base on The easiest way to use Ollama in .NET [](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<string, Embedding<float>>` 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). ", Assign "at most 3 tags" to the expected json: {"id":"10593","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"