El Bruno

⚠️ This blog post was created with the help of AI tools. Yes, I used a bit of magic from language models to organize my thoughts and automate the boring parts, but the geeky fun and the 🤖 in C# are 100% mine.

TL;DR

• Fara1.5-9B now works end-to-end in C# with ElBruno.LocalLLMs.
• The validated multimodal ONNX package is published at:
  • https://huggingface.co/elbruno/Fara1.5-9B-onnx
• You can run image+text inference locally with LocalVisionChatClient and KnownModels.Fara15_9B.
• This closes the loop for end-to-end local agent experiences in:
  • https://github.com/elbruno/ElBruno.MagenticUI

When Microsoft Research introduced MagenticLite, MagenticBrain, and Fara1.5, it made a strong case for practical local agentic experiences:

• Original announcement: https://www.microsoft.com/en-us/research/blog/magenticlite-magenticbrain-fara1-5-an-agentic-experience-optimized-for-small-models/

In plain words: smaller, specialized models can deliver real agentic behavior when the tooling is right.

For this repo, that “tooling is right” moment means: Fara is now actually runnable end-to-end in .NET, not just listed as an idea.

Why this matters for C# end-to-end

The goal of ElBruno.LocalLLMs is to make local model usage feel native in .NET. With Fara support in place:

1. You can auto-download the model from Hugging Face.
2. Run vision prompts with LocalVisionChatClient.
3. Keep everything in a C# workflow without external service dependencies.

That is especially important for app-level experiences like:

• https://github.com/elbruno/ElBruno.MagenticUI

where “agent loop + UI + model inference” should stay coherent in one stack.

Basic usage: Fara in C#

1. Install

dotnet add package ElBruno.LocalLLMs --version 0.20.3

2. Minimal Fara vision client

using ElBruno.LocalLLMs;
using Microsoft.Extensions.AI;
var options = new LocalLLMsOptions
{
    Model = KnownModels.Fara15_9B,
    EnsureModelDownloaded = true,
    ExecutionProvider = ExecutionProvider.Cpu
};
await using var client = new LocalVisionChatClient(options);

3. Ask Fara about an image

var response = await client.GetResponseAsync(
[
    new ChatMessage(ChatRole.User, "Describe the image in one sentence.")
],
new VisionChatOptions
{
    ImagePaths = [@"C:\images\screen.png"],
    MaxOutputTokens = 64
});
Console.WriteLine(response.Text);

Reusing existing Fara sample code

Fara-specific sample code is already in the repo:

• src/samples/FaraVisionAgent

This sample already covers:

• auto-download from elbruno/Fara1.5-9B-onnx
• local --model-path override
• --image-path flow with VisionChatOptions

If you want to start from runnable code instead of snippets, use that sample first.

Validation snapshot

Focused validation for Fara support passed:

• Fara unit tests: 39/39
• Fara Hugging Face checks: 3/3
• Fara vision lifecycle E2E: pass
  • download -> inference -> cache hit -> delete

Detailed report:

• docs/tests/2026-07-28-15-run-results.md
• Issue tracking (closed): #35

Relevant links

• NuGet: https://www.nuget.org/packages/ElBruno.LocalLLMs
• Repo: https://github.com/elbruno/ElBruno.LocalLLMs
• Fara ONNX package: https://huggingface.co/elbruno/Fara1.5-9B-onnx
• Microsoft announcement:
  • https://www.microsoft.com/en-us/research/blog/magenticlite-magenticbrain-fara1-5-an-agentic-experience-optimized-for-small-models/
• MagenticUI .NET app:
  • https://github.com/elbruno/ElBruno.MagenticUI