🦙 Ollama — Local AI

using Microsoft.Agents.AI;
using OllamaSharp;

// 1. Read configuration from environment variables.
var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")
                ?? throw new InvalidOperationException("OLLAMA_ENDPOINT is not set.");
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")
                ?? throw new InvalidOperationException("OLLAMA_MODEL_NAME is not set.");

// 2. Create an OllamaApiClient and wrap it as an AIAgent.
//    AsAIAgent() works identically to the Azure OpenAI / OpenAI path.
AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(
        instructions: "You are a helpful assistant running locally via Ollama.",
        name: "LocalAgent");

// 3. Run the agent — same API as any other AIAgent.
Console.WriteLine(await agent.RunAsync("What is the largest city in France?"));

using Microsoft.Agents.AI;
using OllamaSharp;

var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")!;
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")!;

AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(instructions: "You are a helpful assistant.", name: "StreamAgent");

// RunStreamingAsync works just like with Azure OpenAI — tokens stream in real time.
Console.Write("Agent: ");
await foreach (var update in agent.RunStreamingAsync("Explain what a neural network is in simple terms."))
{
    if (update.Text is not null)
    {
        Console.Write(update.Text);
    }
}
Console.WriteLine();

using System.ComponentModel;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OllamaSharp;

var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")!;
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")!;

// Define a tool function — works exactly the same as with cloud models.
string GetCurrentTime(
    [Description("The timezone identifier, e.g. 'UTC' or 'Europe/Paris'")] string timezone)
    => $"The current time in {timezone} is {DateTime.UtcNow:HH:mm} UTC.";

AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(
        instructions: "You are a helpful assistant. Use your tools to answer questions accurately.",
        name: "ToolAgent",
        tools: [AIFunctionFactory.Create(GetCurrentTime)]);

// The agent will call GetCurrentTime automatically when appropriate.
Console.WriteLine(await agent.RunAsync("What time is it in UTC right now?"));

using Microsoft.Agents.AI;
using OllamaSharp;

var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")!;
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")!;

AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(instructions: "You are a friendly local assistant.", name: "ChatAgent");

// AgentSession manages conversation history — no manual list management needed.
await using AgentSession session = agent.CreateSession();

Console.WriteLine(await session.RunAsync("Hi! I'm learning about planets. My favourite is Saturn."));
Console.WriteLine(await session.RunAsync("What makes its rings so special?"));
Console.WriteLine(await session.RunAsync("How many moons does it have?"));

using System.ComponentModel;
using Microsoft.Agents.AI;
using OllamaSharp;

var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")!;
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")!;

// Define a C# record — Agent Framework generates the JSON schema automatically.
record MovieRecommendation(
    [property: Description("The movie title")] string Title,
    [property: Description("The year of release")] int Year,
    [property: Description("A brief plot summary, max 2 sentences")] string Summary,
    [property: Description("Genre tag such as 'sci-fi', 'drama', or 'comedy'")] string Genre);

AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(
        instructions: "You are a film critic. Respond only with valid JSON matching the requested schema.",
        name: "FilmAgent");

// RunAsync<T> constrains the model to return JSON matching the record's schema.
MovieRecommendation movie = await agent.RunAsync<MovieRecommendation>(
    "Recommend a classic science-fiction film from the 1980s.");

Console.WriteLine($"Title:   {movie.Title} ({movie.Year})");
Console.WriteLine($"Genre:   {movie.Genre}");
Console.WriteLine($"Summary: {movie.Summary}");

using Microsoft.Agents.AI;
using OllamaSharp;

var endpoint  = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT")!;
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME")!;

// Helper: create a new Ollama client for the same local model.
OllamaApiClient NewClient() => new OllamaApiClient(new Uri(endpoint), modelName);

// 1. Create two specialist sub-agents, each with a descriptive name the orchestrator can use.
AIAgent historyAgent = NewClient().AsAIAgent(
    instructions: "You are a history expert. Answer concisely and accurately.",
    name: "HistoryExpert",
    description: "Expert in world history, historical events and figures.");

AIAgent scienceAgent = NewClient().AsAIAgent(
    instructions: "You are a science expert. Explain concepts clearly in plain language.",
    name: "ScienceExpert",
    description: "Expert in physics, chemistry, biology, and astronomy.");

// 2. The orchestrator uses sub-agents as tools via .AsAIFunction().
AIAgent orchestrator = NewClient().AsAIAgent(
    instructions: """
        You are a helpful assistant with access to specialist experts.
        Always delegate to the most appropriate expert using your tools.
        Combine their answers into a single, cohesive response.
        """,
    name: "Orchestrator",
    tools: [historyAgent.AsAIFunction(), scienceAgent.AsAIFunction()]);

// 3. A single call — the orchestrator routes each part of the question to the right expert.
Console.WriteLine(await orchestrator.RunAsync(
    "When did the Berlin Wall fall, and what causes a solar eclipse?"));