AgentScope Java¶

An agent-oriented programming framework for building LLM applications

What is AgentScope Java?¶

AgentScope Java is a powerful framework that enables developers to build LLM-powered applications using agent-oriented programming paradigms. It provides a comprehensive toolkit for creating intelligent agents with tool calling, memory management, multi-agent collaboration, and more.

Key Features¶

Multi-Model Support: DashScope (Qwen), OpenAI, and more LLM providers
Tool System: Annotation-based tool registration and execution with automatic schema generation
Reactive Architecture: Built on Project Reactor for efficient non-blocking operations
Memory Management: Short-term memory and long-term memory with external backends (Mem0)
Multi-Agent Pipelines: Sequential and parallel agent workflows for complex tasks
State Management: Session-based persistence and recovery with JSON storage
Hook System: Extensible event-driven customization for monitoring and control
MCP Support: Model Context Protocol integration for enhanced tool capabilities

Requirements¶

JDK 17 or higher
Maven or Gradle

Quick Start¶

Follow these steps to get started with AgentScope Java:

Installation - Set up AgentScope Java in your project
Key Concepts - Understand core concepts and architecture
Build Your First Agent - Create a working agent

Quick Example¶

import io.agentscope.core.ReActAgent;
import io.agentscope.core.model.DashScopeChatModel;
import io.agentscope.core.message.Msg;

// Create an agent with inline model configuration
var agent = ReActAgent.builder()
    .name("Assistant")
    .model(DashScopeChatModel.builder()
        .apiKey(System.getenv("DASHSCOPE_API_KEY"))
        .modelName("qwen-plus")
        .build())
    .sysPrompt("You are a helpful assistant.")
    .build();

// Call the agent
Msg userMsg = Msg.builder()
    .textContent("Hello!")
    .build();

Msg response = agent.call(userMsg).block();
System.out.println(response.getTextContent());

Advanced Topics¶

Once you’re familiar with the basics, explore these advanced features:

Model Integration¶

Model Integration - Configure different LLM providers

Tools & Knowledge¶

Tool System - Create and use tools with annotation-based registration
MCP - Model Context Protocol support for advanced tool integration
RAG - Retrieval-Augmented Generation for knowledge-enhanced responses

Agent Customization¶

Hook System - Monitor and customize agent behavior with event hooks
Memory Management - Manage conversation history and long-term memory
Planning - Plan management for complex multi-step tasks

Multi-Agent Systems¶

Pipeline - Build multi-agent workflows with sequential and parallel execution
State Management - Persist and restore agent state across sessions

Community¶

GitHub: agentscope-ai/agentscope-java

Discord	DingTalk	WeChat

License¶

AgentScope Java is released under the Apache License 2.0.

Ready to build intelligent agents? Start with the Installation Guide!