azure-ai-projects-dotnet

name	azure-ai-projects-dotnet
description	Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
risk	unknown
source	community
date_added	2026-02-27

Azure.AI.Projects (.NET)

High-level SDK for Azure AI Foundry project operations including agents, connections, datasets, deployments, evaluations, and indexes.

Installation

dotnet add package Azure.AI.Projects
dotnet add package Azure.Identity

# Optional: For versioned agents with OpenAI extensions
dotnet add package Azure.AI.Projects.OpenAI --prerelease

# Optional: For low-level agent operations
dotnet add package Azure.AI.Agents.Persistent --prerelease

Current Versions: GA v1.1.0, Preview v1.2.0-beta.5

Environment Variables

PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o-mini
CONNECTION_NAME=<your-connection-name>
AI_SEARCH_CONNECTION_NAME=<ai-search-connection>

Authentication

using Azure.Identity;
using Azure.AI.Projects;

var endpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
AIProjectClient projectClient = new AIProjectClient(
    new Uri(endpoint), 
    new DefaultAzureCredential());

Client Hierarchy

AIProjectClient
├── Agents          → AIProjectAgentsOperations (versioned agents)
├── Connections     → ConnectionsClient
├── Datasets        → DatasetsClient
├── Deployments     → DeploymentsClient
├── Evaluations     → EvaluationsClient
├── Evaluators      → EvaluatorsClient
├── Indexes         → IndexesClient
├── Telemetry       → AIProjectTelemetry
├── OpenAI          → ProjectOpenAIClient (preview)
└── GetPersistentAgentsClient() → PersistentAgentsClient

Core Workflows

1. Get Persistent Agents Client

// Get low-level agents client from project client
PersistentAgentsClient agentsClient = projectClient.GetPersistentAgentsClient();

// Create agent
PersistentAgent agent = await agentsClient.Administration.CreateAgentAsync(
    model: "gpt-4o-mini",
    name: "Math Tutor",
    instructions: "You are a personal math tutor.");

// Create thread and run
PersistentAgentThread thread = await agentsClient.Threads.CreateThreadAsync();
await agentsClient.Messages.CreateMessageAsync(thread.Id, MessageRole.User, "Solve 3x + 11 = 14");
ThreadRun run = await agentsClient.Runs.CreateRunAsync(thread.Id, agent.Id);

// Poll for completion
do
{
    await Task.Delay(500);
    run = await agentsClient.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);

// Get messages
await foreach (var msg in agentsClient.Messages.GetMessagesAsync(thread.Id))
{
    foreach (var content in msg.ContentItems)
    {
        if (content is MessageTextContent textContent)
            Console.WriteLine(textContent.Text);
    }
}

// Cleanup
await agentsClient.Threads.DeleteThreadAsync(thread.Id);
await agentsClient.Administration.DeleteAgentAsync(agent.Id);

2. Versioned Agents with Tools (Preview)

using Azure.AI.Projects.OpenAI;

// Create agent with web search tool
PromptAgentDefinition agentDefinition = new(model: "gpt-4o-mini")
{
    Instructions = "You are a helpful assistant that can search the web",
    Tools = {
        ResponseTool.CreateWebSearchTool(
            userLocation: WebSearchToolLocation.CreateApproximateLocation(
                country: "US",
                city: "Seattle",
                region: "Washington"
            )
        ),
    }
};

AgentVersion agentVersion = await projectClient.Agents.CreateAgentVersionAsync(
    agentName: "myAgent",
    options: new(agentDefinition));

// Get response client
ProjectResponsesClient responseClient = projectClient.OpenAI.GetProjectResponsesClientForAgent(agentVersion.Name);

// Create response
ResponseResult response = responseClient.CreateResponse("What's the weather in Seattle?");
Console.WriteLine(response.GetOutputText());

// Cleanup
projectClient.Agents.DeleteAgentVersion(agentName: agentVersion.Name, agentVersion: agentVersion.Version);

3. Connections

// List all connections
foreach (AIProjectConnection connection in projectClient.Connections.GetConnections())
{
    Console.WriteLine($"{connection.Name}: {connection.ConnectionType}");
}

// Get specific connection
AIProjectConnection conn = projectClient.Connections.GetConnection(
    connectionName, 
    includeCredentials: true);

// Get default connection
AIProjectConnection defaultConn = projectClient.Connections.GetDefaultConnection(
    includeCredentials: false);

4. Deployments

// List all deployments
foreach (AIProjectDeployment deployment in projectClient.Deployments.GetDeployments())
{
    Console.WriteLine($"{deployment.Name}: {deployment.ModelName}");
}

// Filter by publisher
foreach (var deployment in projectClient.Deployments.GetDeployments(modelPublisher: "Microsoft"))
{
    Console.WriteLine(deployment.Name);
}

// Get specific deployment
ModelDeployment details = (ModelDeployment)projectClient.Deployments.GetDeployment("gpt-4o-mini");

5. Datasets

// Upload single file
FileDataset fileDataset = projectClient.Datasets.UploadFile(
    name: "my-dataset",
    version: "1.0",
    filePath: "data/training.txt",
    connectionName: connectionName);

// Upload folder
FolderDataset folderDataset = projectClient.Datasets.UploadFolder(
    name: "my-dataset",
    version: "2.0",
    folderPath: "data/training",
    connectionName: connectionName,
    filePattern: new Regex(".*\\.txt"));

// Get dataset
AIProjectDataset dataset = projectClient.Datasets.GetDataset("my-dataset", "1.0");

// Delete dataset
projectClient.Datasets.Delete("my-dataset", "1.0");

6. Indexes

// Create Azure AI Search index
AzureAISearchIndex searchIndex = new(aiSearchConnectionName, aiSearchIndexName)
{
    Description = "Sample Index"
};

searchIndex = (AzureAISearchIndex)projectClient.Indexes.CreateOrUpdate(
    name: "my-index",
    version: "1.0",
    index: searchIndex);

// List indexes
foreach (AIProjectIndex index in projectClient.Indexes.GetIndexes())
{
    Console.WriteLine(index.Name);
}

// Delete index
projectClient.Indexes.Delete(name: "my-index", version: "1.0");

7. Evaluations

// Create evaluation configuration
var evaluatorConfig = new EvaluatorConfiguration(id: EvaluatorIDs.Relevance);
evaluatorConfig.InitParams.Add("deployment_name", BinaryData.FromObjectAsJson("gpt-4o"));

// Create evaluation
Evaluation evaluation = new Evaluation(
    data: new InputDataset("<dataset_id>"),
    evaluators: new Dictionary<string, EvaluatorConfiguration> 
    { 
        { "relevance", evaluatorConfig } 
    }
)
{
    DisplayName = "Sample Evaluation"
};

// Run evaluation
Evaluation result = projectClient.Evaluations.Create(evaluation: evaluation);

// Get evaluation
Evaluation getResult = projectClient.Evaluations.Get(result.Name);

// List evaluations
foreach (var eval in projectClient.Evaluations.GetAll())
{
    Console.WriteLine($"{eval.DisplayName}: {eval.Status}");
}

8. Get Azure OpenAI Chat Client

using Azure.AI.OpenAI;
using OpenAI.Chat;

ClientConnection connection = projectClient.GetConnection(typeof(AzureOpenAIClient).FullName!);

if (!connection.TryGetLocatorAsUri(out Uri uri) || uri is null)
    throw new InvalidOperationException("Invalid URI.");

uri = new Uri($"https://{uri.Host}");

AzureOpenAIClient azureOpenAIClient = new AzureOpenAIClient(uri, new DefaultAzureCredential());
ChatClient chatClient = azureOpenAIClient.GetChatClient("gpt-4o-mini");

ChatCompletion result = chatClient.CompleteChat("List all rainbow colors");
Console.WriteLine(result.Content[0].Text);

Available Agent Tools

Tool	Class	Purpose
Code Interpreter	CodeInterpreterToolDefinition	Execute Python code
File Search	FileSearchToolDefinition	Search uploaded files
Function Calling	FunctionToolDefinition	Call custom functions
Bing Grounding	BingGroundingToolDefinition	Web search via Bing
Azure AI Search	AzureAISearchToolDefinition	Search Azure AI indexes
OpenAPI	OpenApiToolDefinition	Call external APIs
Azure Functions	AzureFunctionToolDefinition	Invoke Azure Functions
MCP	MCPToolDefinition	Model Context Protocol tools

Key Types Reference

Type	Purpose
AIProjectClient	Main entry point
PersistentAgentsClient	Low-level agent operations
PromptAgentDefinition	Versioned agent definition
AgentVersion	Versioned agent instance
AIProjectConnection	Connection to Azure resource
AIProjectDeployment	Model deployment info
AIProjectDataset	Dataset metadata
AIProjectIndex	Search index metadata
Evaluation	Evaluation configuration and results

Best Practices

1. Use DefaultAzureCredential for production authentication
2. Use async methods (*Async) for all I/O operations
3. Poll with appropriate delays (500ms recommended) when waiting for runs
4. Clean up resources — delete threads, agents, and files when done
5. Use versioned agents (via Azure.AI.Projects.OpenAI) for production scenarios
6. Store connection IDs rather than names for tool configurations
7. Use includeCredentials: true only when credentials are needed
8. Handle pagination — use AsyncPageable<T> for listing operations

Error Handling

using Azure;

try
{
    var result = await projectClient.Evaluations.CreateAsync(evaluation);
}
catch (RequestFailedException ex)
{
    Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}

Related SDKs

SDK	Purpose	Install
Azure.AI.Projects	High-level project client (this SDK)	dotnet add package Azure.AI.Projects
Azure.AI.Agents.Persistent	Low-level agent operations	dotnet add package Azure.AI.Agents.Persistent
Azure.AI.Projects.OpenAI	Versioned agents with OpenAI	dotnet add package Azure.AI.Projects.OpenAI

Reference Links

Resource	URL
NuGet Package	https://www.nuget.org/packages/Azure.AI.Projects
API Reference	https://learn.microsoft.com/dotnet/api/azure.ai.projects
GitHub Source	https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects
Samples	https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects/samples

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.