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:

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": "13.6.0-preview.1.26479.8"
  }
}

C#

Terminal

aspire add azure-ai-foundry

Learn more about aspire add in the command reference.

Or, choose a manual installation approach:

AppHost.cs

#:package Aspire.Hosting.Foundry@*

AppHost.csproj

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

Nota

This integration was previously shipped as Aspire.Hosting.Azure.AIFoundry. When upgrading from Aspire 13.1 or earlier, replace that package with Aspire.Hosting.Foundry and rename AddAzureAIFoundry(...) to AddFoundry(...). Resource and model types moved from Aspire.Hosting.Azure to Aspire.Hosting.Foundry; add using Aspire.Hosting.Foundry; for direct type references and remove using Aspire.Hosting.Azure; only if your code no longer uses other types from that namespace. Extension methods remain in Aspire.Hosting. Update renamed types such as AIFoundryModel to FoundryModel.

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:

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();

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...

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.

Attenzione

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:

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();

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...

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. The FoundryModel class organizes models by provider family: FoundryModel.Microsoft, FoundryModel.OpenAI, FoundryModel.Cohere, FoundryModel.MistralAI, and others. For example, to use FoundryModel.Microsoft.Phi4Reasoning:

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();

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();

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:

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();

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();

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:

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);

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);

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 Toolbox

A Toolbox is a Foundry data-plane resource that bundles reusable tools behind a single MCP endpoint. Toolboxes don’t have an ARM or Bicep representation — Aspire manages them directly through the Foundry data-plane API. Use AddToolbox on a Foundry project to declare one:

TypeScript

apphost.mts

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

const builder = await createBuilder();

const foundry = await builder.addFoundry('foundry');
const project = await foundry.addProject('project');
const search = await builder.addAzureSearch('search');

const toolbox = await project.addToolbox('field-tools');
await toolbox.withDescription('Tools for field technicians.');
await toolbox.withWebSearchTool({
  name: 'web-search',
  description: 'Search the public web.',
});
await toolbox.withMcpTool('inventory', 'https://inventory.example.com/mcp', {
  serverDescription: 'Inventory MCP server.',
  approvalPolicy: {
    global: FoundryToolboxMcpGlobalApprovalMode.Always,
  },
});
await toolbox.withAISearchTool('knowledge-base', search, 'docs');

const api = await builder.addProject('api', '../Api/Api.csproj');
await api.withReference(toolbox);
await api.waitFor(toolbox);

await builder.build().run();

C#

AppHost.cs

using Aspire.Hosting.Foundry;

var builder = DistributedApplication.CreateBuilder(args);

var foundry = builder.AddFoundry("foundry");
var project = foundry.AddProject("project");
var search = builder.AddAzureSearch("search");

var toolbox = project.AddToolbox("field-tools")
    .WithDescription("Tools for field technicians.")
    .WithWebSearchTool("web-search", "Search the public web.")
    .WithMcpTool(
        "inventory",
        "https://inventory.example.com/mcp",
        new FoundryToolboxMcpToolOptions
        {
            ServerDescription = "Inventory MCP server.",
            ApprovalPolicy = new()
            {
                Global = FoundryToolboxMcpGlobalApprovalMode.Always
            }
        })
    .WithAISearchTool("knowledge-base", search, "docs");

builder.AddProject<Projects.Api>("api")
    .WithReference(toolbox)
    .WaitFor(toolbox);

builder.Build().Run();

Parameter details:

The identity running aspire run or aspire deploy needs the Foundry User role on the project to manage Toolbox versions. Deployed compute resources that reference the Toolbox receive this role on the project automatically. The Search index named docs must already exist and contain data: neither AddAzureSearch nor WithAISearchTool creates or populates it. Toolbox availability depends on the Foundry service preview and its supported regions.

API	Parameter	Description
AddToolbox(...)	name	The Aspire resource name and the Toolbox name.
WithDescription(...)	description	A description persisted with each Toolbox version.
WithWebSearchTool(...)	name, description	Adds a web search tool definition to the Toolbox.
WithMcpTool(...)	name, endpoint, options	Adds an MCP tool definition. endpoint accepts a string URI, an EndpointReference, or a ReferenceExpression for composed URLs. options configures the MCP server label, description, and approval policy.
WithAISearchTool(...)	name, search, indexName, description	Adds an Azure AI Search tool backed by an AddAzureSearch resource and an existing search index.

Attenzione

MCP endpoints must be reachable from the Foundry data plane over HTTPS. Localhost and loopback addresses aren’t reachable, so use a development tunnel for local testing. URI credentials, inline headers, and connection-authenticated MCP servers aren’t supported.

Nota

MCP approval policies are discovery metadata only. The Toolbox service doesn’t enforce approval when a client calls tools/call — the consuming application is responsible for inspecting the returned policy and obtaining approval before invoking a tool.

Aspire reuses the current default Toolbox version when its configuration matches. Otherwise, it creates and promotes a new immutable version, using a deterministic configuration fingerprint to detect changes. The default consumer endpoint always serves the promoted version; set the Toolbox resource’s Version only when a consumer must pin a specific immutable version.

Ownership metadata provides best-effort coordination, not an atomic lease. The service doesn’t expose an ETag or conditional update operation. Aspire checks ownership and the current default before promotion and verifies the result afterward, failing rather than overwriting contradictory concurrent changes.

Use an existing Toolbox

Use the existing-resource methods to validate a remote Toolbox without resolving modeled tools, creating versions, or changing the default:

Method	aspire run	aspire deploy
RunAsExisting()	Validate existing	Reconcile managed
PublishAsExisting()	Reconcile managed	Validate existing
AsExisting()	Validate existing	Validate existing

These methods apply to the Toolbox resource itself. Existing mode validates the remote Toolbox, and a pinned version if supplied, without resolving local tools or promoting a version. See Toolbox connection properties for the consumer’s endpoint, authentication scope, and required request header.

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:

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();

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();

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(...):

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);

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);

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.

Select a hosted agent protocol

AsHostedAgent defaults to the Responses protocol version 2.0.0. If your hosted agent implements a different protocol or protocol version, use the C# AsHostedAgent(project, protocol, version) overload or the polyglot/TypeScript asHostedAgentWithProtocol export. For example, some Microsoft Agent Framework (MAF) agents use the Invocations protocol:

TypeScript

await builder
  .addPythonApp('agent-python', '../agent', 'main:app')
  .withReference(project)
  .withReference(chat)
  .asHostedAgentWithProtocol(project, HostedAgentProtocol.Invocations, '1.0.0');

C#

builder.AddPythonApp("agent-python", "../agent", "main:app")
    .WithReference(project)
    .WithReference(chat)
    .AsHostedAgent(project, HostedAgentProtocol.Invocations, "1.0.0");

The defaulted asHostedAgent entry point and the explicit asHostedAgentWithProtocol entry point are both exported to polyglot AppHosts, so existing AppHosts that call asHostedAgent(project, options?) continue to work unchanged with the Responses 2.0.0 default. Reach for asHostedAgentWithProtocol only when you need a protocol or version other than the default.

Add and publish a prompt agent

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

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();

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();

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.

The following screenshots use a local demonstration fixture with deterministic example responses to illustrate the dashboard’s command dialogs. They don’t show a live Azure AI Foundry invocation.

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:

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();

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...

Attenzione

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:

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();

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...

When the AppHost starts, Aspire automatically starts the Foundry Local service using the foundry CLI (supporting both the legacy foundry service and the current foundry server command generations). It then discovers the service endpoint, loads any cached models before downloading new ones, and stops the service when the AppHost shuts down or is disposed. You do not need to pre-start Foundry Local manually.

This requires the foundry CLI to be installed and available on PATH. For installation instructions, see Foundry Local get started.

The RunAsFoundryLocal method configures the resource to use the local service. It downloads and loads the specified models locally, provides health checks for the local service, and fully manages the Foundry Local lifecycle — including start and stop — through the foundry CLI.

Attenzione

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

Connect to a remote Foundry Local service

Windows Subsystem for Linux (WSL2) and Linux AppHosts can’t manage the Foundry Local lifecycle directly because the foundry CLI targets Windows or macOS. To connect from one of these hosts to a Foundry Local service that’s already running elsewhere (for example, on the Windows host), pass the service’s reachable endpoint to RunAsFoundryLocal:

TypeScript

apphost.mts

const foundry = await builder.addFoundry('foundry')
    .runAsFoundryLocal({ endpoint: 'http://windows-host:5273' });

const chat = await foundry
    .addDeployment('chat', 'Phi-3.5-mini-instruct', { modelVersion: '1', format: 'Microsoft' })
    .withProperties(async (deployment) => {
        await deployment.localModelId.set('Phi-3.5-mini-instruct-generic-gpu:1');
    });

C#

AppHost.cs

var foundry = builder.AddFoundry("foundry")
                     .RunAsFoundryLocal("http://windows-host:5273");

var chat = foundry.AddDeployment("chat", FoundryModel.Local.Phi4Mini)
                  .WithProperties(deployment =>
                  {
                      deployment.LocalModelId = "Phi-4-mini-instruct-generic-gpu:5";
                  });

When you pass an endpoint, Aspire treats the service as unmanaged: it observes the remote service’s health and loaded models but doesn’t start, stop, download, or load models on that host. The model must already be downloaded and loaded on the remote service.

The endpoint must be reachable from the AppHost and every consuming resource. Set LocalModelId (C#) or localModelId (TypeScript) to the model identifier reported by the remote service when the deployment’s model name is an alias rather than the exact identifier the remote service expects.

Assign roles to resources

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

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();

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();

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:

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");
  });

Nota

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 (legacy)
• Azure AI Foundry documentation