This article presents a complete knowledge map of the AgentScope Java framework—including its core components, API quick‑reference, and a high‑frequency Q&A self‑test—to help developers understand and master multi‑agent orchestration on the JVM.
The article explains AgentScope 2.0’s “agent‑as‑tool” philosophy, showing how a Router agent can dynamically delegate tasks to specialist ReActAgents via @Tool methods, and how HarnessAgent’s Subagent feature enables long‑running, user‑direct interactions, with concrete Java code illustrating both patterns.
This article walks Java developers through setting up AgentScope Java 2.0, explains the core ReActAgent and HarnessAgent classes, shows how to configure a DashScope model key, and provides a complete Maven project and code example that streams a typed‑out response from the AI agent.
The article presents a production‑grade solution that lets users describe a form in natural language, then uses a Spring AI Alibaba ReactAgent powered by a ReAct reasoning loop to retrieve templates, validate fields, generate layout, enforce governance, and finally emit a versioned JSON schema ready for deployment.
This article introduces AgentScope Java 1.0.7, showcases its new features such as Ollama integration, Agent Skill support, and Nacos‑based A2A architecture, and walks through a complete boba‑tea‑shop example that demonstrates a Supervisor‑Worker multi‑agent system, ReActAgent configuration, dynamic MCP registration, MySQL session persistence, Mem0 long‑term memory, and AutoContextMemory compression, plus quick deployment options for local, Kubernetes, and Docker environments.
This article explains how to integrate a Human‑In‑The‑Loop (HITL) mechanism into ReactAgent, detailing the motivation, design of interaction, tool description, XML‑based UI rendering, Redis‑driven waiting loop, and the broader architectural parallels with design patterns and other agent frameworks.
