OllamaSharp: .NET Bindings for the Ollama API
The easiest way to use Ollama in .NET
At a glance
- What is it?
- OllamaSharp wraps every Ollama HTTP endpoint in awaitable C# methods, including streaming, model management and tool calling. It is the binding Microsoft's own .NET AI stack builds on, and the trade-offs sit in how tightly it follows Ollama's own API surface.
- Who is it for?
- Adopt OllamaSharp if you are writing C# against a local or remote Ollama server and want streaming, model pulls and tool calls without hand-rolling HTTP and JSON. Skip it if your inference target is not Ollama, because the abstraction is the Ollama API itself and Microsoft.Extensions.AI only helps if you also implement the other providers.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 53 days ago.
- What is it written in?
- Mainly C#, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The gap OllamaSharp fills for C# developers
Ollama exposes an HTTP API. Calling it from .NET means writing request bodies, parsing streamed JSON lines, tracking chat history yourself, and re-implementing the same pull-progress loop in every project. OllamaSharp removes that work by wrapping each endpoint in awaitable methods. The README describes it as providing .NET bindings for the Ollama API, simplifying interactions with Ollama both locally and remotely. The audience is narrow and clear: .NET teams that have decided to run models through Ollama and want a typed client instead of raw HttpClient calls. The README also notes the client is recommended by Microsoft through the Microsoft.Extensions.AI.Ollama package, and that it powers Microsoft Semantic Kernel and .NET Aspire. Those are adoption signals about who depends on the code, not measurements of quality, and they are the reason a .NET developer is likely to encounter this library rather than one of the thinner wrappers.
How the client maps onto Ollama endpoints
The mechanism is a thin, explicit mapping rather than a re-architecture. The README states that OllamaSharp wraps each Ollama API endpoint in awaitable methods that fully support response streaming. A single OllamaApiClient is constructed with a Uri, and a SelectedModel property sets the model used for subsequent operations. From there, methods correspond to endpoints: ListLocalModelsAsync returns locally available models, PullModelAsync streams status objects that carry a Percent and a Status field, and GenerateAsync maps to the /api/generate endpoint for single-turn, context-free completions. For conversation, the Chat class is the recommended path because, per the README, it automatically tracks the full message history including tool calls and their results across turns, exposed through a Messages property. Streaming is expressed as IAsyncEnumerable, so callers consume tokens with await foreach. That design choice matters: the client does not buffer a whole response for you, and it does not hide the fact that a model pull is a long-running streamed operation with intermediate states.
Getting a client running: constructor, model and streaming
Setup is three lines. Create a Uri pointing at the server, which the README shows as http://localhost:11434, construct new OllamaApiClient(uri), then assign ollama.SelectedModel, using a value such as "qwen3.5:35b-a3b". Listing local models is await ollama.ListLocalModelsAsync(). Pulling a model with progress is a loop over ollama.PullModelAsync("qwen3.5:35b-a3b") that prints status.Percent and status.Status for each yielded item. Single-turn generation is a loop over ollama.GenerateAsync("How are you today?") printing stream.Response. Interactive chat is a Chat instance constructed from the client, then await foreach over chat.SendAsync(message) writing each token. The README also notes that a system prompt, images for vision models, structured JSON output and a thinking mode for reasoning models are available, with the details deferred to the Chat and Generate documentation page. For Ollama cloud models, the README's example constructs an HttpClient, sets its BaseAddress, and adds the API key as a default request header before passing that client into a constructor overload. That is the whole configuration surface shown in the README; anything beyond it lives in the advanced configuration page.
Microsoft.Extensions.AI and the provider abstraction
The most consequential integration is that OllamaApiClient implements IChatClient for inference and IEmbeddingGenerator<string, Embedding<float>> for embeddings. The README calls it the first full implementation of those interfaces. The practical effect is that a factory method can return an IChatClient and branch: one path returns new OllamaApiClient(uri, model), another returns an OpenAIChatClient with an API key, and the calling code does not change. This is the strongest argument for choosing OllamaSharp over a bespoke wrapper, but it is also where expectations need calibration. The abstraction is only as portable as the features you use. Tool calling, vision input, thinking mode and structured output are Ollama-side capabilities, and the README presents the tool support as its own subsystem with source generators. If you switch the underlying provider, those capabilities have to exist on the other side too. The README itself frames the abstraction as interesting for apps that might use different providers, which is a conditional, not a guarantee.
Where the Ollama-shaped abstraction becomes a constraint
Because the client is bindings rather than a portable inference layer, it inherits Ollama's operating model. The server has to be reachable at the configured Uri, and the model has to be present locally or pullable, which is why PullModelAsync exists at all. The README's own chat example loops on Console.ReadLine, and the Chat object holds conversation state in memory; nothing in the supplied material describes persistence, session resizing or trimming of that history, so long conversations are a question the documentation does not answer here. Native AOT is opt-in rather than default: the README shows a custom JsonSerializerContext with a [JsonSerializable] attribute for your own types, passed into a constructor or the static factory method, with a separate documentation page for guidance. If your application serializes its own types through this client under AOT, that context is work you own. Finally, the README does not state which Ollama server versions are supported, so endpoint coverage claims should be checked against the server you actually run.
Choosing between OllamaSharp and a direct HTTP client
The real alternative is not another .NET library; it is writing the HTTP calls yourself against Ollama's documented API, or using Microsoft.Extensions.AI.Ollama, which the README links as the Microsoft-recommended package. The difference in approach is maintenance ownership. A hand-written client gives you exactly the endpoints you need and no dependency, at the cost of reimplementing streamed JSON parsing, chat history tracking and pull-progress handling, plus revisiting all of it when Ollama changes. OllamaSharp keeps that mapping in one place and tracks releases, which is visible in the version cadence: 5.4.28, 5.4.29 and 5.4.30 all landed on 2026-07-24. For a team that only ever calls /api/generate once, a direct HttpClient is defensible. For a team building chat, embeddings, model management and tool calls, the hand-written path converges on reimplementing this library badly.
Licence, maintenance and upgrade considerations
OllamaSharp is MIT licensed, which permits commercial and closed-source use with the usual attribution requirement; this is a description of the licence identifier, not legal advice, and you should read the licence text and your organisation's policy. The repository is not archived and releases are frequent, with three patch versions published on the same day in the supplied material. That cadence cuts both ways. You get fixes quickly, and you also need a version pinning and upgrade habit, because a library that follows an external HTTP API will keep moving as that API moves. The upgrade cost is concentrated in two places: the Ollama server version your endpoint runs, and any custom JsonSerializerContext you maintain for Native AOT. Both are things you can test in a single integration run, which is the cheapest way to know whether a package bump is safe.
Editorial conclusion
Adopt OllamaSharp if you are writing C# against a local or remote Ollama server and want streaming, model pulls and tool calls without hand-rolling HTTP and JSON. Skip it if your inference target is not Ollama, because the abstraction is the Ollama API itself and Microsoft.Extensions.AI only helps if you also implement the other providers. Before committing, verify that the Ollama version behind your endpoint supports the endpoints you call, and confirm the current package version on NuGet rather than trusting a pinned number.
Community notes