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Azure Functions .NET samples (#1939)
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Chris Gillum
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@@ -18,6 +18,16 @@
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<Project Path="samples/AgentWebChat/AgentWebChat.ServiceDefaults/AgentWebChat.ServiceDefaults.csproj" />
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<Project Path="samples/AgentWebChat/AgentWebChat.Web/AgentWebChat.Web.csproj" />
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</Folder>
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<Folder Name="/Samples/AzureFunctions/">
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<File Path="samples/AzureFunctions/.editorconfig" />
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<File Path="samples/AzureFunctions/README.md" />
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<Project Path="samples/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
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<Project Path="samples/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
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<Project Path="samples/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
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<Project Path="samples/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
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<Project Path="samples/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
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<Project Path="samples/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
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</Folder>
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<Folder Name="/Samples/GettingStarted/">
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<File Path="samples/GettingStarted/README.md" />
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</Folder>
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# .editorconfig
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[*.cs]
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# See https://github.com/Azure/azure-functions-durable-extension/issues/3173
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dotnet_diagnostic.DURABLE0001.severity = none
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dotnet_diagnostic.DURABLE0002.severity = none
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dotnet_diagnostic.DURABLE0003.severity = none
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dotnet_diagnostic.DURABLE0004.severity = none
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dotnet_diagnostic.DURABLE0005.severity = none
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dotnet_diagnostic.DURABLE0006.severity = none
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@@ -0,0 +1,42 @@
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>net9.0</TargetFramework>
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<AzureFunctionsVersion>v4</AzureFunctionsVersion>
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<OutputType>Exe</OutputType>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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<!-- The Functions build tools don't like namespaces that start with a number -->
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<AssemblyName>SingleAgent</AssemblyName>
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<RootNamespace>SingleAgent</RootNamespace>
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</PropertyGroup>
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<ItemGroup>
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<FrameworkReference Include="Microsoft.AspNetCore.App" />
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</ItemGroup>
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<!-- Azure Functions packages -->
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<ItemGroup>
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<PackageReference Include="Microsoft.Azure.Functions.Worker" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
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<!--
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<ItemGroup>
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<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
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<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
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</ItemGroup>
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-->
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<ItemGroup>
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<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.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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using Azure;
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Hosting.AzureFunctions;
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using Microsoft.Azure.Functions.Worker.Builder;
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using Microsoft.Extensions.Hosting;
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using OpenAI;
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// Get the Azure OpenAI endpoint and deployment name from environment variables.
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string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
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?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
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// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
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string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
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AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
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? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
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: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
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// Set up an AI agent following the standard Microsoft Agent Framework pattern.
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const string JokerName = "Joker";
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const string JokerInstructions = "You are good at telling jokes.";
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AIAgent agent = client.GetChatClient(deploymentName).CreateAIAgent(JokerInstructions, JokerName);
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// Configure the function app to host the AI agent.
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// This will automatically generate HTTP API endpoints for the agent.
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using IHost app = FunctionsApplication
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.CreateBuilder(args)
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.ConfigureFunctionsWebApplication()
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.ConfigureDurableAgents(options => options.AddAIAgent(agent))
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.Build();
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app.Run();
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# Single Agent Sample
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This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that hosts a single AI agent and provides direct HTTP API access for interactive conversations.
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## Key Concepts Demonstrated
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- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
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- Registering agents with the Function app and running them using HTTP.
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- Conversation management (via session IDs) for isolated interactions.
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## Environment Setup
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See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
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## Running the Sample
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With the environment setup and function app running, you can test the sample by sending an HTTP request to the agent endpoint.
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You can use the `demo.http` file to send a message to the agent, or a command line tool like `curl` as shown below:
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Bash (Linux/macOS/WSL):
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```bash
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curl -X POST http://localhost:7071/api/agents/Joker/run \
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-H "Content-Type: text/plain" \
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-d "Tell me a joke about a pirate."
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```
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PowerShell:
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```powershell
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Invoke-RestMethod -Method Post `
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-Uri http://localhost:7071/api/agents/Joker/run `
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-ContentType text/plain `
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-Body "Tell me a joke about a pirate."
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```
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The response from the agent will be displayed in the terminal where you ran `func start`. The expected output will look something like:
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```text
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Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
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```
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# Default endpoint address for local testing
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@authority=http://localhost:7071
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### Prompt the agent
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POST {{authority}}/api/agents/Joker/run
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Content-Type: text/plain
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Tell me a joke about a pirate.
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{
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"version": "2.0",
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"logging": {
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"logLevel": {
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"Microsoft.Agents.AI.DurableTask": "Information",
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"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
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"DurableTask": "Information",
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"Microsoft.DurableTask": "Information"
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}
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},
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"extensions": {
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"durableTask": {
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"hubName": "default",
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"storageProvider": {
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"type": "AzureManaged",
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"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
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}
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}
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}
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}
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{
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"IsEncrypted": false,
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"Values": {
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"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
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"AzureWebJobsStorage": "UseDevelopmentStorage=true",
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"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
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"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
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"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
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}
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}
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+42
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>net9.0</TargetFramework>
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<AzureFunctionsVersion>v4</AzureFunctionsVersion>
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<OutputType>Exe</OutputType>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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<!-- The Functions build tools don't like namespaces that start with a number -->
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<AssemblyName>AgentOrchestration_Chaining</AssemblyName>
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<RootNamespace>AgentOrchestration_Chaining</RootNamespace>
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</PropertyGroup>
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<ItemGroup>
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<FrameworkReference Include="Microsoft.AspNetCore.App" />
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</ItemGroup>
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<!-- Azure Functions packages -->
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<ItemGroup>
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<PackageReference Include="Microsoft.Azure.Functions.Worker" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
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<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
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<!--
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<ItemGroup>
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<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
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<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
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</ItemGroup>
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-->
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<ItemGroup>
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<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.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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using System.Net;
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using System.Text.Json;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.DurableTask;
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using Microsoft.Azure.Functions.Worker;
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using Microsoft.Azure.Functions.Worker.Http;
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using Microsoft.DurableTask;
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using Microsoft.DurableTask.Client;
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namespace AgentOrchestration_Chaining;
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public static class FunctionTriggers
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{
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public sealed record TextResponse(string Text);
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[Function(nameof(RunOrchestrationAsync))]
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public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
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{
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DurableAIAgent writer = context.GetAgent("WriterAgent");
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AgentThread writerThread = writer.GetNewThread();
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AgentRunResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
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message: "Write a concise inspirational sentence about learning.",
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thread: writerThread);
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AgentRunResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
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message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
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thread: writerThread);
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return refined.Result.Text;
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}
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// POST /singleagent/run
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[Function(nameof(StartOrchestrationAsync))]
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public static async Task<HttpResponseData> StartOrchestrationAsync(
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[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "singleagent/run")] HttpRequestData req,
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[DurableClient] DurableTaskClient client)
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{
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string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
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orchestratorName: nameof(RunOrchestrationAsync));
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HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
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await response.WriteAsJsonAsync(new
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{
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message = "Single-agent orchestration started.",
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instanceId,
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statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
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});
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return response;
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}
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// GET /singleagent/status/{instanceId}
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[Function(nameof(GetOrchestrationStatusAsync))]
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public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
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[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "singleagent/status/{instanceId}")] HttpRequestData req,
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string instanceId,
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[DurableClient] DurableTaskClient client)
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{
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OrchestrationMetadata? status = await client.GetInstanceAsync(
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instanceId,
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getInputsAndOutputs: true,
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req.FunctionContext.CancellationToken);
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if (status is null)
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{
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HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
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await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
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return notFound;
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}
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HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
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await response.WriteAsJsonAsync(new
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{
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instanceId = status.InstanceId,
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runtimeStatus = status.RuntimeStatus.ToString(),
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input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
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output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
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failureDetails = status.FailureDetails
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});
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return response;
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}
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private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
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{
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// NOTE: This can be made more robust by considering the value of
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// request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
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string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
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return $"{authority}/api/singleagent/status/{instanceId}";
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}
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}
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@@ -0,0 +1,40 @@
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// Copyright (c) Microsoft. All rights reserved.
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using Azure;
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Hosting.AzureFunctions;
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using Microsoft.Azure.Functions.Worker.Builder;
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using Microsoft.Extensions.Hosting;
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using OpenAI;
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// Get the Azure OpenAI endpoint and deployment name from environment variables.
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string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
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?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
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// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
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string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
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AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
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? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
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: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
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// Single agent used by the orchestration to demonstrate sequential calls on the same thread.
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const string WriterName = "WriterAgent";
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const string WriterInstructions =
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"""
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You refine short pieces of text. When given an initial sentence you enhance it;
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when given an improved sentence you polish it further.
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""";
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AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
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using IHost app = FunctionsApplication
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.CreateBuilder(args)
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.ConfigureFunctionsWebApplication()
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.ConfigureDurableAgents(options => options.AddAIAgent(writerAgent))
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.Build();
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app.Run();
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@@ -0,0 +1,59 @@
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# Single Agent Orchestration Sample
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This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same conversation thread for context continuity.
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## Key Concepts Demonstrated
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- Orchestrating multiple interactions with the same agent in a deterministic order
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- Using the same `AgentThread` across multiple calls to maintain conversational context
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- Durable orchestration with automatic checkpointing and resumption from failures
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- HTTP API integration for starting and monitoring orchestrations
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## Environment Setup
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||||
|
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See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
|
||||
|
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## Running the Sample
|
||||
|
||||
With the environment setup and function app running, you can test the sample by sending an HTTP request to start the orchestration.
|
||||
|
||||
You can use the `demo.http` file to start the orchestration, or a command line tool like `curl` as shown below:
|
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|
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Bash (Linux/macOS/WSL):
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```bash
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curl -X POST http://localhost:7071/api/singleagent/run
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```
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PowerShell:
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```powershell
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Invoke-RestMethod -Method Post -Uri http://localhost:7071/api/singleagent/run
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```
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The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
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```json
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{
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"message": "Single-agent orchestration started.",
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"instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
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"statusQueryGetUri": "http://localhost:7071/api/singleagent/status/86313f1d45fb42eeb50b1852626bf3ff"
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}
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```
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The orchestration will proceed to run the WriterAgent twice in sequence:
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1. First, it writes an inspirational sentence about learning
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2. Then, it refines the initial output using the same conversation thread
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Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
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```json
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{
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||||
"failureDetails": null,
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||||
"input": null,
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||||
"instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
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"output": "Learning serves as the key, opening doors to boundless opportunities and a brighter future.",
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"runtimeStatus": "Completed"
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}
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```
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@@ -0,0 +1,3 @@
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### Start the single-agent orchestration
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POST http://localhost:7071/api/singleagent/run
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||||
@@ -0,0 +1,20 @@
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{
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||||
"version": "2.0",
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||||
"logging": {
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||||
"logLevel": {
|
||||
"Microsoft.Agents.AI.DurableTask": "Information",
|
||||
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
|
||||
"DurableTask": "Information",
|
||||
"Microsoft.DurableTask": "Information"
|
||||
}
|
||||
},
|
||||
"extensions": {
|
||||
"durableTask": {
|
||||
"hubName": "default",
|
||||
"storageProvider": {
|
||||
"type": "AzureManaged",
|
||||
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"IsEncrypted": false,
|
||||
"Values": {
|
||||
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
|
||||
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
|
||||
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
|
||||
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
|
||||
}
|
||||
}
|
||||
+42
@@ -0,0 +1,42 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
<PropertyGroup>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
|
||||
<OutputType>Exe</OutputType>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<Nullable>enable</Nullable>
|
||||
<!-- The Functions build tools don't like namespaces that start with a number -->
|
||||
<AssemblyName>AgentOrchestration_Concurrency</AssemblyName>
|
||||
<RootNamespace>AgentOrchestration_Concurrency</RootNamespace>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<FrameworkReference Include="Microsoft.AspNetCore.App" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Azure Functions packages -->
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
|
||||
<!--
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
|
||||
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
-->
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
@@ -0,0 +1,116 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Net;
|
||||
using System.Text.Json;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.DurableTask;
|
||||
using Microsoft.Azure.Functions.Worker;
|
||||
using Microsoft.Azure.Functions.Worker.Http;
|
||||
using Microsoft.DurableTask;
|
||||
using Microsoft.DurableTask.Client;
|
||||
|
||||
namespace AgentOrchestration_Concurrency;
|
||||
|
||||
public static class FunctionsTriggers
|
||||
{
|
||||
public sealed record TextResponse(string Text);
|
||||
|
||||
[Function(nameof(RunOrchestrationAsync))]
|
||||
public static async Task<object> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
|
||||
{
|
||||
// Get the prompt from the orchestration input
|
||||
string prompt = context.GetInput<string>() ?? throw new InvalidOperationException("Prompt is required");
|
||||
|
||||
// Get both agents
|
||||
DurableAIAgent physicist = context.GetAgent("PhysicistAgent");
|
||||
DurableAIAgent chemist = context.GetAgent("ChemistAgent");
|
||||
|
||||
// Start both agent runs concurrently
|
||||
Task<AgentRunResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
|
||||
|
||||
Task<AgentRunResponse<TextResponse>> chemistTask = chemist.RunAsync<TextResponse>(prompt);
|
||||
|
||||
// Wait for both tasks to complete using Task.WhenAll
|
||||
await Task.WhenAll(physicistTask, chemistTask);
|
||||
|
||||
// Get the results
|
||||
TextResponse physicistResponse = (await physicistTask).Result;
|
||||
TextResponse chemistResponse = (await chemistTask).Result;
|
||||
|
||||
// Return the result as a structured, anonymous type
|
||||
return new
|
||||
{
|
||||
physicist = physicistResponse.Text,
|
||||
chemist = chemistResponse.Text,
|
||||
};
|
||||
}
|
||||
|
||||
// POST /multiagent/run
|
||||
[Function(nameof(StartOrchestrationAsync))]
|
||||
public static async Task<HttpResponseData> StartOrchestrationAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "multiagent/run")] HttpRequestData req,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
// Read the prompt from the request body
|
||||
string? prompt = await req.ReadAsStringAsync();
|
||||
if (string.IsNullOrWhiteSpace(prompt))
|
||||
{
|
||||
HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
|
||||
await badRequestResponse.WriteAsJsonAsync(new { error = "Prompt is required" });
|
||||
return badRequestResponse;
|
||||
}
|
||||
|
||||
string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
|
||||
orchestratorName: nameof(RunOrchestrationAsync),
|
||||
input: prompt);
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
message = "Multi-agent concurrent orchestration started.",
|
||||
prompt,
|
||||
instanceId,
|
||||
statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
// GET /multiagent/status/{instanceId}
|
||||
[Function(nameof(GetOrchestrationStatusAsync))]
|
||||
public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "multiagent/status/{instanceId}")] HttpRequestData req,
|
||||
string instanceId,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
OrchestrationMetadata? status = await client.GetInstanceAsync(
|
||||
instanceId,
|
||||
getInputsAndOutputs: true,
|
||||
req.FunctionContext.CancellationToken);
|
||||
|
||||
if (status is null)
|
||||
{
|
||||
HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
|
||||
await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
|
||||
return notFound;
|
||||
}
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
instanceId = status.InstanceId,
|
||||
runtimeStatus = status.RuntimeStatus.ToString(),
|
||||
input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
|
||||
output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
|
||||
failureDetails = status.FailureDetails
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
|
||||
{
|
||||
// NOTE: This can be made more robust by considering the value of
|
||||
// request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
|
||||
string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
|
||||
return $"{authority}/api/multiagent/status/{instanceId}";
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Azure;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Hosting.AzureFunctions;
|
||||
using Microsoft.Azure.Functions.Worker.Builder;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
using OpenAI;
|
||||
|
||||
// Get the Azure OpenAI endpoint and deployment name from environment variables.
|
||||
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
|
||||
|
||||
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
|
||||
string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
|
||||
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
|
||||
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
|
||||
: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
|
||||
|
||||
// Two agents used by the orchestration to demonstrate concurrent execution.
|
||||
const string PhysicistName = "PhysicistAgent";
|
||||
const string PhysicistInstructions = "You are an expert in physics. You answer questions from a physics perspective.";
|
||||
|
||||
const string ChemistName = "ChemistAgent";
|
||||
const string ChemistInstructions = "You are an expert in chemistry. You answer questions from a chemistry perspective.";
|
||||
|
||||
AIAgent physicistAgent = client.GetChatClient(deploymentName).CreateAIAgent(PhysicistInstructions, PhysicistName);
|
||||
AIAgent chemistAgent = client.GetChatClient(deploymentName).CreateAIAgent(ChemistInstructions, ChemistName);
|
||||
|
||||
using IHost app = FunctionsApplication
|
||||
.CreateBuilder(args)
|
||||
.ConfigureFunctionsWebApplication()
|
||||
.ConfigureDurableAgents(options =>
|
||||
{
|
||||
options.AddAIAgent(physicistAgent);
|
||||
options.AddAIAgent(chemistAgent);
|
||||
})
|
||||
.Build();
|
||||
|
||||
app.Run();
|
||||
@@ -0,0 +1,65 @@
|
||||
# Multi-Agent Concurrent Orchestration Sample
|
||||
|
||||
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create an Azure Functions app that orchestrates concurrent execution of multiple AI agents, each with specialized expertise, to provide comprehensive answers to complex questions.
|
||||
|
||||
## Key Concepts Demonstrated
|
||||
|
||||
- Multi-agent orchestration with specialized AI agents (physics and chemistry)
|
||||
- Concurrent execution using the fan-out/fan-in pattern for improved performance and distributed processing
|
||||
- Response aggregation from multiple agents into a unified result
|
||||
- Durable orchestration with automatic checkpointing and resumption from failures
|
||||
|
||||
## Environment Setup
|
||||
|
||||
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
|
||||
|
||||
## Running the Sample
|
||||
|
||||
With the environment setup and function app running, you can test the sample by sending an HTTP request with a custom prompt to the orchestration.
|
||||
|
||||
You can use the `demo.http` file to send a message to the agents, or a command line tool like `curl` as shown below:
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:7071/api/multiagent/run \
|
||||
-H "Content-Type: text/plain" \
|
||||
-d "What is temperature?"
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/multiagent/run `
|
||||
-ContentType text/plain `
|
||||
-Body "What is temperature?"
|
||||
```
|
||||
|
||||
The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
|
||||
|
||||
```json
|
||||
{
|
||||
"message": "Multi-agent concurrent orchestration started.",
|
||||
"prompt": "What is temperature?",
|
||||
"instanceId": "e7e29999b6b8424682b3539292afc9ed",
|
||||
"statusQueryGetUri": "http://localhost:7071/api/multiagent/status/e7e29999b6b8424682b3539292afc9ed"
|
||||
}
|
||||
```
|
||||
|
||||
The orchestration will run both the PhysicistAgent and ChemistAgent concurrently, asking them the same question. Their responses will be combined to provide a comprehensive answer covering both physical and chemical aspects.
|
||||
|
||||
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
|
||||
|
||||
```json
|
||||
{
|
||||
"failureDetails": null,
|
||||
"input": "What is temperature?",
|
||||
"instanceId": "e7e29999b6b8424682b3539292afc9ed",
|
||||
"output": {
|
||||
"physicist": "Temperature is a measure of the average kinetic energy of particles in a system. From a physics perspective, it represents the thermal energy and determines the direction of heat flow between objects.",
|
||||
"chemist": "From a chemistry perspective, temperature is crucial for chemical reactions as it affects reaction rates through the Arrhenius equation. It influences the equilibrium position of reversible reactions and determines the physical state of substances."
|
||||
},
|
||||
"runtimeStatus": "Completed"
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,5 @@
|
||||
### Start the multi-agent concurrent orchestration
|
||||
POST http://localhost:7071/api/multiagent/run
|
||||
Content-Type: text/plain
|
||||
|
||||
What is temperature?
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"version": "2.0",
|
||||
"logging": {
|
||||
"logLevel": {
|
||||
"Microsoft.Agents.AI.DurableTask": "Information",
|
||||
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
|
||||
"DurableTask": "Information",
|
||||
"Microsoft.DurableTask": "Information"
|
||||
}
|
||||
},
|
||||
"extensions": {
|
||||
"durableTask": {
|
||||
"hubName": "default",
|
||||
"storageProvider": {
|
||||
"type": "AzureManaged",
|
||||
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"IsEncrypted": false,
|
||||
"Values": {
|
||||
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
|
||||
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
|
||||
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
|
||||
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
|
||||
}
|
||||
}
|
||||
+42
@@ -0,0 +1,42 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
<PropertyGroup>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
|
||||
<OutputType>Exe</OutputType>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<Nullable>enable</Nullable>
|
||||
<!-- The Functions build tools don't like namespaces that start with a number -->
|
||||
<AssemblyName>AgentOrchestration_Conditionals</AssemblyName>
|
||||
<RootNamespace>AgentOrchestration_Conditionals</RootNamespace>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<FrameworkReference Include="Microsoft.AspNetCore.App" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Azure Functions packages -->
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
|
||||
<!--
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
|
||||
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
-->
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
@@ -0,0 +1,143 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Net;
|
||||
using System.Text.Json;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.DurableTask;
|
||||
using Microsoft.Azure.Functions.Worker;
|
||||
using Microsoft.Azure.Functions.Worker.Http;
|
||||
using Microsoft.DurableTask;
|
||||
using Microsoft.DurableTask.Client;
|
||||
|
||||
namespace AgentOrchestration_Conditionals;
|
||||
|
||||
public static class FunctionTriggers
|
||||
{
|
||||
[Function(nameof(RunOrchestrationAsync))]
|
||||
public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
|
||||
{
|
||||
// Get the email from the orchestration input
|
||||
Email email = context.GetInput<Email>() ?? throw new InvalidOperationException("Email is required");
|
||||
|
||||
// Get the spam detection agent
|
||||
DurableAIAgent spamDetectionAgent = context.GetAgent("SpamDetectionAgent");
|
||||
AgentThread spamThread = spamDetectionAgent.GetNewThread();
|
||||
|
||||
// Step 1: Check if the email is spam
|
||||
AgentRunResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
|
||||
message:
|
||||
$"""
|
||||
Analyze this email for spam content and return a JSON response with 'is_spam' (boolean) and 'reason' (string) fields:
|
||||
Email ID: {email.EmailId}
|
||||
Content: {email.EmailContent}
|
||||
""",
|
||||
thread: spamThread);
|
||||
DetectionResult result = spamDetectionResponse.Result;
|
||||
|
||||
// Step 2: Conditional logic based on spam detection result
|
||||
if (result.IsSpam)
|
||||
{
|
||||
// Handle spam email
|
||||
return await context.CallActivityAsync<string>(nameof(HandleSpamEmail), result.Reason);
|
||||
}
|
||||
|
||||
// Generate and send response for legitimate email
|
||||
DurableAIAgent emailAssistantAgent = context.GetAgent("EmailAssistantAgent");
|
||||
AgentThread emailThread = emailAssistantAgent.GetNewThread();
|
||||
|
||||
AgentRunResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
|
||||
message:
|
||||
$"""
|
||||
Draft a professional response to this email. Return a JSON response with a 'response' field containing the reply:
|
||||
|
||||
Email ID: {email.EmailId}
|
||||
Content: {email.EmailContent}
|
||||
""",
|
||||
thread: emailThread);
|
||||
|
||||
EmailResponse emailResponse = emailAssistantResponse.Result;
|
||||
|
||||
return await context.CallActivityAsync<string>(nameof(SendEmail), emailResponse.Response);
|
||||
}
|
||||
|
||||
[Function(nameof(HandleSpamEmail))]
|
||||
public static string HandleSpamEmail([ActivityTrigger] string reason)
|
||||
{
|
||||
return $"Email marked as spam: {reason}";
|
||||
}
|
||||
|
||||
[Function(nameof(SendEmail))]
|
||||
public static string SendEmail([ActivityTrigger] string message)
|
||||
{
|
||||
return $"Email sent: {message}";
|
||||
}
|
||||
|
||||
// POST /spamdetection/run
|
||||
[Function(nameof(StartOrchestrationAsync))]
|
||||
public static async Task<HttpResponseData> StartOrchestrationAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "spamdetection/run")] HttpRequestData req,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
// Read the email from the request body
|
||||
Email? email = await req.ReadFromJsonAsync<Email>();
|
||||
if (email is null || string.IsNullOrWhiteSpace(email.EmailContent))
|
||||
{
|
||||
HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
|
||||
await badRequestResponse.WriteAsJsonAsync(new { error = "Email with content is required" });
|
||||
return badRequestResponse;
|
||||
}
|
||||
|
||||
string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
|
||||
orchestratorName: nameof(RunOrchestrationAsync),
|
||||
input: email);
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
message = "Spam detection orchestration started.",
|
||||
emailId = email.EmailId,
|
||||
instanceId,
|
||||
statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
// GET /spamdetection/status/{instanceId}
|
||||
[Function(nameof(GetOrchestrationStatusAsync))]
|
||||
public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "spamdetection/status/{instanceId}")] HttpRequestData req,
|
||||
string instanceId,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
OrchestrationMetadata? status = await client.GetInstanceAsync(
|
||||
instanceId,
|
||||
getInputsAndOutputs: true,
|
||||
req.FunctionContext.CancellationToken);
|
||||
|
||||
if (status is null)
|
||||
{
|
||||
HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
|
||||
await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
|
||||
return notFound;
|
||||
}
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
instanceId = status.InstanceId,
|
||||
runtimeStatus = status.RuntimeStatus.ToString(),
|
||||
input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
|
||||
output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
|
||||
failureDetails = status.FailureDetails
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
|
||||
{
|
||||
// NOTE: This can be made more robust by considering the value of
|
||||
// request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
|
||||
string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
|
||||
return $"{authority}/api/spamdetection/status/{instanceId}";
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Text.Json.Serialization;
|
||||
|
||||
namespace AgentOrchestration_Conditionals;
|
||||
|
||||
/// <summary>
|
||||
/// Represents an email input for spam detection and response generation.
|
||||
/// </summary>
|
||||
public sealed class Email
|
||||
{
|
||||
[JsonPropertyName("email_id")]
|
||||
public string EmailId { get; set; } = string.Empty;
|
||||
|
||||
[JsonPropertyName("email_content")]
|
||||
public string EmailContent { get; set; } = string.Empty;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents the result of spam detection analysis.
|
||||
/// </summary>
|
||||
public sealed class DetectionResult
|
||||
{
|
||||
[JsonPropertyName("is_spam")]
|
||||
public bool IsSpam { get; set; }
|
||||
|
||||
[JsonPropertyName("reason")]
|
||||
public string Reason { get; set; } = string.Empty;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents a generated email response.
|
||||
/// </summary>
|
||||
public sealed class EmailResponse
|
||||
{
|
||||
[JsonPropertyName("response")]
|
||||
public string Response { get; set; } = string.Empty;
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Azure;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Hosting.AzureFunctions;
|
||||
using Microsoft.Azure.Functions.Worker.Builder;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
using OpenAI;
|
||||
|
||||
// Get the Azure OpenAI endpoint and deployment name from environment variables.
|
||||
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
|
||||
|
||||
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
|
||||
string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
|
||||
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
|
||||
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
|
||||
: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
|
||||
|
||||
// Two agents used by the orchestration to demonstrate conditional logic.
|
||||
const string SpamDetectionName = "SpamDetectionAgent";
|
||||
const string SpamDetectionInstructions = "You are a spam detection assistant that identifies spam emails.";
|
||||
|
||||
const string EmailAssistantName = "EmailAssistantAgent";
|
||||
const string EmailAssistantInstructions = "You are an email assistant that helps users draft responses to emails with professionalism.";
|
||||
|
||||
AIAgent spamDetectionAgent = client.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(SpamDetectionInstructions, SpamDetectionName);
|
||||
|
||||
AIAgent emailAssistantAgent = client.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(EmailAssistantInstructions, EmailAssistantName);
|
||||
|
||||
using IHost app = FunctionsApplication
|
||||
.CreateBuilder(args)
|
||||
.ConfigureFunctionsWebApplication()
|
||||
.ConfigureDurableAgents(options =>
|
||||
{
|
||||
options.AddAIAgent(spamDetectionAgent);
|
||||
options.AddAIAgent(emailAssistantAgent);
|
||||
})
|
||||
.Build();
|
||||
|
||||
app.Run();
|
||||
@@ -0,0 +1,113 @@
|
||||
# Multi-Agent Orchestration with Conditionals Sample
|
||||
|
||||
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a multi-agent orchestration workflow that includes conditional logic. The workflow implements a spam detection system that processes emails and takes different actions based on whether the email is identified as spam or legitimate.
|
||||
|
||||
## Key Concepts Demonstrated
|
||||
|
||||
- Multi-agent orchestration with conditional logic and different processing paths
|
||||
- Spam detection using AI agent analysis
|
||||
- Structured output from agents for reliable processing
|
||||
- Activity functions for integrating non-agentic workflow actions
|
||||
|
||||
## Environment Setup
|
||||
|
||||
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
|
||||
|
||||
## Running the Sample
|
||||
|
||||
With the environment setup and function app running, you can test the sample by sending an HTTP request with email data to the orchestration.
|
||||
|
||||
You can use the `demo.http` file to send email data to the agents, or a command line tool like `curl` as shown below:
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
# Test with a legitimate email
|
||||
curl -X POST http://localhost:7071/api/spamdetection/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"email_id": "email-001",
|
||||
"email_content": "Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
|
||||
}'
|
||||
|
||||
# Test with a spam email
|
||||
curl -X POST http://localhost:7071/api/spamdetection/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"email_id": "email-002",
|
||||
"email_content": "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
|
||||
}'
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
# Test with a legitimate email
|
||||
$body = @{
|
||||
email_id = "email-001"
|
||||
email_content = "Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
|
||||
} | ConvertTo-Json
|
||||
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/spamdetection/run `
|
||||
-ContentType application/json `
|
||||
-Body $body
|
||||
|
||||
# Test with a spam email
|
||||
$body = @{
|
||||
email_id = "email-002"
|
||||
email_content = "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
|
||||
} | ConvertTo-Json
|
||||
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/spamdetection/run `
|
||||
-ContentType application/json `
|
||||
-Body $body
|
||||
```
|
||||
|
||||
The response from either input will be a JSON object that looks something like the following, which indicates that the orchestration has started.
|
||||
|
||||
```json
|
||||
{
|
||||
"message": "Spam detection orchestration started.",
|
||||
"emailId": "email-001",
|
||||
"instanceId": "555dbbb63f75406db2edf9f1f092de95",
|
||||
"statusQueryGetUri": "http://localhost:7071/api/spamdetection/status/555dbbb63f75406db2edf9f1f092de95"
|
||||
}
|
||||
```
|
||||
|
||||
The orchestration will:
|
||||
|
||||
1. Analyze the email content using the SpamDetectionAgent
|
||||
2. If spam: Mark the email as spam with a reason
|
||||
3. If legitimate: Use the EmailAssistantAgent to draft a professional response and "send" it
|
||||
|
||||
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response for the legitimate email will be a JSON object that looks something like the following:
|
||||
|
||||
```json
|
||||
{
|
||||
"failureDetails": null,
|
||||
"input": {
|
||||
"email_content": "Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!",
|
||||
"email_id": "email-001"
|
||||
},
|
||||
"instanceId": "555dbbb63f75406db2edf9f1f092de95",
|
||||
"output": "Email sent: Subject: Re: Follow-Up on Quarterly Report\n\nHi [Recipient's Name],\n\nI hope this message finds you well. Thank you for your patience. I will ensure the updated figures for the quarterly report are sent to you by Friday.\n\nIf you have any further questions or need additional information, please feel free to reach out.\n\nBest regards,\n\nJohn",
|
||||
"runtimeStatus": "Completed"
|
||||
}
|
||||
```
|
||||
|
||||
The response for the spam email will be a JSON object that looks something like the following, which indicates that the email was marked as spam:
|
||||
|
||||
```json
|
||||
{
|
||||
"failureDetails": null,
|
||||
"input": {
|
||||
"email_content": "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!",
|
||||
"email_id": "email-002"
|
||||
},
|
||||
"instanceId": "555dbbb63f75406db2edf9f1f092de95",
|
||||
"output": "Email marked as spam: The email contains misleading claims of winning a large sum of money and encourages immediate action, which are common characteristics of spam.",
|
||||
"runtimeStatus": "Completed"
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,18 @@
|
||||
### Test spam detection with a legitimate email
|
||||
POST http://localhost:7071/api/spamdetection/run
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"email_id": "email-001",
|
||||
"email_content": "Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
|
||||
}
|
||||
|
||||
|
||||
### Test spam detection with a spam email
|
||||
POST http://localhost:7071/api/spamdetection/run
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"email_id": "email-002",
|
||||
"email_content": "URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"version": "2.0",
|
||||
"logging": {
|
||||
"logLevel": {
|
||||
"Microsoft.Agents.AI.DurableTask": "Information",
|
||||
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
|
||||
"DurableTask": "Information",
|
||||
"Microsoft.DurableTask": "Information"
|
||||
}
|
||||
},
|
||||
"extensions": {
|
||||
"durableTask": {
|
||||
"hubName": "default",
|
||||
"storageProvider": {
|
||||
"type": "AzureManaged",
|
||||
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"IsEncrypted": false,
|
||||
"Values": {
|
||||
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
|
||||
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
|
||||
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
|
||||
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
|
||||
}
|
||||
}
|
||||
+43
@@ -0,0 +1,43 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
<PropertyGroup>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
|
||||
<OutputType>Exe</OutputType>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<Nullable>enable</Nullable>
|
||||
<!-- The Functions build tools don't like namespaces that start with a number -->
|
||||
<AssemblyName>AgentOrchestration_HITL</AssemblyName>
|
||||
<RootNamespace>AgentOrchestration_HITL</RootNamespace>
|
||||
<NoWarn>$(NoWarn);DURABLE0001;DURABLE0002;DURABLE0003;DURABLE0004;DURABLE0005;DURABLE0006</NoWarn>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<FrameworkReference Include="Microsoft.AspNetCore.App" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Azure Functions packages -->
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
|
||||
<!--
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
|
||||
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
-->
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
@@ -0,0 +1,229 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Net;
|
||||
using System.Text.Json;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.DurableTask;
|
||||
using Microsoft.Azure.Functions.Worker;
|
||||
using Microsoft.Azure.Functions.Worker.Http;
|
||||
using Microsoft.DurableTask;
|
||||
using Microsoft.DurableTask.Client;
|
||||
using Microsoft.Extensions.Logging;
|
||||
|
||||
namespace AgentOrchestration_HITL;
|
||||
|
||||
public static class FunctionTriggers
|
||||
{
|
||||
[Function(nameof(RunOrchestrationAsync))]
|
||||
public static async Task<object> RunOrchestrationAsync(
|
||||
[OrchestrationTrigger] TaskOrchestrationContext context)
|
||||
{
|
||||
// Get the input from the orchestration
|
||||
ContentGenerationInput input = context.GetInput<ContentGenerationInput>()
|
||||
?? throw new InvalidOperationException("Content generation input is required");
|
||||
|
||||
// Get the writer agent
|
||||
DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
|
||||
AgentThread writerThread = writerAgent.GetNewThread();
|
||||
|
||||
// Set initial status
|
||||
context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
|
||||
|
||||
// Step 1: Generate initial content
|
||||
AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
|
||||
message: $"Write a short article about '{input.Topic}'.",
|
||||
thread: writerThread);
|
||||
GeneratedContent content = writerResponse.Result;
|
||||
|
||||
// Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
|
||||
int iterationCount = 0;
|
||||
while (iterationCount++ < input.MaxReviewAttempts)
|
||||
{
|
||||
context.SetCustomStatus(
|
||||
$"Requesting human feedback. Iteration #{iterationCount}. Timeout: {input.ApprovalTimeoutHours} hour(s).");
|
||||
|
||||
// Step 2: Notify user to review the content
|
||||
await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
|
||||
|
||||
// Step 3: Wait for human feedback with configurable timeout
|
||||
HumanApprovalResponse humanResponse;
|
||||
try
|
||||
{
|
||||
humanResponse = await context.WaitForExternalEvent<HumanApprovalResponse>(
|
||||
eventName: "HumanApproval",
|
||||
timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
|
||||
}
|
||||
catch (OperationCanceledException)
|
||||
{
|
||||
// Timeout occurred - treat as rejection
|
||||
context.SetCustomStatus(
|
||||
$"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.");
|
||||
throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
|
||||
}
|
||||
|
||||
if (humanResponse.Approved)
|
||||
{
|
||||
context.SetCustomStatus("Content approved by human reviewer. Publishing content...");
|
||||
|
||||
// Step 4: Publish the approved content
|
||||
await context.CallActivityAsync(nameof(PublishContent), content);
|
||||
|
||||
context.SetCustomStatus($"Content published successfully at {context.CurrentUtcDateTime:s}");
|
||||
return new { content = content.Content };
|
||||
}
|
||||
|
||||
context.SetCustomStatus("Content rejected by human reviewer. Incorporating feedback and regenerating...");
|
||||
|
||||
// Incorporate human feedback and regenerate
|
||||
writerResponse = await writerAgent.RunAsync<GeneratedContent>(
|
||||
message: $"""
|
||||
The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
|
||||
|
||||
Human Feedback: {humanResponse.Feedback}
|
||||
""",
|
||||
thread: writerThread);
|
||||
|
||||
content = writerResponse.Result;
|
||||
}
|
||||
|
||||
// If we reach here, it means we exhausted the maximum number of iterations
|
||||
throw new InvalidOperationException(
|
||||
$"Content could not be approved after {input.MaxReviewAttempts} iterations.");
|
||||
}
|
||||
|
||||
// POST /hitl/run
|
||||
[Function(nameof(StartOrchestrationAsync))]
|
||||
public static async Task<HttpResponseData> StartOrchestrationAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "hitl/run")] HttpRequestData req,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
// Read the input from the request body
|
||||
ContentGenerationInput? input = await req.ReadFromJsonAsync<ContentGenerationInput>();
|
||||
if (input is null || string.IsNullOrWhiteSpace(input.Topic))
|
||||
{
|
||||
HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
|
||||
await badRequestResponse.WriteAsJsonAsync(new { error = "Topic is required" });
|
||||
return badRequestResponse;
|
||||
}
|
||||
|
||||
string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
|
||||
orchestratorName: nameof(RunOrchestrationAsync),
|
||||
input: input);
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
message = "HITL content generation orchestration started.",
|
||||
topic = input.Topic,
|
||||
instanceId,
|
||||
statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
// POST /hitl/approve/{instanceId}
|
||||
[Function(nameof(SendHumanApprovalAsync))]
|
||||
public static async Task<HttpResponseData> SendHumanApprovalAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "hitl/approve/{instanceId}")] HttpRequestData req,
|
||||
string instanceId,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
// Read the approval response from the request body
|
||||
HumanApprovalResponse? approvalResponse = await req.ReadFromJsonAsync<HumanApprovalResponse>();
|
||||
if (approvalResponse is null)
|
||||
{
|
||||
HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
|
||||
await badRequestResponse.WriteAsJsonAsync(new { error = "Approval response is required" });
|
||||
return badRequestResponse;
|
||||
}
|
||||
|
||||
// Send the approval event to the orchestration
|
||||
await client.RaiseEventAsync(instanceId, "HumanApproval", approvalResponse);
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
message = "Human approval sent to orchestration.",
|
||||
instanceId,
|
||||
approved = approvalResponse.Approved
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
// GET /hitl/status/{instanceId}
|
||||
[Function(nameof(GetOrchestrationStatusAsync))]
|
||||
public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
|
||||
[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "hitl/status/{instanceId}")] HttpRequestData req,
|
||||
string instanceId,
|
||||
[DurableClient] DurableTaskClient client)
|
||||
{
|
||||
OrchestrationMetadata? status = await client.GetInstanceAsync(
|
||||
instanceId,
|
||||
getInputsAndOutputs: true,
|
||||
req.FunctionContext.CancellationToken);
|
||||
|
||||
if (status is null)
|
||||
{
|
||||
HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
|
||||
await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
|
||||
return notFound;
|
||||
}
|
||||
|
||||
HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
|
||||
await response.WriteAsJsonAsync(new
|
||||
{
|
||||
instanceId = status.InstanceId,
|
||||
runtimeStatus = status.RuntimeStatus.ToString(),
|
||||
workflowStatus = status.SerializedCustomStatus is not null ? (object)status.ReadCustomStatusAs<JsonElement>() : null,
|
||||
input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
|
||||
output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
|
||||
failureDetails = status.FailureDetails
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
[Function(nameof(NotifyUserForApproval))]
|
||||
public static void NotifyUserForApproval(
|
||||
[ActivityTrigger] GeneratedContent content,
|
||||
FunctionContext functionContext)
|
||||
{
|
||||
ILogger logger = functionContext.GetLogger(nameof(NotifyUserForApproval));
|
||||
|
||||
// In a real implementation, this would send notifications via email, SMS, etc.
|
||||
logger.LogInformation(
|
||||
"""
|
||||
NOTIFICATION: Please review the following content for approval:
|
||||
Title: {Title}
|
||||
Content: {Content}
|
||||
Use the approval endpoint to approve or reject this content.
|
||||
""",
|
||||
content.Title,
|
||||
content.Content);
|
||||
}
|
||||
|
||||
[Function(nameof(PublishContent))]
|
||||
public static void PublishContent(
|
||||
[ActivityTrigger] GeneratedContent content,
|
||||
FunctionContext functionContext)
|
||||
{
|
||||
ILogger logger = functionContext.GetLogger(nameof(PublishContent));
|
||||
|
||||
// In a real implementation, this would publish to a CMS, website, etc.
|
||||
logger.LogInformation(
|
||||
"""
|
||||
PUBLISHING: Content has been published successfully.
|
||||
Title: {Title}
|
||||
Content: {Content}
|
||||
""",
|
||||
content.Title,
|
||||
content.Content);
|
||||
}
|
||||
|
||||
private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
|
||||
{
|
||||
// NOTE: This can be made more robust by considering the value of
|
||||
// request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
|
||||
string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
|
||||
return $"{authority}/api/hitl/status/{instanceId}";
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Text.Json.Serialization;
|
||||
|
||||
namespace AgentOrchestration_HITL;
|
||||
|
||||
/// <summary>
|
||||
/// Represents the input for the Human-in-the-Loop content generation workflow.
|
||||
/// </summary>
|
||||
public sealed class ContentGenerationInput
|
||||
{
|
||||
[JsonPropertyName("topic")]
|
||||
public string Topic { get; set; } = string.Empty;
|
||||
|
||||
[JsonPropertyName("max_review_attempts")]
|
||||
public int MaxReviewAttempts { get; set; } = 3;
|
||||
|
||||
[JsonPropertyName("approval_timeout_hours")]
|
||||
public float ApprovalTimeoutHours { get; set; } = 72;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents the content generated by the writer agent.
|
||||
/// </summary>
|
||||
public sealed class GeneratedContent
|
||||
{
|
||||
[JsonPropertyName("title")]
|
||||
public string Title { get; set; } = string.Empty;
|
||||
|
||||
[JsonPropertyName("content")]
|
||||
public string Content { get; set; } = string.Empty;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents the human approval response.
|
||||
/// </summary>
|
||||
public sealed class HumanApprovalResponse
|
||||
{
|
||||
[JsonPropertyName("approved")]
|
||||
public bool Approved { get; set; }
|
||||
|
||||
[JsonPropertyName("feedback")]
|
||||
public string Feedback { get; set; } = string.Empty;
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Azure;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Hosting.AzureFunctions;
|
||||
using Microsoft.Azure.Functions.Worker.Builder;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
using OpenAI;
|
||||
|
||||
// Get the Azure OpenAI endpoint and deployment name from environment variables.
|
||||
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
|
||||
|
||||
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
|
||||
string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
|
||||
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
|
||||
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
|
||||
: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
|
||||
|
||||
// Single agent used by the orchestration to demonstrate human-in-the-loop workflow.
|
||||
const string WriterName = "WriterAgent";
|
||||
const string WriterInstructions =
|
||||
"""
|
||||
You are a professional content writer who creates high-quality articles on various topics.
|
||||
You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
|
||||
""";
|
||||
|
||||
AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
|
||||
|
||||
using IHost app = FunctionsApplication
|
||||
.CreateBuilder(args)
|
||||
.ConfigureFunctionsWebApplication()
|
||||
.ConfigureDurableAgents(options => options.AddAIAgent(writerAgent))
|
||||
.Build();
|
||||
|
||||
app.Run();
|
||||
@@ -0,0 +1,126 @@
|
||||
# Multi-Agent Orchestration with Human-in-the-Loop Sample
|
||||
|
||||
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a human-in-the-loop (HITL) workflow using a single AI agent. The workflow uses a writer agent to generate content and requires human approval on every iteration, emphasizing the human-in-the-loop pattern.
|
||||
|
||||
## Key Concepts Demonstrated
|
||||
|
||||
- Single-agent orchestration
|
||||
- Human-in-the-loop feedback loop using external events (`WaitForExternalEvent`)
|
||||
- Activity functions for non-agentic workflow steps
|
||||
- Iterative content refinement based on human feedback
|
||||
- Custom status tracking for workflow visibility
|
||||
- Error handling with maximum retry attempts and timeout handling for human approval
|
||||
|
||||
## Environment Setup
|
||||
|
||||
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
|
||||
|
||||
## Running the Sample
|
||||
|
||||
With the environment setup and function app running, you can test the sample by sending an HTTP request with a topic to start the content generation workflow.
|
||||
|
||||
You can use the `demo.http` file to send a topic to the agents, or a command line tool like `curl` as shown below:
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:7071/api/hitl/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"topic": "The Future of Artificial Intelligence",
|
||||
"max_review_attempts": 3,
|
||||
"timeout_minutes": 5
|
||||
}'
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
$body = @{
|
||||
topic = "The Future of Artificial Intelligence"
|
||||
max_review_attempts = 3
|
||||
timeout_minutes = 5
|
||||
} | ConvertTo-Json
|
||||
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/hitl/run `
|
||||
-ContentType application/json `
|
||||
-Body $body
|
||||
```
|
||||
|
||||
The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
|
||||
|
||||
```json
|
||||
{
|
||||
"message": "HITL content generation orchestration started.",
|
||||
"topic": "The Future of Artificial Intelligence",
|
||||
"instanceId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
|
||||
"statusQueryGetUri": "http://localhost:7071/api/hitl/status/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
|
||||
}
|
||||
```
|
||||
|
||||
The orchestration will:
|
||||
|
||||
1. Generate initial content using the WriterAgent
|
||||
2. Notify the user to review the content
|
||||
3. Wait for human feedback via external event (configurable timeout)
|
||||
4. If approved by human, publish the content
|
||||
5. If rejected by human, incorporate feedback and regenerate content
|
||||
6. If approval timeout occurs, treat as rejection and fail the orchestration
|
||||
7. Repeat until human approval is received or maximum loop iterations are reached
|
||||
|
||||
Once the orchestration is waiting for human approval, you can send approval or rejection using the approval endpoint:
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
# Approve the content
|
||||
curl -X POST http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"approved": true,
|
||||
"feedback": "Great article! The content is well-structured and informative."
|
||||
}'
|
||||
|
||||
# Reject the content with feedback
|
||||
curl -X POST http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"approved": false,
|
||||
"feedback": "The article needs more technical depth and better examples."
|
||||
}'
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
# Approve the content
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 `
|
||||
-ContentType application/json `
|
||||
-Body '{ "approved": true, "feedback": "Great article! The content is well-structured and informative." }'
|
||||
|
||||
# Reject the content with feedback
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 `
|
||||
-ContentType application/json `
|
||||
-Body '{ "approved": false, "feedback": "The article needs more technical depth and better examples." }'
|
||||
```
|
||||
|
||||
Once the orchestration has completed, you can get the status by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
|
||||
|
||||
```json
|
||||
{
|
||||
"failureDetails": null,
|
||||
"input": {
|
||||
"topic": "The Future of Artificial Intelligence",
|
||||
"max_review_attempts": 3
|
||||
},
|
||||
"instanceId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
|
||||
"output": {
|
||||
"content": "The Future of Artificial Intelligence is..."
|
||||
},
|
||||
"runtimeStatus": "Completed",
|
||||
"workflowStatus": "Content published successfully at 2025-10-15T12:00:00Z"
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,44 @@
|
||||
### Start the HITL content generation orchestration with default timeout (30 days)
|
||||
POST http://localhost:7071/api/hitl/run
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"topic": "The Future of Artificial Intelligence",
|
||||
"max_review_attempts": 3
|
||||
}
|
||||
|
||||
|
||||
### Start the HITL content generation orchestration with very short timeout for demonstration (~4 seconds)
|
||||
POST http://localhost:7071/api/hitl/run
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"topic": "The Future of Artificial Intelligence",
|
||||
"max_review_attempts": 3,
|
||||
"approval_timeout_hours": 0.001
|
||||
}
|
||||
|
||||
|
||||
### Copy/paste the instanceId from the response above
|
||||
@instanceId=INSTANCE_ID_GOES_HERE
|
||||
|
||||
### Check the status of the orchestration (replace {instanceId} with the actual instance ID from the response above)
|
||||
GET http://localhost:7071/api/hitl/status/{{instanceId}}
|
||||
|
||||
### Send human approval (replace {instanceId} with the actual instance ID)
|
||||
POST http://localhost:7071/api/hitl/approve/{{instanceId}}
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"approved": true,
|
||||
"feedback": "Great article! The content is well-structured and informative."
|
||||
}
|
||||
|
||||
### Send human rejection with feedback (replace {instanceId} with the actual instance ID)
|
||||
POST http://localhost:7071/api/hitl/approve/{{instanceId}}
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"approved": false,
|
||||
"feedback": "The article needs more technical depth and better examples. Please add more specific use cases and implementation details."
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"version": "2.0",
|
||||
"logging": {
|
||||
"logLevel": {
|
||||
"Microsoft.Agents.AI.DurableTask": "Information",
|
||||
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
|
||||
"DurableTask": "Information",
|
||||
"Microsoft.DurableTask": "Information"
|
||||
}
|
||||
},
|
||||
"extensions": {
|
||||
"durableTask": {
|
||||
"hubName": "default",
|
||||
"storageProvider": {
|
||||
"type": "AzureManaged",
|
||||
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"IsEncrypted": false,
|
||||
"Values": {
|
||||
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
|
||||
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
|
||||
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
|
||||
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,42 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
<PropertyGroup>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
|
||||
<OutputType>Exe</OutputType>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<Nullable>enable</Nullable>
|
||||
<!-- The Functions build tools don't like namespaces that start with a number -->
|
||||
<AssemblyName>LongRunningTools</AssemblyName>
|
||||
<RootNamespace>LongRunningTools</RootNamespace>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<FrameworkReference Include="Microsoft.AspNetCore.App" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Azure Functions packages -->
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
|
||||
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
|
||||
<!--
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
|
||||
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
-->
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
@@ -0,0 +1,151 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.DurableTask;
|
||||
using Microsoft.Azure.Functions.Worker;
|
||||
using Microsoft.DurableTask;
|
||||
using Microsoft.Extensions.Logging;
|
||||
|
||||
namespace LongRunningTools;
|
||||
|
||||
public static class FunctionTriggers
|
||||
{
|
||||
[Function(nameof(RunOrchestrationAsync))]
|
||||
public static async Task<object> RunOrchestrationAsync(
|
||||
[OrchestrationTrigger] TaskOrchestrationContext context)
|
||||
{
|
||||
// Get the input from the orchestration
|
||||
ContentGenerationInput input = context.GetInput<ContentGenerationInput>()
|
||||
?? throw new InvalidOperationException("Content generation input is required");
|
||||
|
||||
// Get the writer agent
|
||||
DurableAIAgent writerAgent = context.GetAgent("Writer");
|
||||
AgentThread writerThread = writerAgent.GetNewThread();
|
||||
|
||||
// Set initial status
|
||||
context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
|
||||
|
||||
// Step 1: Generate initial content
|
||||
AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
|
||||
message: $"Write a short article about '{input.Topic}'.",
|
||||
thread: writerThread);
|
||||
GeneratedContent content = writerResponse.Result;
|
||||
|
||||
// Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
|
||||
int iterationCount = 0;
|
||||
while (iterationCount++ < input.MaxReviewAttempts)
|
||||
{
|
||||
context.SetCustomStatus(
|
||||
new
|
||||
{
|
||||
message = "Requesting human feedback.",
|
||||
approvalTimeoutHours = input.ApprovalTimeoutHours,
|
||||
iterationCount,
|
||||
content
|
||||
});
|
||||
|
||||
// Step 2: Notify user to review the content
|
||||
await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
|
||||
|
||||
// Step 3: Wait for human feedback with configurable timeout
|
||||
HumanApprovalResponse humanResponse;
|
||||
try
|
||||
{
|
||||
humanResponse = await context.WaitForExternalEvent<HumanApprovalResponse>(
|
||||
eventName: "HumanApproval",
|
||||
timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
|
||||
}
|
||||
catch (OperationCanceledException)
|
||||
{
|
||||
// Timeout occurred - treat as rejection
|
||||
context.SetCustomStatus(
|
||||
new
|
||||
{
|
||||
message = $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.",
|
||||
iterationCount,
|
||||
content
|
||||
});
|
||||
throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
|
||||
}
|
||||
|
||||
if (humanResponse.Approved)
|
||||
{
|
||||
context.SetCustomStatus(new
|
||||
{
|
||||
message = "Content approved by human reviewer. Publishing content...",
|
||||
content
|
||||
});
|
||||
|
||||
// Step 4: Publish the approved content
|
||||
await context.CallActivityAsync(nameof(PublishContent), content);
|
||||
|
||||
context.SetCustomStatus(new
|
||||
{
|
||||
message = $"Content published successfully at {context.CurrentUtcDateTime:s}",
|
||||
humanFeedback = humanResponse,
|
||||
content
|
||||
});
|
||||
return new { content = content.Content };
|
||||
}
|
||||
|
||||
context.SetCustomStatus(new
|
||||
{
|
||||
message = "Content rejected by human reviewer. Incorporating feedback and regenerating...",
|
||||
humanFeedback = humanResponse,
|
||||
content
|
||||
});
|
||||
|
||||
// Incorporate human feedback and regenerate
|
||||
writerResponse = await writerAgent.RunAsync<GeneratedContent>(
|
||||
message: $"""
|
||||
The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
|
||||
|
||||
Human Feedback: {humanResponse.Feedback}
|
||||
""",
|
||||
thread: writerThread);
|
||||
|
||||
content = writerResponse.Result;
|
||||
}
|
||||
|
||||
// If we reach here, it means we exhausted the maximum number of iterations
|
||||
throw new InvalidOperationException(
|
||||
$"Content could not be approved after {input.MaxReviewAttempts} iterations.");
|
||||
}
|
||||
|
||||
[Function(nameof(NotifyUserForApproval))]
|
||||
public static void NotifyUserForApproval(
|
||||
[ActivityTrigger] GeneratedContent content,
|
||||
FunctionContext functionContext)
|
||||
{
|
||||
ILogger logger = functionContext.GetLogger(nameof(NotifyUserForApproval));
|
||||
|
||||
// In a real implementation, this would send notifications via email, SMS, etc.
|
||||
logger.LogInformation(
|
||||
"""
|
||||
NOTIFICATION: Please review the following content for approval:
|
||||
Title: {Title}
|
||||
Content: {Content}
|
||||
Use the approval endpoint to approve or reject this content.
|
||||
""",
|
||||
content.Title,
|
||||
content.Content);
|
||||
}
|
||||
|
||||
[Function(nameof(PublishContent))]
|
||||
public static void PublishContent(
|
||||
[ActivityTrigger] GeneratedContent content,
|
||||
FunctionContext functionContext)
|
||||
{
|
||||
ILogger logger = functionContext.GetLogger(nameof(PublishContent));
|
||||
|
||||
// In a real implementation, this would publish to a CMS, website, etc.
|
||||
logger.LogInformation(
|
||||
"""
|
||||
PUBLISHING: Content has been published successfully.
|
||||
Title: {Title}
|
||||
Content: {Content}
|
||||
""",
|
||||
content.Title,
|
||||
content.Content);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.Text.Json.Serialization;
|
||||
|
||||
namespace LongRunningTools;
|
||||
|
||||
/// <summary>
|
||||
/// Represents the input for the content generation workflow.
|
||||
/// </summary>
|
||||
public sealed class ContentGenerationInput
|
||||
{
|
||||
[JsonPropertyName("topic")]
|
||||
public string Topic { get; set; } = string.Empty;
|
||||
|
||||
[JsonPropertyName("max_review_attempts")]
|
||||
public int MaxReviewAttempts { get; set; } = 3;
|
||||
|
||||
[JsonPropertyName("approval_timeout_hours")]
|
||||
public float ApprovalTimeoutHours { get; set; } = 72;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents the content generated by the writer agent.
|
||||
/// </summary>
|
||||
public sealed class GeneratedContent
|
||||
{
|
||||
[JsonPropertyName("title")]
|
||||
public string Title { get; set; } = string.Empty;
|
||||
|
||||
[JsonPropertyName("content")]
|
||||
public string Content { get; set; } = string.Empty;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Represents the human approval response.
|
||||
/// </summary>
|
||||
public sealed class HumanApprovalResponse
|
||||
{
|
||||
[JsonPropertyName("approved")]
|
||||
public bool Approved { get; set; }
|
||||
|
||||
[JsonPropertyName("feedback")]
|
||||
public string Feedback { get; set; } = string.Empty;
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Azure;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using LongRunningTools;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Hosting.AzureFunctions;
|
||||
using Microsoft.Azure.Functions.Worker.Builder;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.DependencyInjection;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
using Microsoft.Extensions.Logging;
|
||||
using OpenAI;
|
||||
|
||||
// Get the Azure OpenAI endpoint and deployment name from environment variables.
|
||||
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
|
||||
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
|
||||
|
||||
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
|
||||
string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
|
||||
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
|
||||
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
|
||||
: new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
|
||||
|
||||
// Agent used by the orchestration to write content.
|
||||
const string WriterAgentName = "Writer";
|
||||
const string WriterAgentInstructions =
|
||||
"""
|
||||
You are a professional content writer who creates high-quality articles on various topics.
|
||||
You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
|
||||
""";
|
||||
|
||||
AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterAgentInstructions, WriterAgentName);
|
||||
|
||||
// Agent that can start content generation workflows using tools
|
||||
const string PublisherAgentName = "Publisher";
|
||||
const string PublisherAgentInstructions =
|
||||
"""
|
||||
You are a publishing agent that can manage content generation workflows.
|
||||
You have access to tools to start, monitor, and raise events for content generation workflows.
|
||||
""";
|
||||
|
||||
using IHost app = FunctionsApplication
|
||||
.CreateBuilder(args)
|
||||
.ConfigureFunctionsWebApplication()
|
||||
.ConfigureDurableAgents(options =>
|
||||
{
|
||||
// Add the writer agent used by the orchestration
|
||||
options.AddAIAgent(writerAgent);
|
||||
|
||||
// Define the agent that can start orchestrations from tool calls
|
||||
options.AddAIAgentFactory(PublisherAgentName, sp =>
|
||||
{
|
||||
// Initialize the tools to be used by the agent.
|
||||
Tools publisherTools = new(sp.GetRequiredService<ILogger<Tools>>());
|
||||
|
||||
return client.GetChatClient(deploymentName).CreateAIAgent(
|
||||
instructions: PublisherAgentInstructions,
|
||||
name: PublisherAgentName,
|
||||
services: sp,
|
||||
tools: [
|
||||
AIFunctionFactory.Create(publisherTools.StartContentGenerationWorkflow),
|
||||
AIFunctionFactory.Create(publisherTools.GetWorkflowStatusAsync),
|
||||
AIFunctionFactory.Create(publisherTools.SubmitHumanApprovalAsync),
|
||||
]);
|
||||
});
|
||||
})
|
||||
.Build();
|
||||
|
||||
app.Run();
|
||||
@@ -0,0 +1,129 @@
|
||||
# Long Running Tools Sample
|
||||
|
||||
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create agents with long running tools. This sample builds on the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample by adding a publisher agent that can start and manage content generation workflows. A key difference is that the publisher agent knows the IDs of the workflows it starts, so it can check the status of the workflows and approve or reject them without being explicitly given the context (instance IDs, etc).
|
||||
|
||||
## Key Concepts Demonstrated
|
||||
|
||||
The same key concepts as the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample are demonstrated, but with the following additional concepts:
|
||||
|
||||
- **Long running tools**: Using `DurableAgentContext.Current` to start orchestrations from tool calls
|
||||
- **Multi-agent orchestration**: Agents can start and manage workflows that orchestrate other agents
|
||||
- **Human-in-the-loop (with delegation)**: The agent acts as an intermediary between the human and the workflow. The human remains in the loop, but delegates to the agent to start the workflow and approve or reject the content.
|
||||
|
||||
## Environment Setup
|
||||
|
||||
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
|
||||
|
||||
## Running the Sample
|
||||
|
||||
With the environment setup and function app running, you can test the sample by sending an HTTP request to start the agent, which will then trigger the content generation workflow.
|
||||
|
||||
You can use the `demo.http` file to send requests to the agent, or a command line tool like `curl` as shown below.
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
curl -i -X POST http://localhost:7071/api/agents/publisher/run \
|
||||
-D headers.txt \
|
||||
-H "Content-Type: text/plain" \
|
||||
-d 'Start a content generation workflow for the topic \"The Future of Artificial Intelligence\"'
|
||||
|
||||
# Save the thread ID to a variable and print it to the terminal
|
||||
threadId=$(cat headers.txt | grep "X-Agent-Thread" | cut -d' ' -f2)
|
||||
echo "Thread ID: $threadId"
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/agents/publisher/run `
|
||||
-ResponseHeadersVariable ResponseHeaders `
|
||||
-ContentType text/plain `
|
||||
-Body 'Start a content generation workflow for the topic \"The Future of Artificial Intelligence\"' `
|
||||
|
||||
# Save the thread ID to a variable and print it to the console
|
||||
$threadId = $ResponseHeaders['X-Agent-Thread']
|
||||
Write-Host "Thread ID: $threadId"
|
||||
```
|
||||
|
||||
The response will be a text string that looks something like the following, indicating that the agent request has been received and will be processed:
|
||||
|
||||
```http
|
||||
HTTP/1.1 200 OK
|
||||
Content-Type: text/plain
|
||||
X-Agent-Thread: @publisher@351ec855-7f4d-4527-a60d-498301ced36d
|
||||
|
||||
```
|
||||
|
||||
The `X-Agent-Thread` response header contains the thread ID, which can be used to continue the conversation by passing it as a query parameter to the `run` endpoint. The commands above show how to save the thread ID to a `$threadId` variable for use in subsequent requests.
|
||||
|
||||
Behind the scenes, the publisher agent will:
|
||||
|
||||
1. Start the content generation workflow via a tool call
|
||||
1. The workflow will generate initial content using the Writer agent and wait for human approval, which will be visible in the logs
|
||||
|
||||
Once the workflow is waiting for human approval, you can send approval or rejection by prompting the publisher agent accordingly (e.g. "Approve the content" or "Reject the content with feedback: The article needs more technical depth and better examples."):
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
# Approve the content
|
||||
curl -X POST http://localhost:7071/api/agents/publisher/run?threadId=$threadId \
|
||||
-H "Content-Type: text/plain" \
|
||||
-d 'Approve the content'
|
||||
|
||||
# Reject the content with feedback
|
||||
curl -X POST http://localhost:7071/api/agents/publisher/run?threadId=$threadId \
|
||||
-H "Content-Type: text/plain" \
|
||||
-d 'Reject the content with feedback: The article needs more technical depth and better examples.'
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
# Approve the content
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/agents/publisher/run?threadId=$threadId `
|
||||
-ContentType text/plain `
|
||||
-Body 'Approve the content'
|
||||
|
||||
# Reject the content with feedback
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/agents/publisher/run?threadId=$threadId `
|
||||
-ContentType text/plain `
|
||||
-Body 'Reject the content with feedback: The article needs more technical depth and better examples.'
|
||||
```
|
||||
|
||||
Once the workflow has completed, you can get the status by prompting the publisher agent to give you the status.
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:7071/api/agents/publisher/run?threadId=$threadId \
|
||||
-H "Content-Type: text/plain" \
|
||||
-d 'Get the status of the workflow you previously started'
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
Invoke-RestMethod -Method Post `
|
||||
-Uri http://localhost:7071/api/agents/publisher/run?threadId=$threadId `
|
||||
-ContentType text/plain `
|
||||
-Body 'Get the status of the workflow you previously started'
|
||||
```
|
||||
|
||||
The response from the publisher agent will look something like the following:
|
||||
|
||||
```text
|
||||
The status of the workflow with instance ID **ab1076d6e7ec49d8a2c2474d09b69ded** is as follows:
|
||||
|
||||
- **Execution Status:** Completed
|
||||
- **Workflow Status:** Content published successfully at `2025-10-24T20:42:02`
|
||||
- **Created At:** `2025-10-24T20:41:40.7531781+00:00`
|
||||
- **Last Updated At:** `2025-10-24T20:42:02.1410736+00:00`
|
||||
|
||||
The content has been successfully published.
|
||||
```
|
||||
```
|
||||
@@ -0,0 +1,84 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.ComponentModel;
|
||||
using Microsoft.Agents.AI.DurableTask;
|
||||
using Microsoft.DurableTask.Client;
|
||||
using Microsoft.Extensions.Logging;
|
||||
|
||||
namespace LongRunningTools;
|
||||
|
||||
/// <summary>
|
||||
/// Tools that demonstrate starting orchestrations from agent tool calls.
|
||||
/// </summary>
|
||||
internal sealed class Tools(ILogger<Tools> logger)
|
||||
{
|
||||
private readonly ILogger<Tools> _logger = logger;
|
||||
|
||||
[Description("Starts a content generation workflow and returns the instance ID for tracking.")]
|
||||
public string StartContentGenerationWorkflow([Description("The topic for content generation")] string topic)
|
||||
{
|
||||
this._logger.LogInformation("Starting content generation workflow for topic: {Topic}", topic);
|
||||
|
||||
const int MaxReviewAttempts = 3;
|
||||
const float ApprovalTimeoutHours = 72;
|
||||
|
||||
// Schedule the orchestration, which will start running after the tool call completes.
|
||||
string instanceId = DurableAgentContext.Current.ScheduleNewOrchestration(
|
||||
name: nameof(FunctionTriggers.RunOrchestrationAsync),
|
||||
input: new ContentGenerationInput
|
||||
{
|
||||
Topic = topic,
|
||||
MaxReviewAttempts = MaxReviewAttempts,
|
||||
ApprovalTimeoutHours = ApprovalTimeoutHours
|
||||
});
|
||||
|
||||
this._logger.LogInformation(
|
||||
"Content generation workflow scheduled to be started for topic '{Topic}' with instance ID: {InstanceId}",
|
||||
topic,
|
||||
instanceId);
|
||||
|
||||
return $"Workflow started with instance ID: {instanceId}";
|
||||
}
|
||||
|
||||
[Description("Gets the status of a workflow orchestration.")]
|
||||
public async Task<object> GetWorkflowStatusAsync(
|
||||
[Description("The instance ID of the workflow to check")] string instanceId,
|
||||
[Description("Whether to include detailed information")] bool includeDetails = true)
|
||||
{
|
||||
this._logger.LogInformation("Getting status for workflow instance: {InstanceId}", instanceId);
|
||||
|
||||
// Get the current agent context using the thread-static property
|
||||
OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
|
||||
instanceId,
|
||||
includeDetails);
|
||||
|
||||
if (status is null)
|
||||
{
|
||||
this._logger.LogInformation("Workflow instance '{InstanceId}' not found.", instanceId);
|
||||
return new
|
||||
{
|
||||
instanceId,
|
||||
error = $"Workflow instance '{instanceId}' not found.",
|
||||
};
|
||||
}
|
||||
|
||||
return new
|
||||
{
|
||||
instanceId = status.InstanceId,
|
||||
createdAt = status.CreatedAt,
|
||||
executionStatus = status.RuntimeStatus,
|
||||
workflowStatus = status.SerializedCustomStatus,
|
||||
lastUpdatedAt = status.LastUpdatedAt,
|
||||
failureDetails = status.FailureDetails
|
||||
};
|
||||
}
|
||||
|
||||
[Description("Raises a feedback event for the content generation workflow.")]
|
||||
public async Task SubmitHumanApprovalAsync(
|
||||
[Description("The instance ID of the workflow to submit feedback for")] string instanceId,
|
||||
[Description("Feedback to submit")] HumanApprovalResponse feedback)
|
||||
{
|
||||
this._logger.LogInformation("Submitting human approval for workflow instance: {InstanceId}", instanceId);
|
||||
await DurableAgentContext.Current.RaiseOrchestrationEventAsync(instanceId, "HumanApproval", feedback);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
### Run an agent that can schedule orchestrations as tool calls
|
||||
POST http://localhost:7071/api/agents/publisher/run
|
||||
Content-Type: text/plain
|
||||
|
||||
Start a content generation workflow for the topic 'The Future of Artificial Intelligence'
|
||||
|
||||
|
||||
### Save the session ID from the response to continue the conversation
|
||||
@threadId = <YOUR_THREAD_ID>
|
||||
|
||||
### Check the status of the workflow
|
||||
POST http://localhost:7071/api/agents/publisher/run?threadId={{threadId}}
|
||||
Content-Type: text/plain
|
||||
|
||||
Check the status of the workflow you previously started
|
||||
|
||||
### Reject content with feedback
|
||||
POST http://localhost:7071/api/agents/publisher/run?threadId={{threadId}}
|
||||
Content-Type: text/plain
|
||||
|
||||
Reject the content with feedback: The article needs more technical depth and better examples.
|
||||
|
||||
### Approve content
|
||||
POST http://localhost:7071/api/agents/publisher/run?threadId={{threadId}}
|
||||
Content-Type: text/plain
|
||||
|
||||
Approve the content
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"version": "2.0",
|
||||
"logging": {
|
||||
"logLevel": {
|
||||
"Microsoft.Agents.AI.DurableTask": "Information",
|
||||
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
|
||||
"DurableTask": "Information",
|
||||
"Microsoft.DurableTask": "Information"
|
||||
}
|
||||
},
|
||||
"extensions": {
|
||||
"durableTask": {
|
||||
"hubName": "default",
|
||||
"storageProvider": {
|
||||
"type": "AzureManaged",
|
||||
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"IsEncrypted": false,
|
||||
"Values": {
|
||||
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
|
||||
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
|
||||
"DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
|
||||
"AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,150 @@
|
||||
# Azure Functions Samples
|
||||
|
||||
This directory contains samples for Azure Functions.
|
||||
|
||||
- **[01_SingleAgent](01_SingleAgent)**: A sample that demonstrates how to host a single conversational agent in an Azure Functions app and invoke it directly over HTTP.
|
||||
- **[02_AgentOrchestration_Chaining](02_AgentOrchestration_Chaining)**: A sample that demonstrates how to host a single conversational agent in an Azure Functions app and invoke it using a durable orchestration.
|
||||
- **[03_AgentOrchestration_Concurrency](03_AgentOrchestration_Concurrency)**: A sample that demonstrates how to host multiple agents in an Azure Functions app and run them concurrently using a durable orchestration.
|
||||
- **[04_AgentOrchestration_Conditionals](04_AgentOrchestration_Conditionals)**: A sample that demonstrates how to host multiple agents in an Azure Functions app and run them sequentially using a durable orchestration with conditionals.
|
||||
- **[05_AgentOrchestration_HITL](05_AgentOrchestration_HITL)**: A sample that demonstrates how to implement a human-in-the-loop workflow using durable orchestration, including external event handling for human approval.
|
||||
- **[06_LongRunningTools](06_LongRunningTools)**: A sample that demonstrates how agents can start and interact with durable orchestrations from tool calls to enable long-running tool scenarios.
|
||||
|
||||
## Running the Samples
|
||||
|
||||
These samples are designed to be run locally in a cloned repository.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
The following prerequisites are required to run the samples:
|
||||
|
||||
- [.NET 9.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
|
||||
- [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local) (version 4.x or later)
|
||||
- [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed and authenticated (`az login`) or an API key for the Azure OpenAI service
|
||||
- [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) with a deployed model (gpt-4o-mini or better is recommended)
|
||||
- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/develop-with-durable-task-scheduler) (local emulator or Azure-hosted)
|
||||
- [Docker](https://docs.docker.com/get-docker/) installed if running the Durable Task Scheduler emulator locally
|
||||
|
||||
### Configuring RBAC Permissions for Azure OpenAI
|
||||
|
||||
These samples are configured to use the Azure OpenAI service with RBAC permissions to access the model. You'll need to configure the RBAC permissions for the Azure OpenAI service to allow the Azure Functions app to access the model.
|
||||
|
||||
Below is an example of how to configure the RBAC permissions for the Azure OpenAI service to allow the current user to access the model.
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
az role assignment create \
|
||||
--assignee "yourname@contoso.com" \
|
||||
--role "Cognitive Services OpenAI User" \
|
||||
--scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
az role assignment create `
|
||||
--assignee "yourname@contoso.com" `
|
||||
--role "Cognitive Services OpenAI User" `
|
||||
--scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
|
||||
```
|
||||
|
||||
More information on how to configure RBAC permissions for Azure OpenAI can be found in the [Azure OpenAI documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=cli).
|
||||
|
||||
### Setting an API key for the Azure OpenAI service
|
||||
|
||||
As an alternative to configuring Azure RBAC permissions, you can set an API key for the Azure OpenAI service by setting the `AZURE_OPENAI_KEY` environment variable.
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
export AZURE_OPENAI_KEY="your-api-key"
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
$env:AZURE_OPENAI_KEY="your-api-key"
|
||||
```
|
||||
|
||||
### Start Durable Task Scheduler
|
||||
|
||||
Most samples use the Durable Task Scheduler (DTS) to support hosted agents and durable orchestrations. DTS also allows you to view the status of orchestrations and their inputs and outputs from a web UI.
|
||||
|
||||
To run the Durable Task Scheduler locally, you can use the following `docker` command:
|
||||
|
||||
```bash
|
||||
docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
|
||||
```
|
||||
|
||||
The DTS dashboard will be available at `http://localhost:8080`.
|
||||
|
||||
### Start the Azure Storage Emulator
|
||||
|
||||
All Function apps require an Azure Storage account to store functions-specific state. You can use the Azure Storage Emulator to run a local instance of the Azure Storage service.
|
||||
|
||||
You can run the Azure Storage emulator locally as a standalone process or via a Docker container.
|
||||
|
||||
#### Docker
|
||||
|
||||
```bash
|
||||
docker run -d --name storage-emulator -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite
|
||||
```
|
||||
|
||||
#### Standalone
|
||||
|
||||
```bash
|
||||
npm install -g azurite
|
||||
azurite
|
||||
```
|
||||
|
||||
### Environment Configuration
|
||||
|
||||
Each sample has its own `local.settings.json` file that contains the environment variables for the sample. You'll need to update the `local.settings.json` file with the correct values for your Azure OpenAI resource.
|
||||
|
||||
```json
|
||||
{
|
||||
"Values": {
|
||||
"AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
|
||||
"AZURE_OPENAI_DEPLOYMENT": "your-deployment-name"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Alternatively, you can set the environment variables in the command line.
|
||||
|
||||
### Bash (Linux/macOS/WSL)
|
||||
|
||||
```bash
|
||||
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
|
||||
export AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
|
||||
```
|
||||
|
||||
### PowerShell
|
||||
|
||||
```powershell
|
||||
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
|
||||
$env:AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
|
||||
```
|
||||
|
||||
These environment variables, when set, will override the values in the `local.settings.json` file, making it convenient to test the sample without having to update the `local.settings.json` file.
|
||||
|
||||
### Start the Azure Functions app
|
||||
|
||||
Navigate to the sample directory and start the Azure Functions app:
|
||||
|
||||
```bash
|
||||
cd dotnet/samples/AzureFunctions/01_SingleAgent
|
||||
func start
|
||||
```
|
||||
|
||||
The Azure Functions app will be available at `http://localhost:7071`.
|
||||
|
||||
### Test the Azure Functions app
|
||||
|
||||
The README.md file in each sample directory contains instructions for testing the sample. Each sample also includes a `demo.http` file that can be used to test the sample from the command line. These files can be opened in VS Code with the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) extension or in the Visual Studio IDE.
|
||||
|
||||
### Viewing the sample output
|
||||
|
||||
The Azure Functions app logs are displayed in the terminal where you ran `func start`. This is where most agent output will be displayed. You can adjust logging levels in the `host.json` file as needed.
|
||||
|
||||
You can also see the state of agents and orchestrations in the DTS dashboard.
|
||||
Reference in New Issue
Block a user