from_response(event))
@@ -1032,6 +1036,60 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
                        raw_representation=event,
                    )
                )
+            case "response.output_text.annotation.added":
+                # Handle streaming text annotations (file citations, file paths, etc.)
+                annotation: Any = event.annotation
+
+                def _get_ann_value(key: str) -> Any:
+                    """Extract value from annotation (dict or object)."""
+                    if isinstance(annotation, dict):
+                        return cast("dict[str, Any]", annotation).get(key)
+                    return getattr(annotation, key, None)
+
+                ann_type = _get_ann_value("type")
+                ann_file_id = _get_ann_value("file_id")
+                if ann_type == "file_path":
+                    if ann_file_id:
+                        contents.append(
+                            HostedFileContent(
+                                file_id=str(ann_file_id),
+                                additional_properties={
+                                    "annotation_index": event.annotation_index,
+                                    "index": _get_ann_value("index"),
+                                },
+                                raw_representation=event,
+                            )
+                        )
+                elif ann_type == "file_citation":
+                    if ann_file_id:
+                        contents.append(
+                            HostedFileContent(
+                                file_id=str(ann_file_id),
+                                additional_properties={
+                                    "annotation_index": event.annotation_index,
+                                    "filename": _get_ann_value("filename"),
+                                    "index": _get_ann_value("index"),
+                                },
+                                raw_representation=event,
+                            )
+                        )
+                elif ann_type == "container_file_citation":
+                    if ann_file_id:
+                        contents.append(
+                            HostedFileContent(
+                                file_id=str(ann_file_id),
+                                additional_properties={
+                                    "annotation_index": event.annotation_index,
+                                    "container_id": _get_ann_value("container_id"),
+                                    "filename": _get_ann_value("filename"),
+                                    "start_index": _get_ann_value("start_index"),
+                                    "end_index": _get_ann_value("end_index"),
+                                },
+                                raw_representation=event,
+                            )
+                        )
+                else:
+                    logger.debug("Unparsed annotation type in streaming: %s", ann_type)
            case _:
                logger.debug("Unparsed event of type: %s: %s", event.type, event)

@@ -4,7 +4,7 @@ description = "Microsoft Agent Framework for building AI Agents with Python. Thi
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -35,7 +35,7 @@ dependencies = [
    # connectors and functions
    "openai>=1.99.0",
    "azure-identity>=1,<2",
-    "mcp[ws]>=1.13",
+    "mcp[ws]>=1.23",
    "packaging>=24.1",
 ]

@@ -9,7 +9,7 @@ import pytest
 from mcp import types
 from mcp.client.session import ClientSession
 from mcp.shared.exceptions import McpError
-from pydantic import AnyUrl, ValidationError
+from pydantic import AnyUrl, BaseModel, ValidationError

 from agent_framework import (
    ChatMessage,
@@ -357,122 +357,360 @@ def test_chat_message_to_mcp_types():
    assert isinstance(mcp_contents[1], types.ImageContent)


-def test_get_input_model_from_mcp_tool():
-    """Test creation of input model from MCP tool."""
-    tool = types.Tool(
-        name="test_tool",
-        description="A test tool",
-        inputSchema={
-            "type": "object",
-            "properties": {"param1": {"type": "string"}, "param2": {"type": "number"}},
-            "required": ["param1"],
-        },
-    )
-    model = _get_input_model_from_mcp_tool(tool)
-
-    # Create an instance to verify the model works
-    instance = model(param1="test", param2=42)
-    assert instance.param1 == "test"
-    assert instance.param2 == 42
-
-    # Test validation
-    with pytest.raises(ValidationError):  # Missing required param1
-        model(param2=42)
-
-
-def test_get_input_model_from_mcp_tool_with_nested_object():
-    """Test creation of input model from MCP tool with nested object property."""
-    tool = types.Tool(
-        name="get_customer_detail",
-        description="Get customer details",
-        inputSchema={
-            "type": "object",
-            "properties": {
-                "params": {
-                    "type": "object",
-                    "properties": {"customer_id": {"type": "integer"}},
-                    "required": ["customer_id"],
+@pytest.mark.parametrize(
+    "test_id,input_schema,valid_data,expected_values,invalid_data,validation_check",
+    [
+        # Basic types with required/optional fields
+        (
+            "basic_types",
+            {
+                "type": "object",
+                "properties": {"param1": {"type": "string"}, "param2": {"type": "number"}},
+                "required": ["param1"],
+            },
+            {"param1": "test", "param2": 42},
+            {"param1": "test", "param2": 42},
+            {"param2": 42},  # Missing required param1
+            None,
+        ),
+        # Nested object
+        (
+            "nested_object",
+            {
+                "type": "object",
+                "properties": {
+                    "params": {
+                        "type": "object",
+                        "properties": {"customer_id": {"type": "integer"}},
+                        "required": ["customer_id"],
+                    }
+                },
+                "required": ["params"],
+            },
+            {"params": {"customer_id": 251}},
+            {"params.customer_id": 251},
+            {"params": {}},  # Missing required customer_id
+            lambda instance: isinstance(instance.params, BaseModel),
+        ),
+        # $ref resolution
+        (
+            "ref_schema",
+            {
+                "type": "object",
+                "properties": {"params": {"$ref": "#/$defs/CustomerIdParam"}},
+                "required": ["params"],
+                "$defs": {
+                    "CustomerIdParam": {
+                        "type": "object",
+                        "properties": {"customer_id": {"type": "integer"}},
+                        "required": ["customer_id"],
+                    }
+                },
+            },
+            {"params": {"customer_id": 251}},
+            {"params.customer_id": 251},
+            {"params": {}},  # Missing required customer_id
+            lambda instance: isinstance(instance.params, BaseModel),
+        ),
+        # Array of strings (typed)
+        (
+            "array_of_strings",
+            {
+                "type": "object",
+                "properties": {
+                    "tags": {
+                        "type": "array",
+                        "description": "List of tags",
+                        "items": {"type": "string"},
+                    }
+                },
+                "required": ["tags"],
+            },
+            {"tags": ["tag1", "tag2", "tag3"]},
+            {"tags": ["tag1", "tag2", "tag3"]},
+            None,  # No validation error test for this case
+            None,
+        ),
+        # Array of integers (typed)
+        (
+            "array_of_integers",
+            {
+                "type": "object",
+                "properties": {
+                    "numbers": {
+                        "type": "array",
+                        "description": "List of integers",
+                        "items": {"type": "integer"},
+                    }
+                },
+                "required": ["numbers"],
+            },
+            {"numbers": [1, 2, 3]},
+            {"numbers": [1, 2, 3]},
+            None,
+            None,
+        ),
+        # Array of objects (complex nested)
+        (
+            "array_of_objects",
+            {
+                "type": "object",
+                "properties": {
+                    "users": {
+                        "type": "array",
+                        "description": "List of users",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "id": {"type": "integer", "description": "User ID"},
+                                "name": {"type": "string", "description": "User name"},
+                            },
+                            "required": ["id", "name"],
+                        },
+                    }
+                },
+                "required": ["users"],
+            },
+            {"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]},
+            {"users[0].id": 1, "users[0].name": "Alice", "users[1].id": 2, "users[1].name": "Bob"},
+            {"users": [{"id": 1}]},  # Missing required 'name'
+            lambda instance: all(isinstance(user, BaseModel) for user in instance.users),
+        ),
+        # Deeply nested objects (3+ levels)
+        (
+            "deeply_nested",
+            {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "object",
+                        "properties": {
+                            "filters": {
+                                "type": "object",
+                                "properties": {
+                                    "date_range": {
+                                        "type": "object",
+                                        "properties": {
+                                            "start": {"type": "string"},
+                                            "end": {"type": "string"},
+                                        },
+                                        "required": ["start", "end"],
+                                    },
+                                    "categories": {"type": "array", "items": {"type": "string"}},
+                                },
+                                "required": ["date_range"],
+                            }
+                        },
+                        "required": ["filters"],
+                    }
+                },
+                "required": ["query"],
+            },
+            {
+                "query": {
+                    "filters": {
+                        "date_range": {"start": "2024-01-01", "end": "2024-12-31"},
+                        "categories": ["tech", "science"],
+                    }
                }
            },
-            "required": ["params"],
-        },
-    )
-    model = _get_input_model_from_mcp_tool(tool)
+            {
+                "query.filters.date_range.start": "2024-01-01",
+                "query.filters.date_range.end": "2024-12-31",
+                "query.filters.categories": ["tech", "science"],
+            },
+            {"query": {"filters": {"date_range": {}}}},  # Missing required start and end
+            None,
+        ),
+        # Complex $ref with nested structure
+        (
+            "ref_nested_structure",
+            {
+                "type": "object",
+                "properties": {"order": {"$ref": "#/$defs/OrderParams"}},
+                "required": ["order"],
+                "$defs": {
+                    "OrderParams": {
+                        "type": "object",
+                        "properties": {
+                            "customer": {"$ref": "#/$defs/Customer"},
+                            "items": {"type": "array", "items": {"$ref": "#/$defs/OrderItem"}},
+                        },
+                        "required": ["customer", "items"],
+                    },
+                    "Customer": {
+                        "type": "object",
+                        "properties": {"id": {"type": "integer"}, "email": {"type": "string"}},
+                        "required": ["id", "email"],
+                    },
+                    "OrderItem": {
+                        "type": "object",
+                        "properties": {"product_id": {"type": "string"}, "quantity": {"type": "integer"}},
+                        "required": ["product_id", "quantity"],
+                    },
+                },
+            },
+            {
+                "order": {
+                    "customer": {"id": 123, "email": "test@example.com"},
+                    "items": [{"product_id": "prod1", "quantity": 2}],
+                }
+            },
+            {
+                "order.customer.id": 123,
+                "order.customer.email": "test@example.com",
+                "order.items[0].product_id": "prod1",
+                "order.items[0].quantity": 2,
+            },
+            {"order": {"customer": {"id": 123}, "items": []}},  # Missing email
+            lambda instance: isinstance(instance.order.customer, BaseModel),
+        ),
+        # Mixed types (primitives, arrays, nested objects)
+        (
+            "mixed_types",
+            {
+                "type": "object",
+                "properties": {
+                    "simple_string": {"type": "string"},
+                    "simple_number": {"type": "integer"},
+                    "string_array": {"type": "array", "items": {"type": "string"}},
+                    "nested_config": {
+                        "type": "object",
+                        "properties": {
+                            "enabled": {"type": "boolean"},
+                            "options": {"type": "array", "items": {"type": "string"}},
+                        },
+                        "required": ["enabled"],
+                    },
+                },
+                "required": ["simple_string", "nested_config"],
+            },
+            {
+                "simple_string": "test",
+                "simple_number": 42,
+                "string_array": ["a", "b"],
+                "nested_config": {"enabled": True, "options": ["opt1", "opt2"]},
+            },
+            {
+                "simple_string": "test",
+                "simple_number": 42,
+                "string_array": ["a", "b"],
+                "nested_config.enabled": True,
+                "nested_config.options": ["opt1", "opt2"],
+            },
+            None,
+            None,
+        ),
+        # Empty schema (no properties)
+        (
+            "empty_schema",
+            {"type": "object", "properties": {}},
+            {},
+            {},
+            None,
+            None,
+        ),
+        # All primitive types
+        (
+            "all_primitives",
+            {
+                "type": "object",
+                "properties": {
+                    "string_field": {"type": "string"},
+                    "integer_field": {"type": "integer"},
+                    "number_field": {"type": "number"},
+                    "boolean_field": {"type": "boolean"},
+                },
+            },
+            {"string_field": "test", "integer_field": 42, "number_field": 3.14, "boolean_field": True},
+            {"string_field": "test", "integer_field": 42, "number_field": 3.14, "boolean_field": True},
+            None,
+            None,
+        ),
+        # Edge case: unresolvable $ref (fallback to dict)
+        (
+            "unresolvable_ref",
+            {
+                "type": "object",
+                "properties": {"data": {"$ref": "#/$defs/NonExistent"}},
+                "$defs": {},
+            },
+            {"data": {"key": "value"}},
+            {"data": {"key": "value"}},
+            None,
+            None,
+        ),
+        # Edge case: array without items schema (fallback to bare list)
+        (
+            "array_no_items",
+            {
+                "type": "object",
+                "properties": {"items": {"type": "array"}},
+            },
+            {"items": [1, "two", 3.0]},
+            {"items": [1, "two", 3.0]},
+            None,
+            None,
+        ),
+        # Edge case: object without properties (fallback to dict)
+        (
+            "object_no_properties",
+            {
+                "type": "object",
+                "properties": {"config": {"type": "object"}},
+            },
+            {"config": {"arbitrary": "data", "nested": {"key": "value"}}},
+            {"config": {"arbitrary": "data", "nested": {"key": "value"}}},
+            None,
+            None,
+        ),
+    ],
+)
+def test_get_input_model_from_mcp_tool_parametrized(
+    test_id, input_schema, valid_data, expected_values, invalid_data, validation_check
+):
+    """Parametrized test for JSON schema to Pydantic model conversion.

-    # Create an instance to verify the model works with nested objects
-    instance = model(params={"customer_id": 251})
-    assert instance.params == {"customer_id": 251}
-    assert isinstance(instance.params, dict)
+    This test covers various edge cases including:
+    - Basic types with required/optional fields
+    - Nested objects
+    - $ref resolution
+    - Typed arrays (strings, integers, objects)
+    - Deeply nested structures
+    - Complex $ref with nested structures
+    - Mixed types

-    # Verify model_dump produces the correct nested structure
-    dumped = instance.model_dump()
-    assert dumped == {"params": {"customer_id": 251}}
-
-
-def test_get_input_model_from_mcp_tool_with_ref_schema():
-    """Test creation of input model from MCP tool with $ref schema.
-
-    This simulates a FastMCP tool that uses Pydantic models with $ref in the schema.
-    The schema should be resolved and nested objects should be preserved.
+    To add a new test case, add a tuple to the parametrize decorator with:
+    - test_id: A descriptive name for the test case
+    - input_schema: The JSON schema (inputSchema dict)
+    - valid_data: Valid data to instantiate the model
+    - expected_values: Dict of expected values (supports dot notation for nested access)
+    - invalid_data: Invalid data to test validation errors (None to skip)
+    - validation_check: Optional callable to perform additional validation checks
    """
-    # This is similar to what FastMCP generates when you have:
-    # async def get_customer_detail(params: CustomerIdParam) -> CustomerDetail
-    tool = types.Tool(
-        name="get_customer_detail",
-        description="Get customer details",
-        inputSchema={
-            "type": "object",
-            "properties": {"params": {"$ref": "#/$defs/CustomerIdParam"}},
-            "required": ["params"],
-            "$defs": {
-                "CustomerIdParam": {
-                    "type": "object",
-                    "properties": {"customer_id": {"type": "integer"}},
-                    "required": ["customer_id"],
-                }
-            },
-        },
-    )
+    tool = types.Tool(name="test_tool", description="A test tool", inputSchema=input_schema)
    model = _get_input_model_from_mcp_tool(tool)

-    # Create an instance to verify the model works with $ref schemas
-    instance = model(params={"customer_id": 251})
-    assert instance.params == {"customer_id": 251}
-    assert isinstance(instance.params, dict)
+    # Test valid data
+    instance = model(**valid_data)

-    # Verify model_dump produces the correct nested structure
-    dumped = instance.model_dump()
-    assert dumped == {"params": {"customer_id": 251}}
+    # Check expected values
+    for field_path, expected_value in expected_values.items():
+        # Support dot notation and array indexing for nested access
+        current = instance
+        parts = field_path.replace("]", "").replace("[", ".").split(".")
+        for part in parts:
+            current = current[int(part)] if part.isdigit() else getattr(current, part)
+        assert current == expected_value, f"Field {field_path} = {current}, expected {expected_value}"

+    # Run additional validation checks if provided
+    if validation_check:
+        assert validation_check(instance), f"Validation check failed for {test_id}"

-def test_get_input_model_from_mcp_tool_with_simple_array():
-    """Test array with simple items schema (items schema should be preserved in json_schema_extra)."""
-    tool = types.Tool(
-        name="simple_array_tool",
-        description="Tool with simple array",
-        inputSchema={
-            "type": "object",
-            "properties": {
-                "tags": {
-                    "type": "array",
-                    "description": "List of tags",
-                    "items": {"type": "string"},  # Simple string array
-                }
-            },
-            "required": ["tags"],
-        },
-    )
-    model = _get_input_model_from_mcp_tool(tool)
-
-    # Create an instance
-    instance = model(tags=["tag1", "tag2", "tag3"])
-    assert instance.tags == ["tag1", "tag2", "tag3"]
-
-    # Verify JSON schema still preserves items for simple types
-    json_schema = model.model_json_schema()
-    tags_property = json_schema["properties"]["tags"]
-    assert "items" in tags_property
-    assert tags_property["items"]["type"] == "string"
+    # Test invalid data if provided
+    if invalid_data is not None:
+        with pytest.raises(ValidationError):
+            model(**invalid_data)


 def test_get_input_model_from_mcp_prompt():
@@ -993,6 +993,110 @@ def test_streaming_response_basic_structure() -> None:
    assert response.raw_representation is mock_event


+def test_streaming_annotation_added_with_file_path() -> None:
+    """Test streaming annotation added event with file_path type extracts HostedFileContent."""
+    client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
+    chat_options = ChatOptions()
+    function_call_ids: dict[int, tuple[str, str]] = {}
+
+    mock_event = MagicMock()
+    mock_event.type = "response.output_text.annotation.added"
+    mock_event.annotation_index = 0
+    mock_event.annotation = {
+        "type": "file_path",
+        "file_id": "file-abc123",
+        "index": 42,
+    }
+
+    response = client._create_streaming_response_content(mock_event, chat_options, function_call_ids)
+
+    assert len(response.contents) == 1
+    content = response.contents[0]
+    assert isinstance(content, HostedFileContent)
+    assert content.file_id == "file-abc123"
+    assert content.additional_properties is not None
+    assert content.additional_properties.get("annotation_index") == 0
+    assert content.additional_properties.get("index") == 42
+
+
+def test_streaming_annotation_added_with_file_citation() -> None:
+    """Test streaming annotation added event with file_citation type extracts HostedFileContent."""
+    client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
+    chat_options = ChatOptions()
+    function_call_ids: dict[int, tuple[str, str]] = {}
+
+    mock_event = MagicMock()
+    mock_event.type = "response.output_text.annotation.added"
+    mock_event.annotation_index = 1
+    mock_event.annotation = {
+        "type": "file_citation",
+        "file_id": "file-xyz789",
+        "filename": "sample.txt",
+        "index": 15,
+    }
+
+    response = client._create_streaming_response_content(mock_event, chat_options, function_call_ids)
+
+    assert len(response.contents) == 1
+    content = response.contents[0]
+    assert isinstance(content, HostedFileContent)
+    assert content.file_id == "file-xyz789"
+    assert content.additional_properties is not None
+    assert content.additional_properties.get("filename") == "sample.txt"
+    assert content.additional_properties.get("index") == 15
+
+
+def test_streaming_annotation_added_with_container_file_citation() -> None:
+    """Test streaming annotation added event with container_file_citation type."""
+    client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
+    chat_options = ChatOptions()
+    function_call_ids: dict[int, tuple[str, str]] = {}
+
+    mock_event = MagicMock()
+    mock_event.type = "response.output_text.annotation.added"
+    mock_event.annotation_index = 2
+    mock_event.annotation = {
+        "type": "container_file_citation",
+        "file_id": "file-container123",
+        "container_id": "container-456",
+        "filename": "data.csv",
+        "start_index": 10,
+        "end_index": 50,
+    }
+
+    response = client._create_streaming_response_content(mock_event, chat_options, function_call_ids)
+
+    assert len(response.contents) == 1
+    content = response.contents[0]
+    assert isinstance(content, HostedFileContent)
+    assert content.file_id == "file-container123"
+    assert content.additional_properties is not None
+    assert content.additional_properties.get("container_id") == "container-456"
+    assert content.additional_properties.get("filename") == "data.csv"
+    assert content.additional_properties.get("start_index") == 10
+    assert content.additional_properties.get("end_index") == 50
+
+
+def test_streaming_annotation_added_with_unknown_type() -> None:
+    """Test streaming annotation added event with unknown type is ignored."""
+    client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
+    chat_options = ChatOptions()
+    function_call_ids: dict[int, tuple[str, str]] = {}
+
+    mock_event = MagicMock()
+    mock_event.type = "response.output_text.annotation.added"
+    mock_event.annotation_index = 0
+    mock_event.annotation = {
+        "type": "url_citation",
+        "url": "https://example.com",
+    }
+
+    response = client._create_streaming_response_content(mock_event, chat_options, function_call_ids)
+
+    # url_citation should not produce HostedFileContent
+    assert len(response.contents) == 0
+
+
 def test_service_response_exception_includes_original_error_details() -> None:
    """Test that ServiceResponseException messages include original error details in the new format."""
    client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
@@ -3,6 +3,7 @@
 from typing import Any, cast

 import pytest
+from typing_extensions import Never

 from agent_framework import (
    AgentExecutorRequest,
@@ -52,6 +53,55 @@ def test_concurrent_builder_rejects_duplicate_executors() -> None:
        ConcurrentBuilder().participants([a, b])


+def test_concurrent_builder_rejects_duplicate_executors_from_factories() -> None:
+    """Test that duplicate executor IDs from factories are detected at build time."""
+
+    def create_dup1() -> Executor:
+        return _FakeAgentExec("dup", "A")
+
+    def create_dup2() -> Executor:
+        return _FakeAgentExec("dup", "B")  # same executor id
+
+    builder = ConcurrentBuilder().register_participants([create_dup1, create_dup2])
+    with pytest.raises(ValueError, match="Executor with ID 'dup' has already been created."):
+        builder.build()
+
+
+def test_concurrent_builder_rejects_mixed_participants_and_factories() -> None:
+    """Test that mixing .participants() and .register_participants() raises an error."""
+    # Case 1: participants first, then register_participants
+    with pytest.raises(ValueError, match="Cannot mix .participants"):
+        (
+            ConcurrentBuilder()
+            .participants([_FakeAgentExec("a", "A")])
+            .register_participants([lambda: _FakeAgentExec("b", "B")])
+        )
+
+    # Case 2: register_participants first, then participants
+    with pytest.raises(ValueError, match="Cannot mix .participants"):
+        (
+            ConcurrentBuilder()
+            .register_participants([lambda: _FakeAgentExec("a", "A")])
+            .participants([_FakeAgentExec("b", "B")])
+        )
+
+
+def test_concurrent_builder_rejects_multiple_calls_to_participants() -> None:
+    """Test that multiple calls to .participants() raises an error."""
+    with pytest.raises(ValueError, match=r"participants\(\) has already been called"):
+        (ConcurrentBuilder().participants([_FakeAgentExec("a", "A")]).participants([_FakeAgentExec("b", "B")]))
+
+
+def test_concurrent_builder_rejects_multiple_calls_to_register_participants() -> None:
+    """Test that multiple calls to .register_participants() raises an error."""
+    with pytest.raises(ValueError, match=r"register_participants\(\) has already been called"):
+        (
+            ConcurrentBuilder()
+            .register_participants([lambda: _FakeAgentExec("a", "A")])
+            .register_participants([lambda: _FakeAgentExec("b", "B")])
+        )
+
+
 async def test_concurrent_default_aggregator_emits_single_user_and_assistants() -> None:
    # Three synthetic agent executors
    e1 = _FakeAgentExec("agentA", "Alpha")
@@ -159,6 +209,138 @@ def test_concurrent_custom_aggregator_uses_callback_name_for_id() -> None:
    assert aggregator.id == "summarize"


+async def test_concurrent_with_aggregator_executor_instance() -> None:
+    """Test with_aggregator using an Executor instance (not factory)."""
+
+    class CustomAggregator(Executor):
+        @handler
+        async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+            texts: list[str] = []
+            for r in results:
+                msgs: list[ChatMessage] = r.agent_run_response.messages
+                texts.append(msgs[-1].text if msgs else "")
+            await ctx.yield_output(" & ".join(sorted(texts)))
+
+    e1 = _FakeAgentExec("agentA", "One")
+    e2 = _FakeAgentExec("agentB", "Two")
+
+    aggregator_instance = CustomAggregator(id="instance_aggregator")
+    wf = ConcurrentBuilder().participants([e1, e2]).with_aggregator(aggregator_instance).build()
+
+    completed = False
+    output: str | None = None
+    async for ev in wf.run_stream("prompt: instance test"):
+        if isinstance(ev, WorkflowStatusEvent) and ev.state == WorkflowRunState.IDLE:
+            completed = True
+        elif isinstance(ev, WorkflowOutputEvent):
+            output = cast(str, ev.data)
+        if completed and output is not None:
+            break
+
+    assert completed
+    assert output is not None
+    assert isinstance(output, str)
+    assert output == "One & Two"
+
+
+async def test_concurrent_with_aggregator_executor_factory() -> None:
+    """Test with_aggregator using an Executor factory."""
+
+    class CustomAggregator(Executor):
+        @handler
+        async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+            texts: list[str] = []
+            for r in results:
+                msgs: list[ChatMessage] = r.agent_run_response.messages
+                texts.append(msgs[-1].text if msgs else "")
+            await ctx.yield_output(" | ".join(sorted(texts)))
+
+    e1 = _FakeAgentExec("agentA", "One")
+    e2 = _FakeAgentExec("agentB", "Two")
+
+    wf = (
+        ConcurrentBuilder()
+        .participants([e1, e2])
+        .register_aggregator(lambda: CustomAggregator(id="custom_aggregator"))
+        .build()
+    )
+
+    completed = False
+    output: str | None = None
+    async for ev in wf.run_stream("prompt: factory test"):
+        if isinstance(ev, WorkflowStatusEvent) and ev.state == WorkflowRunState.IDLE:
+            completed = True
+        elif isinstance(ev, WorkflowOutputEvent):
+            output = cast(str, ev.data)
+        if completed and output is not None:
+            break
+
+    assert completed
+    assert output is not None
+    assert isinstance(output, str)
+    assert output == "One | Two"
+
+
+async def test_concurrent_with_aggregator_executor_factory_with_default_id() -> None:
+    """Test with_aggregator using an Executor class directly as factory (with default __init__ parameters)."""
+
+    class CustomAggregator(Executor):
+        def __init__(self, id: str = "default_aggregator") -> None:
+            super().__init__(id)
+
+        @handler
+        async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+            texts: list[str] = []
+            for r in results:
+                msgs: list[ChatMessage] = r.agent_run_response.messages
+                texts.append(msgs[-1].text if msgs else "")
+            await ctx.yield_output(" | ".join(sorted(texts)))
+
+    e1 = _FakeAgentExec("agentA", "One")
+    e2 = _FakeAgentExec("agentB", "Two")
+
+    wf = ConcurrentBuilder().participants([e1, e2]).register_aggregator(CustomAggregator).build()
+
+    completed = False
+    output: str | None = None
+    async for ev in wf.run_stream("prompt: factory test"):
+        if isinstance(ev, WorkflowStatusEvent) and ev.state == WorkflowRunState.IDLE:
+            completed = True
+        elif isinstance(ev, WorkflowOutputEvent):
+            output = cast(str, ev.data)
+        if completed and output is not None:
+            break
+
+    assert completed
+    assert output is not None
+    assert isinstance(output, str)
+    assert output == "One | Two"
+
+
+def test_concurrent_builder_rejects_multiple_calls_to_with_aggregator() -> None:
+    """Test that multiple calls to .with_aggregator() raises an error."""
+
+    def summarize(results: list[AgentExecutorResponse]) -> str:  # type: ignore[override]
+        return str(len(results))
+
+    with pytest.raises(ValueError, match=r"with_aggregator\(\) has already been called"):
+        (ConcurrentBuilder().with_aggregator(summarize).with_aggregator(summarize))
+
+
+def test_concurrent_builder_rejects_multiple_calls_to_register_aggregator() -> None:
+    """Test that multiple calls to .register_aggregator() raises an error."""
+
+    class CustomAggregator(Executor):
+        pass
+
+    with pytest.raises(ValueError, match=r"register_aggregator\(\) has already been called"):
+        (
+            ConcurrentBuilder()
+            .register_aggregator(lambda: CustomAggregator(id="agg1"))
+            .register_aggregator(lambda: CustomAggregator(id="agg2"))
+        )
+
+
 async def test_concurrent_checkpoint_resume_round_trip() -> None:
    storage = InMemoryCheckpointStorage()

@@ -278,3 +460,92 @@ async def test_concurrent_checkpoint_runtime_overrides_buildtime() -> None:

        assert len(runtime_checkpoints) > 0, "Runtime storage should have checkpoints"
        assert len(buildtime_checkpoints) == 0, "Build-time storage should have no checkpoints when overridden"
+
+
+def test_concurrent_builder_rejects_empty_participant_factories() -> None:
+    with pytest.raises(ValueError):
+        ConcurrentBuilder().register_participants([])
+
+
+async def test_concurrent_builder_reusable_after_build_with_participants() -> None:
+    """Test that the builder can be reused to build multiple identical workflows with participants()."""
+    e1 = _FakeAgentExec("agentA", "One")
+    e2 = _FakeAgentExec("agentB", "Two")
+
+    builder = ConcurrentBuilder().participants([e1, e2])
+
+    builder.build()
+
+    assert builder._participants[0] is e1  # type: ignore
+    assert builder._participants[1] is e2  # type: ignore
+    assert builder._participant_factories == []  # type: ignore
+
+
+async def test_concurrent_builder_reusable_after_build_with_factories() -> None:
+    """Test that the builder can be reused to build multiple workflows with register_participants()."""
+    call_count = 0
+
+    def create_agent_executor_a() -> Executor:
+        nonlocal call_count
+        call_count += 1
+        return _FakeAgentExec("agentA", "One")
+
+    def create_agent_executor_b() -> Executor:
+        nonlocal call_count
+        call_count += 1
+        return _FakeAgentExec("agentB", "Two")
+
+    builder = ConcurrentBuilder().register_participants([create_agent_executor_a, create_agent_executor_b])
+
+    # Build the first workflow
+    wf1 = builder.build()
+
+    assert builder._participants == []  # type: ignore
+    assert len(builder._participant_factories) == 2  # type: ignore
+    assert call_count == 2
+
+    # Build the second workflow
+    wf2 = builder.build()
+    assert call_count == 4
+
+    # Verify that the two workflows have different executor instances
+    assert wf1.executors["agentA"] is not wf2.executors["agentA"]
+    assert wf1.executors["agentB"] is not wf2.executors["agentB"]
+
+
+async def test_concurrent_with_register_participants() -> None:
+    """Test workflow creation using register_participants with factories."""
+
+    def create_agent1() -> Executor:
+        return _FakeAgentExec("agentA", "Alpha")
+
+    def create_agent2() -> Executor:
+        return _FakeAgentExec("agentB", "Beta")
+
+    def create_agent3() -> Executor:
+        return _FakeAgentExec("agentC", "Gamma")
+
+    wf = ConcurrentBuilder().register_participants([create_agent1, create_agent2, create_agent3]).build()
+
+    completed = False
+    output: list[ChatMessage] | None = None
+    async for ev in wf.run_stream("test prompt"):
+        if isinstance(ev, WorkflowStatusEvent) and ev.state == WorkflowRunState.IDLE:
+            completed = True
+        elif isinstance(ev, WorkflowOutputEvent):
+            output = cast(list[ChatMessage], ev.data)
+        if completed and output is not None:
+            break
+
+    assert completed
+    assert output is not None
+    messages: list[ChatMessage] = output
+
+    # Expect one user message + one assistant message per participant
+    assert len(messages) == 1 + 3
+    assert messages[0].role == Role.USER
+    assert "test prompt" in messages[0].text
+
+    assistant_texts = {m.text for m in messages[1:]}
+    assert assistant_texts == {"Alpha", "Beta", "Gamma"}
+    assert all(m.role == Role.ASSISTANT for m in messages[1:])
@@ -293,6 +293,20 @@ def test_register_duplicate_name_raises_error():
        builder.register_executor(lambda: MockExecutor(id="executor_2"), name="MyExecutor")


+def test_register_duplicate_id_raises_error():
+    """Test that registering duplicate id raises an error."""
+    builder = WorkflowBuilder()
+
+    # Register first executor
+    builder.register_executor(lambda: MockExecutor(id="executor"), name="MyExecutor1")
+    builder.register_executor(lambda: MockExecutor(id="executor"), name="MyExecutor2")
+    builder.set_start_executor("MyExecutor1")
+
+    # Registering second executor with same ID should raise ValueError
+    with pytest.raises(ValueError, match="Executor with ID 'executor' has already been created."):
+        builder.build()
+
+
 def test_register_agent_basic():
    """Test basic agent registration with lazy initialization."""
    builder = WorkflowBuilder()
@@ -4,7 +4,7 @@ description = "Declarative specification support for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Debug UI for Microsoft Agent Framework with OpenAI-compatible API
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://github.com/microsoft/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Experimental modules for Microsoft Agent Framework"
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Mem0 integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Microsoft Purview (Graph dataSecurityAndGovernance) integration f
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://github.com/microsoft/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Redis integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -4,7 +4,7 @@ description = "Microsoft Agent Framework for building AI Agents with Python. Thi
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
@@ -23,7 +23,7 @@ classifiers = [
  "Typing :: Typed",
 ]
 dependencies = [
-    "agent-framework-core[all]==1.0.0b251209",
+    "agent-framework-core[all]==1.0.0b251211",
 ]

 [dependency-groups]
@@ -25,6 +25,7 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py) | Azure AI Agent with Azure AI Search Example |
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding.py) | Azure AI agent with Bing Grounding search for real-time web information |
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter.py) | Azure AI Agent with Code Interpreter Example |
+| [`getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter_file_generation.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter_file_generation.py) | Azure AI Agent with Code Interpreter File Generation Example |
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_existing_agent.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_existing_agent.py) | Azure AI Agent with Existing Agent Example |
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py) | Azure AI Agent with Existing Thread Example |
 | [`getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py) | Azure AI Agent with Explicit Settings Example |
@@ -47,6 +48,7 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
 | [`getting_started/agents/azure_ai/azure_ai_with_bing_custom_search.py`](./getting_started/agents/azure_ai/azure_ai_with_bing_custom_search.py) | Azure AI Agent with Bing Custom Search Example |
 | [`getting_started/agents/azure_ai/azure_ai_with_browser_automation.py`](./getting_started/agents/azure_ai/azure_ai_with_browser_automation.py) | Azure AI Agent with Browser Automation Example |
 | [`getting_started/agents/azure_ai/azure_ai_with_code_interpreter.py`](./getting_started/agents/azure_ai/azure_ai_with_code_interpreter.py) | Azure AI Agent with Code Interpreter Example |
+| [`getting_started/agents/azure_ai/azure_ai_with_code_interpreter_file_generation.py`](./getting_started/agents/azure_ai/azure_ai_with_code_interpreter_file_generation.py) | Azure AI Agent with Code Interpreter File Generation Example |
 | [`getting_started/agents/azure_ai/azure_ai_with_existing_agent.py`](./getting_started/agents/azure_ai/azure_ai_with_existing_agent.py) | Azure AI Agent with Existing Agent Example |
 | [`getting_started/agents/azure_ai/azure_ai_with_existing_conversation.py`](./getting_started/agents/azure_ai/azure_ai_with_existing_conversation.py) | Azure AI Agent with Existing Conversation Example |
 | [`getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py`](./getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py) | Azure AI Agent with Explicit Settings Example |
@@ -14,6 +14,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to search custom search instances and provide responses with relevant results. Requires a Bing Custom Search connection and instance configured in your Azure AI project. |
 | [`azure_ai_with_browser_automation.py`](azure_ai_with_browser_automation.py) | Shows how to use Browser Automation with Azure AI agents to perform automated web browsing tasks and provide responses based on web interactions. Requires a Browser Automation connection configured in your Azure AI project. |
 | [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
+| [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
 | [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
 | [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
 | [`azure_ai_with_application_endpoint.py`](azure_ai_with_application_endpoint.py) | Demonstrates calling the Azure AI application-scoped endpoint. |
@@ -0,0 +1,111 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import (
+    CitationAnnotation,
+    HostedCodeInterpreterTool,
+    HostedFileContent,
+    TextContent,
+)
+from agent_framework._agents import AgentRunResponseUpdate
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI V2 Code Interpreter File Generation Sample
+
+This sample demonstrates how the V2 AzureAIClient handles file annotations
+when code interpreter generates text files. It shows both non-streaming
+and streaming approaches to verify file ID extraction.
+"""
+
+QUERY = (
+    "Write a simple Python script that creates a text file called 'sample.txt' containing "
+    "'Hello from the code interpreter!' and save it to disk."
+)
+
+
+async def test_non_streaming() -> None:
+    """Test non-streaming response - should have annotations on TextContent."""
+    print("=== Testing Non-Streaming Response ===")
+
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(credential=credential).create_agent(
+            name="V2CodeInterpreterFileAgent",
+            instructions="You are a helpful assistant that can write and execute Python code to create files.",
+            tools=HostedCodeInterpreterTool(),
+        ) as agent,
+    ):
+        print(f"User: {QUERY}\n")
+
+        result = await agent.run(QUERY)
+        print(f"Agent: {result.text}\n")
+
+        # Check for annotations in the response
+        annotations_found: list[str] = []
+        # AgentRunResponse has messages property, which contains ChatMessage objects
+        for message in result.messages:
+            for content in message.contents:
+                if isinstance(content, TextContent) and content.annotations:
+                    for annotation in content.annotations:
+                        if isinstance(annotation, CitationAnnotation) and annotation.file_id:
+                            annotations_found.append(annotation.file_id)
+                            print(f"Found file annotation: file_id={annotation.file_id}")
+
+        if annotations_found:
+            print(f"SUCCESS: Found {len(annotations_found)} file annotation(s)")
+        else:
+            print("WARNING: No file annotations found in non-streaming response")
+
+
+async def test_streaming() -> None:
+    """Test streaming response - check if file content is captured via HostedFileContent."""
+    print("\n=== Testing Streaming Response ===")
+
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(credential=credential).create_agent(
+            name="V2CodeInterpreterFileAgentStreaming",
+            instructions="You are a helpful assistant that can write and execute Python code to create files.",
+            tools=HostedCodeInterpreterTool(),
+        ) as agent,
+    ):
+        print(f"User: {QUERY}\n")
+        annotations_found: list[str] = []
+        text_chunks: list[str] = []
+        file_ids_found: list[str] = []
+
+        async for update in agent.run_stream(QUERY):
+            if isinstance(update, AgentRunResponseUpdate):
+                for content in update.contents:
+                    if isinstance(content, TextContent):
+                        if content.text:
+                            text_chunks.append(content.text)
+                        if content.annotations:
+                            for annotation in content.annotations:
+                                if isinstance(annotation, CitationAnnotation) and annotation.file_id:
+                                    annotations_found.append(annotation.file_id)
+                                    print(f"Found streaming annotation: file_id={annotation.file_id}")
+                    elif isinstance(content, HostedFileContent):
+                        file_ids_found.append(content.file_id)
+                        print(f"Found streaming HostedFileContent: file_id={content.file_id}")
+
+        print(f"\nAgent response: {''.join(text_chunks)[:200]}...")
+
+        if annotations_found or file_ids_found:
+            total = len(annotations_found) + len(file_ids_found)
+            print(f"SUCCESS: Found {total} file reference(s) in streaming")
+        else:
+            print("WARNING: No file annotations found in streaming response")
+
+
+async def main() -> None:
+    print("AzureAIClient Code Interpreter File Generation Test\n")
+    await test_non_streaming()
+    await test_streaming()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -9,6 +9,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAIAgentClient`. It automatically handles all configuration using environment variables. |
 | [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to find real-time information from the web using custom search configurations. Demonstrates how to set up and use HostedWebSearchTool with custom search instances. |
 | [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to find real-time information from the web. Demonstrates web search capabilities with proper source citations and comprehensive error handling. |
+| [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
 | [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
 | [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents. |
 | [`azure_ai_with_existing_thread.py`](azure_ai_with_existing_thread.py) | Shows how to work with a pre-existing thread by providing the thread ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created threads. |
@@ -0,0 +1,102 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import AgentRunResponseUpdate, ChatAgent, HostedCodeInterpreterTool, HostedFileContent
+from agent_framework.azure import AzureAIAgentClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent Code Interpreter File Generation Example
+
+This sample demonstrates using HostedCodeInterpreterTool with AzureAIAgentClient
+to generate a text file and then retrieve it.
+
+The test flow:
+1. Create an agent with code interpreter tool
+2. Ask the agent to generate a txt file using Python code
+3. Capture the file_id from HostedFileContent in the response
+4. Retrieve the file using the agents_client.files API
+"""
+
+
+async def main() -> None:
+    """Test file generation and retrieval with code interpreter."""
+
+    async with AzureCliCredential() as credential:
+        client = AzureAIAgentClient(credential=credential)
+
+        try:
+            async with ChatAgent(
+                chat_client=client,
+                instructions=(
+                    "You are a Python code execution assistant. "
+                    "ALWAYS use the code interpreter tool to execute Python code when asked to create files. "
+                    "Write actual Python code to create files, do not just describe what you would do."
+                ),
+                tools=[HostedCodeInterpreterTool()],
+            ) as agent:
+                # Be very explicit about wanting code execution and a download link
+                query = (
+                    "Use the code interpreter to execute this Python code and then provide me "
+                    "with a download link for the generated file:\n"
+                    "```python\n"
+                    "with open('/mnt/data/sample.txt', 'w') as f:\n"
+                    "    f.write('Hello, World! This is a test file.')\n"
+                    "'/mnt/data/sample.txt'\n"  # Return the path so it becomes downloadable
+                    "```"
+                )
+                print(f"User: {query}\n")
+                print("=" * 60)
+
+                # Collect file_ids from the response
+                file_ids: list[str] = []
+
+                async for chunk in agent.run_stream(query):
+                    if not isinstance(chunk, AgentRunResponseUpdate):
+                        continue
+
+                    for content in chunk.contents:
+                        if content.type == "text":
+                            print(content.text, end="", flush=True)
+                        elif content.type == "hosted_file":
+                            if isinstance(content, HostedFileContent):
+                                file_ids.append(content.file_id)
+                                print(f"\n[File generated: {content.file_id}]")
+
+                print("\n" + "=" * 60)
+
+                # Attempt to retrieve discovered files
+                if file_ids:
+                    print(f"\nAttempting to retrieve {len(file_ids)} file(s):")
+                    for file_id in file_ids:
+                        try:
+                            file_info = await client.agents_client.files.get(file_id)
+                            print(f"  File {file_id}: Retrieved successfully")
+                            print(f"    Filename: {file_info.filename}")
+                            print(f"    Purpose: {file_info.purpose}")
+                            print(f"    Bytes: {file_info.bytes}")
+                        except Exception as e:
+                            print(f"  File {file_id}: FAILED to retrieve - {e}")
+                else:
+                    print("No file IDs were captured from the response.")
+
+                # List all files to see if any exist
+                print("\nListing all files in the agent service:")
+                try:
+                    files_list = await client.agents_client.files.list()
+                    count = 0
+                    for file_info in files_list.data:
+                        count += 1
+                        print(f"  - {file_info.id}: {file_info.filename} ({file_info.purpose})")
+                    if count == 0:
+                        print("  No files found.")
+                except Exception as e:
+                    print(f"  Failed to list files: {e}")
+
+        finally:
+            await client.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -110,6 +110,7 @@ For additional observability samples in Agent Framework, see the [observability
 | Concurrent Orchestration (Default Aggregator) | [orchestration/concurrent_agents.py](./orchestration/concurrent_agents.py) | Fan-out to multiple agents; fan-in with default aggregator returning combined ChatMessages |
 | Concurrent Orchestration (Custom Aggregator) | [orchestration/concurrent_custom_aggregator.py](./orchestration/concurrent_custom_aggregator.py) | Override aggregator via callback; summarize results with an LLM |
 | Concurrent Orchestration (Custom Agent Executors) | [orchestration/concurrent_custom_agent_executors.py](./orchestration/concurrent_custom_agent_executors.py) | Child executors own ChatAgents; concurrent fan-out/fan-in via ConcurrentBuilder |
+| Concurrent Orchestration (Participant Factory) | [orchestration/concurrent_participant_factory.py](./orchestration/concurrent_participant_factory.py) | Use participant factories for state isolation between workflow instances |
 | Group Chat with Agent Manager | [orchestration/group_chat_agent_manager.py](./orchestration/group_chat_agent_manager.py) | Agent-based manager using `set_manager()` to select next speaker |
 | Group Chat Philosophical Debate | [orchestration/group_chat_philosophical_debate.py](./orchestration/group_chat_philosophical_debate.py) | Agent manager moderates long-form, multi-round debate across diverse participants |
 | Group Chat with Simple Function Selector | [orchestration/group_chat_simple_selector.py](./orchestration/group_chat_simple_selector.py) | Group chat with a simple function selector for next speaker |
@@ -17,7 +17,7 @@ to synthesize a concise, consolidated summary from the experts' outputs.
 The workflow completes when all participants become idle.

 Demonstrates:
- ConcurrentBuilder().participants([...]).with_custom_aggregator(callback)
+- ConcurrentBuilder().participants([...]).with_aggregator(callback)
 - Fan-out to agents and fan-in at an aggregator
 - Aggregation implemented via an LLM call (chat_client.get_response)
 - Workflow output yielded with the synthesized summary string
@@ -0,0 +1,169 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from typing import Any, Never
+
+from agent_framework import (
+    ChatAgent,
+    ChatMessage,
+    ConcurrentBuilder,
+    Executor,
+    Role,
+    Workflow,
+    WorkflowContext,
+    handler,
+)
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.identity import AzureCliCredential
+
+"""
+Sample: Concurrent Orchestration with participant factories and Custom Aggregator
+
+Build a concurrent workflow with ConcurrentBuilder that fans out one prompt to
+multiple domain agents and fans in their responses.
+
+Override the default aggregator with a custom Executor class that uses
+AzureOpenAIChatClient.get_response() to synthesize a concise, consolidated summary
+from the experts' outputs.
+
+All participants and the aggregator are created via factory functions that return
+their respective ChatAgent or Executor instances.
+
+Using participant factories allows you to set up proper state isolation between workflow
+instances created by the same builder. This is particularly useful when you need to handle
+requests or tasks in parallel with stateful participants.
+
+Demonstrates:
+- ConcurrentBuilder().register_participants([...]).with_aggregator(callback)
+- Fan-out to agents and fan-in at an aggregator
+- Aggregation implemented via an LLM call (chat_client.get_response)
+- Workflow output yielded with the synthesized summary string
+
+Prerequisites:
+- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
+"""
+
+
+def create_researcher() -> ChatAgent:
+    """Factory function to create a researcher agent instance."""
+    return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
+        instructions=(
+            "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
+            " opportunities, and risks."
+        ),
+        name="researcher",
+    )
+
+
+def create_marketer() -> ChatAgent:
+    """Factory function to create a marketer agent instance."""
+    return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
+        instructions=(
+            "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
+            " aligned to the prompt."
+        ),
+        name="marketer",
+    )
+
+
+def create_legal() -> ChatAgent:
+    """Factory function to create a legal/compliance agent instance."""
+    return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
+        instructions=(
+            "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
+            " based on the prompt."
+        ),
+        name="legal",
+    )
+
+
+class SummarizationExecutor(Executor):
+    """Custom aggregator executor that synthesizes expert outputs into a concise summary."""
+
+    def __init__(self) -> None:
+        super().__init__(id="summarization_executor")
+        self.chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    @handler
+    async def summarize_results(self, results: list[Any], ctx: WorkflowContext[Never, str]) -> None:
+        expert_sections: list[str] = []
+        for r in results:
+            try:
+                messages = getattr(r.agent_run_response, "messages", [])
+                final_text = messages[-1].text if messages and hasattr(messages[-1], "text") else "(no content)"
+                expert_sections.append(f"{getattr(r, 'executor_id', 'expert')}:\n{final_text}")
+            except Exception as e:
+                expert_sections.append(f"{getattr(r, 'executor_id', 'expert')}: (error: {type(e).__name__}: {e})")
+
+        # Ask the model to synthesize a concise summary of the experts' outputs
+        system_msg = ChatMessage(
+            Role.SYSTEM,
+            text=(
+                "You are a helpful assistant that consolidates multiple domain expert outputs "
+                "into one cohesive, concise summary with clear takeaways. Keep it under 200 words."
+            ),
+        )
+        user_msg = ChatMessage(Role.USER, text="\n\n".join(expert_sections))
+
+        response = await self.chat_client.get_response([system_msg, user_msg])
+
+        await ctx.yield_output(response.messages[-1].text if response.messages else "")
+
+
+async def run_workflow(workflow: Workflow, query: str) -> None:
+    events = await workflow.run(query)
+    outputs = events.get_outputs()
+
+    if outputs:
+        print(outputs[0])  # Get the first (and typically only) output
+    else:
+        raise RuntimeError("No outputs received from the workflow.")
+
+
+async def main() -> None:
+    # Create a concurrent builder with participant factories and a custom aggregator
+    # - register_participants([...]) accepts factory functions that return
+    #   AgentProtocol (agents) or Executor instances.
+    # - register_aggregator(...) takes a factory function that returns an Executor instance.
+    concurrent_builder = (
+        ConcurrentBuilder()
+        .register_participants([create_researcher, create_marketer, create_legal])
+        .register_aggregator(SummarizationExecutor)
+    )
+
+    # Build workflow_a
+    workflow_a = concurrent_builder.build()
+
+    # Run workflow_a
+    # Context is maintained across runs
+    print("=== First Run on workflow_a ===")
+    await run_workflow(workflow_a, "We are launching a new budget-friendly electric bike for urban commuters.")
+    print("\n=== Second Run on workflow_a ===")
+    await run_workflow(workflow_a, "Refine your response to focus on the California market.")
+
+    # Build workflow_b
+    # This will create new instances of all participants and the aggregator
+    # The agents will also get new threads
+    workflow_b = concurrent_builder.build()
+    # Run workflow_b
+    # Context is not maintained across instances
+    # Should not expect mentions of electric bikes in the results
+    print("\n=== First Run on workflow_b ===")
+    await run_workflow(workflow_b, "Refine your response to focus on the California market.")
+
+    """
+    Sample Output:
+
+    === First Run on workflow_a ===
+    The budget-friendly electric bike market is poised for significant growth, driven by urbanization, ...
+
+    === Second Run on workflow_a ===
+    Launching a budget-friendly electric bike in California presents significant opportunities, driven ...
+
+    === First Run on workflow_b ===
+    To successfully penetrate the California market, consider these tailored strategies focused on ...
+    """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,241 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Handoff Workflow with Code Interpreter File Generation Sample
+
+This sample demonstrates retrieving file IDs from code interpreter output
+in a handoff workflow context. A triage agent routes to a code specialist
+that generates a text file, and we verify the file_id is captured correctly
+from the streaming AgentRunUpdateEvent events.
+
+Verifies GitHub issue #2718: files generated by code interpreter in
+HandoffBuilder workflows can be properly retrieved.
+
+Toggle USE_V2_CLIENT to switch between:
+    - V1: AzureAIAgentClient (azure-ai-agents SDK)
+    - V2: AzureAIClient (azure-ai-projects 2.x with Responses API)
+
+IMPORTANT: When using V2 AzureAIClient with HandoffBuilder, each agent must
+have its own client instance. The V2 client binds to a single server-side
+agent name, so sharing a client between agents causes routing issues.
+
+Prerequisites:
+    - `az login` (Azure CLI authentication)
+    - V1: AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
+    - V2: AZURE_AI_PROJECT_ENDPOINT, AZURE_AI_MODEL_DEPLOYMENT_NAME
+"""
+
+import asyncio
+from collections.abc import AsyncIterable
+from contextlib import asynccontextmanager
+from collections.abc import AsyncIterator
+
+from agent_framework import (
+    AgentRunUpdateEvent,
+    ChatAgent,
+    HandoffBuilder,
+    HandoffUserInputRequest,
+    HostedCodeInterpreterTool,
+    HostedFileContent,
+    RequestInfoEvent,
+    TextContent,
+    WorkflowEvent,
+    WorkflowRunState,
+    WorkflowStatusEvent,
+)
+from azure.identity.aio import AzureCliCredential
+
+# Toggle between V1 (AzureAIAgentClient) and V2 (AzureAIClient)
+USE_V2_CLIENT = False
+
+
+async def _drain(stream: AsyncIterable[WorkflowEvent]) -> list[WorkflowEvent]:
+    """Collect all events from an async stream."""
+    return [event async for event in stream]
+
+
+def _handle_events(events: list[WorkflowEvent]) -> tuple[list[RequestInfoEvent], list[str]]:
+    """Process workflow events and extract file IDs and pending requests.
+
+    Returns:
+        Tuple of (pending_requests, file_ids_found)
+    """
+    requests: list[RequestInfoEvent] = []
+    file_ids: list[str] = []
+
+    for event in events:
+        if isinstance(event, WorkflowStatusEvent):
+            if event.state in {WorkflowRunState.IDLE, WorkflowRunState.IDLE_WITH_PENDING_REQUESTS}:
+                print(f"[status] {event.state.name}")
+
+        elif isinstance(event, RequestInfoEvent):
+            if isinstance(event.data, HandoffUserInputRequest):
+                print("\n=== Conversation So Far ===")
+                for msg in event.data.conversation:
+                    speaker = msg.author_name or msg.role.value
+                    text = msg.text or ""
+                    txt = text[:200] + "..." if len(text) > 200 else text
+                    print(f"- {speaker}: {txt}")
+                print("===========================\n")
+            requests.append(event)
+
+        elif isinstance(event, AgentRunUpdateEvent):
+            update = event.data
+            if update is None:
+                continue
+            for content in update.contents:
+                if isinstance(content, HostedFileContent):
+                    file_ids.append(content.file_id)
+                    print(f"[Found HostedFileContent: file_id={content.file_id}]")
+                elif isinstance(content, TextContent) and content.annotations:
+                    for annotation in content.annotations:
+                        if hasattr(annotation, "file_id") and annotation.file_id:
+                            file_ids.append(annotation.file_id)
+                            print(f"[Found file annotation: file_id={annotation.file_id}]")
+
+    return requests, file_ids
+
+
+@asynccontextmanager
+async def create_agents_v1(credential: AzureCliCredential) -> AsyncIterator[tuple[ChatAgent, ChatAgent]]:
+    """Create agents using V1 AzureAIAgentClient."""
+    from agent_framework.azure import AzureAIAgentClient
+
+    async with AzureAIAgentClient(credential=credential) as client:
+        triage = client.create_agent(
+            name="triage_agent",
+            instructions=(
+                "You are a triage agent. Route code-related requests to the code_specialist. "
+                "When the user asks to create or generate files, hand off to code_specialist "
+                "by calling handoff_to_code_specialist."
+            ),
+        )
+
+        code_specialist = client.create_agent(
+            name="code_specialist",
+            instructions=(
+                "You are a Python code specialist. Use the code interpreter to execute Python code "
+                "and create files when requested. Always save files to /mnt/data/ directory."
+            ),
+            tools=[HostedCodeInterpreterTool()],
+        )
+
+        yield triage, code_specialist
+
+
+@asynccontextmanager
+async def create_agents_v2(credential: AzureCliCredential) -> AsyncIterator[tuple[ChatAgent, ChatAgent]]:
+    """Create agents using V2 AzureAIClient.
+
+    Each agent needs its own client instance because the V2 client binds
+    to a single server-side agent name.
+    """
+    from agent_framework.azure import AzureAIClient
+
+    async with (
+        AzureAIClient(credential=credential) as triage_client,
+        AzureAIClient(credential=credential) as code_client,
+    ):
+        triage = triage_client.create_agent(
+            name="TriageAgent",
+            instructions=(
+                "You are a triage agent. Your ONLY job is to route requests to the appropriate specialist. "
+                "For code or file creation requests, call handoff_to_CodeSpecialist immediately. "
+                "Do NOT try to complete tasks yourself. Just hand off."
+            ),
+        )
+
+        code_specialist = code_client.create_agent(
+            name="CodeSpecialist",
+            instructions=(
+                "You are a Python code specialist. You have access to a code interpreter tool. "
+                "Use the code interpreter to execute Python code and create files. "
+                "Always save files to /mnt/data/ directory. "
+                "Do NOT discuss handoffs or routing - just complete the coding task directly."
+            ),
+            tools=[HostedCodeInterpreterTool()],
+        )
+
+        yield triage, code_specialist
+
+
+async def main() -> None:
+    """Run a simple handoff workflow with code interpreter file generation."""
+    client_version = "V2 (AzureAIClient)" if USE_V2_CLIENT else "V1 (AzureAIAgentClient)"
+    print(f"=== Handoff Workflow with Code Interpreter File Generation [{client_version}] ===\n")
+
+    async with AzureCliCredential() as credential:
+        create_agents = create_agents_v2 if USE_V2_CLIENT else create_agents_v1
+
+        async with create_agents(credential) as (triage, code_specialist):
+            workflow = (
+                HandoffBuilder()
+                .participants([triage, code_specialist])
+                .set_coordinator(triage)
+                .with_termination_condition(lambda conv: sum(1 for msg in conv if msg.role.value == "user") >= 2)
+                .build()
+            )
+
+            user_inputs = [
+                "Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.",
+                "exit",
+            ]
+            input_index = 0
+            all_file_ids: list[str] = []
+
+            print(f"User: {user_inputs[0]}")
+            events = await _drain(workflow.run_stream(user_inputs[0]))
+            requests, file_ids = _handle_events(events)
+            all_file_ids.extend(file_ids)
+            input_index += 1
+
+            while requests:
+                request = requests[0]
+                if input_index >= len(user_inputs):
+                    break
+                user_input = user_inputs[input_index]
+                print(f"\nUser: {user_input}")
+
+                responses = {request.request_id: user_input}
+                events = await _drain(workflow.send_responses_streaming(responses))
+                requests, file_ids = _handle_events(events)
+                all_file_ids.extend(file_ids)
+                input_index += 1
+
+            print("\n" + "=" * 50)
+            if all_file_ids:
+                print(f"SUCCESS: Found {len(all_file_ids)} file ID(s) in handoff workflow:")
+                for fid in all_file_ids:
+                    print(f"  - {fid}")
+            else:
+                print("WARNING: No file IDs captured from the handoff workflow.")
+            print("=" * 50)
+
+            """
+            Sample Output:
+
+            User: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
+            [Found HostedFileContent: file_id=assistant-JT1sA...]
+
+            === Conversation So Far ===
+            - user: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
+            - triage_agent: I am handing off your request to create the text file "hello.txt" with the specified content to the code specialist. They will assist you shortly.
+            - code_specialist: The file "hello.txt" has been created with the content "Hello from handoff workflow!". You can download it using the link below:
+
+            [hello.txt](sandbox:/mnt/data/hello.txt)
+            ===========================
+
+            [status] IDLE_WITH_PENDING_REQUESTS
+
+            User: exit
+            [status] IDLE
+
+            ==================================================
+            SUCCESS: Found 1 file ID(s) in handoff workflow:
+            - assistant-JT1sA...
+            ==================================================
+            """  # noqa: E501
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -89,7 +89,7 @@ wheels = [

 [[package]]
 name = "agent-framework"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { virtual = "." }
 dependencies = [
    { name = "agent-framework-core", extra = ["all"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -156,7 +156,7 @@ docs = [

 [[package]]
 name = "agent-framework-a2a"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/a2a" }
 dependencies = [
    { name = "a2a-sdk", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -171,7 +171,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-ag-ui"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/ag-ui" }
 dependencies = [
    { name = "ag-ui-protocol", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -201,7 +201,7 @@ provides-extras = ["dev"]

 [[package]]
 name = "agent-framework-anthropic"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/anthropic" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -216,7 +216,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-azure-ai"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/azure-ai" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -235,7 +235,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-azure-ai-search"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/azure-ai-search" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -250,7 +250,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-azurefunctions"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/azurefunctions" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -275,7 +275,7 @@ dev = [{ name = "types-python-dateutil", specifier = ">=2.9.0" }]

 [[package]]
 name = "agent-framework-chatkit"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/chatkit" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -290,7 +290,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-copilotstudio"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/copilotstudio" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -305,7 +305,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-core"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/core" }
 dependencies = [
    { name = "azure-identity", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -356,7 +356,7 @@ requires-dist = [
    { name = "agent-framework-purview", marker = "extra == 'all'", editable = "packages/purview" },
    { name = "agent-framework-redis", marker = "extra == 'all'", editable = "packages/redis" },
    { name = "azure-identity", specifier = ">=1,<2" },
-    { name = "mcp", extras = ["ws"], specifier = ">=1.13" },
+    { name = "mcp", extras = ["ws"], specifier = ">=1.23" },
    { name = "openai", specifier = ">=1.99.0" },
    { name = "opentelemetry-api", specifier = ">=1.39.0" },
    { name = "opentelemetry-exporter-otlp-proto-grpc", specifier = ">=1.39.0" },
@@ -371,7 +371,7 @@ provides-extras = ["all"]

 [[package]]
 name = "agent-framework-declarative"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/declarative" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -396,7 +396,7 @@ dev = [{ name = "types-pyyaml" }]

 [[package]]
 name = "agent-framework-devui"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/devui" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -430,7 +430,7 @@ provides-extras = ["dev", "all"]

 [[package]]
 name = "agent-framework-lab"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/lab" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -521,7 +521,7 @@ dev = [

 [[package]]
 name = "agent-framework-mem0"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/mem0" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -536,7 +536,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-purview"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/purview" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -553,7 +553,7 @@ requires-dist = [

 [[package]]
 name = "agent-framework-redis"
-version = "1.0.0b251209"
+version = "1.0.0b251211"
 source = { editable = "packages/redis" }
 dependencies = [
    { name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Author	SHA1	Message	Date
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>Chris	16230d3b20	Bump actions/checkout from 5 to 6 (#2404 ) Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>	2025-12-11 18:43:52 +00:00
Dmytro StrukandGitHub	8d53b20026	Python: Updated package versions (#2784 ) * Updated package versions * Small fix	2025-12-11 18:39:08 +00:00
Eduard van ValkenburgandGitHub	c376868ec9	Python: added more complete parsing for mcp tool arguments (#2756 ) * added more complete parsing for mcp tool arguments * fixed mypy * added nonlocal model counter, and some fixes * fixes in naming logic * extracted json parsing function, added parametrized test and checked coverage	2025-12-11 17:24:08 +00:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	8bb9927f3c	Bump Azure.AI.AgentServer.AgentFramework from 1.0.0-beta.4 to 1.0.0-beta.5 (#2778 ) --- updated-dependencies: - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-12-11 14:08:20 +00:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	194486c4cc	Bump Azure.Identity from 1.17.0 to 1.17.1 (#2780 ) --- updated-dependencies: - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-12-11 11:04:22 +00:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	0413f4220a	Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.7 to 4.0.4.11 (#2777 ) --- updated-dependencies: - dependency-name: AWSSDK.Extensions.Bedrock.MEAI dependency-version: 4.0.4.11 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-12-11 11:01:19 +00:00
Copilot GitHub rogerbarreto Copilotcopilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>	67e83042cf	.NET: Add Conversation State Sample (Step05) (#2697 ) * Initial plan * Add Agent_OpenAI_Step05_Conversation sample for conversation state management Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Update Program.cs comment to accurately describe the sample Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Update the code to use the ConversationClient more in line with the samples in OpenAI * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Changing sample to use ChatClientAgent and conversationId in GetNewThread --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>	2025-12-11 10:58:42 +00:00
Korolev Dmitry GitHub Mark Wallace	5da1c2fd4c	code ql sm04598 (#2723 ) Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>	2025-12-11 10:34:58 +00:00
SergeyMenshykhandGitHub	989b6ebe71	.NET: [BREAKING] Prevent nulls in AIAgent property (#2719 ) * prevent nulls in AIAgent property * address feedback	2025-12-11 09:50:25 +00:00
Evan MattsonandGitHub	3481914981	Capture file IDs from code interpreter in streaming responses (#2741 )	2025-12-11 07:30:19 +00:00
KurtandGitHub	4c6a5d4aa1	Python: fix: GroupChat ManagerSelectionResponse JSON Schema for OpenAI Structured Outpu… (#2750 ) * fix: ManagerSelectionResponse JSON Schema for OpenAI Structured Output Strict Mode * refactor: install pre-commit then commit again	2025-12-11 02:34:12 +00:00
Tao ChenandGitHub	191779ce80	Python: Add factory pattern to concurrent orchestration builder (#2738 ) * Add factory pattern to concurrent orchestration builder * Update readme * Address AI comments * Fix unit tests * Fix import * Prevent multiple calls to set participants or factories * Add comments * Mitigate warnings * Fix mypy * Address comments * Address Copilot comments * Fix tests	2025-12-11 01:23:28 +00:00