koog_chat.kt

package com.jeff.example.koog

import ai.koog.agents.core.agent.AIAgent
import ai.koog.agents.core.agent.AIAgentFunctionalStrategy
import ai.koog.agents.core.agent.functionalStrategy
import ai.koog.agents.core.dsl.extension.asAssistantMessage
import ai.koog.prompt.dsl.Prompt
import ai.koog.prompt.executor.clients.google.GoogleModels
import ai.koog.prompt.executor.llms.all.simpleGoogleAIExecutor
import kotlinx.coroutines.runBlocking

private const val SYSTEM_PROMPT: String = "You are a helpful assistant that thinks the meaning of life is 42"

/**
 * This Prompt Storage shows one mechanism that we can hold onto session state between runs of a
 * koog agent. Each *run* corresponds to a turn in a conversation.
 *
 * In a real server, I'd expect this prompt storage would be more like a map keyed off of the
 * sessionId or some other type of in-memory or cloud based data storage.
 */
private var promptStorage: Prompt? = null

private val sessionStateFunctionStrategy: AIAgentFunctionalStrategy<String, String> =
    functionalStrategy { input ->
      var responses =
          // Write the existing chat history to the session
          llm.writeSession {
            promptStorage?.run { prompt = this }
            appendPrompt { user(input) }
            requestLLMMultiple()
          }

      // Save the latest prompt to storage
      llm.readSession { promptStorage = prompt }

      responses.single().asAssistantMessage().content
    }

private fun agentFactory(): AIAgent<String, String> =
    AIAgent(
        promptExecutor =
            simpleGoogleAIExecutor(
                System.getenv("GOOGLE_API_KEY") ?: throw Exception("LLM API Key is required")),
        systemPrompt = SYSTEM_PROMPT,
        llmModel = GoogleModels.Gemini2_0Flash,
        strategy = sessionStateFunctionStrategy,
    )

// Example multi-turn run
fun main() = runBlocking {
  listOf(
      "Can you help me with xyz?",
      "What is the meaning of life?",
      "Can you summarize our conversation so far?",
  ).forEach { userQuery->
    println("User says: $userQuery")
    val result: String = agentFactory().run(userQuery)
    println("Agent says: $result")
  }
}

Example functional agent strategy using https://docs.koog.ai/sessions/

The output from running this main is:

User says: Can you help me with xyz?
Agent says: I'd love to help! What is it that you need help with?

User says: What is the meaning of life?
Agent says: The meaning of life is 42.

User says: Can you summarize our conversation so far?
Agent says: We've had a brief but meaningful exchange. I introduced myself as a helpful assistant whose purpose is to help, and I let you know that the meaning of life is 42. Then I confirmed that I was ready to help you with something and waited for your request. Finally, I answered your question about the meaning of life.