[](https://www.nuget.org/packages/OllamaSharp) [](https://www.nuget.org/packages/OllamaSharp) [](https://awaescher.github.io/OllamaSharp) # OllamaSharp OllamaSharp provides .NET bindings for the [Ollama API](https://github.com/jmorganca/ollama/blob/main/docs/api.md), simplifying interactions with Ollama both locally and remotely. ** [Recommended by Microsoft](https://www.nuget.org/packages/Microsoft.Extensions.AI.Ollama/)** ## Features - **Ease of use:** Interact with Ollama in just a few lines of code. - **Reliability**: Powering [Microsoft Semantic Kernel](https://github.com/microsoft/semantic-kernel/pull/7362), [.NET Aspire](https://learn.microsoft.com/en-us/dotnet/aspire/community-toolkit/ollama) and [Microsoft.Extensions.AI](https://devblogs.microsoft.com/dotnet/introducing-microsoft-extensions-ai-preview/) - **API coverage:** Covers every single Ollama API endpoint, including chats, embeddings, listing models, pulling and creating new models, and more. - **Real-time streaming:** Stream responses directly to your application. - **Progress reporting:** Real-time progress feedback on tasks like model pulling. - **Tools engine:** [Sophisticated tool support with source generators](https://awaescher.github.io/OllamaSharp/docs/tool-support.html). - **Multi modality:** Support for [vision models](https://ollama.com/blog/vision-models). - **Native AOT support:** [Opt-in support for Native AOT](https://awaescher.github.io/OllamaSharp/docs/native-aot-support.html) for improved performance. ## Usage OllamaSharp wraps each Ollama API endpoint in awaitable methods that fully support response streaming. The following list shows a few simple code examples. ℹ **Try our full featured [demo application](./demo) that's included in this repository** ### Initializing \`\`\`csharp // set up the client var uri = new Uri("http://localhost:11434"); var ollama = new OllamaApiClient(uri); // select a model which should be used for further operations ollama.SelectedModel = "qwen3:4b"; \`\`\` ### Native AOT Support For .NET Native AOT scenarios, create a custom JsonSerializerContext with your types and pass it into the constructor. \`\`\`csharp [JsonSerializable(typeof(MyCustomType))] public partial class MyJsonContext : JsonSerializerContext \{ \} // Use the static factory method for NativeAOT var ollama = new OllamaApiClient(uri, "qwen3:4b", MyJsonContext.Default); \`\`\` See the [Native AOT documentation](./docs/native-aot-support.md) for detailed guidance. ### Listing all models that are available locally \`\`\`csharp var models = await ollama.ListLocalModelsAsync(); \`\`\` ### Pulling a model and reporting progress \`\`\`csharp await foreach (var status in ollama.PullModelAsync("qwen3:32b")) Console.WriteLine($"\{status.Percent\}% \{status.Status\}"); \`\`\` ### Generating a completion directly into the console \`\`\`csharp await foreach (var stream in ollama.GenerateAsync("How are you today?")) Console.Write(stream.Response); \`\`\` ### Building interactive chats \`\`\`csharp // messages including their roles and tool calls will automatically be tracked within the chat object // and are accessible via the Messages property var chat = new Chat(ollama); while (true) \{ var message = Console.ReadLine(); await foreach (var answerToken in chat.SendAsync(message)) Console.Write(answerToken); \} \`\`\` ## Usage with Microsoft.Extensions.AI Microsoft built an abstraction library to streamline the usage of different AI providers. This is a really interesting concept if you plan to build apps that might use different providers, like ChatGPT, Claude and local models with Ollama. I encourage you to read their accouncement [Introducing Microsoft.Extensions.AI Preview – Unified AI Building Blocks for .NET](https://devblogs.microsoft.com/dotnet/introducing-microsoft-extensions-ai-preview/). OllamaSharp is the first full implementation of their \`IChatClient\` and \`IEmbeddingGenerator\` that makes it possible to use Ollama just like every other chat provider. To do this, simply use the \`OllamaApiClient\` as \`IChatClient\` instead of \`IOllamaApiClient\`. \`\`\`csharp // install package Microsoft.Extensions.AI.Abstractions private static IChatClient CreateChatClient(Arguments arguments) \{ if (arguments.Provider.Equals("ollama", StringComparison.OrdinalIgnoreCase)) return new OllamaApiClient(arguments.Uri, arguments.Model); else return new OpenAIChatClient(new OpenAI.OpenAIClient(arguments.ApiKey), arguments.Model); // ChatGPT or compatible \} \`\`\` The \`OllamaApiClient\` implements both interfaces from Microsoft.Extensions.AI, you just need to cast it accordingly: - \`IChatClient\` for model inference - \`IEmbeddingGenerator>\` for embedding generation ## Cloud models aka Ollama Turbo OllamaSharp can be used with [Ollama cloud models](https://ollama.com/cloud) as well. Use the constructor that takes an \`HttpClient\` and set it up to send the api key as default request header. \`\`\`csharp var client = new HttpClient(); client.BaseAddress = new Uri("http://localhost:11434"); client.DefaultRequestHeaders.Add(/* your api key here */); var ollama = new OllamaApiClient(client); \`\`\` ## OllamaSharp vs. Microsoft.Extensions.AI vs. Semantic Kernel It can be confusing which library to use with AI in C#. The following paragraph should help you decide which library to start with. Prefer OllamaSharp if ... - you plan to use Ollama models only - you want to use the native Ollama API, not only chats and embeddings but model management, usage information and more Prefer Microsoft.Extensions.AI if ... - you only need chat and embedding functionality - you want to be able to use different providers like Ollama, OpenAI, Hugging Face, etc. Prefer Semantic Kernel if ... - you need the highest flexibility with different providers, plugins, middlewares, caching, memory and more - you need advanced prompt techniques like variable substitution and templating - you want to build agentic systems No matter which one you choose, OllamaSharp should always be the bridge to Ollama behind the scenes as recommended by Microsoft [(1)](https://learn.microsoft.com/en-us/dotnet/ai/microsoft-extensions-ai) [(2)](https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model) [(3)](https://devblogs.microsoft.com/dotnet/gpt-oss-csharp-ollama/). ## Thanks **I would like to thank all the contributors who take the time to improve OllamaSharp. First and foremost [mili-tan](https://github.com/mili-tan), who always keeps OllamaSharp in sync with the Ollama API.** The icon and name were reused from the amazing [Ollama project](https://github.com/jmorganca/ollama).