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Chris
2025-11-05 12:26:13 -08:00
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commit 55a4aa2b53
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dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/DevUI_Step01_BasicUsage.csproj
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<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>
dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Program.cs
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// 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.DevUI;
using Microsoft.Agents.AI.Hosting;
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.AddSequentialWorkflow(
"review-workflow",
[assistantBuilder, reviewerBuilder])
.AddAsAIAgent();
if (builder.Environment.IsDevelopment())
{
builder.AddDevUI();
}
var app = builder.Build();
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();
}
}
dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/README.md
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# 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 DevUI services and map the endpoint:
```csharp
builder.AddDevUI();
var app = builder.Build();
app.MapDevUI();
// Add required endpoints
app.MapEntities();
app.MapOpenAIResponses();
app.MapOpenAIConversations();
app.Run();
```
4. Navigate to `/devui` in your browser
dotnet/samples/GettingStarted/DevUI/README.md
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# 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.");
// Add DevUI services
builder.AddDevUI();
var app = builder.Build();
// Map the DevUI endpoint
app.MapDevUI();
// Add required endpoints
app.MapEntities();
app.MapOpenAIResponses();
app.MapOpenAIConversations();
app.Run();
```
Then navigate to `/devui` in your browser.
dotnet/samples/GettingStarted/Workflows/Agents/WorkflowAsAnAgent/WorkflowFactory.cs
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// 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();
}
dotnet/samples/GettingStarted/Workflows/Concurrent/Concurrent/Program.cs
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// 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();
dotnet/samples/GettingStarted/Workflows/Concurrent/MapReduce/Program.cs
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// 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();
}
dotnet/samples/GettingStarted/Workflows/ConditionalEdges/03_MultiSelection/Program.cs
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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) =>
{
dotnet/samples/GettingStarted/Workflows/Observability/WorkflowAsAnAgent/WorkflowHelper.cs
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@@ -24,8 +24,8 @@ internal static class WorkflowHelper
// 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();
}
dotnet/samples/GettingStarted/Workflows/SharedStates/Program.cs
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// Build the workflow by connecting executors sequentially
var workflow = new WorkflowBuilder(fileRead)
.AddFanOutEdge(fileRead, targets: [wordCount, paragraphCount])
.AddFanInEdge(aggregate, sources: [wordCount, paragraphCount])
.AddFanOutEdge(fileRead, [wordCount, paragraphCount])
.AddFanInEdge([wordCount, paragraphCount], aggregate)
.WithOutputFrom(aggregate)
.Build();