Build Intelligent ChatbotsThat Remember, Reason & Act.

using LMKit.Model;
using LMKit.Agents;
using LMKit.TextGeneration;

// Load chat and embedding models
var chatModel = LM.LoadFromModelID("qwen3:8b");
var embedModel = LM.LoadFromModelID("qwen3-embedding:0.6b");

// Create memory with automatic extraction enabled
var memory = new AgentMemory(embedModel)
{
    ExtractionMode = MemoryExtractionMode.LlmBased,
    ExtractionModel = chatModel
};

// Create conversation - memory auto-learns from every turn
var chat = new MultiTurnConversation(chatModel) { Memory = memory };

// Memory is extracted and recalled automatically
var r1 = chat.Submit("I prefer concise answers. My name is Sarah.");
var r2 = chat.Submit("Explain dependency injection.");
// Agent recalls Sarah's name and preference for brevity

using LMKit.Model;
using LMKit.TextGeneration;
using LMKit.FunctionCalling;

public class WeatherTools
{
    [LMFunction("Get current weather for a city")]
    public static string GetWeather(string city)
    {
        return $"Weather in {city}: 72°F, sunny";
    }
}

var model = LM.LoadFromModelID("qwen3:8b");
var chat = new MultiTurnConversation(model);

// Register tools from a class
chat.Tools.ImportFunctions(typeof(WeatherTools));

// Model calls GetWeather when appropriate
var response = chat.Submit("What's the weather in Tokyo?");

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Planning;
using LMKit.Agents.Tools.BuiltIn;

var model = LM.LoadFromModelID("qwen3:8b");

// Build an agent with ReAct planning and web search
var agent = Agent.CreateBuilder(model)
    .WithPlanning(PlanningStrategy.ReAct)
    .WithTools(tools =>
    {
        tools.Register(BuiltInTools.WebSearch);
        tools.Register(BuiltInTools.Calculator);
    })
    .Build();

// Agent reasons step-by-step, searches the web, and computes
var result = await agent.ExecuteAsync(
    "Research the population of the 3 largest cities in France and calculate the total."
);