Set up Azure AI Foundry in the AppHost

This article is the reference for the Aspire Azure AI Foundry hosting integration. It enumerates the AppHost APIs — with examples for both AppHost.cs and apphost.mts — that you use to model a Foundry account, model deployments, Foundry projects, hosted agents, and prompt agents in your AppHost project.

If you’re new to the Azure AI Foundry integration, start with the Get started with Azure AI Foundry integrations guide. For how consuming apps read the connection information this page exposes, see Connect to Azure AI Foundry.

Installation

To start building an Aspire app that uses Azure AI Foundry, install the 📦 Aspire.Hosting.Foundry NuGet package:

C#

Terminal

aspire add azure-ai-foundry

Learn more about aspire add in the command reference.

Or, choose a manual installation approach:

C# — AppHost.cs

#:package Aspire.Hosting.Foundry@*

XML — AppHost.csproj

<PackageReference Include="Aspire.Hosting.Foundry" Version="*" />

Note

This integration was previously shipped as Aspire.Hosting.Azure.AIFoundry. When upgrading from earlier previews, replace that package with Aspire.Hosting.Foundry. If your code referenced package-specific Foundry types such as the old AIFoundryModel, update those references to FoundryModel and add using Aspire.Hosting.Foundry; where needed.

TypeScript

Terminal

aspire add azure-ai-foundry

Learn more about aspire add in the command reference.

This updates your aspire.config.json with the Azure AI Foundry hosting integration package:

aspire.config.json

{
  "packages": {
    "Aspire.Hosting.Foundry": "13.3.0-preview.1.26256.5"
  }
}

For an introduction to working with the Azure AI Foundry hosting integration, see Get started with Azure AI Foundry integrations.

Add a Foundry resource

To add an Azure AI Foundry resource to your AppHost project, call the AddFoundry method providing a name:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");

builder.AddProject<Projects.ExampleProject>()
    .WithReference(foundry);

// After adding all resources, run the app...

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(foundry);

await builder.build().run();

The preceding code adds an Azure AI Foundry resource named foundry to the AppHost project. The WithReference method passes the connection information to the ExampleProject project.

Caution

When you call AddFoundry, it implicitly calls AddAzureProvisioning—which adds support for generating Azure resources dynamically during app startup. The app must configure the appropriate subscription and location. For more information, see Local provisioning: Configuration.

Add a Foundry deployment resource

To add a Foundry deployment resource, call the AddDeployment method with one of the generated FoundryModel entries:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");

var chat = foundry.AddDeployment("chat", FoundryModel.OpenAI.Gpt5Mini);

builder.AddProject<Projects.ExampleProject>()
    .WithReference(chat)
    .WaitFor(chat);

// After adding all resources, run the app...

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');

const chat = await foundry.addDeployment('chat', {
  name: 'gpt-5-mini',
  version: '2025-06-01',
  format: 'OpenAI',
});

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(chat)
  .waitFor(chat);

await builder.build().run();

The preceding code:

• Adds an Azure AI Foundry resource named foundry.
• Adds a Foundry deployment resource named chat using the generated FoundryModel.OpenAI.Gpt5Mini descriptor.

If you need a different generated model descriptor, use the corresponding nested type, such as FoundryModel.Microsoft.Phi4Reasoning:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");

var chat = foundry.AddDeployment("chat", FoundryModel.Microsoft.Phi4Reasoning);

builder.AddProject<Projects.ExampleProject>()
    .WithReference(chat)
    .WaitFor(chat);

builder.Build().Run();

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');

const chat = await foundry.addDeployment('chat', {
  name: 'Phi-4-reasoning',
  version: '1',
  format: 'Microsoft',
});

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(chat)
  .waitFor(chat);

await builder.build().run();

Configure deployment properties

You can customize deployment properties using the WithProperties method:

var chat = foundry.AddDeployment("chat", FoundryModel.OpenAI.Gpt5Mini)
    .WithProperties(deployment =>
    {
        deployment.SkuName = "Standard";
        deployment.SkuCapacity = 10;
    });

The preceding code sets the SKU name to Standard and capacity to 10 for the deployment.

Add a Foundry project

Use the AddProject method to create an Azure AI Foundry project for agents, project-scoped connections, and related Azure resources:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");
var project = foundry.AddProject("my-project");

var chat = project.AddModelDeployment("chat", FoundryModel.OpenAI.Gpt5Mini);

builder.AddProject<Projects.ExampleProject>("api")
    .WithReference(project)
    .WithReference(chat)
    .WaitFor(chat);

builder.Build().Run();

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');
const project = await foundry.addProject('my-project');

const chat = await project.addModelDeployment('chat', {
  name: 'gpt-5-mini',
  version: '2025-06-01',
  format: 'OpenAI',
});

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(project)
  .withReference(chat)
  .waitFor(chat);

await builder.build().run();

The preceding code creates a Foundry project and adds a deployment through AddModelDeployment. When you call WithReference(project), Aspire injects the standard connection string and project-specific connection properties such as the project endpoint and Application Insights connection string into the consuming resource.

Configure a Foundry project

You can customize the Azure resources associated with a Foundry project:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");
var appInsights = builder.AddAzureApplicationInsights("appinsights");
var keyVault = builder.AddAzureKeyVault("keyvault");
var registry = builder.AddAzureContainerRegistry("agents");

var project = foundry.AddProject("my-project")
    .WithAppInsights(appInsights)
    .WithKeyVault(keyVault)
    .WithContainerRegistry(registry);

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');
const appInsights = await builder.addAzureApplicationInsights('appinsights');
const keyVault = await builder.addAzureKeyVault('keyvault');
const registry = await builder.addAzureContainerRegistry('agents');

await foundry
  .addProject('my-project')
  .withAppInsights(appInsights)
  .withKeyVault(keyVault)
  .withContainerRegistry(registry);

AddProject creates a default Azure Container Registry for hosted agents only in publish mode (when deploying to Azure). In local run mode, no default registry is created. Use WithContainerRegistry when you want to point the project at a specific registry.

Add a hosted agent to Azure AI Foundry

Use AsHostedAgent in C# or asHostedAgent in TypeScript to configure an executable or containerized app as a hosted agent in a Foundry project:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");
var project = foundry.AddProject("my-project");
var chat = project.AddModelDeployment("chat", FoundryModel.OpenAI.Gpt5Mini);

builder.AddPythonApp("agent-python", "..\\agent", "main:app")
    .WithReference(project)
    .WithReference(chat)
    .AsHostedAgent(project);

builder.AddProject<Projects.Agent>("agent-dotnet")
    .WithHttpEndpoint(targetPort: 9000)
    .WithReference(project)
    .WithReference(chat)
    .AsHostedAgent(project);

builder.Build().Run();

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');
const project = await foundry.addProject('my-project');
const chat = await project.addModelDeployment('chat', {
  name: 'gpt-5-mini',
  version: '2025-06-01',
  format: 'OpenAI',
});

await builder
  .addPythonApp('agent-python', '../agent', 'main:app')
  .withReference(project)
  .withReference(chat)
  .asHostedAgent(project);

await builder
  .addProject('agent-dotnet', '../Agent/Agent.csproj')
  .withHttpEndpoint({ targetPort: 9000 })
  .withReference(project)
  .withReference(chat)
  .asHostedAgent(project);

await builder.build().run();

In run mode, AsHostedAgent(...) configures a local http endpoint for the agent. Aspire first checks whether the resource already has an endpoint named http. If that endpoint has a target port, Aspire preserves it. If there is no existing http target port, Aspire defaults to 8088.

Aspire also injects the selected port as DEFAULT_AD_PORT, so hosted-agent apps can bind to the right port:

var port = Environment.GetEnvironmentVariable("DEFAULT_AD_PORT") ?? "8088";
builder.WebHost.UseUrls($"http://+:{port}");

If your hosted agent listens on a different port, declare the endpoint before calling AsHostedAgent(...):

C#

var dotnetWeatherAgent = builder.AddProject<Projects.WeatherAgent_Dotnet>("weather-agent-dotnet")
    .WithHttpEndpoint(targetPort: 9000)
    .WithReference(project).WaitFor(project)
    .WithReference(chat).WaitFor(chat);

dotnetWeatherAgent.AsHostedAgent(project);

TypeScript

const dotnetWeatherAgent = await builder
  .addProject(
    'weather-agent-dotnet',
    '../WeatherAgent.Dotnet/WeatherAgent.Dotnet.csproj'
  )
  .withHttpEndpoint({ targetPort: 9000 })
  .withReference(project)
  .waitFor(project)
  .withReference(chat)
  .waitFor(chat);

await dotnetWeatherAgent.asHostedAgent(project);

In run mode, Aspire also opens the agent’s dashboard URL at the selected protocol path, defaults that path to /responses, adds a dashboard command for sending a message to the selected path, and enables OpenTelemetry environment variables for agent instrumentation.

For .NET hosted agents, add your Service Defaults project reference and call builder.AddServiceDefaults() in the hosted-agent app so logs, metrics, traces, HTTP instrumentation, and OTLP export flow into the Aspire dashboard. Then enable telemetry on the Foundry Responses chat client that backs the Microsoft Agent Framework (MAF) agent.

In publish mode, Aspire creates an AzureHostedAgentResource and publishes the container to the project-associated registry.

For C# AppHosts, the parameterless AsHostedAgent() overload reuses an existing Foundry project from the app model or creates one automatically. TypeScript AppHosts pass the project resource explicitly.

Add and publish a prompt agent

For prompt-only scenarios, use AddPromptAgent on a Foundry project:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");
var project = foundry.AddProject("my-project");
var chat = project.AddModelDeployment("chat", FoundryModel.OpenAI.Gpt5Mini);

var webSearch = project.AddWebSearchTool("web-search");

var webResearcher = project.AddPromptAgent(chat, name: "web-researcher",
    instructions: """
        You are the Web Researcher. Use web search for current information,
        cite sources, summarize tradeoffs, and keep answers concise and practical.
        """)
    .WithTool(webSearch);

builder.Build().Run();

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');
const project = await foundry.addProject('my-project');
const chat = await project.addModelDeployment('chat', 'gpt-5-mini', {
  modelVersion: '2025-06-01',
  format: 'OpenAI',
});

const webSearch = await project.addWebSearchTool('web-search');

await project
  .addPromptAgent(
    'web-researcher',
    chat,
    {
      instructions: `
    You are the Web Researcher. Use web search for current information,
    cite sources, summarize tradeoffs, and keep answers concise and practical.
  `,
    }
  )
  .withTool(webSearch);

await builder.build().run();

Parameter details:

API	Parameter	Description
AddPromptAgent(...)	model	The Foundry model deployment resource the prompt agent uses, such as chat from project.AddModelDeployment(...).
AddPromptAgent(...)	name	The Aspire resource name and Foundry agent name. This also drives generated environment variable prefixes, for example web-researcher becomes WEB_RESEARCHER.
AddPromptAgent(...)	instructions	Optional system instructions for the prompt agent. Use this to define behavior, tool-use guidance, and response style.
WithTool(...)	tool	A Foundry tool resource created on the same Foundry project, such as AddWebSearchTool(...), AddCodeInterpreterTool(...), or AddAISearchTool(...).

Prompt agents are deployed to Azure AI Foundry even during local development. Local apps call the cloud-provisioned agent through the injected project endpoint and agent name.

Invoke agents from the Aspire dashboard

Aspire also makes the declared agents easy to try from the dashboard. Prompt agents get a Send Message command from AddPromptAgent(...). Hosted agents configured with AsHostedAgent(...) get a highlighted Send Message command that posts to the selected protocol path, which defaults to the local /responses endpoint.

Connect to an existing Foundry service

You might have an existing Azure AI Foundry service that you want to connect to. You can chain a call to annotate that your FoundryResource is an existing resource:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var existingFoundryName = builder.AddParameter("existingFoundryName");
var existingFoundryResourceGroup = builder.AddParameter("existingFoundryResourceGroup");

var foundry = builder.AddFoundry("foundry")
    .AsExisting(existingFoundryName, existingFoundryResourceGroup);

builder.AddProject<Projects.ExampleProject>()
    .WithReference(foundry);

// After adding all resources, run the app...

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const existingFoundryName = await builder.addParameter('existingFoundryName');
const existingFoundryResourceGroup = await builder.addParameter(
  'existingFoundryResourceGroup'
);

const foundry = await builder
  .addFoundry('foundry')
  .asExisting(existingFoundryName, existingFoundryResourceGroup);

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(foundry);

await builder.build().run();

Caution

When you call RunAsExisting, PublishAsExisting, or AsExisting methods to work with resources that are already present in your Azure subscription, you must add certain configuration values to your AppHost to ensure that Aspire can locate them. The necessary configuration values include SubscriptionId, AllowResourceGroupCreation, ResourceGroup, and Location. If you don’t set them, “Missing configuration” errors appear in the Aspire dashboard. For more information about how to set them, see Use existing Azure resources.

Use Foundry Local for development

Aspire supports the usage of Foundry Local for local development. Add the following to your AppHost project:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry")
    .RunAsFoundryLocal();

var chat = foundry.AddDeployment("chat", FoundryModel.Local.Phi4);

builder.AddProject<Projects.ExampleProject>()
    .WithReference(chat)
    .WaitFor(chat);

// After adding all resources, run the app...

TypeScript

TypeScript — apphost.mts

import { createBuilder } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry').runAsFoundryLocal();

const chat = await foundry.addDeployment('chat', {
  name: 'Phi-4',
  version: '1',
  format: 'Microsoft',
});

await builder
  .addProject('api', '../ExampleProject/ExampleProject.csproj')
  .withReference(chat)
  .waitFor(chat);

await builder.build().run();

When the AppHost starts up, the local foundry service is also started. This requires the local machine to have Foundry Local installed and running.

The RunAsFoundryLocal method configures the resource to run as an emulator. It downloads and loads the specified models locally. The method provides health checks for the local service and automatically manages the Foundry Local lifecycle.

Caution

Foundry Projects aren’t supported when the parent Foundry resource uses RunAsFoundryLocal(). Use account-level deployments for local emulator scenarios.

Assign roles to resources

You can assign specific roles to resources that need to access the Azure AI Foundry service. Use the WithRoleAssignments method:

C#

C# — AppHost.cs

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("chat");

builder.AddProject<Projects.Api>("api")
  .WithRoleAssignments(foundry, CognitiveServicesBuiltInRole.CognitiveServicesUser)
  .WithReference(foundry);

builder.Build().Run();

TypeScript

TypeScript — apphost.mts

import { createBuilder, FoundryRole } from './.aspire/modules/aspire.mjs';

const builder = await createBuilder();

const foundry = await builder.addFoundry('chat');

await builder
  .addProject('api', '../Api/Api.csproj')
  .withFoundryRoleAssignments(foundry, [FoundryRole.CognitiveServicesUser])
  .withReference(foundry);

await builder.build().run();

The preceding code assigns the CognitiveServicesUser role to the api project, granting it the necessary permissions to access the Microsoft Foundry resource.

Provisioning-generated Bicep

If you’re new to Bicep, it’s a domain-specific language for defining Azure resources. With Aspire, you don’t need to write Bicep by-hand, instead the provisioning APIs generate Bicep for you. When you publish your app, the generated Bicep provisions an Azure AI Foundry resource with standard defaults.

Generated Bicep — ai-foundry.bicep

@description('The location for the resource(s) to be deployed.')
param location string = resourceGroup().location

resource ai_foundry 'Microsoft.CognitiveServices/accounts@2024-10-01' = {
  name: take('aifoundry-${uniqueString(resourceGroup().id)}', 64)
  location: location
  identity: {
    type: 'SystemAssigned'
  }
  kind: 'AIServices'
  properties: {
    customSubDomainName: toLower(take(concat('ai-foundry', uniqueString(resourceGroup().id)), 24))
    publicNetworkAccess: 'Enabled'
    disableLocalAuth: true
  }
  sku: {
    name: 'S0'
  }
  tags: {
    'aspire-resource-name': 'ai-foundry'
  }
}

resource chat 'Microsoft.CognitiveServices/accounts/deployments@2024-10-01' = {
  name: 'Phi-4'
  properties: {
    model: {
      format: 'Microsoft'
      name: 'Phi-4'
      version: '1'
    }
  }
  sku: {
    name: 'GlobalStandard'
    capacity: 1
  }
  parent: ai_foundry
}

output aiFoundryApiEndpoint string = ai_foundry.properties.endpoints['AI Foundry API']

output endpoint string = ai_foundry.properties.endpoint

output name string = ai_foundry.name

The preceding Bicep is a module that provisions an Azure Cognitive Services resource configured for AI Services. Additionally, role assignments are created for the Azure resource in a separate module:

Generated Bicep — ai-foundry-roles.bicep

@description('The location for the resource(s) to be deployed.')
param location string = resourceGroup().location

param ai_foundry_outputs_name string

param principalType string

param principalId string

resource ai_foundry 'Microsoft.CognitiveServices/accounts@2024-10-01' existing = {
  name: ai_foundry_outputs_name
}

resource ai_foundry_CognitiveServicesUser 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
  name: guid(ai_foundry.id, principalId, subscriptionResourceId('Microsoft.Authorization/roleDefinitions', 'a97b65f3-24c7-4388-baec-2e87135dc908'))
  properties: {
    principalId: principalId
    roleDefinitionId: subscriptionResourceId('Microsoft.Authorization/roleDefinitions', 'a97b65f3-24c7-4388-baec-2e87135dc908')
    principalType: principalType
  }
  scope: ai_foundry
}

resource ai_foundry_CognitiveServicesOpenAIUser 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
  name: guid(ai_foundry.id, principalId, subscriptionResourceId('Microsoft.Authorization/roleDefinitions', '5e0bd9bd-7b93-4f28-af87-19fc36ad61bd'))
  properties: {
    principalId: principalId
    roleDefinitionId: subscriptionResourceId('Microsoft.Authorization/roleDefinitions', '5e0bd9bd-7b93-4f28-af87-19fc36ad61bd')
    principalType: principalType
  }
  scope: ai_foundry
}

The generated Bicep is a starting point and is influenced by changes to the provisioning infrastructure in C#. Customizations to the Bicep file directly will be overwritten, so make changes through the C# provisioning APIs to ensure they’re reflected in the generated files.

Customize provisioning infrastructure

All Aspire Azure resources are subclasses of the AzureProvisioningResource type. This enables customization of the generated Bicep by providing a fluent API to configure the Azure resources—using the ConfigureInfrastructure API:

C# — AppHost.cs

builder.AddFoundry("foundry")
  .ConfigureInfrastructure(infra =>
  {
      var resources = infra.GetProvisionableResources();
      var account = resources.OfType<CognitiveServicesAccount>().Single();

      account.Sku = new CognitiveServicesSku
      {
          Tier = CognitiveServicesSkuTier.Enterprise,
          Name = "E0"
      };
      account.Tags.Add("ExampleKey", "Example value");
  });

Note

TypeScript AppHosts can use curated provisioning helper APIs when an integration exposes them. This example directly customizes Azure.Provisioning objects through ConfigureInfrastructure, which is currently C#-only unless the integration wraps the scenario in a polyglot-friendly helper.

The preceding code:

• Chains a call to the ConfigureInfrastructure API:
  • The infra parameter is an instance of the AzureResourceInfrastructure type.
  • The provisionable resources are retrieved by calling the GetProvisionableResources method.
  • The single CognitiveServicesAccount resource is retrieved.
  • The Sku property is assigned to a new instance of CognitiveServicesSku with an E0 name and Enterprise tier.
  • A tag is added to the Cognitive Services resource with a key of ExampleKey and a value of Example value.

See also

• Get started with Azure AI Foundry integrations
• Connect to Azure AI Foundry
• Azure AI Inference integration
• Azure AI Foundry documentation