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This commit is contained in:
wehub-resource-sync
2026-07-13 13:39:25 +08:00
commit db620d33df
5151 changed files with 925932 additions and 0 deletions
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// Copyright (c) Microsoft. All rights reserved.
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
/// <summary>
/// The registry of agents used in the workflow.
/// </summary>
/// <param name="chatClient">The <see cref="IChatClient"/> to use as the agent backend.</param>
internal sealed class AgentRegistry(IChatClient chatClient)
{
internal const string IntakeAgentName = "Assistant";
public AIAgent IntakeAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You receive a user request and are responsible for routing to the correct initial expert agent.
""",
IntakeAgentName
);
internal const string LiquidityAnalysisAgentName = "Liquidity Analysis";
public AIAgent LiquidityAnalysisAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Liquidity Analysis.
""",
LiquidityAnalysisAgentName
);
internal const string TaxAnalysisAgentName = "Tax Analysis";
public AIAgent TaxAnalysisAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Tax Analysis.
""",
TaxAnalysisAgentName
);
internal const string ForeignExchangeAgentName = "Foreign Exchange Analysis";
public AIAgent ForeignExchangeAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Foreign Exchange Analysis.
""",
ForeignExchangeAgentName
);
internal const string EquityAgentName = "Equity Analysis";
public AIAgent EquityAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Equity Analysis.
""",
EquityAgentName
);
public IEnumerable<AIAgent> Experts => [this.LiquidityAnalysisAgent, this.TaxAnalysisAgent, this.ForeignExchangeAgent, this.EquityAgent];
public HashSet<AIAgent> All
{
get
{
if (field == null)
{
field = [this.IntakeAgent, .. this.Experts];
}
return field;
}
}
}
@@ -0,0 +1,29 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>MAAIW001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" />
<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\Microsoft.Agents.AI.csproj" />
<!-- Include Workflows source generator when using [MessageHandler] attribute -->
<ProjectReference Include="$(RepoRoot)/dotnet/src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj"
OutputItemType="Analyzer"
ReferenceOutputAssembly="false"
GlobalPropertiesToRemove="TargetFramework" />
</ItemGroup>
</Project>
@@ -0,0 +1,125 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
IChatClient chatClient = projectClient.ProjectOpenAIClient
.GetChatClient(deploymentName)
.AsIChatClient();
Workflow workflow = CreateWorkflow(chatClient);
await RunWorkflowAsync(workflow).ConfigureAwait(false);
static Workflow CreateWorkflow(IChatClient chatClient)
{
AgentRegistry agents = new(chatClient);
HandoffWorkflowBuilder handoffBuilder = AgentWorkflowBuilder.CreateHandoffBuilderWith(agents.IntakeAgent);
// Add a handoff to each of the experts from every agent in the registry (experts + Intake)
foreach (AIAgent expert in agents.Experts)
{
handoffBuilder.WithHandoffs(agents.All.Except([expert]), expert);
}
// Let agents request more user information and return to the asking agent (rather than going back to the intake agent)
handoffBuilder.EnableReturnToPrevious();
return handoffBuilder.Build();
}
static async Task RunWorkflowAsync(Workflow workflow)
{
using CancellationTokenSource cts = CreateConsoleCancelKeySource();
await using StreamingRun run = await InProcessExecution.OpenStreamingAsync(workflow, cancellationToken: cts.Token)
.ConfigureAwait(false);
bool hadError = false;
do
{
Console.Write("> ");
string userInput = Console.ReadLine() ?? string.Empty;
if (userInput.Equals("exit", StringComparison.OrdinalIgnoreCase))
{
break;
}
await run.TrySendMessageAsync(userInput);
string? speakingAgent = null;
await foreach (WorkflowEvent evt in run.WatchStreamAsync(cts.Token))
{
switch (evt)
{
case AgentResponseUpdateEvent update:
{
if (speakingAgent == null || speakingAgent != update.Update.AuthorName)
{
speakingAgent = update.Update.AuthorName;
Console.Write($"\n{speakingAgent}: ");
}
Console.Write(update.Update.Text);
break;
}
case WorkflowErrorEvent workflowError:
{
Console.ForegroundColor = ConsoleColor.Red;
if (workflowError.Exception != null)
{
Console.WriteLine($"\nWorkflow error: {workflowError.Exception}");
}
else
{
Console.WriteLine("\nUnknown workflow error occurred.");
}
Console.ResetColor();
hadError = true;
break;
}
case WorkflowWarningEvent workflowWarning when workflowWarning.Data is string message:
{
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine(message);
Console.ResetColor();
break;
}
}
}
} while (!hadError);
}
static CancellationTokenSource CreateConsoleCancelKeySource()
{
CancellationTokenSource cts = new();
// Normally, support a way to detach events, but in this case this is a termination signal, so cleanup will happen
// as part of application shutdown.
Console.CancelKeyPress += (s, args) =>
{
cts.Cancel();
// We handle cleanup + termination ourselves
args.Cancel = true;
};
return cts;
}
@@ -0,0 +1,23 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);MAAIW001;OPENAI001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,193 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample ports the Python Magentic orchestration sample to .NET.
// A Magentic workflow coordinates a researcher and a coder, streams orchestration
// events as the plan evolves, and prints the final conversation transcript.
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Agents.AI.Workflows.Specialized.Magentic;
using Microsoft.Extensions.AI;
namespace WorkflowMagenticOrchestrationSample;
/// <summary>
/// Demonstrates Magentic orchestration with a researcher, a coder, and an LLM manager.
/// </summary>
/// <remarks>
/// Pre-requisites:
/// - An Azure AI Foundry project endpoint and model deployment must be configured.
/// - Run <c>az login</c> before executing the sample.
/// </remarks>
public static class Program
{
private const string TaskPrompt =
"I am preparing a report on the energy efficiency of different machine learning model architectures. " +
"Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 " +
"on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). " +
"Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 " +
"VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model " +
"per task type (image classification, text classification, and text generation).";
private static async Task Main()
{
string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
AIAgent researcherAgent = projectClient.AsAIAgent(
deploymentName,
name: "ResearcherAgent",
description: "Specialist in research and information gathering.",
instructions: "You are a researcher. Find relevant information without doing additional computation or quantitative analysis.");
AIAgent coderAgent = projectClient.AsAIAgent(
deploymentName,
name: "CoderAgent",
description: "A helpful assistant that writes and executes code to analyze data.",
instructions: "You solve quantitative questions by writing and running code. Show the analysis and the computation process clearly.",
tools: [new HostedCodeInterpreterTool()]);
AIAgent managerAgent = projectClient.AsAIAgent(
deploymentName,
name: "MagenticManager",
description: "Orchestrator that coordinates the research and coding workflow.",
instructions: "You coordinate the team to complete complex tasks efficiently.");
Workflow workflow = new MagenticWorkflowBuilder(managerAgent)
.AddParticipants([researcherAgent, coderAgent])
.WithName("Magentic Orchestration Workflow")
.WithDescription("Coordinates a researcher and coder to solve a complex analytical task.")
.RequirePlanSignoff(false)
.WithMaxRounds(10)
.WithMaxStalls(3)
.WithMaxResets(2)
.Build();
Console.WriteLine("Building Magentic workflow...");
Console.WriteLine();
Console.WriteLine($"Task: {TaskPrompt}");
Console.WriteLine();
Console.WriteLine("Starting workflow execution...");
await using StreamingRun run = await InProcessExecution.RunStreamingAsync(
workflow,
new List<ChatMessage> { new(ChatRole.User, TaskPrompt) });
await run.TrySendMessageAsync(new TurnToken(emitEvents: true));
string? lastResponseId = null;
WorkflowOutputEvent? finalOutput = null;
await foreach (WorkflowEvent workflowEvent in run.WatchStreamAsync())
{
switch (workflowEvent)
{
case AgentResponseUpdateEvent updateEvent:
WriteStreamingUpdate(updateEvent, ref lastResponseId);
break;
case MagenticPlanCreatedEvent planCreated:
WriteMagenticMessage("Initial Plan", planCreated.FullTaskLedger.Text);
PauseIfInteractive();
break;
case MagenticReplannedEvent replanned:
WriteMagenticMessage("Replanned", replanned.FullTaskLedger.Text);
PauseIfInteractive();
break;
case MagenticProgressLedgerUpdatedEvent progressUpdated:
WriteMagenticMessage("Progress Ledger", FormatProgressLedger(progressUpdated.ProgressLedger));
PauseIfInteractive();
break;
case WorkflowOutputEvent outputEvent when outputEvent.Is<List<ChatMessage>>():
finalOutput = outputEvent;
break;
case WorkflowErrorEvent workflowError:
Console.ForegroundColor = ConsoleColor.Red;
Console.Error.WriteLine(workflowError.Exception?.ToString() ?? "Unknown workflow error occurred.");
Console.ResetColor();
break;
case ExecutorFailedEvent executorFailed:
Console.ForegroundColor = ConsoleColor.Red;
Console.Error.WriteLine($"Executor '{executorFailed.ExecutorId}' failed with {(executorFailed.Data is null ? "unknown error" : $"exception {executorFailed.Data}")}.");
Console.ResetColor();
break;
}
}
if (finalOutput?.As<List<ChatMessage>>() is { } transcript)
{
Console.WriteLine();
Console.WriteLine(new string('=', 80));
Console.WriteLine();
Console.WriteLine("Final Conversation Transcript:");
Console.WriteLine();
foreach (ChatMessage message in transcript)
{
Console.WriteLine($"{message.AuthorName ?? message.Role.ToString()}: {message.Text}");
Console.WriteLine();
}
}
}
private static void WriteStreamingUpdate(AgentResponseUpdateEvent updateEvent, ref string? lastResponseId)
{
string responseId = updateEvent.Update.ResponseId ?? updateEvent.Update.MessageId ?? updateEvent.ExecutorId;
if (!string.Equals(responseId, lastResponseId, StringComparison.Ordinal))
{
if (lastResponseId is not null)
{
Console.WriteLine();
Console.WriteLine();
}
Console.Write($"- {updateEvent.ExecutorId}: ");
lastResponseId = responseId;
}
if (!string.IsNullOrEmpty(updateEvent.Update.Text))
{
Console.Write(updateEvent.Update.Text);
}
}
private static void WriteMagenticMessage(string title, string? content)
{
Console.WriteLine();
Console.WriteLine($"[Magentic {title}]");
Console.WriteLine(content);
}
private static string FormatProgressLedger(MagenticProgressLedger ledger) =>
string.Join(Environment.NewLine,
$"Request satisfied: {ledger.IsRequestSatisfied}",
$"In loop: {ledger.IsInLoop}",
$"Making progress: {ledger.IsProgressBeingMade}",
$"Next speaker: {ledger.NextSpeaker}",
$"Instruction: {ledger.InstructionOrQuestion}");
private static void PauseIfInteractive()
{
if (Console.IsInputRedirected || Console.IsOutputRedirected)
{
return;
}
Console.Write("Press Enter to continue...");
Console.ReadLine();
Console.WriteLine();
}
}
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# Magentic Orchestration Sample
This sample showcases the Magentic Orchestration Pattern in .NET, setting up a team with three roles:
- **ResearcherAgent** gathers factual background information.
- **CoderAgent** uses `HostedCodeInterpreterTool` for quantitative analysis.
- **MagenticManager** plans the work, tracks progress, and decides who should act next.
## What This Sample Demonstrates
- Building a Magentic workflow with `MagenticWorkflowBuilder`
- Combining standard responses-based agents with a code interpreter-enabled participant
- Streaming orchestration events such as the initial plan, replans, and progress-ledger updates
- Printing the final multi-agent conversation transcript
## Prerequisites
- `FOUNDRY_PROJECT_ENDPOINT` set to your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL` set to your model deployment name (defaults to `gpt-5.4-mini`)
- `az login` completed before running the sample
## Running the Sample
```bash
dotnet run
```
## Expected Output
The sample prints:
1. The original task prompt
2. Streamed updates from the participating agents
3. Magentic plan and progress-ledger events as the workflow coordinates the team
4. The final conversation transcript returned by the workflow
## Related Samples
- [Handoff Orchestration](../Handoff) - another multi-agent orchestration pattern in .NET workflows
- [Python Magentic workflow sample](../../../../../python/samples/03-workflows/orchestrations/magentic.py) - the source scenario that this sample ports