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.NET: Add Magentic Orchestration Sample (#5823)
* Add Magentic orchestration sample scaffold Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/8799740a-74d8-4100-b6f6-76dcd0418c87 Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> * Validate Magentic orchestration sample Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/8799740a-74d8-4100-b6f6-76dcd0418c87 Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> * Document follow-up changes for the Magentic .NET sample Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/caa3488f-d6f5-494d-a928-a45d6a98b3c3 Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> * Remove CHANGES.md from Magentic sample Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/ffab38e2-37f9-4643-a782-20680573965a Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> * Fix PauseIfInteractive to also skip when stdout is redirected Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/07ddf735-29cc-4775-b588-fd71ca76fa58 Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> * fix: Update for PR Review Feedback * fix: Update Sample README for PR Feedback --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: lokitoth <6936551+lokitoth@users.noreply.github.com> Co-authored-by: Jacob Alber <jaalber@microsoft.com>
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@@ -281,6 +281,7 @@
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</Folder>
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<Folder Name="/Samples/03-workflows/Orchestration/">
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<Project Path="samples/03-workflows/Orchestration/Handoff/Handoff.csproj" />
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<Project Path="samples/03-workflows/Orchestration/Magentic/Magentic.csproj" />
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</Folder>
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<Folder Name="/Samples/03-workflows/Observability/">
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<Project Path="samples/03-workflows/Observability/ApplicationInsights/ApplicationInsights.csproj" />
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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<NoWarn>$(NoWarn);MAAIW001;OPENAI001</NoWarn>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.Projects" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// This sample ports the Python Magentic orchestration sample to .NET.
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// A Magentic workflow coordinates a researcher and a coder, streams orchestration
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// events as the plan evolves, and prints the final conversation transcript.
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using Azure.AI.Projects;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Workflows;
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using Microsoft.Agents.AI.Workflows.Specialized.Magentic;
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using Microsoft.Extensions.AI;
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namespace WorkflowMagenticOrchestrationSample;
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/// <summary>
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/// Demonstrates Magentic orchestration with a researcher, a coder, and an LLM manager.
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/// </summary>
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/// <remarks>
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/// Pre-requisites:
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/// - An Azure AI Foundry project endpoint and model deployment must be configured.
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/// - Run <c>az login</c> before executing the sample.
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/// </remarks>
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public static class Program
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{
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private const string TaskPrompt =
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"I am preparing a report on the energy efficiency of different machine learning model architectures. " +
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"Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 " +
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"on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). " +
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"Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 " +
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"VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model " +
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"per task type (image classification, text classification, and text generation).";
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private static async Task Main()
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{
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string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
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// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
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AIAgent researcherAgent = projectClient.AsAIAgent(
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deploymentName,
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name: "ResearcherAgent",
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description: "Specialist in research and information gathering.",
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instructions: "You are a researcher. Find relevant information without doing additional computation or quantitative analysis.");
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AIAgent coderAgent = projectClient.AsAIAgent(
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deploymentName,
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name: "CoderAgent",
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description: "A helpful assistant that writes and executes code to analyze data.",
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instructions: "You solve quantitative questions by writing and running code. Show the analysis and the computation process clearly.",
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tools: [new HostedCodeInterpreterTool()]);
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AIAgent managerAgent = projectClient.AsAIAgent(
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deploymentName,
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name: "MagenticManager",
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description: "Orchestrator that coordinates the research and coding workflow.",
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instructions: "You coordinate the team to complete complex tasks efficiently.");
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Workflow workflow = new MagenticWorkflowBuilder(managerAgent)
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.AddParticipants([researcherAgent, coderAgent])
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.WithName("Magentic Orchestration Workflow")
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.WithDescription("Coordinates a researcher and coder to solve a complex analytical task.")
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.RequirePlanSignoff(false)
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.WithMaxRounds(10)
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.WithMaxStalls(3)
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.WithMaxResets(2)
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.Build();
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Console.WriteLine("Building Magentic workflow...");
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Console.WriteLine();
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Console.WriteLine($"Task: {TaskPrompt}");
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Console.WriteLine();
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Console.WriteLine("Starting workflow execution...");
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await using StreamingRun run = await InProcessExecution.RunStreamingAsync(
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workflow,
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new List<ChatMessage> { new(ChatRole.User, TaskPrompt) });
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await run.TrySendMessageAsync(new TurnToken(emitEvents: true));
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string? lastResponseId = null;
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WorkflowOutputEvent? finalOutput = null;
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await foreach (WorkflowEvent workflowEvent in run.WatchStreamAsync())
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{
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switch (workflowEvent)
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{
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case AgentResponseUpdateEvent updateEvent:
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WriteStreamingUpdate(updateEvent, ref lastResponseId);
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break;
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case MagenticPlanCreatedEvent planCreated:
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WriteMagenticMessage("Initial Plan", planCreated.FullTaskLedger.Text);
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PauseIfInteractive();
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break;
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case MagenticReplannedEvent replanned:
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WriteMagenticMessage("Replanned", replanned.FullTaskLedger.Text);
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PauseIfInteractive();
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break;
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case MagenticProgressLedgerUpdatedEvent progressUpdated:
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WriteMagenticMessage("Progress Ledger", FormatProgressLedger(progressUpdated.ProgressLedger));
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PauseIfInteractive();
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break;
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case WorkflowOutputEvent outputEvent when outputEvent.Is<List<ChatMessage>>():
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finalOutput = outputEvent;
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break;
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case WorkflowErrorEvent workflowError:
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Console.ForegroundColor = ConsoleColor.Red;
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Console.Error.WriteLine(workflowError.Exception?.ToString() ?? "Unknown workflow error occurred.");
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Console.ResetColor();
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break;
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case ExecutorFailedEvent executorFailed:
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Console.ForegroundColor = ConsoleColor.Red;
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Console.Error.WriteLine($"Executor '{executorFailed.ExecutorId}' failed with {(executorFailed.Data is null ? "unknown error" : $"exception {executorFailed.Data}")}.");
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Console.ResetColor();
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break;
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}
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}
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if (finalOutput?.As<List<ChatMessage>>() is { } transcript)
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{
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Console.WriteLine();
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Console.WriteLine(new string('=', 80));
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Console.WriteLine();
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Console.WriteLine("Final Conversation Transcript:");
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Console.WriteLine();
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foreach (ChatMessage message in transcript)
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{
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Console.WriteLine($"{message.AuthorName ?? message.Role.ToString()}: {message.Text}");
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Console.WriteLine();
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}
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}
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}
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private static void WriteStreamingUpdate(AgentResponseUpdateEvent updateEvent, ref string? lastResponseId)
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{
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string responseId = updateEvent.Update.ResponseId ?? updateEvent.Update.MessageId ?? updateEvent.ExecutorId;
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if (!string.Equals(responseId, lastResponseId, StringComparison.Ordinal))
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{
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if (lastResponseId is not null)
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{
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Console.WriteLine();
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Console.WriteLine();
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}
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Console.Write($"- {updateEvent.ExecutorId}: ");
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lastResponseId = responseId;
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}
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if (!string.IsNullOrEmpty(updateEvent.Update.Text))
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{
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Console.Write(updateEvent.Update.Text);
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}
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}
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private static void WriteMagenticMessage(string title, string? content)
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{
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Console.WriteLine();
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Console.WriteLine($"[Magentic {title}]");
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Console.WriteLine(content);
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}
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private static string FormatProgressLedger(MagenticProgressLedger ledger) =>
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string.Join(Environment.NewLine,
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$"Request satisfied: {ledger.IsRequestSatisfied}",
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$"In loop: {ledger.IsInLoop}",
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$"Making progress: {ledger.IsProgressBeingMade}",
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$"Next speaker: {ledger.NextSpeaker}",
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$"Instruction: {ledger.InstructionOrQuestion}");
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private static void PauseIfInteractive()
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{
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if (Console.IsInputRedirected || Console.IsOutputRedirected)
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{
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return;
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}
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Console.Write("Press Enter to continue...");
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Console.ReadLine();
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Console.WriteLine();
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}
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}
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# Magentic Orchestration Sample
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This sample showcases the Magentic Orchestration Pattern in .NET, setting up a team with three roles:
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- **ResearcherAgent** gathers factual background information.
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- **CoderAgent** uses `HostedCodeInterpreterTool` for quantitative analysis.
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- **MagenticManager** plans the work, tracks progress, and decides who should act next.
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## What This Sample Demonstrates
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- Building a Magentic workflow with `MagenticWorkflowBuilder`
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- Combining standard responses-based agents with a code interpreter-enabled participant
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- Streaming orchestration events such as the initial plan, replans, and progress-ledger updates
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- Printing the final multi-agent conversation transcript
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## Prerequisites
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- `AZURE_AI_PROJECT_ENDPOINT` set to your Azure AI Foundry project endpoint
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- `AZURE_AI_MODEL_DEPLOYMENT_NAME` set to your model deployment name (defaults to `gpt-5.4-mini`)
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- `az login` completed before running the sample
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## Running the Sample
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```bash
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dotnet run
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```
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## Expected Output
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The sample prints:
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1. The original task prompt
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2. Streamed updates from the participating agents
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3. Magentic plan and progress-ledger events as the workflow coordinates the team
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4. The final conversation transcript returned by the workflow
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## Related Samples
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- [Handoff Orchestration](../Handoff) - another multi-agent orchestration pattern in .NET workflows
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- [Python Magentic workflow sample](../../../../../python/samples/03-workflows/orchestrations/magentic.py) - the source scenario that this sample ports
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@@ -62,3 +62,4 @@ Once completed, please proceed to the other samples listed below.
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| Sample | Concepts |
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|--------|----------|
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| [Handoff Orchestration](./Orchestration/Handoff) | Introduces the Handoff Orchestration pattern |
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| [Magentic Orchestration](./Orchestration/Magentic) | Coordinates multiple agents with a Magentic manager, streamed plan events, and a final transcript |
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