mirror of
https://github.com/microsoft/agent-framework.git
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Moved by agent (#4094)
This commit is contained in:
Chris
GitHub
-21
@@ -1,21 +0,0 @@
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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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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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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</ItemGroup>
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<ItemGroup>
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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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-162
@@ -1,162 +0,0 @@
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to add a basic custom memory component to an agent.
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// The memory component subscribes to all messages added to the conversation and
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// extracts the user's name and age if provided.
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// The component adds a prompt to ask for this information if it is not already known
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// and provides it to the model before each invocation if known.
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using System.Text;
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using System.Text.Json;
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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.Extensions.AI;
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using OpenAI.Chat;
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using SampleApp;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
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// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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ChatClient chatClient = new AzureOpenAIClient(
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new Uri(endpoint),
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new DefaultAzureCredential())
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.GetChatClient(deploymentName);
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// Create the agent and provide a factory to add our custom memory component to
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// all sessions created by the agent. Here each new memory component will have its own
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// user info object, so each session will have its own memory.
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// In real world applications/services, where the user info would be persisted in a database,
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// and preferably shared between multiple sessions used by the same user, ensure that the
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// factory reads the user id from the current context and scopes the memory component
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// and its storage to that user id.
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AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
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{
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ChatOptions = new() { Instructions = "You are a friendly assistant. Always address the user by their name." },
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AIContextProviders = [new UserInfoMemory(chatClient.AsIChatClient())]
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});
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// Create a new session for the conversation.
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AgentSession session = await agent.CreateSessionAsync();
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Console.WriteLine(">> Use session with blank memory\n");
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// Invoke the agent and output the text result.
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Console.WriteLine(await agent.RunAsync("Hello, what is the square root of 9?", session));
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Console.WriteLine(await agent.RunAsync("My name is Ruaidhrí", session));
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Console.WriteLine(await agent.RunAsync("I am 20 years old", session));
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// We can serialize the session. The serialized state will include the state of the memory component.
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JsonElement sesionElement = await agent.SerializeSessionAsync(session);
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Console.WriteLine("\n>> Use deserialized session with previously created memories\n");
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// Later we can deserialize the session and continue the conversation with the previous memory component state.
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var deserializedSession = await agent.DeserializeSessionAsync(sesionElement);
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Console.WriteLine(await agent.RunAsync("What is my name and age?", deserializedSession));
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Console.WriteLine("\n>> Read memories using memory component\n");
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// It's possible to access the memory component via the agent's GetService method.
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var userInfo = agent.GetService<UserInfoMemory>()?.GetUserInfo(deserializedSession);
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// Output the user info that was captured by the memory component.
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Console.WriteLine($"MEMORY - User Name: {userInfo?.UserName}");
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Console.WriteLine($"MEMORY - User Age: {userInfo?.UserAge}");
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Console.WriteLine("\n>> Use new session with previously created memories\n");
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// It is also possible to set the memories using a memory component on an individual session.
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// This is useful if we want to start a new session, but have it share the same memories as a previous session.
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var newSession = await agent.CreateSessionAsync();
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if (userInfo is not null && agent.GetService<UserInfoMemory>() is UserInfoMemory newSessionMemory)
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{
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newSessionMemory.SetUserInfo(newSession, userInfo);
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}
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// Invoke the agent and output the text result.
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// This time the agent should remember the user's name and use it in the response.
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Console.WriteLine(await agent.RunAsync("What is my name and age?", newSession));
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namespace SampleApp
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{
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/// <summary>
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/// Sample memory component that can remember a user's name and age.
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/// </summary>
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internal sealed class UserInfoMemory : AIContextProvider
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{
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private readonly ProviderSessionState<UserInfo> _sessionState;
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private readonly IChatClient _chatClient;
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public UserInfoMemory(IChatClient chatClient, Func<AgentSession?, UserInfo>? stateInitializer = null)
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: base(null, null)
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{
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this._sessionState = new ProviderSessionState<UserInfo>(
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stateInitializer ?? (_ => new UserInfo()),
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this.GetType().Name);
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this._chatClient = chatClient;
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}
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public override string StateKey => this._sessionState.StateKey;
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public UserInfo GetUserInfo(AgentSession session)
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=> this._sessionState.GetOrInitializeState(session);
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public void SetUserInfo(AgentSession session, UserInfo userInfo)
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=> this._sessionState.SaveState(session, userInfo);
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protected override async ValueTask StoreAIContextAsync(InvokedContext context, CancellationToken cancellationToken = default)
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{
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var userInfo = this._sessionState.GetOrInitializeState(context.Session);
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// Try and extract the user name and age from the message if we don't have it already and it's a user message.
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if ((userInfo.UserName is null || userInfo.UserAge is null) && context.RequestMessages.Any(x => x.Role == ChatRole.User))
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{
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var result = await this._chatClient.GetResponseAsync<UserInfo>(
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context.RequestMessages,
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new ChatOptions()
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{
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Instructions = "Extract the user's name and age from the message if present. If not present return nulls."
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},
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cancellationToken: cancellationToken);
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userInfo.UserName ??= result.Result.UserName;
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userInfo.UserAge ??= result.Result.UserAge;
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}
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this._sessionState.SaveState(context.Session, userInfo);
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}
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protected override ValueTask<AIContext> ProvideAIContextAsync(InvokingContext context, CancellationToken cancellationToken = default)
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{
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var userInfo = this._sessionState.GetOrInitializeState(context.Session);
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StringBuilder instructions = new();
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// If we don't already know the user's name and age, add instructions to ask for them, otherwise just provide what we have to the context.
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instructions
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.AppendLine(
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userInfo.UserName is null ?
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"Ask the user for their name and politely decline to answer any questions until they provide it." :
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$"The user's name is {userInfo.UserName}.")
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.AppendLine(
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userInfo.UserAge is null ?
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"Ask the user for their age and politely decline to answer any questions until they provide it." :
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$"The user's age is {userInfo.UserAge}.");
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return new ValueTask<AIContext>(new AIContext
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{
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Instructions = instructions.ToString()
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});
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}
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}
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internal sealed class UserInfo
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{
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public string? UserName { get; set; }
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public int? UserAge { get; set; }
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}
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}
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@@ -6,4 +6,4 @@ These samples show how to create an agent with the Agent Framework that uses Mem
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|---|---|
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|[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
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|[Memory with MemoryStore](./AgentWithMemory_Step02_MemoryUsingMem0/)|This sample demonstrates how to create and run an agent that uses the Mem0 service to extract and retrieve individual memories.|
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|[Custom Memory Implementation](./AgentWithMemory_Step03_CustomMemory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
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|[Custom Memory Implementation](../../01-get-started/04_memory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
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@@ -1,21 +0,0 @@
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<Project Sdk="Microsoft.NET.Sdk">
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||||
<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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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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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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</ItemGroup>
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<ItemGroup>
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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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@@ -1,29 +0,0 @@
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to create and use a simple AI agent with Azure OpenAI as the backend.
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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 OpenAI.Chat;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
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// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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AIAgent agent = new AzureOpenAIClient(
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new Uri(endpoint),
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new DefaultAzureCredential())
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.GetChatClient(deploymentName)
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.AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
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// Invoke the agent and output the text result.
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Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
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// Invoke the agent with streaming support.
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await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
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{
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Console.WriteLine(update);
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}
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-21
@@ -1,21 +0,0 @@
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<Project Sdk="Microsoft.NET.Sdk">
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||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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|
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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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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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</ItemGroup>
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<ItemGroup>
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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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@@ -1,36 +0,0 @@
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to create and use a simple AI agent with a multi-turn conversation.
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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 OpenAI.Chat;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
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|
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
|
||||
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
|
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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AIAgent agent = new AzureOpenAIClient(
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new Uri(endpoint),
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new DefaultAzureCredential())
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.GetChatClient(deploymentName)
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.AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
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// Invoke the agent with a multi-turn conversation, where the context is preserved in the session object.
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AgentSession session = await agent.CreateSessionAsync();
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Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
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Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
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// Invoke the agent with a multi-turn conversation and streaming, where the context is preserved in the session object.
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session = await agent.CreateSessionAsync();
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await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate.", session))
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{
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Console.WriteLine(update);
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}
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await foreach (var update in agent.RunStreamingAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session))
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{
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Console.WriteLine(update);
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}
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-21
@@ -1,21 +0,0 @@
|
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<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFrameworks>net10.0</TargetFrameworks>
|
||||
|
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<Nullable>enable</Nullable>
|
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<ImplicitUsings>enable</ImplicitUsings>
|
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</PropertyGroup>
|
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|
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<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
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@@ -1,37 +0,0 @@
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// Copyright (c) Microsoft. All rights reserved.
|
||||
|
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// This sample demonstrates how to use a ChatClientAgent with function tools.
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// It shows both non-streaming and streaming agent interactions using menu-related tools.
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using System.ComponentModel;
|
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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.Extensions.AI;
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using OpenAI.Chat;
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|
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
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|
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[Description("Get the weather for a given location.")]
|
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static string GetWeather([Description("The location to get the weather for.")] string location)
|
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=> $"The weather in {location} is cloudy with a high of 15°C.";
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|
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// Create the chat client and agent, and provide the function tool to the agent.
|
||||
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
|
||||
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
|
||||
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new DefaultAzureCredential())
|
||||
.GetChatClient(deploymentName)
|
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.AsAIAgent(instructions: "You are a helpful assistant", tools: [AIFunctionFactory.Create(GetWeather)]);
|
||||
|
||||
// Non-streaming agent interaction with function tools.
|
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Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));
|
||||
|
||||
// Streaming agent interaction with function tools.
|
||||
await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
|
||||
{
|
||||
Console.WriteLine(update);
|
||||
}
|
||||
@@ -26,9 +26,6 @@ Before you begin, ensure you have the following prerequisites:
|
||||
|
||||
|Sample|Description|
|
||||
|---|---|
|
||||
|[Running a simple agent](./Agent_Step01_Running/)|This sample demonstrates how to create and run a basic agent with instructions|
|
||||
|[Multi-turn conversation with a simple agent](./Agent_Step02_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
|
||||
|[Using function tools with a simple agent](./Agent_Step03_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|
||||
|[Using OpenAPI function tools with a simple agent](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/AgentFrameworkMigration/AzureOpenAI/Step04_ToolCall_WithOpenAPI)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)|
|
||||
|[Using function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
|
||||
|[Structured output with a simple agent](./Agent_Step05_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
|
||||
|
||||
@@ -12,7 +12,6 @@ Please begin with the [Foundational](./_Foundational) samples in order. These th
|
||||
|
||||
| Sample | Concepts |
|
||||
|--------|----------|
|
||||
| [Executors and Edges](./_Foundational/01_ExecutorsAndEdges) | Minimal workflow with basic executors and edges |
|
||||
| [Streaming](./_Foundational/02_Streaming) | Extends workflows with event streaming |
|
||||
| [Agents](./_Foundational/03_AgentsInWorkflows) | Use agents in workflows |
|
||||
| [Agentic Workflow Patterns](./_Foundational/04_AgentWorkflowPatterns) | Demonstrates common agentic workflow patterns |
|
||||
|
||||
-15
@@ -1,15 +0,0 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFrameworks>net10.0</TargetFrameworks>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -1,63 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using Microsoft.Agents.AI.Workflows;
|
||||
|
||||
namespace WorkflowExecutorsAndEdgesSample;
|
||||
|
||||
/// <summary>
|
||||
/// This sample introduces the concepts of executors and edges in a workflow.
|
||||
///
|
||||
/// Workflows are built from executors (processing units) connected by edges (data flow paths).
|
||||
/// In this example, we create a simple text processing pipeline that:
|
||||
/// 1. Takes input text and converts it to uppercase using an UppercaseExecutor
|
||||
/// 2. Takes the uppercase text and reverses it using a ReverseTextExecutor
|
||||
///
|
||||
/// The executors are connected sequentially, so data flows from one to the next in order.
|
||||
/// For input "Hello, World!", the workflow produces "!DLROW ,OLLEH".
|
||||
/// </summary>
|
||||
public static class Program
|
||||
{
|
||||
private static async Task Main()
|
||||
{
|
||||
// Create the executors
|
||||
Func<string, string> uppercaseFunc = s => s.ToUpperInvariant();
|
||||
var uppercase = uppercaseFunc.BindAsExecutor("UppercaseExecutor");
|
||||
|
||||
ReverseTextExecutor reverse = new();
|
||||
|
||||
// Build the workflow by connecting executors sequentially
|
||||
WorkflowBuilder builder = new(uppercase);
|
||||
builder.AddEdge(uppercase, reverse).WithOutputFrom(reverse);
|
||||
var workflow = builder.Build();
|
||||
|
||||
// Execute the workflow with input data
|
||||
await using Run run = await InProcessExecution.RunAsync(workflow, "Hello, World!");
|
||||
foreach (WorkflowEvent evt in run.NewEvents)
|
||||
{
|
||||
if (evt is ExecutorCompletedEvent executorComplete)
|
||||
{
|
||||
Console.WriteLine($"{executorComplete.ExecutorId}: {executorComplete.Data}");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Second executor: reverses the input text and completes the workflow.
|
||||
/// </summary>
|
||||
internal sealed class ReverseTextExecutor() : Executor<string, string>("ReverseTextExecutor")
|
||||
{
|
||||
/// <summary>
|
||||
/// Processes the input message by reversing the text.
|
||||
/// </summary>
|
||||
/// <param name="message">The input text to reverse</param>
|
||||
/// <param name="context">Workflow context for accessing workflow services and adding events</param>
|
||||
/// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests.
|
||||
/// The default is <see cref="CancellationToken.None"/>.</param>
|
||||
/// <returns>The input text reversed</returns>
|
||||
public override ValueTask<string> HandleAsync(string message, IWorkflowContext context, CancellationToken cancellationToken = default)
|
||||
{
|
||||
// Because we do not suppress it, the returned result will be yielded as an output from this executor.
|
||||
return ValueTask.FromResult(string.Concat(message.Reverse()));
|
||||
}
|
||||
}
|
||||
+1
-1
@@ -146,8 +146,8 @@ I cannot process this request as it appears to contain unsafe content.
|
||||
|
||||
## Related Samples
|
||||
|
||||
- **05_first_workflow** - Basic executor and edge concepts
|
||||
- **03_AgentsInWorkflows** - Introduction to using agents in workflows
|
||||
- **01_ExecutorsAndEdges** - Basic executor and edge concepts
|
||||
- **02_Streaming** - Understanding streaming events
|
||||
- **Concurrent** - Parallel processing with fan-out/fan-in patterns
|
||||
|
||||
|
||||
Reference in New Issue
Block a user