Merge branch 'main' into feature-foundry-agents

This commit is contained in:
Chris
2025-11-10 08:42:17 -08:00
committed by GitHub
161 changed files with 17581 additions and 5794 deletions
-1
View File
@@ -1 +0,0 @@
launchSettings.json
@@ -0,0 +1,24 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net9.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<UserSecretsId>b9c3f1e1-2fb4-5g29-0e52-53e2b7g9gf21</UserSecretsId>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Bcl.AsyncInterfaces" VersionOverride="10.0.0-rc.2.25502.107" />
<PackageReference Include="System.Net.ServerSentEvents" VersionOverride="10.0.0-rc.2.25502.107" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.AGUI\Microsoft.Agents.AI.AGUI.csproj" />
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,11 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
namespace AGUIDojoServer;
[JsonSerializable(typeof(WeatherInfo))]
[JsonSerializable(typeof(Recipe))]
[JsonSerializable(typeof(Ingredient))]
[JsonSerializable(typeof(RecipeResponse))]
internal sealed partial class AGUIDojoServerSerializerContext : JsonSerializerContext;
@@ -0,0 +1,98 @@
// Copyright (c) Microsoft. All rights reserved.
using System.ComponentModel;
using System.Text.Json;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using ChatClient = OpenAI.Chat.ChatClient;
namespace AGUIDojoServer;
internal static class ChatClientAgentFactory
{
private static AzureOpenAIClient? s_azureOpenAIClient;
private static string? s_deploymentName;
public static void Initialize(IConfiguration configuration)
{
string endpoint = configuration["AZURE_OPENAI_ENDPOINT"] ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
s_deploymentName = configuration["AZURE_OPENAI_DEPLOYMENT_NAME"] ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
s_azureOpenAIClient = new AzureOpenAIClient(
new Uri(endpoint),
new DefaultAzureCredential());
}
public static ChatClientAgent CreateAgenticChat()
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().CreateAIAgent(
name: "AgenticChat",
description: "A simple chat agent using Azure OpenAI");
}
public static ChatClientAgent CreateBackendToolRendering()
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().CreateAIAgent(
name: "BackendToolRenderer",
description: "An agent that can render backend tools using Azure OpenAI",
tools: [AIFunctionFactory.Create(
GetWeather,
name: "get_weather",
description: "Get the weather for a given location.",
AGUIDojoServerSerializerContext.Default.Options)]);
}
public static ChatClientAgent CreateHumanInTheLoop()
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().CreateAIAgent(
name: "HumanInTheLoopAgent",
description: "An agent that involves human feedback in its decision-making process using Azure OpenAI");
}
public static ChatClientAgent CreateToolBasedGenerativeUI()
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().CreateAIAgent(
name: "ToolBasedGenerativeUIAgent",
description: "An agent that uses tools to generate user interfaces using Azure OpenAI");
}
public static ChatClientAgent CreateAgenticUI()
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().CreateAIAgent(
name: "AgenticUIAgent",
description: "An agent that generates agentic user interfaces using Azure OpenAI");
}
public static AIAgent CreateSharedState(JsonSerializerOptions options)
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
var baseAgent = chatClient.AsIChatClient().CreateAIAgent(
name: "SharedStateAgent",
description: "An agent that demonstrates shared state patterns using Azure OpenAI");
return new SharedStateAgent(baseAgent, options);
}
[Description("Get the weather for a given location.")]
private static WeatherInfo GetWeather([Description("The location to get the weather for.")] string location) => new()
{
Temperature = 20,
Conditions = "sunny",
Humidity = 50,
WindSpeed = 10,
FeelsLike = 25
};
}
@@ -0,0 +1,17 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
namespace AGUIDojoServer;
internal sealed class Ingredient
{
[JsonPropertyName("icon")]
public string Icon { get; set; } = string.Empty;
[JsonPropertyName("name")]
public string Name { get; set; } = string.Empty;
[JsonPropertyName("amount")]
public string Amount { get; set; } = string.Empty;
}
@@ -0,0 +1,45 @@
// Copyright (c) Microsoft. All rights reserved.
using AGUIDojoServer;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.AspNetCore.HttpLogging;
using Microsoft.Extensions.Options;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
builder.Services.AddHttpLogging(logging =>
{
logging.LoggingFields = HttpLoggingFields.RequestPropertiesAndHeaders | HttpLoggingFields.RequestBody
| HttpLoggingFields.ResponsePropertiesAndHeaders | HttpLoggingFields.ResponseBody;
logging.RequestBodyLogLimit = int.MaxValue;
logging.ResponseBodyLogLimit = int.MaxValue;
});
builder.Services.AddHttpClient().AddLogging();
builder.Services.ConfigureHttpJsonOptions(options => options.SerializerOptions.TypeInfoResolverChain.Add(AGUIDojoServerSerializerContext.Default));
builder.Services.AddAGUI();
WebApplication app = builder.Build();
app.UseHttpLogging();
// Initialize the factory
ChatClientAgentFactory.Initialize(app.Configuration);
// Map the AG-UI agent endpoints for different scenarios
app.MapAGUI("/agentic_chat", ChatClientAgentFactory.CreateAgenticChat());
app.MapAGUI("/backend_tool_rendering", ChatClientAgentFactory.CreateBackendToolRendering());
app.MapAGUI("/human_in_the_loop", ChatClientAgentFactory.CreateHumanInTheLoop());
app.MapAGUI("/tool_based_generative_ui", ChatClientAgentFactory.CreateToolBasedGenerativeUI());
app.MapAGUI("/agentic_generative_ui", ChatClientAgentFactory.CreateAgenticUI());
var jsonOptions = app.Services.GetRequiredService<IOptions<Microsoft.AspNetCore.Http.Json.JsonOptions>>();
app.MapAGUI("/shared_state", ChatClientAgentFactory.CreateSharedState(jsonOptions.Value.SerializerOptions));
await app.RunAsync();
public partial class Program { }
@@ -0,0 +1,12 @@
{
"profiles": {
"AGUIDojoServer": {
"commandName": "Project",
"launchBrowser": true,
"environmentVariables": {
"ASPNETCORE_ENVIRONMENT": "Development"
},
"applicationUrl": "http://localhost:5018"
}
}
}
@@ -0,0 +1,26 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
namespace AGUIDojoServer;
internal sealed class Recipe
{
[JsonPropertyName("title")]
public string Title { get; set; } = string.Empty;
[JsonPropertyName("skill_level")]
public string SkillLevel { get; set; } = string.Empty;
[JsonPropertyName("cooking_time")]
public string CookingTime { get; set; } = string.Empty;
[JsonPropertyName("special_preferences")]
public List<string> SpecialPreferences { get; set; } = [];
[JsonPropertyName("ingredients")]
public List<Ingredient> Ingredients { get; set; } = [];
[JsonPropertyName("instructions")]
public List<string> Instructions { get; set; } = [];
}
@@ -0,0 +1,13 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
namespace AGUIDojoServer;
#pragma warning disable CA1812 // Used for the JsonSchema response format
internal sealed class RecipeResponse
#pragma warning restore CA1812
{
[JsonPropertyName("recipe")]
public Recipe Recipe { get; set; } = new();
}
@@ -0,0 +1,106 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Diagnostics.CodeAnalysis;
using System.Runtime.CompilerServices;
using System.Text.Json;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
namespace AGUIDojoServer;
[SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated by ChatClientAgentFactory.CreateSharedState")]
internal sealed class SharedStateAgent : DelegatingAIAgent
{
private readonly JsonSerializerOptions _jsonSerializerOptions;
public SharedStateAgent(AIAgent innerAgent, JsonSerializerOptions jsonSerializerOptions)
: base(innerAgent)
{
this._jsonSerializerOptions = jsonSerializerOptions;
}
public override Task<AgentRunResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
{
return this.RunStreamingAsync(messages, thread, options, cancellationToken).ToAgentRunResponseAsync(cancellationToken);
}
public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
IEnumerable<ChatMessage> messages,
AgentThread? thread = null,
AgentRunOptions? options = null,
[EnumeratorCancellation] CancellationToken cancellationToken = default)
{
if (options is not ChatClientAgentRunOptions { ChatOptions.AdditionalProperties: { } properties } chatRunOptions ||
!properties.TryGetValue("ag_ui_state", out JsonElement state))
{
await foreach (var update in this.InnerAgent.RunStreamingAsync(messages, thread, options, cancellationToken).ConfigureAwait(false))
{
yield return update;
}
yield break;
}
var firstRunOptions = new ChatClientAgentRunOptions
{
ChatOptions = chatRunOptions.ChatOptions.Clone(),
AllowBackgroundResponses = chatRunOptions.AllowBackgroundResponses,
ContinuationToken = chatRunOptions.ContinuationToken,
ChatClientFactory = chatRunOptions.ChatClientFactory,
};
// Configure JSON schema response format for structured state output
firstRunOptions.ChatOptions.ResponseFormat = ChatResponseFormat.ForJsonSchema<RecipeResponse>(
schemaName: "RecipeResponse",
schemaDescription: "A response containing a recipe with title, skill level, cooking time, preferences, ingredients, and instructions");
ChatMessage stateUpdateMessage = new(
ChatRole.System,
[
new TextContent("Here is the current state in JSON format:"),
new TextContent(state.GetRawText()),
new TextContent("The new state is:")
]);
var firstRunMessages = messages.Append(stateUpdateMessage);
var allUpdates = new List<AgentRunResponseUpdate>();
await foreach (var update in this.InnerAgent.RunStreamingAsync(firstRunMessages, thread, firstRunOptions, cancellationToken).ConfigureAwait(false))
{
allUpdates.Add(update);
// Yield all non-text updates (tool calls, etc.)
bool hasNonTextContent = update.Contents.Any(c => c is not TextContent);
if (hasNonTextContent)
{
yield return update;
}
}
var response = allUpdates.ToAgentRunResponse();
if (response.TryDeserialize(this._jsonSerializerOptions, out JsonElement stateSnapshot))
{
byte[] stateBytes = JsonSerializer.SerializeToUtf8Bytes(
stateSnapshot,
this._jsonSerializerOptions.GetTypeInfo(typeof(JsonElement)));
yield return new AgentRunResponseUpdate
{
Contents = [new DataContent(stateBytes, "application/json")]
};
}
else
{
yield break;
}
var secondRunMessages = messages.Concat(response.Messages).Append(
new ChatMessage(
ChatRole.System,
[new TextContent("Please provide a concise summary of the state changes in at most two sentences.")]));
await foreach (var update in this.InnerAgent.RunStreamingAsync(secondRunMessages, thread, options, cancellationToken).ConfigureAwait(false))
{
yield return update;
}
}
}
@@ -0,0 +1,23 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json.Serialization;
namespace AGUIDojoServer;
internal sealed class WeatherInfo
{
[JsonPropertyName("temperature")]
public int Temperature { get; init; }
[JsonPropertyName("conditions")]
public string Conditions { get; init; } = string.Empty;
[JsonPropertyName("humidity")]
public int Humidity { get; init; }
[JsonPropertyName("wind_speed")]
public int WindSpeed { get; init; }
[JsonPropertyName("feelsLike")]
public int FeelsLike { get; init; }
}
@@ -0,0 +1,9 @@
{
"Logging": {
"LogLevel": {
"Default": "Information",
"Microsoft.AspNetCore": "Warning",
"Microsoft.AspNetCore.HttpLogging.HttpLoggingMiddleware": "Information"
}
}
}
@@ -0,0 +1,10 @@
{
"Logging": {
"LogLevel": {
"Default": "Information",
"Microsoft.AspNetCore": "Warning",
"Microsoft.AspNetCore.HttpLogging.HttpLoggingMiddleware": "Information"
}
},
"AllowedHosts": "*"
}
@@ -0,0 +1,12 @@
{
"profiles": {
"AGUIServer": {
"commandName": "Project",
"launchBrowser": true,
"environmentVariables": {
"ASPNETCORE_ENVIRONMENT": "Development"
},
"applicationUrl": "http://localhost:5100;https://localhost:5101"
}
}
}
@@ -107,8 +107,8 @@ app.UseSwaggerUI(options => options.SwaggerEndpoint("/openapi/v1.json", "Agents
app.UseExceptionHandler();
// attach a2a with simple message communication
app.MapA2A(agentName: "pirate", path: "/a2a/pirate");
app.MapA2A(agentName: "knights-and-knaves", path: "/a2a/knights-and-knaves", agentCard: new()
app.MapA2A(pirateAgentBuilder, path: "/a2a/pirate");
app.MapA2A(knightsKnavesAgentBuilder, path: "/a2a/knights-and-knaves", agentCard: new()
{
Name = "Knights and Knaves",
Description = "An agent that helps you solve the knights and knaves puzzle.",
@@ -0,0 +1,9 @@
# Agent Framework Retrieval Augmented Generation (RAG)
These samples show how to create an agent with the Agent Framework that uses Memory to remember previous conversations or facts from previous conversations.
|Sample|Description|
|---|---|
|[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
|[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.|
|[Custom Memory Implementation](./AgentWithMemory_Step03_CustomMemory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
@@ -1,6 +1,6 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use Qdrant to add retrieval augmented generation (RAG) capabilities to an AI agent.
// This sample shows how to use Qdrant with a custom schema to add retrieval augmented generation (RAG) capabilities to an AI agent.
// While the sample is using Qdrant, it can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
// The TextSearchProvider runs a search against the vector store before each model invocation and injects the results into the model context.
@@ -1,11 +1,11 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
// capabilities to an AI agent. The provider runs a search against an external knowledge base
// capabilities to an AI agent. This shows a mock implementation of a search function,
// which can be replaced with any custom search logic to query any external knowledge base.
// The provider invokes the custom search function
// before each model invocation and injects the results into the model context.
// Also see the AgentWithRAG folder for more advanced RAG scenarios.
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
@@ -5,4 +5,5 @@ These samples show how to create an agent with the Agent Framework that uses Ret
|Sample|Description|
|---|---|
|[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).|
|[RAG with external Vector Store and custom schema](./AgentWithRAG_Step02_ExternalDataSourceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store. It also uses a custom schema for the documents stored in the vector store.|
|[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.|
|[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.|
@@ -1,28 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net9.0</TargetFramework>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
<PackageReference Include="Microsoft.SemanticKernel.Plugins.OpenApi" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
<ItemGroup>
<None Update="OpenAPISpec.json">
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
</None>
</ItemGroup>
</Project>
@@ -1,354 +0,0 @@
{
"openapi": "3.0.1",
"info": {
"title": "Github Versions API",
"version": "1.0.0"
},
"servers": [
{
"url": "https://api.github.com"
}
],
"components": {
"schemas": {
"basic-error": {
"title": "Basic Error",
"description": "Basic Error",
"type": "object",
"properties": {
"message": {
"type": "string"
},
"documentation_url": {
"type": "string"
},
"url": {
"type": "string"
},
"status": {
"type": "string"
}
}
},
"label": {
"title": "Label",
"description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).",
"type": "object",
"properties": {
"id": {
"description": "Unique identifier for the label.",
"type": "integer",
"format": "int64",
"example": 208045946
},
"node_id": {
"type": "string",
"example": "MDU6TGFiZWwyMDgwNDU5NDY="
},
"url": {
"description": "URL for the label",
"example": "https://api.github.com/repositories/42/labels/bug",
"type": "string",
"format": "uri"
},
"name": {
"description": "The name of the label.",
"example": "bug",
"type": "string"
},
"description": {
"description": "Optional description of the label, such as its purpose.",
"type": "string",
"example": "Something isn't working",
"nullable": true
},
"color": {
"description": "6-character hex code, without the leading #, identifying the color",
"example": "FFFFFF",
"type": "string"
},
"default": {
"description": "Whether this label comes by default in a new repository.",
"type": "boolean",
"example": true
}
},
"required": [
"id",
"node_id",
"url",
"name",
"description",
"color",
"default"
]
},
"tag": {
"title": "Tag",
"description": "Tag",
"type": "object",
"properties": {
"name": {
"type": "string",
"example": "v0.1"
},
"commit": {
"type": "object",
"properties": {
"sha": {
"type": "string"
},
"url": {
"type": "string",
"format": "uri"
}
},
"required": [
"sha",
"url"
]
},
"zipball_url": {
"type": "string",
"format": "uri",
"example": "https://github.com/octocat/Hello-World/zipball/v0.1"
},
"tarball_url": {
"type": "string",
"format": "uri",
"example": "https://github.com/octocat/Hello-World/tarball/v0.1"
},
"node_id": {
"type": "string"
}
},
"required": [
"name",
"node_id",
"commit",
"zipball_url",
"tarball_url"
]
}
},
"examples": {
"label-items": {
"value": [
{
"id": 208045946,
"node_id": "MDU6TGFiZWwyMDgwNDU5NDY=",
"url": "https://api.github.com/repos/octocat/Hello-World/labels/bug",
"name": "bug",
"description": "Something isn't working",
"color": "f29513",
"default": true
},
{
"id": 208045947,
"node_id": "MDU6TGFiZWwyMDgwNDU5NDc=",
"url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement",
"name": "enhancement",
"description": "New feature or request",
"color": "a2eeef",
"default": false
}
]
},
"tag-items": {
"value": [
{
"name": "v0.1",
"commit": {
"sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
"url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
},
"zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
"tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
"node_id": "MDQ6VXNlcjE="
}
]
}
},
"parameters": {
"owner": {
"name": "owner",
"description": "The account owner of the repository. The name is not case sensitive.",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
"repo": {
"name": "repo",
"description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
"per-page": {
"name": "per_page",
"description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
"in": "query",
"schema": {
"type": "integer",
"default": 30
}
},
"page": {
"name": "page",
"description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
"in": "query",
"schema": {
"type": "integer",
"default": 1
}
}
},
"responses": {
"not_found": {
"description": "Resource not found",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/basic-error"
}
}
}
}
},
"headers": {
"link": {
"example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
"schema": {
"type": "string"
}
}
}
},
"paths": {
"/repos/{owner}/{repo}/tags": {
"get": {
"summary": "List repository tags",
"description": "",
"tags": [
"repos"
],
"operationId": "repos/list-tags",
"externalDocs": {
"description": "API method documentation",
"url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
},
"parameters": [
{
"$ref": "#/components/parameters/owner"
},
{
"$ref": "#/components/parameters/repo"
},
{
"$ref": "#/components/parameters/per-page"
},
{
"$ref": "#/components/parameters/page"
}
],
"responses": {
"200": {
"description": "Response",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/tag"
}
},
"examples": {
"default": {
"$ref": "#/components/examples/tag-items"
}
}
}
},
"headers": {
"Link": {
"$ref": "#/components/headers/link"
}
}
}
},
"x-github": {
"githubCloudOnly": false,
"enabledForGitHubApps": true,
"category": "repos",
"subcategory": "repos"
}
}
},
"/repos/{owner}/{repo}/labels": {
"get": {
"summary": "List labels for a repository",
"description": "Lists all labels for a repository.",
"tags": [
"issues"
],
"operationId": "issues/list-labels-for-repo",
"externalDocs": {
"description": "API method documentation",
"url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
},
"parameters": [
{
"$ref": "#/components/parameters/owner"
},
{
"$ref": "#/components/parameters/repo"
},
{
"$ref": "#/components/parameters/per-page"
},
{
"$ref": "#/components/parameters/page"
}
],
"responses": {
"200": {
"description": "Response",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/label"
}
},
"examples": {
"default": {
"$ref": "#/components/examples/label-items"
}
}
}
},
"headers": {
"Link": {
"$ref": "#/components/headers/link"
}
}
},
"404": {
"$ref": "#/components/responses/not_found"
}
},
"x-github": {
"githubCloudOnly": false,
"enabledForGitHubApps": true,
"category": "issues",
"subcategory": "labels"
}
}
}
}
}
@@ -1,33 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to use a ChatClientAgent with function tools provided via an OpenAPI spec.
// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Plugins.OpenApi;
using OpenAI;
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";
// Load the OpenAPI Spec from a file.
KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
// Convert the Semantic Kernel plugin to Agent Framework function tools.
// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
Kernel kernel = new();
List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
// Create the chat client and agent, and provide the OpenAPI function tools to the agent.
AIAgent agent = new AzureOpenAIClient(
new Uri(endpoint),
new AzureCliCredential())
.GetChatClient(deploymentName)
.CreateAIAgent(instructions: "You are a helpful assistant", tools: tools);
// Run the agent with the OpenAPI function tools.
Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github."));
@@ -28,8 +28,8 @@ Before you begin, ensure you have the following prerequisites:
|---|---|
|[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_Step03.1_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|[Using OpenAPI function tools with a simple agent](./Agent_Step03.2_UsingFunctionTools_FromOpenAPI/)|This sample demonstrates how to create function tools from an OpenAPI spec and use them 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|
|[Persisted conversations with a simple agent](./Agent_Step06_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
@@ -39,14 +39,11 @@ Before you begin, ensure you have the following prerequisites:
|[Exposing a simple agent as MCP tool](./Agent_Step10_AsMcpTool/)|This sample demonstrates how to expose an agent as an MCP tool|
|[Using images with a simple agent](./Agent_Step11_UsingImages/)|This sample demonstrates how to use image multi-modality with an AI agent|
|[Exposing a simple agent as a function tool](./Agent_Step12_AsFunctionTool/)|This sample demonstrates how to expose an agent as a function tool|
|[Using memory with an agent](./Agent_Step13_Memory/)|This sample demonstrates how to create a simple memory component and use it with an agent|
|[Background responses with tools and persistence](./Agent_Step13_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|[Using middleware with an agent](./Agent_Step14_Middleware/)|This sample demonstrates how to use middleware with an agent|
|[Using plugins with an agent](./Agent_Step15_Plugins/)|This sample demonstrates how to use plugins with an agent|
|[Reducing chat history size](./Agent_Step16_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
|[Background responses](./Agent_Step17_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
|[Adding RAG with text search](./Agent_Step18_TextSearchRag/)|This sample demonstrates how to enrich agent responses with retrieval augmented generation using the text search provider|
|[Using Mem0-backed memory](./Agent_Step19_Mem0Provider/)|This sample demonstrates how to use the Mem0Provider to persist and recall memories across conversations|
|[Background responses with tools and persistence](./Agent_Step20_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
## Running the samples from the console
@@ -64,13 +64,14 @@ internal static class Program
return AgentWorkflowBuilder.BuildSequential(workflowName: key, agents: agents);
}).AddAsAIAgent();
if (builder.Environment.IsDevelopment())
{
builder.AddDevUI();
}
builder.Services.AddOpenAIResponses();
builder.Services.AddOpenAIConversations();
var app = builder.Build();
app.MapOpenAIResponses();
app.MapOpenAIConversations();
if (builder.Environment.IsDevelopment())
{
app.MapDevUI();
@@ -0,0 +1,13 @@
{
"profiles": {
"DevUI_Step01_BasicUsage": {
"commandName": "Project",
"launchUrl": "devui",
"launchBrowser": true,
"environmentVariables": {
"ASPNETCORE_ENVIRONMENT": "Development"
},
"applicationUrl": "https://localhost:50516;http://localhost:50518"
}
}
}
@@ -63,17 +63,23 @@ To add DevUI to your ASP.NET Core application:
.AddAsAIAgent();
```
3. Add DevUI services and map the endpoint:
3. Add OpenAI services and map the endpoints for OpenAI and DevUI:
```csharp
builder.AddDevUI();
// Register services for OpenAI responses and conversations (also required for DevUI)
builder.Services.AddOpenAIResponses();
builder.Services.AddOpenAIConversations();
var app = builder.Build();
app.MapDevUI();
// Add required endpoints
app.MapEntities();
// Map endpoints for OpenAI responses and conversations (also required for DevUI)
app.MapOpenAIResponses();
app.MapOpenAIConversations();
if (builder.Environment.IsDevelopment())
{
// Map DevUI endpoint to /devui
app.MapDevUI();
}
app.Run();
```
+10 -7
View File
@@ -38,19 +38,22 @@ builder.Services.AddChatClient(chatClient);
// Register your agents
builder.AddAIAgent("my-agent", "You are a helpful assistant.");
// Add DevUI services
builder.AddDevUI();
// Register services for OpenAI responses and conversations (also required for DevUI)
builder.Services.AddOpenAIResponses();
builder.Services.AddOpenAIConversations();
var app = builder.Build();
// Map the DevUI endpoint
app.MapDevUI();
// Add required endpoints
app.MapEntities();
// Map endpoints for OpenAI responses and conversations (also required for DevUI)
app.MapOpenAIResponses();
app.MapOpenAIConversations();
if (builder.Environment.IsDevelopment())
{
// Map DevUI endpoint to /devui
app.MapDevUI();
}
app.Run();
```
+2
View File
@@ -9,6 +9,8 @@ of the agent framework.
|---|---|
|[Agents](./Agents/README.md)|Step by step instructions for getting started with agents|
|[Agent Providers](./AgentProviders/README.md)|Getting started with creating agents using various providers|
|[Agents With Retrieval Augmented Generation (RAG)](./AgentWithRAG/README.md)|Adding Retrieval Augmented Generation (RAG) capabilities to your agents.|
|[Agents With Memory](./AgentWithMemory/README.md)|Adding Memory capabilities to your agents.|
|[A2A](./A2A/README.md)|Getting started with A2A (Agent-to-Agent) specific features|
|[Agent Open Telemetry](./AgentOpenTelemetry/README.md)|Getting started with OpenTelemetry for agents|
|[Agent With OpenAI exchange types](./AgentWithOpenAI/README.md)|Using OpenAI exchange types with agents|