Updated package versions (#2027 )

Add Microsoft Agent Framework logo to assets (#2007 )
DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021 )
2026-06-16 21:04:09 +08:00 · 2025-11-08 18:09:28 +00:00 · 2025-11-08 07:27:33 +00:00 · 2025-11-08 03:25:08 +00:00 · 2025-11-07 23:45:15 +00:00 · 2025-11-07 23:28:32 +00:00
777 changed files with 73466 additions and 11549 deletions
@@ -12,11 +12,14 @@ ignorePatterns:
  - pattern: "https:\/\/platform.openai.com"
  - pattern: "http:\/\/localhost"
  - pattern: "http:\/\/127.0.0.1"
+  - pattern: "https:\/\/localhost"
+  - pattern: "https:\/\/127.0.0.1"
  - pattern: "0001-spec.md"
  - pattern: "0001-madr-architecture-decisions.md"
  - pattern: "https://api.powerplatform.com/.default"
  - pattern: "https://your-resource.openai.azure.com/"
  - pattern: "http://host.docker.internal"
+  - pattern: "https://openai.github.io/openai-agents-js/openai/agents/classes/"
 # excludedDirs:
  # Folders which include links to localhost, since it's not ignored with regular expressions
 baseUrl: https://github.com/microsoft/agent-framework/
@@ -1052,7 +1052,7 @@ AgentThread thread = agent.GetNewThread();

 **Add Agent Framework Packages:**
 ```xml
-<PackageReference Include="Microsoft.Agents.AI.AzureAI" />
+<PackageReference Include="Microsoft.Agents.AI.AzureAI.Persistent" />
 <PackageReference Include="Azure.Identity" />
 ```
 </configuration_changes>
@@ -74,6 +74,7 @@ jobs:
            .
            .github
            dotnet
+            python
            workflow-samples

      - name: Setup dotnet
@@ -18,7 +18,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        python-version: ["3.10"]
+        python-version: ["3.10", "3.14"]
    runs-on: ubuntu-latest
    continue-on-error: true
    defaults:
@@ -16,13 +16,39 @@ env:
  UV_CACHE_DIR: /tmp/.uv-cache

 jobs:
+  paths-filter:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: read
+    outputs:
+      pythonChanges: ${{ steps.filter.outputs.python}}
+    steps:
+      - uses: actions/checkout@v5
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            python:
+              - 'python/**'
+      # run only if 'python' files were changed
+      - name: python tests
+        if: steps.filter.outputs.python == 'true'
+        run: echo "Python file"
+      # run only if not 'python' files were changed
+      - name: not python tests
+        if: steps.filter.outputs.python != 'true'
+        run: echo "NOT python file"
+
  python-lab-tests:
    name: Python Lab Tests
+    needs: paths-filter
+    if: needs.paths-filter.outputs.pythonChanges == 'true'
    runs-on: ${{ matrix.os }}
    strategy:
      fail-fast: true
      matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
        # TODO(ekzhu): re-enable macos-latest when this is fixed: https://github.com/actions/runner-images/issues/11881
        os: [ubuntu-latest, windows-latest]
    env:
@@ -60,6 +60,8 @@ jobs:
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
+      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
@@ -16,7 +16,7 @@ jobs:
    strategy:
      fail-fast: true
      matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
        # todo: add macos-latest when problems are resolved
        os: [ubuntu-latest, windows-latest]
    env:
@@ -203,4 +203,12 @@ agents.md

 # AI
 .claude/
-WARP.md
+WARP.md
+
+# Frontend
+**/frontend/node_modules/
+**/frontend/.vite/
+**/frontend/dist/
+
+# Database files
+*.db
@@ -0,0 +1,22 @@
+# Welcome to the Agent Framework Community
+
+Below are some ways that you can get involved in the Agent Framework Community.
+
+## Engage on GitHub
+
+- [Discussions](https://github.com/microsoft/agent-framework/discussions): Ask questions, provide feedback and ideas to what you'd like to see from the Agent Framework.
+- [Issues](https://github.com/microsoft/agent-framework/issues) - If you find a bug, unexpected behavior or have a feature request, please open an issue.
+- [Pull Requests](https://github.com/microsoft/agent-framework/pulls) - We welcome contributions! Please see our [Contributing Guide](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)
+
+We do our best to respond to each submission.
+
+## Public Community Office Hours
+
+We regularly have Community Office Hours that are open to the **public** to join.
+
+Add Agent Framework events to your calendar. We are running two community calls to accommodate different time zones for Q&A Office Hours:
+
+- **Americas & EMEA timezone:** Every Wednesday at 8:00 AM Pacific Time/17:00 CET. Adjusted for daylight savings. Join here: [AF-AG-SK-Americas-Europe-OfficeHours](https://aka.ms/sk-officehours).
+- **Asia Pacific timezone:** The second Wednesday of every month at 4:00 PM Pacific Time Wednesday. In much of Asia this occurs on Thursday local time. Adjusted for daylight savings. Join here: [AF-AG-SK-APAC-OfficeHours](https://aka.ms/sk-apac-officehours).
+
+If you are unable to make it live, all meetings will be recorded and posted online.
@@ -23,23 +23,23 @@ To download nightly builds follow the following steps:
    <configuration>
      <packageSources>
        <add key="nuget.org" value="https://api.nuget.org/v3/index.json" protocolVersion="3" />
-        <add key="github" value="https://nuget.pkg.github.com/microsoft/index.json" />
+        <add key="GitHubMicrosoft" value="https://nuget.pkg.github.com/microsoft/index.json" />
      </packageSources>
    
      <packageSourceMapping>
        <packageSource key="nuget.org">
          <package pattern="*" />
        </packageSource>
-        <packageSource key="github">
+        <packageSource key="GitHubMicrosoft">
          <package pattern="*nightly"/>
        </packageSource>
      </packageSourceMapping>
    
      <packageSourceCredentials>
-        <github>
-            <add key="Username" value="<Your GitHub Id>" />
-            <add key="ClearTextPassword" value="<Your Personal Access Token>" />
-          </github>
+        <GitHubMicrosoft>
+          <add key="Username" value="<Your GitHub Id>" />
+          <add key="ClearTextPassword" value="<Your Personal Access Token>" />
+        </GitHubMicrosoft>
      </packageSourceCredentials>
    </configuration>
    ```
@@ -22,7 +22,6 @@ This document aims to provide options and capture the decision on how to model t
 See various features that would need to be supported via this type of mechanism, plus how various other frameworks support this:

 - Also see [dotnet issue 6492](https://github.com/dotnet/extensions/issues/6492), which discusses the need for a similar pattern in the context of MCP approvals.
- Also see [the openai RunToolApprovalItem](https://openai.github.io/openai-agents-js/openai/agents/classes/runtoolapprovalitem/).
 - Also see [the openai human-in-the-loop guide](https://openai.github.io/openai-agents-js/guides/human-in-the-loop/#approval-requests).
 - Also see [the openai MCP guide](https://openai.github.io/openai-agents-js/guides/mcp/#optional-approval-flow).
 - Also see [MCP Approval Requests from OpenAI](https://platform.openai.com/docs/guides/tools-remote-mcp#approvals).
@@ -0,0 +1,95 @@
+---
+status: accepted
+contact: javiercn
+date: 2025-10-29
+deciders: javiercn, DeagleGross, moonbox3, markwallace-microsoft
+consulted: Agent Framework team
+informed: .NET community
+---
+
+# AG-UI Protocol Support for .NET Agent Framework
+
+## Context and Problem Statement
+
+The .NET Agent Framework needed a standardized way to enable communication between AI agents and user-facing applications with support for streaming, real-time updates, and bidirectional communication. Without AG-UI protocol support, .NET agents could not interoperate with the growing ecosystem of AG-UI-compatible frontends and agent frameworks (LangGraph, CrewAI, Pydantic AI, etc.), limiting the framework's adoption and utility.
+
+The AG-UI (Agent-User Interaction) protocol is an open, lightweight, event-based protocol that addresses key challenges in agentic applications including streaming support for long-running agents, event-driven architecture for nondeterministic behavior, and protocol interoperability that complements MCP (tool/context) and A2A (agent-to-agent) protocols.
+
+## Decision Drivers
+
+- Need for streaming communication between agents and client applications
+- Requirement for protocol interoperability with other AI frameworks
+- Support for long-running, multi-turn conversation sessions
+- Real-time UI updates for nondeterministic agent behavior
+- Standardized approach to agent-to-UI communication
+- Framework abstraction to protect consumers from protocol changes
+
+## Considered Options
+
+1. **Implement AG-UI event types as public API surface** - Expose AG-UI event models directly to consumers
+2. **Use custom AIContent types for lifecycle events** - Create new content types (RunStartedContent, RunFinishedContent, RunErrorContent)
+3. **Current approach** - Internal event types with framework-native abstractions
+
+## Decision Outcome
+
+Chosen option: "Current approach with internal event types and framework-native abstractions", because it:
+
+- Protects consumers from protocol changes by keeping AG-UI events internal
+- Maintains framework abstractions through conversion at boundaries
+- Uses existing framework types (AgentRunResponseUpdate, ChatMessage) for public API
+- Focuses on core text streaming functionality
+- Leverages existing properties (ConversationId, ResponseId, ErrorContent) instead of custom types
+- Provides bidirectional client and server support
+
+### Implementation Details
+
+**In Scope:**
+1. **Client-side AG-UI consumption** (`Microsoft.Agents.AI.AGUI` package)
+   - `AGUIAgent` class for connecting to remote AG-UI servers
+   - `AGUIAgentThread` for managing conversation threads
+   - HTTP/SSE streaming support
+   - Event-to-framework type conversion
+
+2. **Server-side AG-UI hosting** (`Microsoft.Agents.AI.Hosting.AGUI.AspNetCore` package)
+   - `MapAGUIAgent` extension method for ASP.NET Core
+   - Server-Sent Events (SSE) response formatting
+   - Framework-to-event type conversion
+   - Agent factory pattern for per-request instantiation
+
+3. **Text streaming events**
+   - Lifecycle events: `RunStarted`, `RunFinished`, `RunError`
+   - Text message events: `TextMessageStart`, `TextMessageContent`, `TextMessageEnd`
+   - Thread and run ID management via `ConversationId` and `ResponseId`
+
+### Key Design Decisions
+
+1. **Event Models as Internal Types** - AG-UI event types are internal with conversion via extension methods; public API uses the existing types in Microsoft.Extensions.AI as those are the abstractions people are familiar with
+
+2. **No Custom Content Types** - Run lifecycle communicated through existing `ChatResponseUpdate` properties (`ConversationId`, `ResponseId`) and standard `ErrorContent` type
+
+3. **Agent Factory Pattern** - `MapAGUIAgent` uses factory function `(messages) => AIAgent` to allow request-specific agent configuration supporting multi-tenancy
+
+4. **Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentRunResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentRunResponseUpdate`)
+
+5. **Thread Management** - `AGUIAgentThread` stores only `ThreadId` with thread ID communicated via `ConversationId`; applications manage persistence for parity with other implementations and to be compliant with the protocol. Future extensions will support having the server manage the conversation.
+
+6. **Custom JSON Converter** - Uses custom polymorphic deserialization via `BaseEventJsonConverter` instead of built-in System.Text.Json support to handle AG-UI protocol's flexible discriminator positioning
+
+### Consequences
+
+**Positive:**
+- .NET developers can consume AG-UI servers from any framework
+- .NET agents accessible from any AG-UI-compatible client
+- Standardized streaming communication patterns
+- Protected from protocol changes through internal implementation
+- Symmetric conversion logic between client and server
+- Framework-native public API surface
+
+**Negative:**
+- Custom JSON converter required (internal implementation detail)
+- Shared code uses preprocessor directives (`#if ASPNETCORE`)
+- Additional abstraction layer between protocol and public API
+
+**Neutral:**
+- Initial implementation focused on text streaming
+- Applications responsible for thread persistence
@@ -15,9 +15,9 @@
    <PackageVersion Include="Aspire.Hosting.AppHost" Version="$(AspireAppHostSdkVersion)" />
    <PackageVersion Include="Aspire.Hosting.Azure.CognitiveServices" Version="$(AspireAppHostSdkVersion)" />
    <PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
-    <PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="9.8.0" />
+    <PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="9.9.0" />
    <!-- Azure.* -->
-    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.6" />
+    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.7" />
    <PackageVersion Include="Azure.AI.OpenAI" Version="2.5.0-beta.1" />
    <PackageVersion Include="Azure.Identity" Version="1.17.0" />
    <PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
@@ -30,6 +30,7 @@
    <PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="9.0.10" />
    <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.0-rc.2.25502.107" />
+    <PackageVersion Include="System.Net.Http.Json" Version="9.0.10" />
    <PackageVersion Include="System.Net.ServerSentEvents" Version="9.0.10" />
    <PackageVersion Include="System.Text.Json" Version="9.0.10" />
    <PackageVersion Include="System.Threading.Channels" Version="9.0.10" />
@@ -40,18 +41,19 @@
    <PackageVersion Include="OpenTelemetry.Exporter.Console" Version="1.13.1" />
    <PackageVersion Include="OpenTelemetry.Exporter.InMemory" Version="1.13.1" />
    <PackageVersion Include="OpenTelemetry.Exporter.OpenTelemetryProtocol" Version="1.13.1" />
-    <PackageVersion Include="OpenTelemetry.Extensions.Hosting" Version="1.12.0" />
-    <PackageVersion Include="OpenTelemetry.Instrumentation.AspNetCore" Version="1.12.0" />
-    <PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.12.0" />
+    <PackageVersion Include="OpenTelemetry.Extensions.Hosting" Version="1.13.1" />
+    <PackageVersion Include="OpenTelemetry.Instrumentation.AspNetCore" Version="1.13.0" />
+    <PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.13.0" />
    <PackageVersion Include="OpenTelemetry.Instrumentation.Runtime" Version="1.12.0" />
    <!-- Microsoft.AspNetCore.* -->
    <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="9.0.10" />
    <PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="9.0.4" />
    <!-- Microsoft.Extensions.* -->
-    <PackageVersion Include="Microsoft.Extensions.AI" Version="9.10.1" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="9.10.1" />
+    <PackageVersion Include="Microsoft.Extensions.AI" Version="9.10.2" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="9.10.2" />
    <PackageVersion Include="Microsoft.Extensions.AI.AzureAIInference" Version="9.10.0-preview.1.25513.3" />
-    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="9.10.1-preview.1.25521.4" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="9.10.2-preview.1.25552.1" />
+    <PackageVersion Include="Microsoft.Extensions.Caching.Memory" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.Configuration" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.Configuration.Binder" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.Configuration.EnvironmentVariables" Version="9.0.10" />
@@ -65,12 +67,16 @@
    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="$(AspireAppHostSdkVersion)" />
-    <!-- Vector Stores -->
+    <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
+    <!-- Semantic Kernel -->
    <PackageVersion Include="Microsoft.SemanticKernel" Version="1.66.0" />
-    <PackageVersion Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.66.0-preview" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Core" Version="1.66.0" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.OpenAI" Version="1.66.0-preview" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.AzureAI" Version="1.66.0-preview" />
+    <PackageVersion Include="Microsoft.SemanticKernel.Plugins.OpenApi" Version="1.66.0" />
+    <!-- Vector Stores -->
+    <PackageVersion Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.66.0-preview" />
+    <PackageVersion Include="Microsoft.SemanticKernel.Connectors.Qdrant" Version="1.66.0-preview" />
    <!-- Agent SDKs -->
    <PackageVersion Include="Microsoft.Agents.CopilotStudio.Client" Version="1.2.41" />
    <!-- A2A -->
@@ -80,12 +86,12 @@
    <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="4.0.4.1" />
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.4.2" />
    <PackageVersion Include="Microsoft.ML.OnnxRuntimeGenAI" Version="0.10.0" />
    <PackageVersion Include="OllamaSharp" Version="5.4.8" />
-    <PackageVersion Include="OpenAI" Version="2.5.0" />
+    <PackageVersion Include="OpenAI" Version="2.6.0" />
    <!-- Identity -->
-    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.77.1" />
+    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.78.0" />
    <!-- Workflows -->
    <PackageVersion Include="Microsoft.Bot.ObjectModel" Version="1.2025.1003.2" />
    <PackageVersion Include="Microsoft.Bot.ObjectModel.Json" Version="1.2025.1003.2" />
@@ -95,7 +101,7 @@
    <PackageVersion Include="System.Linq.Async" Version="6.0.3" />
    <!-- Test -->
    <PackageVersion Include="FluentAssertions" Version="8.8.0" />
-    <PackageVersion Include="Microsoft.AspNetCore.Mvc.Testing" Version="9.0.10" />
+    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Version="9.0.10" />
    <PackageVersion Include="Microsoft.NET.Test.Sdk" Version="18.0.0" />
    <PackageVersion Include="Moq" Version="[4.18.4]" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Abstractions" Version="1.66.0" />
@@ -18,6 +18,10 @@
    <Project Path="samples/AgentWebChat/AgentWebChat.ServiceDefaults/AgentWebChat.ServiceDefaults.csproj" />
    <Project Path="samples/AgentWebChat/AgentWebChat.Web/AgentWebChat.Web.csproj" />
  </Folder>
+  <Folder Name="/Samples/AGUIClientServer/">
+    <Project Path="samples/AGUIClientServer/AGUIClient/AGUIClient.csproj" />
+    <Project Path="samples/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/">
    <File Path="samples/GettingStarted/README.md" />
  </Folder>
@@ -43,7 +47,8 @@
    <File Path="samples/GettingStarted/Agents/README.md" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step01_Running/Agent_Step01_Running.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step02_MultiturnConversation/Agent_Step02_MultiturnConversation.csproj" />
-    <Project Path="samples/GettingStarted/Agents/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step03.1_UsingFunctionTools/Agent_Step03.1_UsingFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step03.2_UsingFunctionTools_FromOpenAPI/Agent_Step03.2_UsingFunctionTools_FromOpenAPI.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
@@ -58,16 +63,31 @@
    <Project Path="samples/GettingStarted/Agents/Agent_Step15_Plugins/Agent_Step15_Plugins.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step16_ChatReduction/Agent_Step16_ChatReduction.csproj" />
    <Project Path="samples/GettingStarted/Agents/Agent_Step17_BackgroundResponses/Agent_Step17_BackgroundResponses.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step18_TextSearchRag/Agent_Step18_TextSearchRag.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step19_Mem0Provider/Agent_Step19_Mem0Provider.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step20_BackgroundResponsesWithToolsAndPersistence/Agent_Step20_BackgroundResponsesWithToolsAndPersistence.csproj" />
+    <Project Path="samples/GettingStarted/Agents/Agent_Step21_ChatHistoryMemoryProvider/Agent_Step21_ChatHistoryMemoryProvider.csproj" />
+  </Folder>
+  <Folder Name="/Samples/GettingStarted/DevUI/">
+    <File Path="samples/GettingStarted/DevUI/README.md" />
+    <Project Path="samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/DevUI_Step01_BasicUsage.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/AgentWithOpenAI/">
    <File Path="samples/GettingStarted/AgentWithOpenAI/README.md" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Agent_OpenAI_Step01_Running.csproj" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
  </Folder>
+  <Folder Name="/Samples/GettingStarted/AgentWithRAG/">
+    <File Path="samples/GettingStarted/AgentWithRAG/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_ExternalDataSourceRAG/AgentWithRAG_Step02_ExternalDataSourceRAG.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/ModelContextProtocol/">
+    <File Path="samples/GettingStarted/ModelContextProtocol/README.md" />
    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server_Auth/Agent_MCP_Server_Auth.csproj" />
    <Project Path="samples/GettingStarted/ModelContextProtocol/FoundryAgent_Hosted_MCP/FoundryAgent_Hosted_MCP.csproj" />
+    <Project Path="samples/GettingStarted/ModelContextProtocol/ResponseAgent_Hosted_MCP/ResponseAgent_Hosted_MCP.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/Observability/">
    <Project Path="samples/GettingStarted/AgentOpenTelemetry/AgentOpenTelemetry.csproj" />
@@ -120,6 +140,7 @@
  <Folder Name="/Samples/GettingStarted/Workflows/Observability/">
    <Project Path="samples/GettingStarted/Workflows/Observability/ApplicationInsights/ApplicationInsights.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Observability/AspireDashboard/AspireDashboard.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Observability/WorkflowAsAnAgent/WorkflowAsAnAgentObservability.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/Workflows/Visualization/">
    <Project Path="samples/GettingStarted/Workflows/Visualization/Visualization.csproj" Id="99bf0bc6-2440-428e-b3e7-d880e4b7a5fd" />
@@ -132,9 +153,12 @@
    <Project Path="samples/GettingStarted/Workflows/_Foundational/05_MultiModelService/05_MultiModelService.csproj" />
    <Project Path="samples/GettingStarted/Workflows/_Foundational/06_SubWorkflows/06_SubWorkflows.csproj" />
    <Project Path="samples/GettingStarted/Workflows/_Foundational/07_MixedWorkflowAgentsAndExecutors/07_MixedWorkflowAgentsAndExecutors.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/_Foundational/08_WriterCriticWorkflow/08_WriterCriticWorkflow.csproj" />
  </Folder>
-  <Folder Name="/Samples/SemanticKernelMigration/">
-    <File Path="samples/SemanticKernelMigration/README.md" />
+  <Folder Name="/Samples/Catalog/">
+    <Project Path="samples/Catalog/AgentsInWorkflows/AgentsInWorkflows.csproj" />
+    <Project Path="samples/Catalog/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
+    <Project Path="samples/Catalog/DeepResearchAgent/DeepResearchAgent.csproj" />
  </Folder>
  <Folder Name="/Solution Items/">
    <File Path=".editorconfig" />
@@ -258,10 +282,13 @@
  <Folder Name="/src/">
    <Project Path="src/Microsoft.Agents.AI.A2A/Microsoft.Agents.AI.A2A.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Abstractions/Microsoft.Agents.AI.Abstractions.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.OpenAI/Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Mem0/Microsoft.Agents.AI.Mem0.csproj" />
@@ -275,6 +302,7 @@
    <Project Path="tests/AgentConformance.IntegrationTests/AgentConformance.IntegrationTests.csproj" />
    <Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
    <Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.IntegrationTests/Microsoft.Agents.AI.Mem0.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests.csproj" />
    <Project Path="tests/OpenAIAssistant.IntegrationTests/OpenAIAssistant.IntegrationTests.csproj" />
@@ -284,8 +312,10 @@
  <Folder Name="/Tests/UnitTests/">
    <Project Path="tests/Microsoft.Agents.AI.A2A.UnitTests/Microsoft.Agents.AI.A2A.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Abstractions.UnitTests/Microsoft.Agents.AI.Abstractions.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.AzureAI.UnitTests/Microsoft.Agents.AI.AzureAI.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.Tests/Microsoft.Agents.AI.Hosting.A2A.Tests.csproj" Id="2a1c544d-237d-4436-8732-ba0c447ac06b" />
+    <Project Path="tests/Microsoft.Agents.AI.AGUI.UnitTests/Microsoft.Agents.AI.AGUI.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.UnitTests/Microsoft.Agents.AI.Hosting.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.UnitTests/Microsoft.Agents.AI.Mem0.UnitTests.csproj" />
@@ -294,4 +324,4 @@
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
  </Folder>
-</Solution>
+</Solution>
@@ -2,9 +2,9 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251028.1</PackageVersion>
-    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251028.1</PackageVersion>
-    <GitTag>1.0.0-preview.251028.1</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251107.1</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251107.1</PackageVersion>
+    <GitTag>1.0.0-preview.251107.1</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>
@@ -1 +0,0 @@
-launchSettings.json
@@ -20,7 +20,7 @@
    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.A2A\Microsoft.Agents.AI.Hosting.A2A.csproj" />

    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.A2A\Microsoft.Agents.AI.A2A.csproj" />
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>

@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <UserSecretsId>a8b2e9f0-1ea3-4f18-9d41-42d1a6f8fe10</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="System.CommandLine" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+    <PackageReference Include="Microsoft.Bcl.AsyncInterfaces" VersionOverride="10.0.0-rc.2.25502.107" />
+    <PackageReference Include="System.Net.ServerSentEvents" VersionOverride="10.0.0-rc.2.25502.107" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,12 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use the AG-UI client to connect to a remote AG-UI server
+// and display streaming updates including conversation/response metadata, text content, and errors.
+
+using System.Text.Json.Serialization;
+
+namespace AGUIClient;
+
+[JsonSerializable(typeof(SensorRequest))]
+[JsonSerializable(typeof(SensorResponse))]
+internal sealed partial class AGUIClientSerializerContext : JsonSerializerContext;
@@ -0,0 +1,213 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use the AG-UI client to connect to a remote AG-UI server
+// and display streaming updates including conversation/response metadata, text content, and errors.
+
+using System.CommandLine;
+using System.ComponentModel;
+using System.Reflection;
+using System.Text;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AGUI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.Configuration;
+using Microsoft.Extensions.Logging;
+
+namespace AGUIClient;
+
+public static class Program
+{
+    public static async Task<int> Main(string[] args)
+    {
+        // Create root command with options
+        RootCommand rootCommand = new("AGUIClient");
+        rootCommand.SetAction((_, ct) => HandleCommandsAsync(ct));
+
+        // Run the command
+        return await rootCommand.Parse(args).InvokeAsync();
+    }
+
+    private static async Task HandleCommandsAsync(CancellationToken cancellationToken)
+    {
+        // Set up the logging
+        using ILoggerFactory loggerFactory = LoggerFactory.Create(builder =>
+        {
+            builder.AddConsole();
+            builder.SetMinimumLevel(LogLevel.Information);
+        });
+        ILogger logger = loggerFactory.CreateLogger("AGUIClient");
+
+        // Retrieve configuration settings
+        IConfigurationRoot configRoot = new ConfigurationBuilder()
+            .AddEnvironmentVariables()
+            .AddUserSecrets(Assembly.GetExecutingAssembly())
+            .Build();
+
+        string serverUrl = configRoot["AGUI_SERVER_URL"] ?? "http://localhost:5100";
+
+        logger.LogInformation("Connecting to AG-UI server at: {ServerUrl}", serverUrl);
+
+        // Create the AG-UI client agent
+        using HttpClient httpClient = new()
+        {
+            Timeout = TimeSpan.FromSeconds(60)
+        };
+
+        var changeBackground = AIFunctionFactory.Create(
+            () =>
+            {
+                Console.ForegroundColor = ConsoleColor.DarkBlue;
+                Console.WriteLine("Changing color to blue");
+            },
+            name: "change_background_color",
+            description: "Change the console background color to dark blue."
+        );
+
+        var readClientClimateSensors = AIFunctionFactory.Create(
+            ([Description("The sensors measurements to include in the response")] SensorRequest request) =>
+            {
+                return new SensorResponse()
+                {
+                    Temperature = 22.5,
+                    Humidity = 45.0,
+                    AirQualityIndex = 75
+                };
+            },
+            name: "read_client_climate_sensors",
+            description: "Reads the climate sensor data from the client device.",
+            serializerOptions: AGUIClientSerializerContext.Default.Options
+        );
+
+        var chatClient = new AGUIChatClient(
+            httpClient,
+            serverUrl,
+            jsonSerializerOptions: AGUIClientSerializerContext.Default.Options);
+
+        AIAgent agent = chatClient.CreateAIAgent(
+            name: "agui-client",
+            description: "AG-UI Client Agent",
+            tools: [changeBackground, readClientClimateSensors]);
+
+        AgentThread thread = agent.GetNewThread();
+        List<ChatMessage> messages = [new(ChatRole.System, "You are a helpful assistant.")];
+        try
+        {
+            while (true)
+            {
+                // Get user message
+                Console.Write("\nUser (:q or quit to exit): ");
+                string? message = Console.ReadLine();
+                if (string.IsNullOrWhiteSpace(message))
+                {
+                    Console.WriteLine("Request cannot be empty.");
+                    continue;
+                }
+
+                if (message is ":q" or "quit")
+                {
+                    break;
+                }
+
+                messages.Add(new(ChatRole.User, message));
+
+                // Call RunStreamingAsync to get streaming updates
+                bool isFirstUpdate = true;
+                string? threadId = null;
+                var updates = new List<ChatResponseUpdate>();
+                await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: cancellationToken))
+                {
+                    // Use AsChatResponseUpdate to access ChatResponseUpdate properties
+                    ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
+                    updates.Add(chatUpdate);
+                    if (chatUpdate.ConversationId != null)
+                    {
+                        threadId = chatUpdate.ConversationId;
+                    }
+
+                    // Display run started information from the first update
+                    if (isFirstUpdate && threadId != null && update.ResponseId != null)
+                    {
+                        Console.ForegroundColor = ConsoleColor.Yellow;
+                        Console.WriteLine($"\n[Run Started - Thread: {threadId}, Run: {update.ResponseId}]");
+                        Console.ResetColor();
+                        isFirstUpdate = false;
+                    }
+
+                    // Display different content types with appropriate formatting
+                    foreach (AIContent content in update.Contents)
+                    {
+                        switch (content)
+                        {
+                            case TextContent textContent:
+                                Console.ForegroundColor = ConsoleColor.Cyan;
+                                Console.Write(textContent.Text);
+                                Console.ResetColor();
+                                break;
+
+                            case FunctionCallContent functionCallContent:
+                                Console.ForegroundColor = ConsoleColor.Green;
+                                Console.WriteLine($"\n[Function Call - Name: {functionCallContent.Name}, Arguments: {PrintArguments(functionCallContent.Arguments)}]");
+                                Console.ResetColor();
+                                break;
+
+                            case FunctionResultContent functionResultContent:
+                                Console.ForegroundColor = ConsoleColor.Magenta;
+                                if (functionResultContent.Exception != null)
+                                {
+                                    Console.WriteLine($"\n[Function Result - Exception: {functionResultContent.Exception}]");
+                                }
+                                else
+                                {
+                                    Console.WriteLine($"\n[Function Result - Result: {functionResultContent.Result}]");
+                                }
+                                Console.ResetColor();
+                                break;
+
+                            case ErrorContent errorContent:
+                                Console.ForegroundColor = ConsoleColor.Red;
+                                string code = errorContent.AdditionalProperties?["Code"] as string ?? "Unknown";
+                                Console.WriteLine($"\n[Error - Code: {code}, Message: {errorContent.Message}]");
+                                Console.ResetColor();
+                                break;
+                        }
+                    }
+                }
+                if (updates.Count > 0 && !updates[^1].Contents.Any(c => c is TextContent))
+                {
+                    var lastUpdate = updates[^1];
+                    Console.ForegroundColor = ConsoleColor.Yellow;
+                    Console.WriteLine();
+                    Console.WriteLine($"[Run Ended - Thread: {threadId}, Run: {lastUpdate.ResponseId}]");
+                    Console.ResetColor();
+                }
+                messages.Clear();
+                Console.WriteLine();
+            }
+        }
+        catch (OperationCanceledException)
+        {
+            logger.LogInformation("AGUIClient operation was canceled.");
+        }
+        catch (Exception ex) when (ex is not OutOfMemoryException and not StackOverflowException and not ThreadAbortException and not AccessViolationException)
+        {
+            logger.LogError(ex, "An error occurred while running the AGUIClient");
+            return;
+        }
+    }
+
+    private static string PrintArguments(IDictionary<string, object?>? arguments)
+    {
+        if (arguments == null)
+        {
+            return "";
+        }
+        var builder = new StringBuilder();
+        builder.AppendLine();
+        foreach (var kvp in arguments)
+        {
+            builder.AppendLine($"   Name: {kvp.Key}");
+            builder.AppendLine($"   Value: {kvp.Value}");
+        }
+        return builder.ToString();
+    }
+}
@@ -0,0 +1,34 @@
+# AG-UI Client
+
+This is a console application that demonstrates how to connect to an AG-UI server and interact with remote agents using the AG-UI protocol.
+
+## Features
+
+- Connects to an AG-UI server endpoint
+- Displays streaming updates with color-coded output:
+  - **Yellow**: Run started notifications
+  - **Cyan**: Agent text responses (streamed)
+  - **Green**: Run finished notifications
+  - **Red**: Error messages (if any)
+- Interactive prompt loop for sending messages
+
+## Configuration
+
+Set the following environment variable to specify the AG-UI server URL:
+
+```powershell
+$env:AGUI_SERVER_URL="http://localhost:5100"
+```
+
+If not set, the default is `http://localhost:5100`.
+
+## Running the Client
+
+1. Make sure the AG-UI server is running
+2. Run the client:
+   ```bash
+   cd AGUIClient
+   dotnet run
+   ```
+3. Enter your messages and observe the streaming updates
+4. Type `:q` or `quit` to exit
@@ -0,0 +1,13 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use the AG-UI client to connect to a remote AG-UI server
+// and display streaming updates including conversation/response metadata, text content, and errors.
+
+namespace AGUIClient;
+
+internal sealed class SensorRequest
+{
+    public bool IncludeTemperature { get; set; } = true;
+    public bool IncludeHumidity { get; set; } = true;
+    public bool IncludeAirQualityIndex { get; set; } = true;
+}
@@ -0,0 +1,13 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use the AG-UI client to connect to a remote AG-UI server
+// and display streaming updates including conversation/response metadata, text content, and errors.
+
+namespace AGUIClient;
+
+internal sealed class SensorResponse
+{
+    public double Temperature { get; set; }
+    public double Humidity { get; set; }
+    public int AirQualityIndex { get; set; }
+}
@@ -0,0 +1,24 @@
+<Project Sdk="Microsoft.NET.Sdk.Web">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <UserSecretsId>a8b2e9f0-1ea3-4f18-9d41-42d1a6f8fe10</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Bcl.AsyncInterfaces" VersionOverride="10.0.0-rc.2.25502.107" />
+    <PackageReference Include="System.Net.ServerSentEvents" VersionOverride="10.0.0-rc.2.25502.107" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,17 @@
+@host = http://localhost:5100
+
+### Send a message to the AG-UI agent
+POST {{host}}/
+Content-Type: application/json
+
+{
+  "threadId": "thread_123",
+  "runId": "run_456",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the capital of France?"
+    }
+  ],
+  "context": {}
+}
@@ -0,0 +1,9 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+
+namespace AGUIServer;
+
+[JsonSerializable(typeof(ServerWeatherForecastRequest))]
+[JsonSerializable(typeof(ServerWeatherForecastResponse))]
+internal sealed partial class AGUIServerSerializerContext : JsonSerializerContext;
@@ -0,0 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+using AGUIServer;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
+builder.Services.AddHttpClient().AddLogging();
+builder.Services.ConfigureHttpJsonOptions(options => options.SerializerOptions.TypeInfoResolverChain.Add(AGUIServerSerializerContext.Default));
+builder.Services.AddAGUI();
+
+WebApplication app = builder.Build();
+
+string endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Create the AI agent with tools
+var agent = new AzureOpenAIClient(
+        new Uri(endpoint),
+        new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(
+        name: "AGUIAssistant",
+        tools: [
+            AIFunctionFactory.Create(
+                () => DateTimeOffset.UtcNow,
+                name: "get_current_time",
+                description: "Get the current UTC time."
+            ),
+            AIFunctionFactory.Create(
+                ([Description("The weather forecast request")]ServerWeatherForecastRequest request) => {
+                    return new ServerWeatherForecastResponse()
+                    {
+                        Summary = "Sunny",
+                        TemperatureC = 25,
+                        Date = request.Date
+                    };
+                },
+                name: "get_server_weather_forecast",
+                description: "Gets the forecast for a specific location and date",
+                AGUIServerSerializerContext.Default.Options)
+        ]);
+
+// Map the AG-UI agent endpoint
+app.MapAGUI("/", agent);
+
+await app.RunAsync();
@@ -0,0 +1,12 @@
+{
+  "profiles": {
+    "AGUIServer": {
+      "commandName": "Project",
+      "launchBrowser": true,
+      "environmentVariables": {
+        "ASPNETCORE_ENVIRONMENT": "Development"
+      },
+      "applicationUrl": "http://localhost:5100;https://localhost:5101"
+    }
+  }
+}
@@ -0,0 +1,9 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace AGUIServer;
+
+internal sealed class ServerWeatherForecastRequest
+{
+    public DateTime Date { get; set; }
+    public string Location { get; set; } = "Seattle";
+}
@@ -0,0 +1,12 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace AGUIServer;
+
+internal sealed class ServerWeatherForecastResponse
+{
+    public string Summary { get; set; } = "";
+
+    public int TemperatureC { get; set; }
+
+    public DateTime Date { get; set; }
+}
@@ -0,0 +1,208 @@
+# AG-UI Client and Server Sample
+
+This sample demonstrates how to use the AG-UI (Agent UI) protocol to enable communication between a client application and a remote agent server. The AG-UI protocol provides a standardized way for clients to interact with AI agents.
+
+## Overview
+
+The demonstration has two components:
+
+1. **AGUIServer** - An ASP.NET Core web server that hosts an AI agent and exposes it via the AG-UI protocol
+2. **AGUIClient** - A console application that connects to the AG-UI server and displays streaming updates
+
+> **Warning**
+> The AG-UI protocol is still under development and changing.
+> We will try to keep these samples updated as the protocol evolves.
+
+## Configuring Environment Variables
+
+Configure the required Azure OpenAI environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="<<your-model-endpoint>>"
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4.1-mini"
+```
+
+> **Note:** This sample uses `DefaultAzureCredential` for authentication. Make sure you're authenticated with Azure (e.g., via `az login`, Visual Studio, or environment variables).
+
+## Running the Sample
+
+### Step 1: Start the AG-UI Server
+
+```bash
+cd AGUIServer
+dotnet build
+dotnet run --urls "http://localhost:5100"
+```
+
+The server will start and listen on `http://localhost:5100`.
+
+### Step 2: Testing with the REST Client (Optional)
+
+Before running the client, you can test the server using the included `.http` file:
+
+1. Open [./AGUIServer/AGUIServer.http](./AGUIServer/AGUIServer.http) in Visual Studio or VS Code with the REST Client extension
+2. Send a test request to verify the server is working
+3. Observe the server-sent events stream in the response
+
+Sample request:
+```http
+POST http://localhost:5100/
+Content-Type: application/json
+
+{
+  "threadId": "thread_123",
+  "runId": "run_456",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the capital of France?"
+    }
+  ],
+  "context": {}
+}
+```
+
+### Step 3: Run the AG-UI Client
+
+In a new terminal window:
+
+```bash
+cd AGUIClient
+dotnet run
+```
+
+Optionally, configure a different server URL:
+
+```powershell
+$env:AGUI_SERVER_URL="http://localhost:5100"
+```
+
+### Step 4: Interact with the Agent
+
+1. The client will connect to the AG-UI server
+2. Enter your message at the prompt
+3. Observe the streaming updates with color-coded output:
+   - **Yellow**: Run started notification showing thread and run IDs
+   - **Cyan**: Agent's text response (streamed character by character)
+   - **Green**: Run finished notification
+   - **Red**: Error messages (if any occur)
+4. Type `:q` or `quit` to exit
+
+## Sample Output
+
+```
+AGUIClient> dotnet run
+info: AGUIClient[0]
+      Connecting to AG-UI server at: http://localhost:5100
+
+User (:q or quit to exit): What is the capital of France?
+
+[Run Started - Thread: thread_abc123, Run: run_xyz789]
+The capital of France is Paris. It is known for its rich history, culture, and iconic landmarks such as the Eiffel Tower and the Louvre Museum.
+[Run Finished - Thread: thread_abc123, Run: run_xyz789]
+
+User (:q or quit to exit): Tell me a fun fact about space
+
+[Run Started - Thread: thread_abc123, Run: run_def456]
+Here's a fun fact: A day on Venus is longer than its year! Venus takes about 243 Earth days to rotate once on its axis, but only about 225 Earth days to orbit the Sun.
+[Run Finished - Thread: thread_abc123, Run: run_def456]
+
+User (:q or quit to exit): :q
+```
+
+## How It Works
+
+### Server Side
+
+The `AGUIServer` uses the `MapAGUI` extension method to expose an agent through the AG-UI protocol:
+
+```csharp
+AIAgent agent = new OpenAIClient(apiKey)
+    .GetChatClient(model)
+    .CreateAIAgent(
+        instructions: "You are a helpful assistant.",
+        name: "AGUIAssistant");
+
+app.MapAGUI("/", agent);
+```
+
+This automatically handles:
+- HTTP POST requests with message payloads
+- Converting agent responses to AG-UI event streams
+- Server-sent events (SSE) formatting
+- Thread and run management
+
+### Client Side
+
+The `AGUIClient` uses the `AGUIChatClient` to connect to the remote server:
+
+```csharp
+using HttpClient httpClient = new();
+var chatClient = new AGUIChatClient(
+    httpClient,
+    endpoint: serverUrl,
+    modelId: "agui-client",
+    jsonSerializerOptions: null);
+
+AIAgent agent = chatClient.CreateAIAgent(
+    instructions: null,
+    name: "agui-client",
+    description: "AG-UI Client Agent",
+    tools: []);
+
+bool isFirstUpdate = true;
+AgentRunResponseUpdate? currentUpdate = null;
+
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+{
+    // First update indicates run started
+    if (isFirstUpdate)
+    {
+        Console.WriteLine($"[Run Started - Thread: {update.ConversationId}, Run: {update.ResponseId}]");
+        isFirstUpdate = false;
+    }
+    
+    currentUpdate = update;
+    
+    foreach (AIContent content in update.Contents)
+    {
+        switch (content)
+        {
+            case TextContent textContent:
+                // Display streaming text
+                Console.Write(textContent.Text);
+                break;
+            case ErrorContent errorContent:
+                // Display error notification
+                Console.WriteLine($"[Error: {errorContent.Message}]");
+                break;
+        }
+    }
+}
+
+// Last update indicates run finished
+if (currentUpdate != null)
+{
+    Console.WriteLine($"\n[Run Finished - Thread: {currentUpdate.ConversationId}, Run: {currentUpdate.ResponseId}]");
+}
+```
+
+The `RunStreamingAsync` method:
+1. Sends messages to the server via HTTP POST
+2. Receives server-sent events (SSE) stream
+3. Parses events into `AgentRunResponseUpdate` objects
+4. Yields updates as they arrive for real-time display
+
+## Key Concepts
+
+- **Thread**: Represents a conversation context that persists across multiple runs (accessed via `ConversationId` property)
+- **Run**: A single execution of the agent for a given set of messages (identified by `ResponseId` property)
+- **AgentRunResponseUpdate**: Contains the response data with:
+  - `ResponseId`: The unique run identifier
+  - `ConversationId`: The thread/conversation identifier
+  - `Contents`: Collection of content items (TextContent, ErrorContent, etc.)
+- **Run Lifecycle**: 
+  - The **first** `AgentRunResponseUpdate` in a run indicates the run has started
+  - Subsequent updates contain streaming content as the agent processes
+  - The **last** `AgentRunResponseUpdate` in a run indicates the run has finished
+  - If an error occurs, the update will contain `ErrorContent`
@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
+<Project Sdk="Microsoft.NET.Sdk.Web">

  <PropertyGroup>
    <TargetFramework>net9.0</TargetFramework>
@@ -13,7 +13,7 @@
    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting\Microsoft.Agents.AI.Hosting.csproj" />
    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.A2A.AspNetCore\Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
-	  <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.OpenAI\Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.OpenAI\Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
    <ProjectReference Include="..\AgentWebChat.ServiceDefaults\AgentWebChat.ServiceDefaults.csproj" />
  </ItemGroup>

@@ -37,4 +37,4 @@
  </ItemGroup>
  <!-- A2A dependency -->

-</Project>
+</Project>
@@ -0,0 +1,17 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.AI;
+
+namespace AgentWebChat.AgentHost.Custom;
+
+public class CustomAITool : AITool
+{
+}
+
+public class CustomFunctionTool : AIFunction
+{
+    protected override ValueTask<object?> InvokeCoreAsync(AIFunctionArguments arguments, CancellationToken cancellationToken)
+    {
+        return new ValueTask<object?>(arguments.Context?.Count ?? 0);
+    }
+}
@@ -2,6 +2,7 @@

 using A2A.AspNetCore;
 using AgentWebChat.AgentHost;
+using AgentWebChat.AgentHost.Custom;
 using AgentWebChat.AgentHost.Utilities;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Hosting;
@@ -20,14 +21,16 @@ builder.Services.AddProblemDetails();
 // Configure the chat model and our agent.
 builder.AddKeyedChatClient("chat-model");

-builder.AddAIAgent(
+var pirateAgentBuilder = builder.AddAIAgent(
    "pirate",
    instructions: "You are a pirate. Speak like a pirate",
    description: "An agent that speaks like a pirate.",
    chatClientServiceKey: "chat-model")
+    .WithAITool(new CustomAITool())
+    .WithAITool(new CustomFunctionTool())
    .WithInMemoryThreadStore();

-builder.AddAIAgent("knights-and-knaves", (sp, key) =>
+var knightsKnavesAgentBuilder = builder.AddAIAgent("knights-and-knaves", (sp, key) =>
 {
    var chatClient = sp.GetRequiredKeyedService<IChatClient>("chat-model");

@@ -78,8 +81,21 @@ var literatureAgent = builder.AddAIAgent("literator",
    description: "An agent that helps with literature.",
    chatClientServiceKey: "chat-model");

-builder.AddSequentialWorkflow("science-sequential-workflow", [chemistryAgent, mathsAgent, literatureAgent]).AddAsAIAgent();
-builder.AddConcurrentWorkflow("science-concurrent-workflow", [chemistryAgent, mathsAgent, literatureAgent]).AddAsAIAgent();
+var scienceSequentialWorkflow = builder.AddWorkflow("science-sequential-workflow", (sp, key) =>
+{
+    List<IHostedAgentBuilder> usedAgents = [chemistryAgent, mathsAgent, literatureAgent];
+    var agents = usedAgents.Select(ab => sp.GetRequiredKeyedService<AIAgent>(ab.Name));
+    return AgentWorkflowBuilder.BuildSequential(workflowName: key, agents: agents);
+}).AddAsAIAgent();
+
+var scienceConcurrentWorkflow = builder.AddWorkflow("science-concurrent-workflow", (sp, key) =>
+{
+    List<IHostedAgentBuilder> usedAgents = [chemistryAgent, mathsAgent, literatureAgent];
+    var agents = usedAgents.Select(ab => sp.GetRequiredKeyedService<AIAgent>(ab.Name));
+    return AgentWorkflowBuilder.BuildConcurrent(workflowName: key, agents: agents);
+}).AddAsAIAgent();
+
+builder.AddOpenAIChatCompletions();
 builder.AddOpenAIResponses();

 var app = builder.Build();
@@ -104,8 +120,8 @@ app.MapA2A(agentName: "knights-and-knaves", path: "/a2a/knights-and-knaves", age

 app.MapOpenAIResponses();

-app.MapOpenAIChatCompletions("pirate");
-app.MapOpenAIChatCompletions("knights-and-knaves");
+app.MapOpenAIChatCompletions(pirateAgentBuilder);
+app.MapOpenAIChatCompletions(knightsKnavesAgentBuilder);

 // Map the agents HTTP endpoints
 app.MapAgentDiscovery("/agents");
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,82 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
+// capabilities to an AI agent. The provider runs a search against an external knowledge base
+// before each model invocation and injects the results into the model context.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation and keep a short rolling window of conversation context.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+    RecentMessageMemoryLimit = 6,
+};
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+        AIContextProviderFactory = ctx => new TextSearchProvider(MockSearchAsync, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about returns\n");
+Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
+
+Console.WriteLine("\n>> Asking about shipping\n");
+Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
+
+Console.WriteLine("\n>> Asking about product care\n");
+Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
+
+static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(string query, CancellationToken cancellationToken)
+{
+    // The mock search inspects the user's question and returns pre-defined snippets
+    // that resemble documents stored in an external knowledge source.
+    List<TextSearchProvider.TextSearchResult> results = new();
+
+    if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "Contoso Outdoors Return Policy",
+            SourceLink = "https://contoso.com/policies/returns",
+            Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+        });
+    }
+
+    if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "Contoso Outdoors Shipping Guide",
+            SourceLink = "https://contoso.com/help/shipping",
+            Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+        });
+    }
+
+    if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "TrailRunner Tent Care Instructions",
+            SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+            Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+        });
+    }
+
+    return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
+}
@@ -0,0 +1,41 @@
+# What this sample demonstrates
+
+This sample demonstrates how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent. The provider runs a search against an external knowledge base before each model invocation and injects the results into the model context.
+
+Key features:
+- Configuring TextSearchProvider with custom search behavior
+- Running searches before AI invocations to provide relevant context
+- Managing conversation memory with a rolling window approach
+- Citing source documents in AI responses
+
+## Prerequisites
+
+Before running this sample, ensure you have:
+
+1. An Azure OpenAI endpoint configured
+2. A deployment of a chat model (e.g., gpt-4o-mini)
+3. Azure CLI installed and authenticated
+
+## Environment Variables
+
+Set the following environment variables:
+
+```powershell
+# Replace with your Azure OpenAI endpoint
+$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
+
+# Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## How It Works
+
+The sample uses a mock search function that demonstrates the RAG pattern:
+
+1. When the user asks a question, the TextSearchProvider intercepts it
+2. The search function looks for relevant documents based on the query
+3. Retrieved documents are injected into the model's context
+4. The AI responds using both its training and the provided context
+5. The agent can cite specific source documents in its answers
+
+The mock search function returns pre-defined snippets for demonstration purposes. In a production scenario, you would replace this with actual searches against your knowledge base (e.g., Azure AI Search, vector database, etc.).
@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,48 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to integrate AI agents into a workflow pipeline.
+// Three translation agents are connected sequentially to create a translation chain:
+// English → French → Spanish → English, showing how agents can be composed as workflow executors.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Extensions.AI;
+
+// Set up the Azure OpenAI client
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+IChatClient chatClient = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .AsIChatClient();
+
+// Create agents
+AIAgent frenchAgent = GetTranslationAgent("French", chatClient);
+AIAgent spanishAgent = GetTranslationAgent("Spanish", chatClient);
+AIAgent englishAgent = GetTranslationAgent("English", chatClient);
+
+// Build the workflow by adding executors and connecting them
+Workflow workflow = new WorkflowBuilder(frenchAgent)
+    .AddEdge(frenchAgent, spanishAgent)
+    .AddEdge(spanishAgent, englishAgent)
+.Build();
+
+// Execute the workflow
+await using StreamingRun run = await InProcessExecution.StreamAsync(workflow, new ChatMessage(ChatRole.User, "Hello World!"));
+
+// Must send the turn token to trigger the agents.
+// The agents are wrapped as executors. When they receive messages,
+// they will cache the messages and only start processing when they receive a TurnToken.
+await run.TrySendMessageAsync(new TurnToken(emitEvents: true));
+await foreach (WorkflowEvent evt in run.WatchStreamAsync())
+{
+    if (evt is AgentRunUpdateEvent executorComplete)
+    {
+        Console.WriteLine($"{executorComplete.ExecutorId}: {executorComplete.Data}");
+    }
+}
+
+static ChatClientAgent GetTranslationAgent(string targetLanguage, IChatClient chatClient) =>
+    new(chatClient, $"You are a translation assistant that translates the provided text to {targetLanguage}.");
@@ -0,0 +1,26 @@
+# What this sample demonstrates
+
+This sample demonstrates the use of AI agents as executors within a workflow.
+
+This workflow uses three translation agents:
+1. French Agent - translates input text to French
+2. Spanish Agent - translates French text to Spanish
+3. English Agent - translates Spanish text back to English
+
+The agents are connected sequentially, creating a translation chain that demonstrates how AI-powered components can be seamlessly integrated into workflow pipelines.
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Agents.Persistent" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,52 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create an Azure AI Foundry Agent with the Deep Research Tool.
+
+using Azure.AI.Agents.Persistent;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deepResearchDeploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEEP_RESEARCH_DEPLOYMENT_NAME") ?? "o3-deep-research";
+var modelDeploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o";
+var bingConnectionId = Environment.GetEnvironmentVariable("BING_CONNECTION_ID") ?? throw new InvalidOperationException("BING_CONNECTION_ID is not set.");
+
+// Configure extended network timeout for long-running Deep Research tasks.
+PersistentAgentsAdministrationClientOptions persistentAgentsClientOptions = new();
+persistentAgentsClientOptions.Retry.NetworkTimeout = TimeSpan.FromMinutes(20);
+
+// Get a client to create/retrieve server side agents with.
+PersistentAgentsClient persistentAgentsClient = new(endpoint, new AzureCliCredential(), persistentAgentsClientOptions);
+
+// Define and configure the Deep Research tool.
+DeepResearchToolDefinition deepResearchTool = new(new DeepResearchDetails(
+    bingGroundingConnections: [new(bingConnectionId)],
+    model: deepResearchDeploymentName)
+ );
+
+// Create an agent with the Deep Research tool on the Azure AI agent service.
+AIAgent agent = await persistentAgentsClient.CreateAIAgentAsync(
+    model: modelDeploymentName,
+    name: "DeepResearchAgent",
+    instructions: "You are a helpful Agent that assists in researching scientific topics.",
+    tools: [deepResearchTool]);
+
+const string Task = "Research the current state of studies on orca intelligence and orca language, " +
+    "including what is currently known about orcas' cognitive capabilities and communication systems.";
+
+Console.WriteLine($"# User: '{Task}'");
+Console.WriteLine();
+
+try
+{
+    AgentThread thread = agent.GetNewThread();
+
+    await foreach (var response in agent.RunStreamingAsync(Task, thread))
+    {
+        Console.Write(response.Text);
+    }
+}
+finally
+{
+    await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
+}
@@ -0,0 +1,47 @@
+# What this sample demonstrates
+
+This sample demonstrates how to create an Azure AI Agent with the Deep Research Tool, which leverages the o3-deep-research reasoning model to perform comprehensive research on complex topics.
+
+Key features:
+- Configuring and using the Deep Research Tool with Bing grounding
+- Creating a persistent AI agent with deep research capabilities
+- Executing deep research queries and retrieving results
+
+## Prerequisites
+
+Before running this sample, ensure you have:
+
+1. An Azure AI Foundry project set up
+2. A deep research model deployment (e.g., o3-deep-research)
+3. A model deployment (e.g., gpt-4o)
+4. A Bing Connection configured in your Azure AI Foundry project
+5. Azure CLI installed and authenticated
+
+**Important**: Please visit the following documentation for detailed setup instructions:
+- [Deep Research Tool Documentation](https://aka.ms/agents-deep-research)
+- [Research Tool Setup](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/deep-research#research-tool-setup)
+
+Pay special attention to the purple `Note` boxes in the Azure documentation.
+
+**Note**: The Bing Connection ID must be from the **project**, not the resource. It has the following format:
+
+```
+/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/connections/<connection_name>
+```
+
+## Environment Variables
+
+Set the following environment variables:
+
+```powershell
+# Replace with your Azure AI Foundry project endpoint
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"
+
+# Replace with your Bing connection ID from the project
+$env:BING_CONNECTION_ID="/subscriptions/.../connections/your-bing-connection"
+
+# Optional, defaults to o3-deep-research
+$env:AZURE_FOUNDRY_PROJECT_DEEP_RESEARCH_DEPLOYMENT_NAME="o3-deep-research"
+
+# Optional, defaults to gpt-4o
+$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o"
@@ -23,7 +23,7 @@ A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
 AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();

 // Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
-AIAgent a2aAgent = await agentCard.GetAIAgentAsync();
+AIAgent a2aAgent = agentCard.GetAIAgent();

 // Create the main agent, and provide the a2a agent skills as a function tools.
 AIAgent agent = new AzureOpenAIClient(
@@ -125,7 +125,7 @@ var agent = new ChatClientAgent(instrumentedChatClient,
    instructions: "You are a helpful assistant that provides concise and informative responses.",
    tools: [AIFunctionFactory.Create(GetWeatherAsync)])
    .AsBuilder()
-    .UseOpenTelemetry(SourceName) // enable telemetry at the agent level
+    .UseOpenTelemetry(SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
    .Build();

 var thread = agent.GetNewThread();
@@ -134,6 +134,8 @@ appLogger.LogInformation("Agent created successfully with ID: {AgentId}", agent.

 // Create a parent span for the entire agent session
 using var sessionActivity = activitySource.StartActivity("Agent Session");
+Console.WriteLine($"Trace ID: {sessionActivity?.TraceId} ");
+
 var sessionId = Guid.NewGuid().ToString("N");
 sessionActivity?
    .SetTag("agent.name", "OpenTelemetryDemoAgent")
@@ -147,7 +149,7 @@ using (appLogger.BeginScope(new Dictionary<string, object> { ["SessionId"] = ses

    while (true)
    {
-        Console.Write("You: ");
+        Console.Write("You (or 'exit' to quit): ");
        var userInput = Console.ReadLine();

        if (string.IsNullOrWhiteSpace(userInput) || userInput.Equals("exit", StringComparison.OrdinalIgnoreCase))
@@ -14,7 +14,7 @@
  </ItemGroup>

  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
  </ItemGroup>

 </Project>
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,105 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent.
+// The sample uses an In-Memory vector store, which can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
+// The TextSearchProvider runs a search against the vector store via the TextSearchStore before each model invocation and injects the results into the model context.
+// The TextSearchStore is a sample store implementation that hardcodes a storage schema and uses the vector store to store and retrieve documents.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Agents.AI.Samples;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+
+AzureOpenAIClient azureOpenAIClient = new(
+    new Uri(endpoint),
+    new AzureCliCredential());
+
+// Create an In-Memory vector store that uses the Azure OpenAI embedding model to generate embeddings.
+VectorStore vectorStore = new InMemoryVectorStore(new()
+{
+    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+});
+
+// Create a store that defines a storage schema, and uses the vector store to store and retrieve documents.
+TextSearchStore textSearchStore = new(vectorStore, "product-and-policy-info", 3072);
+
+// Upload sample documents into the store.
+await textSearchStore.UpsertDocumentsAsync(GetSampleDocuments());
+
+// Create an adapter function that the TextSearchProvider can use to run searches against the TextSearchStore.
+Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+{
+    // Here we are limiting the search results to the single top result to demonstrate that we are accurately matching
+    // specific search results for each question, but in a real world case, more results should be used.
+    var searchResults = await textSearchStore.SearchAsync(text, 1, ct);
+    return searchResults.Select(r => new TextSearchProvider.TextSearchResult
+    {
+        SourceName = r.SourceName,
+        SourceLink = r.SourceLink,
+        Text = r.Text ?? string.Empty,
+        RawRepresentation = r
+    });
+};
+
+// Configure the options for the TextSearchProvider.
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+};
+
+// Create the AI agent with the TextSearchProvider as the AI context provider.
+AIAgent agent = azureOpenAIClient
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+        AIContextProviderFactory = ctx => new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about returns\n");
+Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
+
+Console.WriteLine("\n>> Asking about shipping\n");
+Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
+
+Console.WriteLine("\n>> Asking about product care\n");
+Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
+
+// Produces some sample search documents.
+// Each one contains a source name and link, which the agent can use to cite sources in its responses.
+static IEnumerable<TextSearchDocument> GetSampleDocuments()
+{
+    yield return new TextSearchDocument
+    {
+        SourceId = "return-policy-001",
+        SourceName = "Contoso Outdoors Return Policy",
+        SourceLink = "https://contoso.com/policies/returns",
+        Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+    };
+    yield return new TextSearchDocument
+    {
+        SourceId = "shipping-guide-001",
+        SourceName = "Contoso Outdoors Shipping Guide",
+        SourceLink = "https://contoso.com/help/shipping",
+        Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+    };
+    yield return new TextSearchDocument
+    {
+        SourceId = "tent-care-001",
+        SourceName = "TrailRunner Tent Care Instructions",
+        SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+        Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+    };
+}
@@ -0,0 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Represents a document that can be used for Retrieval Augmented Generation (RAG) that stores textual data.
+/// </summary>
+public sealed class TextSearchDocument
+{
+    /// <summary>
+    /// Gets or sets an optional list of namespaces that the document should belong to.
+    /// </summary>
+    /// <remarks>
+    /// A namespace is a logical grouping of documents, e.g. may include a group id to scope the document to a specific group of users.
+    /// </remarks>
+    public IList<string> Namespaces { get; set; } = [];
+
+    /// <summary>
+    /// Gets or sets the content as text.
+    /// </summary>
+    public string? Text { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional source ID for the document.
+    /// </summary>
+    /// <remarks>
+    /// This ID should be unique within the collection that the document is stored in, and can
+    /// be used to map back to the source artifact for this document.
+    /// If updates need to be made later or the source document was deleted and this document
+    /// also needs to be deleted, this id can be used to find the document again.
+    /// </remarks>
+    public string? SourceId { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional name for the source document.
+    /// </summary>
+    /// <remarks>
+    /// This can be used to provide display names for citation links when the document is referenced as
+    /// part of a response to a query.
+    /// </remarks>
+    public string? SourceName { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional link back to the source of the document.
+    /// </summary>
+    /// <remarks>
+    /// This can be used to provide citation links when the document is referenced as
+    /// part of a response to a query.
+    /// </remarks>
+    public string? SourceLink { get; set; }
+}
@@ -0,0 +1,392 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Linq.Expressions;
+using System.Text.RegularExpressions;
+using Microsoft.Extensions.VectorData;
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// A class that allows for easy storage and retrieval of documents in a Vector Store for Retrieval Augmented Generation (RAG).
+/// </summary>
+/// <remarks>
+/// <para>
+/// This class provides an opinionated schema for storing documents in a vector store. It is valuable for simple scenarios
+/// where you want to store text + embedding, or a reference to an external document + embedding without needing to customize the schema.
+/// If you want to control the schema yourself, use an implementation of <see cref="VectorStoreCollection{TKey, TRecord}"/> directly instead.
+/// </para>
+/// <para>
+/// This class and its related types are currently provided as a sample implementation, but may be promoted to a first-class supported API in future releases.
+/// </para>
+/// </remarks>
+public sealed partial class TextSearchStore : IDisposable
+{
+#if NET
+    [GeneratedRegex(@"\p{L}+", RegexOptions.IgnoreCase, "en-US")]
+    private static partial Regex AnyLanguageWordRegex();
+
+    private static readonly Func<string, ICollection<string>> s_defaultWordSegmenter = text => AnyLanguageWordRegex().Matches(text).Select(x => x.Value).ToList();
+#else
+    private static readonly Regex s_anyLanguageWordRegex = new(@"\p{L}+", RegexOptions.Compiled);
+    private static Regex AnyLanguageWordRegex() => s_anyLanguageWordRegex;
+
+    private static readonly Func<string, ICollection<string>> s_defaultWordSegmenter = text =>
+    {
+        List<string> words = new();
+        foreach (Match word in AnyLanguageWordRegex().Matches(text))
+        {
+            words.Add(word.Value);
+        }
+        return words;
+    };
+#endif
+
+    private readonly VectorStore _vectorStore;
+    private readonly TextSearchStoreOptions _options;
+    private readonly Func<string, ICollection<string>> _wordSegmenter;
+
+    private readonly VectorStoreCollection<object, Dictionary<string, object?>> _vectorStoreRecordCollection;
+    private readonly SemaphoreSlim _collectionInitializationLock = new(1, 1);
+    private bool _collectionInitialized;
+    private bool _disposedValue;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="TextSearchStore"/> class.
+    /// </summary>
+    /// <param name="vectorStore">The vector store to store and read the memories from.</param>
+    /// <param name="collectionName">The name of the collection in the vector store to store and read the memories from.</param>
+    /// <param name="vectorDimensions">The number of dimensions to use for the memory embeddings.</param>
+    /// <param name="options">Options to configure the behavior of this class.</param>
+    /// <exception cref="NotSupportedException">Thrown if the key type provided is not supported.</exception>
+    public TextSearchStore(
+        VectorStore vectorStore,
+        string collectionName,
+        int vectorDimensions,
+        TextSearchStoreOptions? options = default)
+    {
+        // Verify
+        if (vectorStore is null)
+        {
+            throw new ArgumentNullException(nameof(vectorStore));
+        }
+
+        if (string.IsNullOrWhiteSpace(collectionName))
+        {
+            throw new ArgumentException("Collection name cannot be null or whitespace.", nameof(collectionName));
+        }
+
+        if (vectorDimensions < 1)
+        {
+            throw new ArgumentOutOfRangeException(nameof(vectorDimensions), "Vector dimensions must be greater than zero.");
+        }
+
+        if (options?.KeyType is not null && options.KeyType != typeof(string) && options.KeyType != typeof(Guid))
+        {
+            throw new NotSupportedException($"Unsupported key of type '{options.KeyType.Name}'");
+        }
+
+        if (options?.KeyType is not null && options.KeyType != typeof(string) && options?.UseSourceIdAsPrimaryKey is true)
+        {
+            throw new NotSupportedException($"The {nameof(TextSearchStoreOptions.UseSourceIdAsPrimaryKey)} option can only be used when the key type is 'string'.");
+        }
+
+        // Assign
+        this._vectorStore = vectorStore;
+        this._options = options ?? new TextSearchStoreOptions();
+        this._wordSegmenter = this._options.WordSegmenter ?? s_defaultWordSegmenter;
+
+        // Create a definition so that we can use the dimensions provided at runtime.
+        VectorStoreCollectionDefinition ragDocumentDefinition = new()
+        {
+            Properties = new List<VectorStoreProperty>()
+            {
+                new VectorStoreKeyProperty("Key", this._options.KeyType ?? typeof(string)),
+                new VectorStoreDataProperty("Namespaces", typeof(List<string>)) { IsIndexed = true },
+                new VectorStoreDataProperty("SourceId", typeof(string)) { IsIndexed = true },
+                new VectorStoreDataProperty("Text", typeof(string)) { IsFullTextIndexed = true },
+                new VectorStoreDataProperty("SourceName", typeof(string)),
+                new VectorStoreDataProperty("SourceLink", typeof(string)),
+                new VectorStoreVectorProperty("TextEmbedding", typeof(string), vectorDimensions),
+            }
+        };
+
+        this._vectorStoreRecordCollection = this._vectorStore.GetDynamicCollection(collectionName, ragDocumentDefinition);
+    }
+
+    /// <summary>
+    /// Upserts a batch of text chunks into the vector store.
+    /// </summary>
+    /// <param name="textChunks">The text chunks to upload.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>A task that completes when the documents have been upserted.</returns>
+    public async Task UpsertTextAsync(IEnumerable<string> textChunks, CancellationToken cancellationToken = default)
+    {
+        if (textChunks == null)
+        {
+            throw new ArgumentNullException(nameof(textChunks));
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        var storageDocuments = textChunks.Select(textChunk =>
+        {
+            // Without text we cannot generate a vector.
+            if (string.IsNullOrWhiteSpace(textChunk))
+            {
+                throw new ArgumentException("One of the provided text chunks is null.", nameof(textChunks));
+            }
+
+            return new Dictionary<string, object?>
+            {
+                { "Key", this.GenerateUniqueKey(null) },
+                { "Namespaces", new List<string>() },
+                { "Text", textChunk },
+                { "TextEmbedding", textChunk },
+            };
+        });
+
+        await vectorStoreRecordCollection.UpsertAsync(storageDocuments, cancellationToken).ConfigureAwait(false);
+    }
+
+    /// <summary>
+    /// Upserts a batch of documents into the vector store.
+    /// </summary>
+    /// <param name="documents">The documents to upload.</param>
+    /// <param name="options">Optional options to control the upsert behavior.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>A task that completes when the documents have been upserted.</returns>
+    public async Task UpsertDocumentsAsync(IEnumerable<TextSearchDocument> documents, TextSearchStoreUpsertOptions? options = null, CancellationToken cancellationToken = default)
+    {
+        if (documents is null)
+        {
+            throw new ArgumentNullException(nameof(documents));
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        var storageDocuments = documents.Select(document =>
+        {
+            if (document is null)
+            {
+                throw new ArgumentNullException(nameof(documents), "One of the provided documents is null.");
+            }
+
+            // Without text we cannot generate a vector.
+            if (string.IsNullOrWhiteSpace(document.Text))
+            {
+                throw new ArgumentException($"The {nameof(TextSearchDocument.Text)} property must be set.", nameof(document));
+            }
+
+            // If we aren't persisting the text, we need a source id or link to refer back to the original document.
+            if (options?.DoNotPersistSourceText is true && string.IsNullOrWhiteSpace(document.SourceId) && string.IsNullOrWhiteSpace(document.SourceLink))
+            {
+                throw new ArgumentException($"Either the {nameof(TextSearchDocument.SourceId)} or {nameof(TextSearchDocument.SourceLink)} properties must be set when the {nameof(TextSearchStoreUpsertOptions.DoNotPersistSourceText)} setting is true.", nameof(document));
+            }
+
+            var key = this.GenerateUniqueKey(this._options.UseSourceIdAsPrimaryKey ?? false ? document.SourceId : null);
+
+            return new Dictionary<string, object?>()
+            {
+                { "Key", key },
+                { "Namespaces", document.Namespaces.ToList() },
+                { "SourceId", document.SourceId },
+                { "Text", options?.DoNotPersistSourceText is true ? null : document.Text },
+                { "SourceName", document.SourceName },
+                { "SourceLink", document.SourceLink },
+                { "TextEmbedding", document.Text },
+            };
+        });
+
+        await vectorStoreRecordCollection.UpsertAsync(storageDocuments, cancellationToken).ConfigureAwait(false);
+    }
+
+    /// <summary>
+    /// Search the database for documents similar to the provided query.
+    /// </summary>
+    /// <param name="query">The text query to find similar documents to.</param>
+    /// <param name="top">The maximum number of results to return.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The search results.</returns>
+    public async Task<IEnumerable<TextSearchDocument>> SearchAsync(string query, int top, CancellationToken cancellationToken = default)
+    {
+        var searchResult = await this.SearchCoreAsync(query, top, cancellationToken).ConfigureAwait(false);
+
+        return searchResult.Select(x => new TextSearchDocument()
+        {
+            Namespaces = (List<string>)x["Namespaces"]!,
+            Text = (string?)x["Text"],
+            SourceId = (string?)x["SourceId"],
+            SourceName = (string?)x["SourceName"],
+            SourceLink = (string?)x["SourceLink"],
+        });
+    }
+
+    /// <summary>
+    /// Internal search implementation with hydration of id / link only storage.
+    /// </summary>
+    /// <param name="query">The text query to find similar documents to.</param>
+    /// <param name="top">The maximum number of results to return.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The search results.</returns>
+    private async Task<IEnumerable<Dictionary<string, object?>>> SearchCoreAsync(string query, int top, CancellationToken cancellationToken = default)
+    {
+        // Short circuit if the query is empty.
+        if (string.IsNullOrWhiteSpace(query))
+        {
+            return [];
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        // If the user has not opted out of hybrid search, check if the vector store supports it.
+        var hybridSearchCollection = this._options.UseHybridSearch ?? true ?
+            vectorStoreRecordCollection.GetService(typeof(IKeywordHybridSearchable<Dictionary<string, object?>>)) as IKeywordHybridSearchable<Dictionary<string, object?>> :
+            null;
+
+        // Optional filter to limit the search to a specific namespace.
+        Expression<Func<Dictionary<string, object?>, bool>>? filter = string.IsNullOrWhiteSpace(this._options.SearchNamespace) ? null : x => ((List<string>)x["Namespaces"]!).Contains(this._options.SearchNamespace);
+
+        // Execute a hybrid search if possible, otherwise perform a regular vector search.
+        var searchResult = hybridSearchCollection is null
+            ? vectorStoreRecordCollection.SearchAsync(
+                query,
+                top,
+                options: new()
+                {
+                    Filter = filter,
+                },
+                cancellationToken: cancellationToken)
+            : hybridSearchCollection.HybridSearchAsync(
+                query,
+                this._wordSegmenter(query),
+                top,
+                options: new()
+                {
+                    Filter = filter,
+                },
+                cancellationToken: cancellationToken);
+
+        // Retrieve the documents from the search results.
+        List<Dictionary<string, object?>> searchResponseDocs = new();
+        await foreach (var searchResponseDoc in searchResult.WithCancellation(cancellationToken).ConfigureAwait(false))
+        {
+            searchResponseDocs.Add(searchResponseDoc.Record);
+        }
+
+        // Find any source ids and links for which the text needs to be retrieved.
+        var sourceIdsToRetrieve = searchResponseDocs
+            .Where(x => string.IsNullOrWhiteSpace((string?)x["Text"]))
+            .Select(x => new TextSearchStoreOptions.SourceRetrievalRequest((string?)x["SourceId"], (string?)x["SourceLink"]))
+            .ToList();
+
+        // If we have none, we can return early.
+        if (sourceIdsToRetrieve.Count == 0)
+        {
+            return searchResponseDocs;
+        }
+
+        if (this._options.SourceRetrievalCallback is null)
+        {
+            throw new InvalidOperationException($"The {nameof(TextSearchStoreOptions.SourceRetrievalCallback)} option must be set if retrieving documents without stored text.");
+        }
+
+        // Retrieve the source text for the documents that need it.
+        var retrievalResponses = await this._options.SourceRetrievalCallback(sourceIdsToRetrieve).ConfigureAwait(false);
+
+        if (retrievalResponses is null)
+        {
+            throw new InvalidOperationException($"The {nameof(TextSearchStoreOptions.SourceRetrievalCallback)} must return a non-null value.");
+        }
+
+        // Update the retrieved documents with the retrieved text.
+        return searchResponseDocs.GroupJoin(
+            retrievalResponses,
+            searchResponseDoc => (searchResponseDoc["SourceId"], searchResponseDoc["SourceLink"]),
+            retrievalResponse => (retrievalResponse.SourceId, retrievalResponse.SourceLink),
+            (searchResponseDoc, textRetrievalResponse) => (searchResponseDoc, textRetrievalResponse))
+            .SelectMany(
+                joinedSet => joinedSet.textRetrievalResponse.DefaultIfEmpty(),
+                (combined, textRetrievalResponse) =>
+                {
+                    combined.searchResponseDoc["Text"] = textRetrievalResponse?.Text ?? combined.searchResponseDoc["Text"];
+                    return combined.searchResponseDoc;
+                });
+    }
+
+    /// <summary>
+    /// Thread safe method to get the collection and ensure that it is created at least once.
+    /// </summary>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The created collection.</returns>
+    private async Task<VectorStoreCollection<object, Dictionary<string, object?>>> EnsureCollectionExistsAsync(CancellationToken cancellationToken)
+    {
+        // Return immediately if the collection is already created, no need to do any locking in this case.
+        if (this._collectionInitialized)
+        {
+            return this._vectorStoreRecordCollection;
+        }
+
+        // Wait on a lock to ensure that only one thread can create the collection.
+        await this._collectionInitializationLock.WaitAsync(cancellationToken).ConfigureAwait(false);
+
+        // If multiple threads waited on the lock, and the first already created the collection,
+        // we can return immediately without doing any work in subsequent threads.
+        if (this._collectionInitialized)
+        {
+            this._collectionInitializationLock.Release();
+            return this._vectorStoreRecordCollection;
+        }
+
+        // Only the winning thread should reach this point and create the collection.
+        try
+        {
+            await this._vectorStoreRecordCollection.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+            this._collectionInitialized = true;
+        }
+        finally
+        {
+            this._collectionInitializationLock.Release();
+        }
+
+        return this._vectorStoreRecordCollection;
+    }
+
+    /// <summary>
+    /// Generates a unique key for the RAG document.
+    /// </summary>
+    /// <param name="sourceId">Source id of the source document for this RAG document.</param>
+    /// <returns>A new unique key.</returns>
+    /// <exception cref="NotSupportedException">Thrown if the requested key type is not supported.</exception>
+    private object GenerateUniqueKey(string? sourceId)
+        => this._options.KeyType switch
+        {
+            _ when (this._options.KeyType == null || this._options.KeyType == typeof(string)) && !string.IsNullOrWhiteSpace(sourceId) => sourceId!,
+            _ when this._options.KeyType == null || this._options.KeyType == typeof(string) => Guid.NewGuid().ToString(),
+            _ when this._options.KeyType == typeof(Guid) => Guid.NewGuid(),
+
+            _ => throw new NotSupportedException($"Unsupported key of type '{this._options.KeyType.Name}'")
+        };
+
+    /// <inheritdoc/>
+    private void Dispose(bool disposing)
+    {
+        if (!this._disposedValue)
+        {
+            if (disposing)
+            {
+                this._vectorStoreRecordCollection.Dispose();
+                this._collectionInitializationLock.Dispose();
+            }
+
+            this._disposedValue = true;
+        }
+    }
+
+    /// <inheritdoc/>
+    public void Dispose()
+    {
+        // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method
+        this.Dispose(disposing: true);
+        GC.SuppressFinalize(this);
+    }
+}
@@ -0,0 +1,140 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Contains options for the <see cref="TextSearchStore"/>.
+/// </summary>
+public sealed class TextSearchStoreOptions
+{
+    /// <summary>
+    /// Gets or sets an optional namespace to pre-filter the possible
+    /// records with when doing a vector search.
+    /// </summary>
+    public string? SearchNamespace { get; init; }
+
+    /// <summary>
+    /// Gets or sets a value indicating whether to use the source ID as the primary key for records.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// Using the source ID as the primary key allows for easy updates from the source for any changed
+    /// records, since those records can just be upserted again, and will overwrite the previous version
+    /// of the same record.
+    /// </para>
+    /// <para>
+    /// This setting can only be used when the chosen key type is a string.
+    /// </para>
+    /// </remarks>
+    /// <value>
+    /// Defaults to <c>false</c> if not set.
+    /// </value>
+    public bool? UseSourceIdAsPrimaryKey { get; init; }
+
+    /// <summary>
+    /// Gets or sets a value indicating whether to use hybrid search if it is available for the provided vector store.
+    /// </summary>
+    /// <value>
+    /// Defaults to <c>true</c> if not set.
+    /// </value>
+    public bool? UseHybridSearch { get; init; }
+
+    /// <summary>
+    /// Gets or sets a word segmenter function to split search text into separate words for the purposes of hybrid search.
+    /// This will not be used if <see cref="UseHybridSearch"/> is set to <c>false</c>.
+    /// </summary>
+    /// <remarks>
+    /// Defaults to a simple text-character-based segmenter that splits the text by any character that is not a text character.
+    /// </remarks>
+    public Func<string, ICollection<string>>? WordSegmenter { get; init; }
+
+    /// <summary>
+    /// Gets or sets the type of key to use for records in the text search store.
+    /// </summary>
+    /// <remarks>
+    /// Make sure to pick a key type that is supported by the underlying vector store.
+    /// Note that you have to choose <see cref="string"/> when using <see cref="UseSourceIdAsPrimaryKey"/>.
+    /// </remarks>
+    /// <value>Defaults to <see cref="string"/> if not set. Only <see cref="string"/> and <see cref="Guid"/> is currently supported.</value>
+    public Type? KeyType { get; init; }
+
+    /// <summary>
+    /// Gets or sets an optional callback to load the source text using the source id or source link
+    /// if the source text is not persisted in the database.
+    /// </summary>
+    /// <remarks>
+    /// The response should include the source id or source link, as provided in the request,
+    /// plus the source text loaded from the source.
+    /// </remarks>
+    public Func<List<SourceRetrievalRequest>, Task<IEnumerable<SourceRetrievalResponse>>>? SourceRetrievalCallback { get; init; }
+
+    /// <summary>
+    /// Represents a request to the <see cref="SourceRetrievalCallback"/>.
+    /// </summary>
+    public sealed class SourceRetrievalRequest
+    {
+        /// <summary>
+        /// Initializes a new instance of the <see cref="SourceRetrievalRequest"/> class.
+        /// </summary>
+        /// <param name="sourceId">The source ID of the document to retrieve.</param>
+        /// <param name="sourceLink">The source link of the document to retrieve.</param>
+        public SourceRetrievalRequest(string? sourceId, string? sourceLink)
+        {
+            this.SourceId = sourceId;
+            this.SourceLink = sourceLink;
+        }
+
+        /// <summary>
+        /// Gets or sets the source ID of the document to retrieve.
+        /// </summary>
+        public string? SourceId { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source link of the document to retrieve.
+        /// </summary>
+        public string? SourceLink { get; set; }
+    }
+
+    /// <summary>
+    /// Represents a response from the <see cref="SourceRetrievalCallback"/>.
+    /// </summary>
+    public sealed class SourceRetrievalResponse
+    {
+        /// <summary>
+        /// Initializes a new instance of the <see cref="SourceRetrievalResponse"/> class.
+        /// </summary>
+        /// <param name="request">The request matching this response.</param>
+        /// <param name="text">The source text that was retrieved.</param>
+        public SourceRetrievalResponse(SourceRetrievalRequest request, string text)
+        {
+            if (request == null)
+            {
+                throw new ArgumentNullException(nameof(request));
+            }
+
+            if (text == null)
+            {
+                throw new ArgumentNullException(nameof(text));
+            }
+
+            this.SourceId = request.SourceId;
+            this.SourceLink = request.SourceLink;
+            this.Text = text;
+        }
+
+        /// <summary>
+        /// Gets or sets the source ID of the document that was retrieved.
+        /// </summary>
+        public string? SourceId { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source link of the document that was retrieved.
+        /// </summary>
+        public string? SourceLink { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source text of the document that was retrieved.
+        /// </summary>
+        public string Text { get; set; }
+    }
+}
@@ -0,0 +1,17 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Contains options for <see cref="TextSearchStore.UpsertDocumentsAsync(IEnumerable{TextSearchDocument}, TextSearchStoreUpsertOptions?, CancellationToken)"/>.
+/// </summary>
+public sealed class TextSearchStoreUpsertOptions
+{
+    /// <summary>
+    /// Gets or sets a value indicating whether the source text should be persisted in the database.
+    /// </summary>
+    /// <value>
+    /// Defaults to <see langword="false"/> if not set.
+    /// </value>
+    public bool DoNotPersistSourceText { get; init; }
+}
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.Qdrant" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,132 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use Qdrant to add retrieval augmented generation (RAG) capabilities to an AI agent.
+// While the sample is using Qdrant, it can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
+// The TextSearchProvider runs a search against the vector store before each model invocation and injects the results into the model context.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.Qdrant;
+using OpenAI;
+using Qdrant.Client;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
+var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";
+
+AzureOpenAIClient azureOpenAIClient = new(
+    new Uri(endpoint),
+    new AzureCliCredential());
+
+// Create a Qdrant vector store that uses the Azure OpenAI embedding model to generate embeddings.
+QdrantClient client = new("localhost");
+VectorStore vectorStore = new QdrantVectorStore(client, ownsClient: true, new()
+{
+    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+});
+
+// Create a collection and upsert some text into it.
+var documentationCollection = vectorStore.GetCollection<Guid, DocumentationChunk>("documentation");
+await documentationCollection.EnsureCollectionDeletedAsync(); // Clear out any data from previous runs.
+await documentationCollection.EnsureCollectionExistsAsync();
+await UploadDataFromMarkdown(afOverviewUrl, "Microsoft Agent Framework Overview", documentationCollection, 2000, 200);
+await UploadDataFromMarkdown(afMigrationUrl, "Semantic Kernel to Microsoft Agent Framework Migration Guide", documentationCollection, 2000, 200);
+
+// Create an adapter function that the TextSearchProvider can use to run searches against the collection.
+Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+{
+    List<TextSearchProvider.TextSearchResult> results = [];
+    await foreach (var result in documentationCollection.SearchAsync(text, 5, cancellationToken: ct))
+    {
+        results.Add(new TextSearchProvider.TextSearchResult
+        {
+            SourceName = result.Record.SourceName,
+            SourceLink = result.Record.SourceLink,
+            Text = result.Record.Text ?? string.Empty,
+            RawRepresentation = result
+        });
+    }
+    return results;
+};
+
+// Configure the options for the TextSearchProvider.
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+    // Use up to 4 recent messages when searching so that searches
+    // still produce valuable results even when the user is referring
+    // back to previous messages in their request.
+    RecentMessageMemoryLimit = 5
+};
+
+// Create the AI agent with the TextSearchProvider as the AI context provider.
+AIAgent agent = azureOpenAIClient
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for the Microsoft Agent Framework. Answer questions using the provided context and cite the source document when available. Keep responses brief.",
+        AIContextProviderFactory = ctx => new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about SK threads\n");
+Console.WriteLine(await agent.RunAsync("Hi! How do I create a thread in Semantic Kernel?", thread));
+
+// Here we are asking a very vague question when taken out of context,
+// but since we are including previous messages in our search using RecentMessageMemoryLimit
+// the RAG search should still produce useful results.
+Console.WriteLine("\n>> Asking about AF threads\n");
+Console.WriteLine(await agent.RunAsync("and in Agent Framework?", thread));
+
+Console.WriteLine("\n>> Contrasting Approaches\n");
+Console.WriteLine(await agent.RunAsync("Please contrast the two approaches", thread));
+
+Console.WriteLine("\n>> Asking about ancestry\n");
+Console.WriteLine(await agent.RunAsync("What are the predecessors to the Agent Framework?", thread));
+
+static async Task UploadDataFromMarkdown(string markdownUrl, string sourceName, VectorStoreCollection<Guid, DocumentationChunk> vectorStoreCollection, int chunkSize, int overlap)
+{
+    // Download the markdown from the given url.
+    using HttpClient client = new();
+    var markdown = await client.GetStringAsync(new Uri(markdownUrl));
+
+    // Chunk it into separate parts with some overlap between chunks
+    var chunks = new List<DocumentationChunk>();
+    for (int i = 0; i < markdown.Length; i += chunkSize)
+    {
+        var chunk = new DocumentationChunk
+        {
+            Key = Guid.NewGuid(),
+            SourceLink = markdownUrl,
+            SourceName = sourceName,
+            Text = markdown.Substring(i, Math.Min(chunkSize + overlap, markdown.Length - i))
+        };
+        chunks.Add(chunk);
+    }
+
+    // Upsert each chunk into the provided vector store.
+    await vectorStoreCollection.UpsertAsync(chunks);
+}
+
+// Data model that defines the database schema we want to use.
+internal sealed class DocumentationChunk
+{
+    [VectorStoreKey]
+    public Guid Key { get; set; }
+    [VectorStoreData]
+    public string SourceLink { get; set; } = string.Empty;
+    [VectorStoreData]
+    public string SourceName { get; set; } = string.Empty;
+    [VectorStoreData]
+    public string Text { get; set; } = string.Empty;
+    [VectorStoreVector(Dimensions: 3072)]
+    public string Embedding => this.Text;
+}
@@ -0,0 +1,60 @@
+# Agent Framework Retrieval Augmented Generation (RAG) with an external Vector Store with a custom schema
+
+This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store.
+It also uses a custom schema for the documents stored in the vector store.
+This sample uses Qdrant for the vector store, but this can easily be swapped out for any vector store that has a Microsoft.Extensions.VectorStore implementation.
+
+## Prerequisites
+
+- .NET 8.0 SDK or later
+- Azure OpenAI service endpoint
+- Both a chat completion and embedding deployment configured in the Azure OpenAI resource
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
+- An existing Qdrant instance. You can use a managed service or run a local instance using Docker, but the sample assumes the instance is running locally.
+
+**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+
+**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Running the sample from the console
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large"  # Optional, defaults to text-embedding-3-large
+```
+
+If the variables are not set, you will be prompted for the values when running the samples.
+
+To use Qdrant in docker locally, start your Qdrant instance using the default port mappings.
+
+```powershell
+docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
+```
+
+Execute the following command to build the sample:
+
+```powershell
+dotnet build
+```
+
+Execute the following command to run the sample:
+
+```powershell
+dotnet run --no-build
+```
+
+Or just build and run in one step:
+
+```powershell
+dotnet run
+```
+
+## Running the sample from Visual Studio
+
+Open the solution in Visual Studio and set the sample project as the startup project. Then, run the project using the built-in debugger or by pressing `F5`.
+
+You will be prompted for any required environment variables if they are not already set.
@@ -0,0 +1,8 @@
+# Agent Framework Retrieval Augmented Generation (RAG)
+
+These samples show how to create an agent with the Agent Framework that uses Retrieval Augmented Generation (RAG) to enhance its responses with information from a knowledge base.
+
+|Sample|Description|
+|---|---|
+|[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).|
+|[RAG with external Vector Store and custom schema](./AgentWithRAG_Step02_ExternalDataSourceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store. It also uses a custom schema for the documents stored in the vector store.|
@@ -0,0 +1,28 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Plugins.OpenApi" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="OpenAPISpec.json">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,354 @@
+{
+    "openapi": "3.0.1",
+    "info": {
+        "title": "Github Versions API",
+        "version": "1.0.0"
+    },
+    "servers": [
+        {
+            "url": "https://api.github.com"
+        }
+    ],
+    "components": {
+        "schemas": {
+            "basic-error": {
+                "title": "Basic Error",
+                "description": "Basic Error",
+                "type": "object",
+                "properties": {
+                    "message": {
+                        "type": "string"
+                    },
+                    "documentation_url": {
+                        "type": "string"
+                    },
+                    "url": {
+                        "type": "string"
+                    },
+                    "status": {
+                        "type": "string"
+                    }
+                }
+            },
+            "label": {
+                "title": "Label",
+                "description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).",
+                "type": "object",
+                "properties": {
+                    "id": {
+                        "description": "Unique identifier for the label.",
+                        "type": "integer",
+                        "format": "int64",
+                        "example": 208045946
+                    },
+                    "node_id": {
+                        "type": "string",
+                        "example": "MDU6TGFiZWwyMDgwNDU5NDY="
+                    },
+                    "url": {
+                        "description": "URL for the label",
+                        "example": "https://api.github.com/repositories/42/labels/bug",
+                        "type": "string",
+                        "format": "uri"
+                    },
+                    "name": {
+                        "description": "The name of the label.",
+                        "example": "bug",
+                        "type": "string"
+                    },
+                    "description": {
+                        "description": "Optional description of the label, such as its purpose.",
+                        "type": "string",
+                        "example": "Something isn't working",
+                        "nullable": true
+                    },
+                    "color": {
+                        "description": "6-character hex code, without the leading #, identifying the color",
+                        "example": "FFFFFF",
+                        "type": "string"
+                    },
+                    "default": {
+                        "description": "Whether this label comes by default in a new repository.",
+                        "type": "boolean",
+                        "example": true
+                    }
+                },
+                "required": [
+                    "id",
+                    "node_id",
+                    "url",
+                    "name",
+                    "description",
+                    "color",
+                    "default"
+                ]
+            },
+            "tag": {
+                "title": "Tag",
+                "description": "Tag",
+                "type": "object",
+                "properties": {
+                    "name": {
+                        "type": "string",
+                        "example": "v0.1"
+                    },
+                    "commit": {
+                        "type": "object",
+                        "properties": {
+                            "sha": {
+                                "type": "string"
+                            },
+                            "url": {
+                                "type": "string",
+                                "format": "uri"
+                            }
+                        },
+                        "required": [
+                            "sha",
+                            "url"
+                        ]
+                    },
+                    "zipball_url": {
+                        "type": "string",
+                        "format": "uri",
+                        "example": "https://github.com/octocat/Hello-World/zipball/v0.1"
+                    },
+                    "tarball_url": {
+                        "type": "string",
+                        "format": "uri",
+                        "example": "https://github.com/octocat/Hello-World/tarball/v0.1"
+                    },
+                    "node_id": {
+                        "type": "string"
+                    }
+                },
+                "required": [
+                    "name",
+                    "node_id",
+                    "commit",
+                    "zipball_url",
+                    "tarball_url"
+                ]
+            }
+        },
+        "examples": {
+            "label-items": {
+                "value": [
+                    {
+                        "id": 208045946,
+                        "node_id": "MDU6TGFiZWwyMDgwNDU5NDY=",
+                        "url": "https://api.github.com/repos/octocat/Hello-World/labels/bug",
+                        "name": "bug",
+                        "description": "Something isn't working",
+                        "color": "f29513",
+                        "default": true
+                    },
+                    {
+                        "id": 208045947,
+                        "node_id": "MDU6TGFiZWwyMDgwNDU5NDc=",
+                        "url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement",
+                        "name": "enhancement",
+                        "description": "New feature or request",
+                        "color": "a2eeef",
+                        "default": false
+                    }
+                ]
+            },
+            "tag-items": {
+                "value": [
+                    {
+                        "name": "v0.1",
+                        "commit": {
+                            "sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
+                            "url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
+                        },
+                        "zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
+                        "tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
+                        "node_id": "MDQ6VXNlcjE="
+                    }
+                ]
+            }
+        },
+        "parameters": {
+            "owner": {
+                "name": "owner",
+                "description": "The account owner of the repository. The name is not case sensitive.",
+                "in": "path",
+                "required": true,
+                "schema": {
+                    "type": "string"
+                }
+            },
+            "repo": {
+                "name": "repo",
+                "description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
+                "in": "path",
+                "required": true,
+                "schema": {
+                    "type": "string"
+                }
+            },
+            "per-page": {
+                "name": "per_page",
+                "description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
+                "in": "query",
+                "schema": {
+                    "type": "integer",
+                    "default": 30
+                }
+            },
+            "page": {
+                "name": "page",
+                "description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
+                "in": "query",
+                "schema": {
+                    "type": "integer",
+                    "default": 1
+                }
+            }
+        },
+        "responses": {
+            "not_found": {
+                "description": "Resource not found",
+                "content": {
+                    "application/json": {
+                        "schema": {
+                            "$ref": "#/components/schemas/basic-error"
+                        }
+                    }
+                }
+            }
+        },
+        "headers": {
+            "link": {
+                "example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
+                "schema": {
+                    "type": "string"
+                }
+            }
+        }
+    },
+    "paths": {
+        "/repos/{owner}/{repo}/tags": {
+            "get": {
+                "summary": "List repository tags",
+                "description": "",
+                "tags": [
+                    "repos"
+                ],
+                "operationId": "repos/list-tags",
+                "externalDocs": {
+                    "description": "API method documentation",
+                    "url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
+                },
+                "parameters": [
+                    {
+                        "$ref": "#/components/parameters/owner"
+                    },
+                    {
+                        "$ref": "#/components/parameters/repo"
+                    },
+                    {
+                        "$ref": "#/components/parameters/per-page"
+                    },
+                    {
+                        "$ref": "#/components/parameters/page"
+                    }
+                ],
+                "responses": {
+                    "200": {
+                        "description": "Response",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "type": "array",
+                                    "items": {
+                                        "$ref": "#/components/schemas/tag"
+                                    }
+                                },
+                                "examples": {
+                                    "default": {
+                                        "$ref": "#/components/examples/tag-items"
+                                    }
+                                }
+                            }
+                        },
+                        "headers": {
+                            "Link": {
+                                "$ref": "#/components/headers/link"
+                            }
+                        }
+                    }
+                },
+                "x-github": {
+                    "githubCloudOnly": false,
+                    "enabledForGitHubApps": true,
+                    "category": "repos",
+                    "subcategory": "repos"
+                }
+            }
+        },
+        "/repos/{owner}/{repo}/labels": {
+            "get": {
+                "summary": "List labels for a repository",
+                "description": "Lists all labels for a repository.",
+                "tags": [
+                    "issues"
+                ],
+                "operationId": "issues/list-labels-for-repo",
+                "externalDocs": {
+                    "description": "API method documentation",
+                    "url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
+                },
+                "parameters": [
+                    {
+                        "$ref": "#/components/parameters/owner"
+                    },
+                    {
+                        "$ref": "#/components/parameters/repo"
+                    },
+                    {
+                        "$ref": "#/components/parameters/per-page"
+                    },
+                    {
+                        "$ref": "#/components/parameters/page"
+                    }
+                ],
+                "responses": {
+                    "200": {
+                        "description": "Response",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "type": "array",
+                                    "items": {
+                                        "$ref": "#/components/schemas/label"
+                                    }
+                                },
+                                "examples": {
+                                    "default": {
+                                        "$ref": "#/components/examples/label-items"
+                                    }
+                                }
+                            }
+                        },
+                        "headers": {
+                            "Link": {
+                                "$ref": "#/components/headers/link"
+                            }
+                        }
+                    },
+                    "404": {
+                        "$ref": "#/components/responses/not_found"
+                    }
+                },
+                "x-github": {
+                    "githubCloudOnly": false,
+                    "enabledForGitHubApps": true,
+                    "category": "issues",
+                    "subcategory": "labels"
+                }
+            }
+        }
+    }
+}
@@ -0,0 +1,33 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use a ChatClientAgent with function tools provided via an OpenAPI spec.
+// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Plugins.OpenApi;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// Load the OpenAPI Spec from a file.
+KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
+
+// Convert the Semantic Kernel plugin to Agent Framework function tools.
+// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
+Kernel kernel = new();
+List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
+
+// Create the chat client and agent, and provide the OpenAPI function tools to the agent.
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(instructions: "You are a helpful assistant", tools: tools);
+
+// Run the agent with the OpenAPI function tools.
+Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github."));
@@ -18,7 +18,7 @@
  </ItemGroup>

  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
  </ItemGroup>
  
 </Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use background responses with ChatClientAgent and OpenAI Responses.
+// This sample shows how to use background responses with ChatClientAgent and Azure OpenAI Responses.

 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -1,6 +1,6 @@
 # What This Sample Shows

-This sample demonstrates how to use background responses with ChatCompletionAgent and OpenAI Responses for long-running operations. Background responses support:
+This sample demonstrates how to use background responses with ChatCompletionAgent and Azure OpenAI Responses for long-running operations. Background responses support:

 - **Polling for completion** - Non-streaming APIs can start a background operation and return a continuation token. Poll with the token until the response completes.
 - **Resuming after interruption** - Streaming APIs can be interrupted and resumed from the last update using the continuation token.
@@ -14,7 +14,10 @@ For more information, see the [official documentation](https://learn.microsoft.c
 Before you begin, ensure you have the following prerequisites:

 - .NET 8.0 SDK or later
- OpenAI api key
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

 Set the following environment variables:

@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,84 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
+// capabilities to an AI agent. The provider runs a search against an external knowledge base
+// before each model invocation and injects the results into the model context.
+
+// Also see the AgentWithRAG folder for more advanced RAG scenarios.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation and keep a short rolling window of conversation context.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+    RecentMessageMemoryLimit = 6,
+};
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+        AIContextProviderFactory = ctx => new TextSearchProvider(MockSearchAsync, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about returns\n");
+Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
+
+Console.WriteLine("\n>> Asking about shipping\n");
+Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
+
+Console.WriteLine("\n>> Asking about product care\n");
+Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
+
+static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(string query, CancellationToken cancellationToken)
+{
+    // The mock search inspects the user's question and returns pre-defined snippets
+    // that resemble documents stored in an external knowledge source.
+    List<TextSearchProvider.TextSearchResult> results = new();
+
+    if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "Contoso Outdoors Return Policy",
+            SourceLink = "https://contoso.com/policies/returns",
+            Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+        });
+    }
+
+    if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "Contoso Outdoors Shipping Guide",
+            SourceLink = "https://contoso.com/help/shipping",
+            Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+        });
+    }
+
+    if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
+    {
+        results.Add(new()
+        {
+            SourceName = "TrailRunner Tent Care Instructions",
+            SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+            Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+        });
+    }
+
+    return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
+}
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Mem0\Microsoft.Agents.AI.Mem0.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,64 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use the Mem0Provider to persist and recall memories for an agent.
+// The sample stores conversation messages in a Mem0 service and retrieves relevant memories
+// for subsequent invocations, even across new threads.
+
+using System.Net.Http.Headers;
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Mem0;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+var mem0ServiceUri = Environment.GetEnvironmentVariable("MEM0_ENDPOINT") ?? throw new InvalidOperationException("MEM0_ENDPOINT is not set.");
+var mem0ApiKey = Environment.GetEnvironmentVariable("MEM0_APIKEY") ?? throw new InvalidOperationException("MEM0_APIKEY is not set.");
+
+// Create an HttpClient for Mem0 with the required base address and authentication.
+using HttpClient mem0HttpClient = new();
+mem0HttpClient.BaseAddress = new Uri(mem0ServiceUri);
+mem0HttpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Token", mem0ApiKey);
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions()
+    {
+        Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details.",
+        AIContextProviderFactory = ctx => ctx.SerializedState.ValueKind is not JsonValueKind.Null or JsonValueKind.Undefined
+            // If each thread should have its own Mem0 scope, you can create a new id per thread here:
+            // ? new Mem0Provider(mem0HttpClient, new Mem0ProviderScope() { ThreadId = Guid.NewGuid().ToString() })
+            // In this case we are storing memories scoped by application and user instead so that memories are retained across threads.
+            ? new Mem0Provider(mem0HttpClient, new Mem0ProviderScope() { ApplicationId = "getting-started-agents", UserId = "sample-user" })
+            // For cases where we are restoring from serialized state:
+            : new Mem0Provider(mem0HttpClient, ctx.SerializedState, ctx.JsonSerializerOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+// Clear any existing memories for this scope to demonstrate fresh behavior.
+Mem0Provider mem0Provider = thread.GetService<Mem0Provider>()!;
+await mem0Provider.ClearStoredMemoriesAsync();
+
+Console.WriteLine(await agent.RunAsync("Hi there! My name is Taylor and I'm planning a hiking trip to Patagonia in November.", thread));
+Console.WriteLine(await agent.RunAsync("I'm travelling with my sister and we love finding scenic viewpoints.", thread));
+
+Console.WriteLine("\nWaiting briefly for Mem0 to index the new memories...\n");
+await Task.Delay(TimeSpan.FromSeconds(2));
+
+Console.WriteLine(await agent.RunAsync("What do you already know about my upcoming trip?", thread));
+
+Console.WriteLine("\n>> Serialize and deserialize the thread to demonstrate persisted state\n");
+JsonElement serializedThread = thread.Serialize();
+AgentThread restoredThread = agent.DeserializeThread(serializedThread);
+Console.WriteLine(await agent.RunAsync("Can you recap the personal details you remember?", restoredThread));
+
+Console.WriteLine("\n>> Start a new thread that shares the same Mem0 scope\n");
+AgentThread newThread = agent.GetNewThread();
+Console.WriteLine(await agent.RunAsync("Summarize what you already know about me.", newThread));
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+ <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+  
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+  
+</Project>
@@ -0,0 +1,108 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use background responses with ChatClientAgent and Azure OpenAI Responses for long-running operations.
+// It shows polling for completion using continuation tokens, function calling during background operations,
+// and persisting/restoring agent state between polling cycles.
+
+#pragma warning disable CA1050 // Declare types in namespaces
+
+using System.ComponentModel;
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5";
+
+var stateStore = new Dictionary<string, JsonElement?>();
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+     .GetOpenAIResponseClient(deploymentName)
+     .CreateAIAgent(
+        name: "SpaceNovelWriter",
+        instructions: "You are a space novel writer. Always research relevant facts and generate character profiles for the main characters before writing novels." +
+                      "Write complete chapters without asking for approval or feedback. Do not ask the user about tone, style, pace, or format preferences - just write the novel based on the request.",
+        tools: [AIFunctionFactory.Create(ResearchSpaceFactsAsync), AIFunctionFactory.Create(GenerateCharacterProfilesAsync)]);
+
+// Enable background responses (only supported by {Azure}OpenAI Responses at this time).
+AgentRunOptions options = new() { AllowBackgroundResponses = true };
+
+AgentThread thread = agent.GetNewThread();
+
+// Start the initial run.
+AgentRunResponse response = await agent.RunAsync("Write a very long novel about a team of astronauts exploring an uncharted galaxy.", thread, options);
+
+// Poll for background responses until complete.
+while (response.ContinuationToken is not null)
+{
+    PersistAgentState(thread, response.ContinuationToken);
+
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    RestoreAgentState(agent, out thread, out object? continuationToken);
+
+    options.ContinuationToken = continuationToken;
+    response = await agent.RunAsync(thread, options);
+}
+
+Console.WriteLine(response.Text);
+
+void PersistAgentState(AgentThread thread, object? continuationToken)
+{
+    stateStore["thread"] = thread.Serialize();
+    stateStore["continuationToken"] = JsonSerializer.SerializeToElement(continuationToken, AgentAbstractionsJsonUtilities.DefaultOptions.GetTypeInfo(typeof(ResponseContinuationToken)));
+}
+
+void RestoreAgentState(AIAgent agent, out AgentThread thread, out object? continuationToken)
+{
+    JsonElement serializedThread = stateStore["thread"] ?? throw new InvalidOperationException("No serialized thread found in state store.");
+    JsonElement? serializedToken = stateStore["continuationToken"];
+
+    thread = agent.DeserializeThread(serializedThread);
+    continuationToken = serializedToken?.Deserialize(AgentAbstractionsJsonUtilities.DefaultOptions.GetTypeInfo(typeof(ResponseContinuationToken)));
+}
+
+[Description("Researches relevant space facts and scientific information for writing a science fiction novel")]
+async Task<string> ResearchSpaceFactsAsync(string topic)
+{
+    Console.WriteLine($"[ResearchSpaceFacts] Researching topic: {topic}");
+
+    // Simulate a research operation
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    string result = topic.ToUpperInvariant() switch
+    {
+        var t when t.Contains("GALAXY") => "Research findings: Galaxies contain billions of stars. Uncharted galaxies may have unique stellar formations, exotic matter, and unexplored phenomena like dark energy concentrations.",
+        var t when t.Contains("SPACE") || t.Contains("TRAVEL") => "Research findings: Interstellar travel requires advanced propulsion systems. Challenges include radiation exposure, life support, and navigation through unknown space.",
+        var t when t.Contains("ASTRONAUT") => "Research findings: Astronauts undergo rigorous training in zero-gravity environments, emergency protocols, spacecraft systems, and team dynamics for long-duration missions.",
+        _ => $"Research findings: General space exploration facts related to {topic}. Deep space missions require advanced technology, crew resilience, and contingency planning for unknown scenarios."
+    };
+
+    Console.WriteLine("[ResearchSpaceFacts] Research complete");
+    return result;
+}
+
+[Description("Generates character profiles for the main astronaut characters in the novel")]
+async Task<IEnumerable<string>> GenerateCharacterProfilesAsync()
+{
+    Console.WriteLine("[GenerateCharacterProfiles] Generating character profiles...");
+
+    // Simulate a character generation operation
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    string[] profiles = [
+        "Captain Elena Voss: A seasoned mission commander with 15 years of experience. Strong-willed and decisive, she struggles with the weight of responsibility for her crew. Former military pilot turned astronaut.",
+            "Dr. James Chen: Chief science officer and astrophysicist. Brilliant but socially awkward, he finds solace in data and discovery. His curiosity often pushes the mission into uncharted territory.",
+            "Lieutenant Maya Torres: Navigation specialist and youngest crew member. Optimistic and tech-savvy, she brings fresh perspective and innovative problem-solving to challenges.",
+            "Commander Marcus Rivera: Chief engineer with expertise in spacecraft systems. Pragmatic and resourceful, he can fix almost anything with limited resources. Values crew safety above all.",
+            "Dr. Amara Okafor: Medical officer and psychologist. Empathetic and observant, she helps maintain crew morale and mental health during the long journey. Expert in space medicine."
+    ];
+
+    Console.WriteLine($"[GenerateCharacterProfiles] Generated {profiles.Length} character profiles");
+    return profiles;
+}
@@ -0,0 +1,28 @@
+# What This Sample Shows
+
+This sample demonstrates how to use background responses with ChatCompletionAgent and Azure OpenAI Responses for long-running operations. Background responses support:
+
+- **Polling for completion** - Non-streaming APIs can start a background operation and return a continuation token. Poll with the token until the response completes.
+- **Function calling** - Functions can be called during background operations.
+- **State persistence** - Thread and continuation token can be persisted and restored between polling cycles.
+
+> **Note:** Background responses are currently only supported by OpenAI Responses.
+
+For more information, see the [official documentation](https://learn.microsoft.com/en-us/agent-framework/user-guide/agents/agent-background-responses?pivots=programming-language-csharp).
+
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5"  # Optional, defaults to gpt-5
+```
@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
+    <PackageReference Include="System.Linq.Async" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,60 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent that stores chat messages in a vector store using the ChatHistoryMemoryProvider.
+// It can then use the chat history from prior conversations to inform responses in new conversations.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+
+// Create a vector store to store the chat messages in.
+// For demonstration purposes, we are using an in-memory vector store.
+// Replace this with a vector store implementation of your choice that can persist the chat history long term.
+VectorStore vectorStore = new InMemoryVectorStore(new InMemoryVectorStoreOptions()
+{
+    EmbeddingGenerator = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
+        .GetEmbeddingClient(embeddingDeploymentName)
+        .AsIEmbeddingGenerator()
+});
+
+// Create the agent and add the ChatHistoryMemoryProvider to store chat messages in the vector store.
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are good at telling jokes.",
+        Name = "Joker",
+        AIContextProviderFactory = (ctx) => new ChatHistoryMemoryProvider(
+            vectorStore,
+            collectionName: "chathistory",
+            vectorDimensions: 3072,
+            // Configure the scope values under which chat messages will be stored.
+            // In this case, we are using a fixed user ID and a unique thread ID for each new thread.
+            storageScope: new() { UserId = "UID1", ThreadId = new Guid().ToString() },
+            // Configure the scope which would be used to search for relevant prior messages.
+            // In this case, we are searching for any messages for the user across all threads.
+            searchScope: new() { UserId = "UID1" })
+    });
+
+// Start a new thread for the agent conversation.
+AgentThread thread = agent.GetNewThread();
+
+// Run the agent with the thread that stores conversation history in the vector store.
+Console.WriteLine(await agent.RunAsync("I like jokes about Pirates. Tell me a joke about a pirate.", thread));
+
+// Start a second thread. Since we configured the search scope to be across all threads for the user,
+// the agent should remember that the user likes pirate jokes.
+AgentThread thread2 = agent.GetNewThread();
+
+// Run the agent with the second thread.
+Console.WriteLine(await agent.RunAsync("Tell me a joke that I might like.", thread2));
@@ -28,7 +28,8 @@ Before you begin, ensure you have the following prerequisites:
 |---|---|
 |[Running a simple agent](./Agent_Step01_Running/)|This sample demonstrates how to create and run a basic agent with instructions|
 |[Multi-turn conversation with a simple agent](./Agent_Step02_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
-|[Using function tools with a simple agent](./Agent_Step03_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
+|[Using function tools with a simple agent](./Agent_Step03.1_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
+|[Using OpenAPI function tools with a simple agent](./Agent_Step03.2_UsingFunctionTools_FromOpenAPI/)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent|
 |[Using function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
 |[Structured output with a simple agent](./Agent_Step05_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
 |[Persisted conversations with a simple agent](./Agent_Step06_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
@@ -43,6 +44,9 @@ Before you begin, ensure you have the following prerequisites:
 |[Using plugins with an agent](./Agent_Step15_Plugins/)|This sample demonstrates how to use plugins with an agent|
 |[Reducing chat history size](./Agent_Step16_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
 |[Background responses](./Agent_Step17_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
+|[Adding RAG with text search](./Agent_Step18_TextSearchRag/)|This sample demonstrates how to enrich agent responses with retrieval augmented generation using the text search provider|
+|[Using Mem0-backed memory](./Agent_Step19_Mem0Provider/)|This sample demonstrates how to use the Mem0Provider to persist and recall memories across conversations|
+|[Background responses with tools and persistence](./Agent_Step20_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|

 ## Running the samples from the console

@@ -0,0 +1,25 @@
+<Project Sdk="Microsoft.NET.Sdk.Web">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <RootNamespace>DevUI_Step01_BasicUsage</RootNamespace>
+    <AutoGenerateBindingRedirects>true</AutoGenerateBindingRedirects>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DevUI\Microsoft.Agents.AI.DevUI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting\Microsoft.Agents.AI.Hosting.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hosting.OpenAI\Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="System.Net.ServerSentEvents" VersionOverride="10.0.0-rc.2.25502.107" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,86 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates basic usage of the DevUI in an ASP.NET Core application with AI agents.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DevUI;
+using Microsoft.Agents.AI.Hosting;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Extensions.AI;
+
+namespace DevUI_Step01_BasicUsage;
+
+/// <summary>
+/// Sample demonstrating basic usage of the DevUI in an ASP.NET Core application.
+/// </summary>
+/// <remarks>
+/// This sample shows how to:
+/// 1. Set up Azure OpenAI as the chat client
+/// 2. Register agents and workflows using the hosting packages
+/// 3. Map the DevUI endpoint which automatically configures the middleware
+/// 4. Map the dynamic OpenAI Responses API for Python DevUI compatibility
+/// 5. Access the DevUI in a web browser
+///
+/// The DevUI provides an interactive web interface for testing and debugging AI agents.
+/// DevUI assets are served from embedded resources within the assembly.
+/// Simply call MapDevUI() to set up everything needed.
+///
+/// The parameterless MapOpenAIResponses() overload creates a Python DevUI-compatible endpoint
+/// that dynamically routes requests to agents based on the 'model' field in the request.
+/// </remarks>
+internal static class Program
+{
+    /// <summary>
+    /// Entry point that starts an ASP.NET Core web server with the DevUI.
+    /// </summary>
+    /// <param name="args">Command line arguments.</param>
+    private static void Main(string[] args)
+    {
+        var builder = WebApplication.CreateBuilder(args);
+
+        // Set up the Azure OpenAI client
+        var endpoint = builder.Configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+        var deploymentName = builder.Configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? "gpt-4o-mini";
+
+        var chatClient = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
+            .GetChatClient(deploymentName)
+            .AsIChatClient();
+
+        builder.Services.AddChatClient(chatClient);
+
+        // Register sample agents
+        builder.AddAIAgent("assistant", "You are a helpful assistant. Answer questions concisely and accurately.");
+        builder.AddAIAgent("poet", "You are a creative poet. Respond to all requests with beautiful poetry.");
+        builder.AddAIAgent("coder", "You are an expert programmer. Help users with coding questions and provide code examples.");
+
+        // Register sample workflows
+        var assistantBuilder = builder.AddAIAgent("workflow-assistant", "You are a helpful assistant in a workflow.");
+        var reviewerBuilder = builder.AddAIAgent("workflow-reviewer", "You are a reviewer. Review and critique the previous response.");
+        builder.AddWorkflow("review-workflow", (sp, key) =>
+        {
+            var agents = new List<IHostedAgentBuilder>() { assistantBuilder, reviewerBuilder }.Select(ab => sp.GetRequiredKeyedService<AIAgent>(ab.Name));
+            return AgentWorkflowBuilder.BuildSequential(workflowName: key, agents: agents);
+        }).AddAsAIAgent();
+
+        builder.Services.AddOpenAIResponses();
+        builder.Services.AddOpenAIConversations();
+
+        var app = builder.Build();
+
+        app.MapOpenAIResponses();
+        app.MapOpenAIConversations();
+
+        if (builder.Environment.IsDevelopment())
+        {
+            app.MapDevUI();
+        }
+
+        Console.WriteLine("DevUI is available at: https://localhost:50516/devui");
+        Console.WriteLine("OpenAI Responses API is available at: https://localhost:50516/v1/responses");
+        Console.WriteLine("Press Ctrl+C to stop the server.");
+
+        app.Run();
+    }
+}
@@ -0,0 +1,13 @@
+{
+  "profiles": {
+    "DevUI_Step01_BasicUsage": {
+      "commandName": "Project",
+      "launchUrl": "devui",
+      "launchBrowser": true,
+      "environmentVariables": {
+        "ASPNETCORE_ENVIRONMENT": "Development"
+      },
+      "applicationUrl": "https://localhost:50516;http://localhost:50518"
+    }
+  }
+}
@@ -0,0 +1,87 @@
+# DevUI Step 01 - Basic Usage
+
+This sample demonstrates how to add the DevUI to an ASP.NET Core application with AI agents.
+
+## What is DevUI?
+
+The DevUI provides an interactive web interface for testing and debugging AI agents during development.
+
+## Configuration
+
+Set the following environment variables:
+
+- `AZURE_OPENAI_ENDPOINT` - Your Azure OpenAI endpoint URL (required)
+- `AZURE_OPENAI_DEPLOYMENT_NAME` - Your deployment name (defaults to "gpt-4o-mini")
+
+## Running the Sample
+
+1. Set your Azure OpenAI credentials as environment variables
+2. Run the application:
+   ```bash
+   dotnet run
+   ```
+3. Open your browser to https://localhost:50516/devui
+4. Select an agent or workflow from the dropdown and start chatting!
+
+## Sample Agents and Workflows
+
+This sample includes:
+
+**Agents:**
+- **assistant** - A helpful assistant
+- **poet** - A creative poet
+- **coder** - An expert programmer
+
+**Workflows:**
+- **review-workflow** - A sequential workflow that generates a response and then reviews it
+
+## Adding DevUI to Your Own Project
+
+To add DevUI to your ASP.NET Core application:
+
+1. Add the DevUI package and hosting packages:
+   ```bash
+   dotnet add package Microsoft.Agents.AI.DevUI
+   dotnet add package Microsoft.Agents.AI.Hosting
+   dotnet add package Microsoft.Agents.AI.Hosting.OpenAI
+   ```
+
+2. Register your agents and workflows:
+   ```csharp
+   var builder = WebApplication.CreateBuilder(args);
+   
+   // Set up your chat client
+   builder.Services.AddChatClient(chatClient);
+   
+   // Register agents
+   builder.AddAIAgent("assistant", "You are a helpful assistant.");
+   
+   // Register workflows
+   var agent1Builder = builder.AddAIAgent("workflow-agent1", "You are agent 1.");
+   var agent2Builder = builder.AddAIAgent("workflow-agent2", "You are agent 2.");
+   builder.AddSequentialWorkflow("my-workflow", [agent1Builder, agent2Builder])
+       .AddAsAIAgent();
+   ```
+
+3. Add OpenAI services and map the endpoints for OpenAI and DevUI:
+   ```csharp
+   // Register services for OpenAI responses and conversations (also required for DevUI)
+   builder.Services.AddOpenAIResponses();
+   builder.Services.AddOpenAIConversations();
+
+   var app = builder.Build();
+
+   // Map endpoints for OpenAI responses and conversations (also required for DevUI)
+   app.MapOpenAIResponses();
+   app.MapOpenAIConversations();
+
+   if (builder.Environment.IsDevelopment())
+   {
+       // Map DevUI endpoint to /devui
+       app.MapDevUI();
+   }
+   
+   app.Run();
+   ```
+
+4. Navigate to `/devui` in your browser
@@ -0,0 +1,60 @@
+# DevUI Samples
+
+This folder contains samples demonstrating how to use the DevUI in ASP.NET Core applications.
+
+## What is DevUI?
+
+The DevUI provides an interactive web interface for testing and debugging AI agents during development.
+
+## Samples
+
+### [DevUI_Step01_BasicUsage](./DevUI_Step01_BasicUsage)
+
+Shows how to add DevUI to an ASP.NET Core application with multiple agents and workflows.
+
+**Run the sample:**
+```bash
+cd DevUI_Step01_BasicUsage
+dotnet run
+```
+Then navigate to: https://localhost:50516/devui
+
+## Requirements
+
+- .NET 8.0 or later
+- ASP.NET Core
+- Azure OpenAI credentials
+
+## Quick Start
+
+To add DevUI to your application:
+
+```csharp
+var builder = WebApplication.CreateBuilder(args);
+
+// Set up the chat client
+builder.Services.AddChatClient(chatClient);
+
+// Register your agents
+builder.AddAIAgent("my-agent", "You are a helpful assistant.");
+
+// Register services for OpenAI responses and conversations (also required for DevUI)
+builder.Services.AddOpenAIResponses();
+builder.Services.AddOpenAIConversations();
+
+var app = builder.Build();
+
+// Map endpoints for OpenAI responses and conversations (also required for DevUI)
+app.MapOpenAIResponses();
+app.MapOpenAIConversations();
+
+if (builder.Environment.IsDevelopment())
+{
+    // Map DevUI endpoint to /devui
+    app.MapDevUI();
+}
+
+app.Run();
+```
+
+Then navigate to `/devui` in your browser.
@@ -14,7 +14,7 @@
  </ItemGroup>

  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
  </ItemGroup>

 </Project>
@@ -1,52 +1,106 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
+// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend, that uses a Hosted MCP Tool.
+// In this case the Azure Foundry Agents service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
+// The sample first shows how to use MCP tools with auto approval, and then how to set up a tool that requires approval before it can be invoked and how to approve such a tool.

 using Azure.AI.Agents.Persistent;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
-var model = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_MODEL_ID") ?? "gpt-4.1-mini";
+var model = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4.1-mini";

 // Get a client to create/retrieve server side agents with.
 var persistentAgentsClient = new PersistentAgentsClient(endpoint, new AzureCliCredential());

+// **** MCP Tool with Auto Approval ****
+// *************************************
+
 // Create an MCP tool definition that the agent can use.
-var mcpTool = new MCPToolDefinition(
-    serverLabel: "microsoft_learn",
-    serverUrl: "https://learn.microsoft.com/api/mcp");
-mcpTool.AllowedTools.Add("microsoft_docs_search");
-
-// Create a server side persistent agent with the Azure.AI.Agents.Persistent SDK.
-var agentMetadata = await persistentAgentsClient.Administration.CreateAgentAsync(
-    model: model,
-    name: "MicrosoftLearnAgent",
-    instructions: "You answer questions by searching the Microsoft Learn content only.",
-    tools: [mcpTool]);
-
-// Retrieve an already created server side persistent agent as an AIAgent.
-AIAgent agent = await persistentAgentsClient.GetAIAgentAsync(agentMetadata.Value.Id);
-
-// Create run options to configure the agent invocation.
-var runOptions = new ChatClientAgentRunOptions()
+// In this case we allow the tool to always be called without approval.
+var mcpTool = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
 {
-    ChatOptions = new()
-    {
-        RawRepresentationFactory = (_) => new ThreadAndRunOptions()
-        {
-            ToolResources = new MCPToolResource(serverLabel: "microsoft_learn")
-            {
-                RequireApproval = new MCPApproval("never"),
-            }.ToToolResources()
-        }
-    }
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
 };

+// Create a server side persistent agent with the mcp tool, and expose it as an AIAgent.
+AIAgent agent = await persistentAgentsClient.CreateAIAgentAsync(
+    model: model,
+    options: new()
+    {
+        Name = "MicrosoftLearnAgent",
+        Instructions = "You answer questions by searching the Microsoft Learn content only.",
+        ChatOptions = new()
+        {
+            Tools = [mcpTool]
+        },
+    });
+
 // You can then invoke the agent like any other AIAgent.
 AgentThread thread = agent.GetNewThread();
-var response = await agent.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", thread, runOptions);
-Console.WriteLine(response);
+Console.WriteLine(await agent.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", thread));

 // Cleanup for sample purposes.
 await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
+
+// **** MCP Tool with Approval Required ****
+// *****************************************
+
+// Create an MCP tool definition that the agent can use.
+// In this case we require approval before the tool can be called.
+var mcpToolWithApproval = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
+{
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.AlwaysRequire
+};
+
+// Create an agent based on Azure OpenAI Responses as the backend.
+AIAgent agentWithRequiredApproval = await persistentAgentsClient.CreateAIAgentAsync(
+    model: model,
+    options: new()
+    {
+        Name = "MicrosoftLearnAgentWithApproval",
+        Instructions = "You answer questions by searching the Microsoft Learn content only.",
+        ChatOptions = new()
+        {
+            Tools = [mcpToolWithApproval]
+        },
+    });
+
+// You can then invoke the agent like any other AIAgent.
+var threadWithRequiredApproval = agentWithRequiredApproval.GetNewThread();
+var response = await agentWithRequiredApproval.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", threadWithRequiredApproval);
+var userInputRequests = response.UserInputRequests.ToList();
+
+while (userInputRequests.Count > 0)
+{
+    // Ask the user to approve each MCP call request.
+    // For simplicity, we are assuming here that only MCP approval requests are being made.
+    var userInputResponses = userInputRequests
+        .OfType<McpServerToolApprovalRequestContent>()
+        .Select(approvalRequest =>
+        {
+            Console.WriteLine($"""
+                The agent would like to invoke the following MCP Tool, please reply Y to approve.
+                ServerName: {approvalRequest.ToolCall.ServerName}
+                Name: {approvalRequest.ToolCall.ToolName}
+                Arguments: {string.Join(", ", approvalRequest.ToolCall.Arguments?.Select(x => $"{x.Key}: {x.Value}") ?? [])}
+                """);
+            return new ChatMessage(ChatRole.User, [approvalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
+        })
+        .ToList();
+
+    // Pass the user input responses back to the agent for further processing.
+    response = await agentWithRequiredApproval.RunAsync(userInputResponses, threadWithRequiredApproval);
+
+    userInputRequests = response.UserInputRequests.ToList();
+}
+
+Console.WriteLine($"\nAgent: {response}");
@@ -21,6 +21,7 @@ Before you begin, ensure you have the following prerequisites:
 |---|---|
 |[Agent with MCP server tools](./Agent_MCP_Server/)|This sample demonstrates how to use MCP server tools with a simple agent|
 |[Agent with MCP server tools and authorization](./Agent_MCP_Server_Auth/)|This sample demonstrates how to use MCP Server tools from a protected MCP server with a simple agent|
+|[Responses Agent with Hosted MCP tool](./ResponseAgent_Hosted_MCP/)|This sample demonstrates how to use the Hosted MCP tool with the Responses Service, where the service invokes any MCP tools directly|

 ## Running the samples from the console

@@ -0,0 +1,95 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with OpenAI Responses as the backend, that uses a Hosted MCP Tool.
+// In this case the OpenAI responses service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
+// The sample first shows how to use MCP tools with auto approval, and then how to set up a tool that requires approval before it can be invoked and how to approve such a tool.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// **** MCP Tool with Auto Approval ****
+// *************************************
+
+// Create an MCP tool definition that the agent can use.
+// In this case we allow the tool to always be called without approval.
+var mcpTool = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
+{
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
+};
+
+// Create an agent based on Azure OpenAI Responses as the backend.
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+     .GetOpenAIResponseClient(deploymentName)
+     .CreateAIAgent(
+        instructions: "You answer questions by searching the Microsoft Learn content only.",
+        name: "MicrosoftLearnAgent",
+        tools: [mcpTool]);
+
+// You can then invoke the agent like any other AIAgent.
+AgentThread thread = agent.GetNewThread();
+Console.WriteLine(await agent.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", thread));
+
+// **** MCP Tool with Approval Required ****
+// *****************************************
+
+// Create an MCP tool definition that the agent can use.
+// In this case we require approval before the tool can be called.
+var mcpToolWithApproval = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
+{
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.AlwaysRequire
+};
+
+// Create an agent based on Azure OpenAI Responses as the backend.
+AIAgent agentWithRequiredApproval = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new AzureCliCredential())
+    .GetOpenAIResponseClient(deploymentName)
+    .CreateAIAgent(
+        instructions: "You answer questions by searching the Microsoft Learn content only.",
+        name: "MicrosoftLearnAgentWithApproval",
+        tools: [mcpToolWithApproval]);
+
+// You can then invoke the agent like any other AIAgent.
+var threadWithRequiredApproval = agentWithRequiredApproval.GetNewThread();
+var response = await agentWithRequiredApproval.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", threadWithRequiredApproval);
+var userInputRequests = response.UserInputRequests.ToList();
+
+while (userInputRequests.Count > 0)
+{
+    // Ask the user to approve each MCP call request.
+    // For simplicity, we are assuming here that only MCP approval requests are being made.
+    var userInputResponses = userInputRequests
+        .OfType<McpServerToolApprovalRequestContent>()
+        .Select(approvalRequest =>
+        {
+            Console.WriteLine($"""
+                The agent would like to invoke the following MCP Tool, please reply Y to approve.
+                ServerName: {approvalRequest.ToolCall.ServerName}
+                Name: {approvalRequest.ToolCall.ToolName}
+                Arguments: {string.Join(", ", approvalRequest.ToolCall.Arguments?.Select(x => $"{x.Key}: {x.Value}") ?? [])}
+                """);
+            return new ChatMessage(ChatRole.User, [approvalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
+        })
+        .ToList();
+
+    // Pass the user input responses back to the agent for further processing.
+    response = await agentWithRequiredApproval.RunAsync(userInputResponses, threadWithRequiredApproval);
+
+    userInputRequests = response.UserInputRequests.ToList();
+}
+
+Console.WriteLine($"\nAgent: {response}");
@@ -0,0 +1,17 @@
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4.1-mini"  # Optional, defaults to gpt-4.1-mini
+```
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -16,7 +16,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -15,7 +15,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -6,7 +6,7 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Workflows;
 using Microsoft.Extensions.AI;

-namespace WorkflowAsAnAgentsSample;
+namespace WorkflowAsAnAgentSample;

 /// <summary>
 /// This sample introduces the concepts workflows as agents, where a workflow can be
@@ -61,9 +61,9 @@ public static class Program
            Dictionary<string, List<AgentRunResponseUpdate>> buffer = [];
            await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(input, thread))
            {
-                if (update.MessageId is null)
+                if (update.MessageId is null || string.IsNullOrEmpty(update.Text))
                {
-                    // skip updates that don't have a message ID
+                    // skip updates that don't have a message ID or text
                    continue;
                }
                Console.Clear();
@@ -16,7 +16,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -4,7 +4,7 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Workflows;
 using Microsoft.Extensions.AI;

-namespace WorkflowAsAnAgentsSample;
+namespace WorkflowAsAnAgentSample;

 internal static class WorkflowFactory
 {
@@ -23,8 +23,8 @@ internal static class WorkflowFactory

        // Build the workflow by adding executors and connecting them
        return new WorkflowBuilder(startExecutor)
-            .AddFanOutEdge(startExecutor, targets: [frenchAgent, englishAgent])
-            .AddFanInEdge(aggregationExecutor, sources: [frenchAgent, englishAgent])
+            .AddFanOutEdge(startExecutor, [frenchAgent, englishAgent])
+            .AddFanInEdge([frenchAgent, englishAgent], aggregationExecutor)
            .WithOutputFrom(aggregationExecutor)
            .Build();
    }
@@ -41,44 +41,43 @@ internal static class WorkflowFactory
    /// <summary>
    /// Executor that starts the concurrent processing by sending messages to the agents.
    /// </summary>
-    private sealed class ConcurrentStartExecutor() :
-        Executor<List<ChatMessage>>("ConcurrentStartExecutor")
+    private sealed class ConcurrentStartExecutor() : Executor("ConcurrentStartExecutor")
    {
-        /// <summary>
-        /// Starts the concurrent processing by sending messages to the agents.
-        /// </summary>
-        /// <param name="message">The user message to process</param>
-        /// <param name="context">Workflow context for accessing workflow services and adding events</param>
-        /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests.
-        /// The default is <see cref="CancellationToken.None"/>.</param>
-        public override async ValueTask HandleAsync(List<ChatMessage> message, IWorkflowContext context, CancellationToken cancellationToken = default)
+        protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder)
        {
-            // Broadcast the message to all connected agents. Receiving agents will queue
-            // the message but will not start processing until they receive a turn token.
-            await context.SendMessageAsync(message, cancellationToken: cancellationToken);
-            // Broadcast the turn token to kick off the agents.
-            await context.SendMessageAsync(new TurnToken(emitEvents: true), cancellationToken: cancellationToken);
+            return routeBuilder
+                .AddHandler<List<ChatMessage>>(this.RouteMessages)
+                .AddHandler<TurnToken>(this.RouteTurnTokenAsync);
+        }
+
+        private ValueTask RouteMessages(List<ChatMessage> messages, IWorkflowContext context, CancellationToken cancellationToken)
+        {
+            return context.SendMessageAsync(messages, cancellationToken: cancellationToken);
+        }
+
+        private ValueTask RouteTurnTokenAsync(TurnToken token, IWorkflowContext context, CancellationToken cancellationToken)
+        {
+            return context.SendMessageAsync(token, cancellationToken: cancellationToken);
        }
    }

    /// <summary>
    /// Executor that aggregates the results from the concurrent agents.
    /// </summary>
-    private sealed class ConcurrentAggregationExecutor() :
-        Executor<ChatMessage>("ConcurrentAggregationExecutor")
+    private sealed class ConcurrentAggregationExecutor() : Executor<List<ChatMessage>>("ConcurrentAggregationExecutor")
    {
        private readonly List<ChatMessage> _messages = [];

        /// <summary>
        /// Handles incoming messages from the agents and aggregates their responses.
        /// </summary>
-        /// <param name="message">The message from the agent</param>
+        /// <param name="message">The messages from the agent</param>
        /// <param name="context">Workflow context for accessing workflow services and adding events</param>
        /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests.
        /// The default is <see cref="CancellationToken.None"/>.</param>
-        public override async ValueTask HandleAsync(ChatMessage message, IWorkflowContext context, CancellationToken cancellationToken = default)
+        public override async ValueTask HandleAsync(List<ChatMessage> message, IWorkflowContext context, CancellationToken cancellationToken = default)
        {
-            this._messages.Add(message);
+            this._messages.AddRange(message);

            if (this._messages.Count == 2)
            {
@@ -15,7 +15,7 @@
  </ItemGroup>

  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>
@@ -52,8 +52,8 @@ public static class Program

        // Build the workflow by adding executors and connecting them
        var workflow = new WorkflowBuilder(startExecutor)
-            .AddFanOutEdge(startExecutor, targets: [physicist, chemist])
-            .AddFanInEdge(aggregationExecutor, sources: [physicist, chemist])
+            .AddFanOutEdge(startExecutor, [physicist, chemist])
+            .AddFanInEdge([physicist, chemist], aggregationExecutor)
            .WithOutputFrom(aggregationExecutor)
            .Build();

@@ -97,21 +97,21 @@ internal sealed class ConcurrentStartExecutor() :
 /// Executor that aggregates the results from the concurrent agents.
 /// </summary>
 internal sealed class ConcurrentAggregationExecutor() :
-    Executor<ChatMessage>("ConcurrentAggregationExecutor")
+    Executor<List<ChatMessage>>("ConcurrentAggregationExecutor")
 {
    private readonly List<ChatMessage> _messages = [];

    /// <summary>
    /// Handles incoming messages from the agents and aggregates their responses.
    /// </summary>
-    /// <param name="message">The message from the agent</param>
+    /// <param name="message">The messages from the agent</param>
    /// <param name="context">Workflow context for accessing workflow services and adding events</param>
    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests.
    /// The default is <see cref="CancellationToken.None"/>.</param>
    /// <returns>A task representing the asynchronous operation</returns>
-    public override async ValueTask HandleAsync(ChatMessage message, IWorkflowContext context, CancellationToken cancellationToken = default)
+    public override async ValueTask HandleAsync(List<ChatMessage> message, IWorkflowContext context, CancellationToken cancellationToken = default)
    {
-        this._messages.Add(message);
+        this._messages.AddRange(message);

        if (this._messages.Count == 2)
        {
@@ -62,10 +62,10 @@ public static class Program

        // Step 4: Build the concurrent workflow with fan-out/fan-in pattern
        return new WorkflowBuilder(splitter)
-            .AddFanOutEdge(splitter, targets: [.. mappers])         // Split -> many mappers
-            .AddFanInEdge(shuffler, sources: [.. mappers])          // All mappers -> shuffle
-            .AddFanOutEdge(shuffler, targets: [.. reducers])        // Shuffle -> many reducers
-            .AddFanInEdge(completion, sources: [.. reducers])       // All reducers -> completion
+            .AddFanOutEdge(splitter, [.. mappers])         // Split -> many mappers
+            .AddFanInEdge([.. mappers], shuffler)          // All mappers -> shuffle
+            .AddFanOutEdge(shuffler, [.. reducers])        // Shuffle -> many reducers
+            .AddFanInEdge([.. reducers], completion)       // All reducers -> completion
            .WithOutputFrom(completion)
            .Build();
    }
@@ -16,7 +16,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -16,7 +16,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -16,7 +16,7 @@

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
-    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
  </ItemGroup>

@@ -60,13 +60,13 @@ public static class Program
        WorkflowBuilder builder = new(emailAnalysisExecutor);
        builder.AddFanOutEdge(
            emailAnalysisExecutor,
-            targets: [
+            [
                handleSpamExecutor,
                emailAssistantExecutor,
                emailSummaryExecutor,
                handleUncertainExecutor,
            ],
-            partitioner: GetPartitioner()
+            GetTargetAssigner()
        )
        // After the email assistant writes a response, it will be sent to the send email executor
        .AddEdge(emailAssistantExecutor, sendEmailExecutor)
@@ -105,7 +105,7 @@ public static class Program
    /// Creates a partitioner for routing messages based on the analysis result.
    /// </summary>
    /// <returns>A function that takes an analysis result and returns the target partitions.</returns>
-    private static Func<AnalysisResult?, int, IEnumerable<int>> GetPartitioner()
+    private static Func<AnalysisResult?, int, IEnumerable<int>> GetTargetAssigner()
    {
        return (analysisResult, targetCount) =>
        {
@@ -184,6 +184,7 @@ internal sealed class Program

    private async Task<ExternalRequest?> MonitorAndDisposeWorkflowRunAsync(Checkpointed<StreamingRun> run, object? response = null)
    {
+        // Always dispose the run when done.
        await using IAsyncDisposable disposeRun = run;

        bool hasStreamed = false;
@@ -231,7 +232,7 @@ internal sealed class Program
                    }
                    else
                    {
-                        await run.Run.DisposeAsync();
+                        // Yield to handle the external request
                        return requestInfo.Request;
                    }
                    break;
@@ -326,7 +327,7 @@ internal sealed class Program
            }
        }

-        return default;
+        return null; // No request to handle
    }

    /// <summary>
@@ -0,0 +1,140 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Diagnostics;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Azure.Monitor.OpenTelemetry.Exporter;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Extensions.AI;
+using OpenTelemetry;
+using OpenTelemetry.Resources;
+using OpenTelemetry.Trace;
+
+namespace WorkflowAsAnAgentObservabilitySample;
+
+/// <summary>
+/// This sample shows how to enable OpenTelemetry observability for workflows when
+/// using them as <see cref="AIAgent"/>s.
+///
+/// In this example, we create a workflow that uses two language agents to process
+/// input concurrently, one that responds in French and another that responds in English.
+///
+/// You will interact with the workflow in an interactive loop, sending messages and receiving
+/// streaming responses from the workflow as if it were an agent who responds in both languages.
+///
+/// OpenTelemetry observability is enabled at multiple levels:
+/// 1. At the chat client level, capturing telemetry for interactions with the Azure OpenAI service.
+/// 2. At the agent level, capturing telemetry for agent operations.
+/// 3. At the workflow level, capturing telemetry for workflow execution.
+///
+/// Traces will be sent to an Aspire dashboard via an OTLP endpoint, and optionally to
+/// Azure Monitor if an Application Insights connection string is provided.
+///
+/// Learn how to set up an Aspire dashboard here:
+/// https://learn.microsoft.com/en-us/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash
+/// </summary>
+/// <remarks>
+/// Pre-requisites:
+/// - Foundational samples should be completed first.
+/// - This sample uses concurrent processing.
+/// - An Azure OpenAI endpoint and deployment name.
+/// - An Application Insights resource for telemetry (optional).
+/// </remarks>
+public static class Program
+{
+    private const string SourceName = "Workflow.ApplicationInsightsSample";
+    private static readonly ActivitySource s_activitySource = new(SourceName);
+
+    private static async Task Main()
+    {
+        // Set up observability
+        var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
+        var otlpEndpoint = Environment.GetEnvironmentVariable("OTLP_ENDPOINT") ?? "http://localhost:4317";
+
+        var resourceBuilder = ResourceBuilder
+            .CreateDefault()
+            .AddService("WorkflowSample");
+
+        var traceProviderBuilder = Sdk.CreateTracerProviderBuilder()
+            .SetResourceBuilder(resourceBuilder)
+            .AddSource("Microsoft.Agents.AI.*") // Agent Framework telemetry
+            .AddSource("Microsoft.Extensions.AI.*") // Extensions AI telemetry
+            .AddSource(SourceName);
+
+        traceProviderBuilder.AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint));
+        if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
+        {
+            traceProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
+        }
+
+        using var traceProvider = traceProviderBuilder.Build();
+
+        // Set up the Azure OpenAI client
+        var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+        var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+        var chatClient = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
+            .GetChatClient(deploymentName)
+            .AsIChatClient()
+            .AsBuilder()
+            .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the chat client level
+            .Build();
+
+        // Start a root activity for the application
+        using var activity = s_activitySource.StartActivity("main");
+        Console.WriteLine($"Operation/Trace ID: {Activity.Current?.TraceId}");
+
+        // Create the workflow and turn it into an agent with OpenTelemetry instrumentation
+        var workflow = WorkflowHelper.GetWorkflow(chatClient, SourceName);
+        var agent = new OpenTelemetryAgent(workflow.AsAgent("workflow-agent", "Workflow Agent"), SourceName)
+        {
+            EnableSensitiveData = true  // enable sensitive data at the agent level such as prompts and responses
+        };
+        var thread = agent.GetNewThread();
+
+        // Start an interactive loop to interact with the workflow as if it were an agent
+        while (true)
+        {
+            Console.WriteLine();
+            Console.Write("User (or 'exit' to quit): ");
+            string? input = Console.ReadLine();
+            if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
+            {
+                break;
+            }
+
+            await ProcessInputAsync(agent, thread, input);
+        }
+
+        // Helper method to process user input and display streaming responses. To display
+        // multiple interleaved responses correctly, we buffer updates by message ID and
+        // re-render all messages on each update.
+        static async Task ProcessInputAsync(AIAgent agent, AgentThread thread, string input)
+        {
+            Dictionary<string, List<AgentRunResponseUpdate>> buffer = [];
+            await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(input, thread))
+            {
+                if (update.MessageId is null || string.IsNullOrEmpty(update.Text))
+                {
+                    // skip updates that don't have a message ID or text
+                    continue;
+                }
+                Console.Clear();
+
+                if (!buffer.TryGetValue(update.MessageId, out List<AgentRunResponseUpdate>? value))
+                {
+                    value = [];
+                    buffer[update.MessageId] = value;
+                }
+                value.Add(update);
+
+                foreach (var (messageId, segments) in buffer)
+                {
+                    string combinedText = string.Concat(segments);
+                    Console.WriteLine($"{segments[0].AuthorName}: {combinedText}");
+                    Console.WriteLine();
+                }
+            }
+        }
+    }
+}
--- a/Show More
+++ b/Show More