The Two API Levels

acton-ai exposes two API levels that share the same actor-based runtime. The high-level API (ActonAI, PromptBuilder, Conversation) handles actor setup and message routing for you. The low-level API (Kernel, Agent, ToolRegistry, MemoryStore, LLMProvider) gives you direct control over every actor in the system.

High-level API

The high-level API is the recommended starting point. It provides a facade that hides actor setup, subscription management, and message routing behind a fluent builder pattern.

Key types

A complete example

use acton_ai::prelude::*;

#[tokio::main]
async fn main() -> Result<(), ActonAIError> {
    let runtime = ActonAI::builder()
        .app_name("my-app")
        .ollama("qwen2.5:7b")
        .with_builtins()
        .launch()
        .await?;

    // Single prompt with streaming
    let response = runtime
        .prompt("List the files in the current directory")
        .system("Use tools when appropriate.")
        .on_token(|t| print!("{t}"))
        .collect()
        .await?;

    println!("\n[{} tokens]", response.token_count);

    // Multi-turn conversation
    let conv = runtime.conversation()
        .system("You are a helpful coding assistant.")
        .build()
        .await;

    let r1 = conv.send("What is Rust?").await?;
    println!("{}", r1.text);

    let r2 = conv.send("How does ownership work?").await?;
    println!("{}", r2.text);

    runtime.shutdown().await?;
    Ok(())
}

What the high-level API handles for you

• Spawns the ActorRuntime, Kernel, and LLMProvider actors
• Creates temporary StreamCollector actors for each collect() call
• Subscribes collectors to the correct broadcast messages
• Runs the tool execution loop (send prompt, execute tools, send results, repeat)
• Manages conversation history in Conversation via an actor mailbox
• Resolves named providers when .provider("name") is used

Low-level API

The low-level API gives you direct access to the actor primitives. Use it when you need:

• Custom actor topologies (e.g., multiple agents with different tool sets)
• Direct message handling with lifecycle hooks
• Custom stream processing with stateful actors
• Integration with your own actor-based system
• Multi-agent collaboration with task delegation

Key types

A complete low-level example

This example is equivalent to the high-level version but uses actors directly:

use acton_ai::prelude::*;
use std::sync::Arc;
use tokio::sync::Notify;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // 1. Launch the actor runtime
    let mut runtime = ActonApp::launch_async().await;

    // 2. Spawn the kernel
    let kernel_config = KernelConfig::default().with_app_name("my-app");
    let _kernel = Kernel::spawn_with_config(&mut runtime, kernel_config).await;

    // 3. Spawn the LLM provider
    let provider_config = ProviderConfig::ollama("qwen2.5:7b");
    let provider_handle = LLMProvider::spawn(&mut runtime, provider_config).await;

    // 4. Set up a completion signal
    let done = Arc::new(Notify::new());
    let done_signal = done.clone();

    // 5. Create a custom actor to collect the response
    let mut collector = runtime.new_actor::<ResponseCollector>();

    // Handle stream tokens
    collector.mutate_on::<LLMStreamToken>(move |actor, envelope| {
        let token = &envelope.message().token;
        actor.model.buffer.push_str(token);
        print!("{token}");
        Reply::ready()
    });

    // Handle stream end
    collector.mutate_on::<LLMStreamEnd>(move |_actor, _envelope| {
        done_signal.notify_one();
        Reply::ready()
    });

    // Subscribe to streaming events
    collector.handle().subscribe::<LLMStreamToken>().await;
    collector.handle().subscribe::<LLMStreamEnd>().await;
    collector.handle().subscribe::<LLMStreamStart>().await;

    let _collector_handle = collector.start().await;

    // 6. Build and send the request
    let request = LLMRequest::builder()
        .system("You are a helpful assistant.")
        .user("What is the capital of France?")
        .build();

    provider_handle.send(request).await;

    // 7. Wait for completion
    done.notified().await;

    // 8. Shutdown
    runtime.shutdown_all().await?;
    Ok(())
}

#[acton_actor]
struct ResponseCollector {
    buffer: String,
}

When to use which level

How the high-level API wraps the low-level primitives

Understanding the mapping helps if you ever need to migrate:

Migration path: high to low level

If you start with the high-level API and later need low-level control, you can adopt incrementally:

Step 1: Access the runtime

The ActonAI facade exposes the underlying ActorRuntime:

let actor_runtime = runtime.runtime();
// Now you can spawn your own actors on the same runtime

Step 2: Access provider handles

Get the raw ActorHandle for any provider:

// Default provider
let handle = runtime.provider_handle();

// Named provider
let claude_handle = runtime.provider_handle_named("claude").unwrap();

// Send a raw LLMRequest
let request = LLMRequest::builder()
    .system("You are helpful.")
    .user("Hello")
    .build();

claude_handle.send(request).await;

Step 3: Mix high and low level

You can use PromptBuilder for most prompts while using low-level actors for specialized processing:

// High-level for simple prompts
let response = runtime.prompt("Quick question").collect().await?;

// Low-level for custom stream processing
let mut my_actor = runtime.runtime().clone().new_actor::<MyProcessor>();
my_actor.handle().subscribe::<LLMStreamToken>().await;
let handle = my_actor.start().await;

// Send a request that your custom actor will handle
runtime.provider_handle().send(custom_request).await;

Next steps

• Actor Model for AI -- Understand the actor model foundation
• Providers and Configuration -- Configure LLM providers
• Tools and Streaming -- Tool execution and streaming details