ADK-Rust

Rust Agent Development Kit (ADK-Rust) - Build AI agents in Rust with modular components for models, tools, memory, realtime voice, and more.

A flexible framework for developing AI agents with simplicity and power. Model-agnostic, deployment-agnostic, optimized for frontier AI models. Includes support for realtime voice agents with OpenAI Realtime API and Gemini Live API.

Quick Start

1. Create a new project:

cargo new my_agent && cd my_agent

2. Add dependencies:

[dependencies]
adk-rust = "0.2.0"
tokio = { version = "1.40", features = ["full"] }
dotenvy = "0.15"

3. Set your API key:

echo 'GOOGLE_API_KEY=your-key' > .env

4. Write src/main.rs:

use adk_rust::prelude::*;
use adk_rust::Launcher;
use std::sync::Arc;

#[tokio::main]
async fn main() -> AnyhowResult<()> {
    dotenvy::dotenv().ok();
    let api_key = std::env::var("GOOGLE_API_KEY")?;
    let model = GeminiModel::new(&api_key, "gemini-2.5-flash")?;

    let agent = LlmAgentBuilder::new("assistant")
        .instruction("You are a helpful assistant.")
        .model(Arc::new(model))
        .build()?;

    Launcher::new(Arc::new(agent)).run().await?;
    Ok(())
}

5. Run:

cargo run

Adding Tools

let agent = LlmAgentBuilder::new("researcher")
    .instruction("Search the web and summarize findings.")
    .model(Arc::new(model))
    .tool(Arc::new(GoogleSearchTool::new()))
    .build()?;

Workflow Agents

// Sequential execution
let pipeline = SequentialAgent::new("pipeline", vec![agent1, agent2, agent3]);

// Parallel execution
let parallel = ParallelAgent::new("analysis", vec![analyst1, analyst2]);

// Loop until condition (max 5 iterations)
let loop_agent = LoopAgent::new("refiner", vec![agent])
    .with_max_iterations(5);

Multi-Agent Systems

let coordinator = LlmAgentBuilder::new("coordinator")
    .instruction("Delegate tasks to specialists.")
    .model(model)
    .sub_agent(code_agent)
    .sub_agent(test_agent)
    .build()?;

Realtime Voice Agents

Build voice-enabled AI assistants with bidirectional audio streaming:

use adk_realtime::{RealtimeAgent, openai::OpenAIRealtimeModel, RealtimeModel};

let model: Arc<dyn RealtimeModel> = Arc::new(
    OpenAIRealtimeModel::new(&api_key, "gpt-4o-realtime-preview-2024-12-17")
);

let agent = RealtimeAgent::builder("voice_assistant")
    .model(model)
    .instruction("You are a helpful voice assistant.")
    .voice("alloy")
    .server_vad()  // Voice activity detection
    .build()?;

Features:

• OpenAI Realtime API & Gemini Live API
• Bidirectional audio (PCM16, G711)
• Server-side VAD
• Real-time tool calling
• Multi-agent handoffs

Graph-Based Workflows

Build complex workflows using LangGraph-style graph agents:

use adk_graph::prelude::*;

let agent = GraphAgent::builder("processor")
    .node_fn("fetch", |ctx| async move { /* ... */ })
    .node_fn("transform", |ctx| async move { /* ... */ })
    .edge(START, "fetch")
    .edge("fetch", "transform")
    .edge("transform", END)
    .checkpointer(SqliteCheckpointer::new("state.db").await?)
    .build()?;

Features:

• Cyclic graphs for ReAct patterns
• Conditional routing
• State management with reducers
• Checkpointing (memory, SQLite)
• Human-in-the-loop interrupts

Browser Automation

Give agents web browsing capabilities with 46 tools:

use adk_browser::{BrowserSession, BrowserToolset, BrowserConfig};
use std::sync::Arc;

let config = BrowserConfig::new().webdriver_url("http://localhost:4444");
let session = Arc::new(BrowserSession::new(config));
let toolset = BrowserToolset::new(session);
let tools = toolset.all_tools();  // 46 browser tools

let mut builder = LlmAgentBuilder::new("web_agent")
    .model(model);

for tool in tools {
    builder = builder.tool(tool);
}

let agent = builder.build()?;

Tools include navigation, extraction, forms, screenshots, JavaScript execution, and more.

Agent Evaluation

Test and validate agent behavior:

use adk_eval::{Evaluator, EvaluationConfig, EvaluationCriteria};

let evaluator = Evaluator::new(EvaluationConfig::with_criteria(
    EvaluationCriteria::exact_tools().with_response_similarity(0.8)
));

let report = evaluator.evaluate_file(agent, "tests/agent.test.json").await?;
assert!(report.all_passed());

Deployment

# Console mode (default)
cargo run

# Server mode
cargo run -- serve --port 8080

Installation Options

# Full (default)
adk-rust = "0.2.0"

# Minimal
adk-rust = { version = "0.2.1", default-features = false, features = ["minimal"] }

# Custom
adk-rust = { version = "0.2.1", default-features = false, features = ["agents", "gemini", "tools"] }

Documentation

• API Reference
• Official Guides
• Examples

License

Apache 2.0