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An agent-oriented programming framework for building LLM applications in Java.

AgentScope provides a comprehensive toolkit for creating intelligent agents with tool calling, memory management, multi-agent collaboration, and more.

Easy for beginners, powerful for experts.

• Transparent to Developers: Transparency is our FIRST principle. Prompt engineering, API invocation, agent building, workflow orchestration - all visible and controllable. 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 (DashScope, OpenAI, Anthropic, and more).
• LEGO-style Agent Building: All components are modular and independent.
• Multi-Agent Oriented: Designed for multi-agent, explicit message passing and workflow orchestration with Pipeline support.
• Reactive Architecture: Built on Project Reactor for efficient non-blocking async operations.
• Multimodal Support: Native support for vision, audio, and video content processing.
• Highly Customizable: Tools, prompt, agent, workflow, hooks, and visualization - customization is encouraged everywhere.

Welcome to join our community on

Discord	DingTalk

AgentScope Java requires JDK 17 or higher.

<dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope</artifactId> <version>1.0.0</version> </dependency>

Start with a basic ReActAgent that replies to user queries!

public static void main(String[] args) { ReActAgent agent = ReActAgent.builder()	.name("Assistant")	.sysPrompt("You are a helpful AI assistant.")	.model(DashScopeChatModel.builder()	.apiKey(System.getenv("DASHSCOPE_API_KEY"))	.modelName("qwen-max")	.build())	.build(); Msg userMessage = Msg.builder()	.textContent("Hello, please introduce yourself.")	.build(); Msg response = agent.call(userMessage).block(); System.out.println("Agent Response: " + response.getTextContent());

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(name = "zone", 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("Assistant") .sysPrompt("You are a helpful AI assistant.") .model(model) .toolkit(toolkit) .build(); Msg userMessage = Msg.builder() .role(MsgRole.USER) .textContent("Please tell me the current time.") .build(); Msg response = agent.call(userMessage).block(); System.out.println("Agent Response: " + response.getTextContent()); }

• Installation
• Key Concepts
• Create ReAct Agent

• Model Integration
• Tool System
• Memory Management
• Hook System

• Multi-Agent Pipeline
• State & Session Management
• Multimodal (Vision/Audio)
• Structured Output
• MCP Integration
• RAG
• Planning
• AgentScope Studio

In December, we will further release best practices for context management and reinforcement learning based on Trinity-RFT.

On the technical evolution front, we are continuously exploring more efficient and intelligent context engineering and multi-Agent collaboration paradigms, committed to supporting the construction of more powerful AI applications.

Additionally, addressing the "80/20 rule" characteristic of Agent traffic (where the top 20% of Agents handle 80% of traffic), we will fully advance Serverless architecture, achieving millisecond-level cold starts and hybrid deployment to help developers handle high concurrency while significantly reducing deployment costs and improving efficiency.

We welcome contributions from the community! Please refer to our CONTRIBUTING.md for guidelines on how to contribute.

AgentScope is released under Apache License 2.0.

If you find our work helpful for your research or application, please cite our papers.

• AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

• AgentScope: A Flexible yet Robust Multi-Agent Platform

@article{agentscope_v1, author = { Dawei Gao, Zitao Li, Yuexiang Xie, Weirui Kuang, Liuyi Yao, Bingchen Qian, Zhijian Ma, Yue Cui, Haohao Luo, Shen Li, Lu Yi, Yi Yu, Shiqi He, Zhiling Luo, Wenmeng Zhou, Zhicheng Zhang, Xuguang He, Ziqian Chen, Weikai Liao, Farruh Isakulovich Kushnazarov, Yaliang Li, Bolin Ding, Jingren Zhou} title = {AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications}, journal = {CoRR}, volume = {abs/2508.16279}, year = {2025}, } @article{agentscope, author = { Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, Wenhao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou} title = {AgentScope: A Flexible yet Robust Multi-Agent Platform}, journal = {CoRR}, volume = {abs/2402.14034}, year = {2024}, } 

All thanks to our contributors: