functional-agents

Functional agents

With functional agents, you implement the logic as a function that handles user input, interacts with LLMs, calls tools if necessary, and produces the final output. Compared to graph-based agents, this usually means faster prototyping with the following downsides:

• Not easy to visualize
• No state persistence

??? note "Prerequisites"

--8<-- "quickstart-snippets.md:prerequisites"

--8<-- "quickstart-snippets.md:dependencies"

--8<-- "quickstart-snippets.md:api-key"

Examples on this page assume that you are running Llama 3.2 locally via Ollama.

This page describes how to implement a functional strategy to quickly prototype some custom logic for your agent.

Create a minimal functional agent

To create a minimal functional agent, use the same AIAgent interface as for a basic agent and pass an instance of AIAgentFunctionalStrategy to it. You can define a functional strategy that expects an input and returns an output, makes one LLM call, then returns the content of the assistant message from the response.

In Kotlin, the most convenient way is to use the functionalStrategy {...} DSL method. In Java, you can use the functionalStrategy method on the AIAgent builder.

=== "Kotlin"

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.agents.core.agent.functionalStrategy
import ai.koog.prompt.message.MessagePart
import ai.koog.prompt.executor.llms.all.simpleOllamaAIExecutor
import ai.koog.prompt.executor.ollama.client.OllamaModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
val strategy = functionalStrategy<String, String> { input ->
    val response = requestLLM(input)
    response.parts.filterIsInstance<MessagePart.Text>().joinToString("\n") { it.text }
}

val mathAgent = AIAgent(
    promptExecutor = simpleOllamaAIExecutor(),
    llmModel = OllamaModels.Meta.LLAMA_3_2,
    strategy = strategy
)

fun main() = runBlocking {
    val result = mathAgent.run("What is 12 × 9?")
    println(result)
}
```
<!--- KNIT example-functional-agent-01.kt -->