OllamaSharp: .NET bindings for the Ollama API, and where they stop being enough

The gap OllamaSharp fills for Ollama on C#

Ollama exposes an HTTP API. A .NET application that wants to talk to it can post JSON to /api/chat and parse the streamed response by hand, which means writing a serializer, handling newline-delimited JSON chunks, and re-implementing every endpoint as the API grows. OllamaSharp exists to remove that work. The README describes it as providing .NET bindings for the Ollama API, "simplifying interactions with Ollama both locally and remotely", and states that it covers every single Ollama API endpoint, including chats, embeddings, listing models, pulling and creating new models.

The audience is narrow and specific: C# and .NET developers who have an Ollama server running somewhere, either on localhost:11434 or on a remote host, and who want to call it from application code rather than from a shell. The README also notes that the package is recommended by Microsoft through Microsoft.Extensions.AI.Ollama, and that it powers Microsoft Semantic Kernel and .NET Aspire. Those are integration points, not endorsements of fitness for any particular workload.

What it is not: it does not run models. There is no inference engine inside the package. If Ollama is not installed and serving on the other end of the URI, OllamaApiClient has nothing to talk to.

How OllamaApiClient maps Ollama endpoints to awaitable methods

The architecture is a client wrapper. You construct an OllamaApiClient with a Uri, set SelectedModel, and then call methods that correspond to Ollama endpoints. According to the README, GenerateAsync maps to /api/generate, ListLocalModelsAsync returns locally available models, and PullModelAsync returns a stream of status objects with Percent and Status fields that you can print as progress.

Two design decisions matter more than the endpoint list. The first is streaming. Responses come back as IAsyncEnumerable, so `await foreach` yields tokens as they arrive instead of buffering a full completion. The second is state. The Chat class, which the README calls the recommended way to build conversational applications, tracks the full message history across turns, including tool calls and their results, and exposes it through a Messages property. That means you do not resend context manually, but it also means the chat object holds growing state for the life of the conversation.

The package also implements IChatClient and IEmbeddingGenerator<string, Embedding<float>> from Microsoft.Extensions.AI. The README frames this as useful when an app might use different providers, showing a factory that returns either an OllamaApiClient or an OpenAIChatClient behind the same interface. That abstraction is the most portable part of the library, and it is worth designing around if provider switching is plausible.

Installing OllamaSharp from nuget and sending a first prompt

OllamaSharp ships as a nuget package, and the homepage field points at nuget.org/packages/OllamaSharp. Add it to a project with the dotnet CLI. The README does not pin a version, so the command below installs the latest available.

dotnet add package OllamaSharp

Before any code runs, an Ollama server must be reachable. The README's initialization example uses http://localhost:11434, which is the default Ollama port, and sets SelectedModel to a model name. That model has to exist on the server, either already pulled or pulled through the client.

var uri = new Uri("http://localhost:11434");
var ollama = new OllamaApiClient(uri);
ollama.SelectedModel = "qwen3.5:35b-a3b";

For a first real use, the README shows a streaming single-turn completion. GenerateAsync is described as ideal for single-turn, context-free completions, and the loop writes each token to the console as it arrives.

await foreach (var stream in ollama.GenerateAsync("How are you today?"))
    Console.Write(stream.Response);

If the server is running and the model is present, that loop prints the answer incrementally rather than waiting for the whole response. To confirm what is actually available locally before prompting, ListLocalModelsAsync returns the installed models.

var models = await ollama.ListLocalModelsAsync();

For anything conversational, the README steers you to the Chat class instead. It takes the client in its constructor, and SendAsync returns tokens the same way. The difference is that message history accumulates inside the chat object, so a follow-up question can refer to the previous turn.

var chat = new Chat(ollama);

while (true)
{
    var message = Console.ReadLine();
    await foreach (var answerToken in chat.SendAsync(message))
        Console.Write(answerToken);
}

Where the thin-client design becomes a limitation

The most consequential limitation is the one already implied by the architecture: OllamaSharp is a client, so every call is a network call to a separate process. If the Ollama daemon is down, unreachable, or running an older API than the client expects, the failure surfaces at call time, not at build time. There is no offline mode and no embedded fallback.

Model management is similarly delegated. PullModelAsync streams progress, but the download, the disk usage and the model storage all live on the Ollama host. An application that ships to end users cannot assume a model is present; it has to check ListLocalModelsAsync and pull, which can mean gigabytes of transfer on first run. The README does not document rollback or cancellation semantics for a partially completed pull.

The tool support story is where the documentation gets more specific and the constraints tighten. The README points to a tools engine with source generators and a dedicated tool support page, and separately mentions Native AOT support as opt-in, requiring a custom JsonSerializerContext passed into a constructor overload. Both of those are compile-time features. Reflection-based serialization that works fine in a normal build can break under AOT or trimming, which is exactly the scenario the Native AOT documentation addresses. If you rely on dynamically shaped tool arguments, verify that path before assuming it survives trimming.

Finally, the README documents the cloud model path only partially. It shows creating an HttpClient with a BaseAddress and adding an API key as a default request header, and the snippet is cut off mid-constructor. The full constructor signature is not visible in the README, so treat the cloud setup as something to confirm against the API reference rather than copy from the front page.

OllamaSharp against calling the Ollama HTTP API directly

The realistic alternative is not another .NET library so much as doing the work yourself: an HttpClient pointed at the Ollama host, System.Text.Json for serialization, and manual parsing of the newline-delimited JSON stream that Ollama returns. That approach has real advantages. You depend on nothing but the framework, you control exactly which fields you deserialize, and you are never waiting on a package release to pick up a new endpoint.

The difference in approach is where the maintenance burden sits. With hand-rolled calls, you own the request shapes and the streaming parser, and every Ollama API change is your change. With OllamaSharp, the endpoint mapping is the package's job, and the cost is a dependency plus the risk that the binding lags the server. The README's claim of covering every Ollama API endpoint is the whole value proposition; if you only ever call /api/generate with one model, the hand-rolled version is a few dozen lines and no dependency.

A second alternative is the Microsoft.Extensions.AI abstraction layer. OllamaSharp implements IChatClient, so an application written against that interface can swap in a different provider without touching call sites. That is a different kind of alternative: not a replacement for OllamaSharp but a way to make OllamaSharp replaceable. The trade-off is that you code against the abstraction's surface, not the full Ollama API, so Ollama-specific features that fall outside IChatClient require dropping back to OllamaApiClient.

Maintenance, licensing and the upgrade path

The repository is not archived, and the last push was on 2026-07-24. The most recent releases listed are 5.4.30, 5.4.29 and 5.4.28, all dated 2026-07-24, which suggests patch releases are cut close together when needed. The project is MIT licensed, which permits commercial use, modification and redistribution provided the copyright notice and permission notice are retained. That is a permissive arrangement, but it is not legal advice; if your organisation has specific obligations around attribution or bundled dependencies, have the LICENSE file reviewed rather than relying on the licence identifier alone.

The upgrade cost is dominated by the Ollama server, not the package. Because the client tracks the server's API, a server upgrade that changes request or response shapes can require a matching package upgrade, and vice versa. Pinning the package version without pinning the Ollama version leaves that coupling unmanaged. The README does not document a compatibility matrix between OllamaSharp versions and Ollama server versions, so that relationship has to be established empirically in your own environment. For teams that depend on tool calling or Native AOT, those are the areas where a version bump is most likely to require code changes, since both involve generated or compile-time artifacts rather than plain runtime calls.

Editorial conclusion

Adopt OllamaSharp if you already run an Ollama server and want C# code that streams tokens, calls tools and can be swapped behind IChatClient without rewriting your application. Do not adopt it if you need to embed inference in-process, ship a single binary with no external service, or target a runtime where the Ollama daemon cannot run. Before committing, verify that your Ollama server version exposes the endpoints you depend on, and check whether the source-generated tool path in the tool support documentation covers your parameter types.

Frequently asked questions

Official sources

1. awaescher/OllamaSharp on GitHub
2. License: MIT
3. Project website
4. README
5. Releases