AgentScope Implementation for Java

This is the Java implementation of AgentScope. Please note that this project is still experimental and under active development.

✨ Why AgentScope?

Easy for beginners, powerful for experts.

• Transparent to Developers: Transparent is our FIRST principle. Prompt engineering, API invocation, agent building, workflow orchestration, all are visible and controllable for developers. No deep encapsulation or implicit magic.
• Realtime Steering: Native support for realtime interruption and customized handling.
• More Agentic: Support agentic tools management, agentic long-term memory control and agentic RAG, etc.
• Model Agnostic: Programming once, run with all models.
• LEGO-style Agent Building: All components are modular and independent.
• Multi-Agent Oriented: Designed for multi-agent, explicit message passing and workflow orchestration, NO deep encapsulation.
• Highly Customizable: Tools, prompt, agent, workflow, third-party libs & visualization, customization is encouraged everywhere.

🚀 Quickstart

Installation

AgentScope Java requires jdk 17 or higher.

<dependency>
    <groupId>io.agentscope</groupId>
    <artifactId>agentscope-core</artifactId>
    <version>0.2.1</version>
</dependency>

Hello AgentScope!

Start with a basic ReActAgent that replies to user queries!

public static void main(String[] args) {
    Model model = DashScopeChatModel.builder()
		.apiKey(System.getenv("DASHSCOPE_API_KEY"))
		.modelName("qwen-max")
		.build();

    ReActAgent agent = ReActAgent.builder()
    .name("hello-world-agent")
    .sysPrompt("You are a helpful AI assistant. Be concise and friendly. " +
               "When thinking through problems, use <thinking>...</thinking> tags to show your reasoning.")
    .model(model)
    .memory(new InMemoryMemory())
    .formatter(new DashScopeChatFormatter())
    .build();

    Msg userMessage = Msg.builder()
        .role(MsgRole.USER)
        .textContent("Hello, please introduce yourself.")
        .build();
    Msg response = agent.reply(userMessage).block();

    System.out.println("Agent Response: " + response.getTextContent());
}

Equip Agent with Tools

1. Define Tool

   Define a tool class with methods annotated with @Tool. Here's an example SimpleTools class with a time tool:

   public class SimpleTools {
   	@Tool(name = "get_time", description = "Get current time string of a time zone")
   	public String getTime(@ToolParam(description = "Time zone, e.g., Beijing") String zone) {
   		LocalDateTime now = LocalDateTime.now();
   		return now.format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"));
   	}
   }

2. Register Tool to ReActAgent

   Register the tool class through Toolkit using the registerTool method:

   public static void main(String[] args) {
   	Model model = DashScopeChatModel.builder()
   		.apiKey(System.getenv("DASHSCOPE_API_KEY"))
   		.modelName("qwen-max")
   		.build();
   
   	Toolkit toolkit = new Toolkit();
   	toolkit.registerTool(new SimpleTools());
   
   	ReActAgent agent = ReActAgent.builder()
   		.name("hello-world-agent")
   		.sysPrompt("You are a helpful AI assistant.")
   		.model(model)
   		.toolkit(toolkit)
   		.memory(new InMemoryMemory())
   		.formatter(new DashScopeChatFormatter())
   		.build();
   
   	Msg userMessage = Msg.builder()
   			.role(MsgRole.USER)
   			.textContent("Please tell me the current time.")
   			.build();
   
   	Msg response = agent.reply(userMessage).block();
   	System.out.println("Agent Response: " + response.getTextContent());
   }

📖 Documentation

• Create Message
• Create ReAct Agent
• Model
• Tool
• Memory
• Prompt Formatter

🏗️ Roadmap

In the upcoming versions, AgentScope Java version will focus on improving the following features.

• Multi-modal
• Multi-Agent
• Tracing
• AgentScope Studio

⚖️ License

AgentScope is released under Apache License 2.0.