> ## Documentation Index > Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # C# (.NET) > Integrate Portkey in your `.NET` app easily using the OpenAI library and get advanced monitoring, routing, and enterprise features. > [card] Enterprise-Ready > > Designed for Fortune 500 needs – security, compliance, observability, and Azure integration out of the box. > [card] > > ![](https://img.shields.io/nuget/v/OpenAI.svg) > > [Open](https://www.nuget.org/packages/OpenAI/) ## Building Enterprise LLM Apps with .NET `.NET` is Microsoft's battle-tested framework trusted by Fortune 500 companies. It's now easier than ever to build LLM apps. You get: | | | | - | - | | **Battle-Tested Security** | Built-in identity management, secret rotation, and compliance standards | | **Production Performance** | High-throughput processing with advanced memory management | | **Azure Integration** | Seamless Azure OpenAI and Active Directory support | Combined with Portkey's enterprise features, you get everything needed for mission-critical LLM deployments. Monitor costs, ensure reliability, maintain compliance, and scale with confidence. ## Portkey Features | | | | :- | :- | | **Complete Observability** | Monitor costs, latency, and performance metrics | | **Provider Flexibility** | Route to 250+ LLMs (like Claude, Gemini, Llama, self-hosted etc.) without code changes | | **Smart Caching** | Reduce costs & time by caching frequent requests | | **High Reliability** | Automatic fallback and load balancing across providers | | **Prompt Management** | Use Portkey as a centralized hub to version, experiment with prompts, and call them using a single ID | | **Continuous Improvement** | Improve your app by capturing and analyzing user feedback | | **Enterprise Ready** | Budget controls, rate limits, model-provisioning, and role-based access | ## Supported Clients | | | | - | - | | `ChatClient` | ✅ Fully Supported | | `EmbeddingClient` | ✅ Fully Supported | | `ImageClient` | 🚧 Coming Soon | | `BatchClient` | 🚧 Coming Soon | | `AudioClient` | 🚧 Coming Soon | ## Implementation Overview 1. Install OpenAI SDK 2. Create Portkey client by extending OpenAI client 3. Use the client in your application to make requests ### 1. Install the NuGet package Add the OpenAI [NuGet](https://www.nuget.org/) package to your .NET project: ```sh theme={"system"} dotnet add package OpenAI ``` ### 2. Create Portkey Client Extension The OpenAI package does not support directly modifying the base URL or passing additional headers. So, we write a simple function to extend OpenAI's `ChatClient` or `EmbeddingClient` to create a new `PortkeyClient`. > [tab] Chat > > ```csharp theme={"system"} > using OpenAI; > using OpenAI.Chat; > using System.ClientModel; > using System.ClientModel.Primitives; > > public static class PortkeyClient > { > private class HeaderPolicy : PipelinePolicy > { > private readonly Dictionary _headers; > public HeaderPolicy(Dictionary headers) => _headers = headers; > > public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) > { > foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); > if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); > } > > public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) > { > Process(message, pipeline, index); > return ValueTask.CompletedTask; > } > } > > public static OpenAIClient CreateClient(Dictionary headers) > { > var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; > options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); > return new OpenAIClient(new ApiKeyCredential("dummy"), options); > } > > public static ChatClient CreateChatClient(Dictionary headers, string model) > { > var client = CreateClient(headers); > return client.GetChatClient(model); > } > } > ``` > [tab] Embedding > > ```csharp theme={"system"} > using OpenAI; > using OpenAI.Embeddings; > using System.ClientModel; > using System.ClientModel.Primitives; > > public static class PortkeyClient > { > private class HeaderPolicy : PipelinePolicy > { > private readonly Dictionary _headers; > public HeaderPolicy(Dictionary headers) => _headers = headers; > > public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) > { > foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); > if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); > } > > public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) > { > Process(message, pipeline, index); > return ValueTask.CompletedTask; > } > } > > public static EmbeddingClient CreateEmbeddingClient(Dictionary headers, string model) > { > var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; > options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); > return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetEmbeddingClient(model); > } > } > ``` ### 3. Use the Portkey Client After creating the extension above, you can pass any [Portkey supported headers](https://docs.portkey.ai/docs/api-reference/inference-api/headers.md) directly while creating the new client. > [tab] Chat > > ```csharp theme={"system"} > // Define Portkey headers > var headers = new Dictionary { > // Required headers > { "x-portkey-api-key", "..." }, // Your Portkey API key > { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog > > // Optional headers > { "x-portkey-trace-id", "my-app" }, // Custom trace identifier > { "x-portkey-config", "..." }, // Send Config ID > // Add any other Portkey headers as needed > }; > > // Create client > var client = PortkeyClient.CreateChatClient( > headers: headers, > model: "gpt-4o" > ); > > // Make request > var response = client.CompleteChat(new UserChatMessage("Hello!")); > Console.WriteLine(response.Value.Content[0].Text); > ``` > > > [!TIP] > > > > Add providers in the [Model Catalog](https://docs.portkey.ai/docs/product/model-catalog.md) to get a provider slug (e.g., `@openai-prod`). The legacy `x-portkey-virtual-key` header is still supported. > [tab] Embedding > > ```csharp theme={"system"} > // Define Portkey headers > var headers = new Dictionary { > // Required headers > { "x-portkey-api-key", "..." }, // Your Portkey API key > { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog > > // Optional headers > { "x-portkey-trace-id", "..." }, // Custom trace identifier > { "x-portkey-config", "..." }, // Send Config ID > // Add any other Portkey headers as needed > }; > > // Create embedding client through Portkey > var client = PortkeyClient.CreateEmbeddingClient( > headers: headers, > model: "text-embedding-3-large" > ); > > // Text that we want to embed > string description = "Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa," > + " and a really helpful concierge. The location is perfect -- right downtown, close to all the tourist" > + " attractions. We highly recommend this hotel."; > > // Generate embedding > var embeddingResult = client.GenerateEmbedding(description); > var vector = embeddingResult.Value.ToFloats(); > > Console.WriteLine($"Full embedding dimensions: {vector.Length}"); > ``` > [!NOTE] > > While we show common headers here, you can pass any Portkey-supported headers to enable features like custom metadata, fallbacks, caching, retries, and more. ### 4. View Your Request in Portkey Logs This request will now be logged on Portkey: ![](https://mintcdn.com/portkey-docs/Hf1XgyjG_b79ym4Q/images/api-ref/sdks/c-sharp.png?fit=max\&auto=format\&n=Hf1XgyjG_b79ym4Q\&q=85\&s=de636a1bc077efc6fad4efbe61219e91) ## Chat Completions Example Add your Azure OpenAI details in the [Model Catalog](https://docs.portkey.ai/docs/integrations/llms/azure-openai#portkey-sdk-integration-with-azure-openai) to get a provider slug. ```csharp [expandable] theme={"system"} using OpenAI; using OpenAI.Chat; using System.ClientModel; using System.ClientModel.Primitives; public static class Portkey { private class HeaderPolicy : PipelinePolicy { private readonly Dictionary _headers; public HeaderPolicy(Dictionary headers) => _headers = headers; public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) { foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); } public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) { Process(message, pipeline, index); return ValueTask.CompletedTask; } } public static ChatClient CreateChatClient(Dictionary headers, string model) { var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetChatClient(model); } } public class Program { public static void Main() { var client = Portkey.CreateChatClient( headers: new Dictionary { { "x-portkey-api-key", "PORTKEY API KEY" }, { "x-portkey-provider", "@azure-openai-prod" }, // Provider slug from Model Catalog { "x-portkey-trace-id", "dotnet" } }, model: "gpt-4o" // Model name configured in your Azure deployment ); Console.WriteLine(client.CompleteChat(new UserChatMessage("1729")).Value.Content[0].Text); } } ``` ## Embedding Example ```csharp [expandable] theme={"system"} using OpenAI; using OpenAI.Embeddings; using System.ClientModel; using System.ClientModel.Primitives; public static class PortkeyClient { private class HeaderPolicy : PipelinePolicy { private readonly Dictionary _headers; public HeaderPolicy(Dictionary headers) => _headers = headers; public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) { foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); } public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) { Process(message, pipeline, index); return ValueTask.CompletedTask; } } public static EmbeddingClient CreateEmbeddingClient(Dictionary headers, string model) { var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetEmbeddingClient(model); } } class Program { static void Main() { // Define Portkey headers var headers = new Dictionary { // Required headers { "x-portkey-api-key", "..." }, // Your Portkey API key { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog // Optional headers { "x-portkey-trace-id", "..." }, // Custom trace identifier { "x-portkey-config", "..." }, // Send Config ID // Add any other Portkey headers as needed }; // Create embedding client through Portkey var client = PortkeyClient.CreateEmbeddingClient( headers: headers, model: "text-embedding-3-large" ); // Text that we want to embed string description = "Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa," + " and a really helpful concierge. The location is perfect -- right downtown, close to all the tourist" + " attractions. We highly recommend this hotel."; // Generate embedding var embeddingResult = client.GenerateEmbedding(description); var vector = embeddingResult.Value.ToFloats(); Console.WriteLine($"Full embedding dimensions: {vector.Length}"); } } ``` ## Microsoft Semantic Kernel Example We can make use of the [Portkey client we created above](https://docs.portkey.ai/docs/api-reference/inference-api/sdks/c-sharp.md#2-create-portkey-client-extension) to initialize the Semantic Kernel. (Please make use of the `CreateClient` method and not `CreateChatClient` method to create the client) ```csharp [expandable] theme={"system"} using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.ChatCompletion; public class Program { public static async Task Main() { var headers = new Dictionary { // Required headers { "x-portkey-api-key", "..." }, // Your Portkey API key { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog // Optional headers // { "x-portkey-trace-id", "my-app" }, // Custom trace identifier // { "x-portkey-config", "..." }, // Send Config ID // Add any other Portkey headers as needed }; // Create client var client = PortkeyClient.CreateClient(headers); var builder = Kernel.CreateBuilder().AddOpenAIChatCompletion("gpt-4", client); Kernel kernel = builder.Build(); var chatCompletionService = kernel.GetRequiredService(); var history = new ChatHistory(); // Initiate a back-and-forth chat string? userInput; do { // Collect user input Console.Write("User > "); userInput = Console.ReadLine(); // Add user input history.AddUserMessage(userInput); // Get the response from the AI var result = await chatCompletionService.GetChatMessageContentAsync( history, null, kernel: kernel); // Print the results Console.WriteLine("Assistant > " + result); // Add the message from the agent to the chat history history.AddMessage(result.Role, result.Content ?? string.Empty); } while (userInput is not null); } } ``` ## More Features > [tab] Async Usage > > You can also use the `PortkeyClient` to send `Async` requests: > > ```csharp theme={"system"} > var completion = await client.CompleteChatAsync(new UserChatMessage("Hello!")); > Console.WriteLine(completion.Value.Content[0].Text); > ``` > [tab] Multi-Turn Conversation > > Use the `SystemChatMessage` and `UserChatMessage` properties from the OpenAI package to create multi-turn conversations: > > ```csharp theme={"system"} > var messages = new List > { > new SystemChatMessage("You are a helpful assistant."), > new UserChatMessage("What is the capital of France?") > }; > > var completion = client.CompleteChat(messages); > messages.Add(new AssistantChatMessage(completion)); > ``` > [tab] Call Anthropic Models > > Switching providers is just a matter of changing your AI Provider slug. Change it to Anthropic, set the model name, and start making requests to Anthropic from the OpenAI .NET library. > > ```csharp {41,44} [expandable] theme={"system"} > using OpenAI; > using OpenAI.Chat; > using System.ClientModel; > using System.ClientModel.Primitives; > > public static class Portkey > { > private class HeaderPolicy : PipelinePolicy > { > private readonly Dictionary _headers; > public HeaderPolicy(Dictionary headers) => _headers = headers; > > public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) > { > foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); > if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); > } > > public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) > { > Process(message, pipeline, index); > return ValueTask.CompletedTask; > } > } > > public static ChatClient CreateChatClient(Dictionary headers, string model) > { > var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; > options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); > return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetChatClient(model); > } > } > > public class Program > { > public static void Main() > { > var client = Portkey.CreateChatClient( > headers: new Dictionary { > { "x-portkey-api-key", "PORTKEY API KEY" }, > { "x-portkey-provider", "@anthropic-prod" }, // Provider slug from Model Catalog > { "x-portkey-trace-id", "dotnet" } > }, > model: "claude-sonnet-4-20250514" > ); > > Console.WriteLine(client.CompleteChat(new UserChatMessage("1729")).Value.Content[0].Text); > } > } > ``` > [tab] Call Vertex Models > > Similarly, just change your provider to the Vertex AI Provider: > > ```csharp {41,44} [expandable] theme={"system"} > using OpenAI; > using OpenAI.Chat; > using System.ClientModel; > using System.ClientModel.Primitives; > > public static class Portkey > { > private class HeaderPolicy : PipelinePolicy > { > private readonly Dictionary _headers; > public HeaderPolicy(Dictionary headers) => _headers = headers; > > public override void Process(PipelineMessage message, IReadOnlyList pipeline, int index) > { > foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value); > if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1); > } > > public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList pipeline, int index) > { > Process(message, pipeline, index); > return ValueTask.CompletedTask; > } > } > > public static ChatClient CreateChatClient(Dictionary headers, string model) > { > var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") }; > options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall); > return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetChatClient(model); > } > } > > public class Program > { > public static void Main() > { > var client = Portkey.CreateChatClient( > headers: new Dictionary { > { "x-portkey-api-key", "PORTKEY API KEY" }, > { "x-portkey-provider", "@vertex-ai-prod" }, // Provider slug from Model Catalog > { "x-portkey-trace-id", "dotnet" } > }, > model: "gemini-2.0-flash" > ); > > Console.WriteLine(client.CompleteChat(new UserChatMessage("1729")).Value.Content[0].Text); > } > } > ``` # Next Steps * [Call local models](https://docs.portkey.ai/docs/integrations/llms/byollm.md) * [Enable cache](https://docs.portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic.md) * [Setup fallbacks](https://docs.portkey.ai/docs/product/ai-gateway/fallbacks.md) * [Loadbalance requests against multiple instances](https://docs.portkey.ai/docs/product/ai-gateway/load-balancing.md) * [Append metadata with requests](https://docs.portkey.ai/docs/product/observability/metadata.md) # Need Help? Reach out to the Portkey team via the [support portal](https://support.portkey.ai/forms/customer-portal-ticket-form) *** > [card] [Portkey is now PRISMA AIRS AI Gateway. See it in action.](https://www.paloaltonetworks.in/ai-security/ai-gateway?utm_source=portkey\&utm_medium=referral\&utm_campaign=prisma_airs\&utm_content=docs_nav#contact) > > Contact Us ## Related topics - [Inference.net](https://docs.portkey.ai/docs/integrations/llms/inference.net.md) - [ACA](https://docs.portkey.ai/docs/self-hosting/hybrid-deployments/azure/aca.md) - [Cato Networks](https://docs.portkey.ai/docs/integrations/guardrails/cato.md) - [Palo Alto Networks Prisma AIRS](https://docs.portkey.ai/docs/integrations/guardrails/palo-alto-panw-prisma.md) - [Supported SDKs](https://docs.portkey.ai/docs/api-reference/sdk/list.md) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.