agentscope

Build reactive multi-agent Java systems with messaging, memory, hooks, and tools.

1.3k|259|Updated Mar 16, 2026

Search Tags:#llm #agent #multi-agent #java #reactive #tool-integration #project reactor

Ready to run

npx skills add https://github.com/Stonewuu/ai-fusion-video --skill agentscope

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentscope
Source: https://github.com/Stonewuu/ai-fusion-video/tree/main/.agents/skills/agentscope
Command: npx skills add https://github.com/Stonewuu/ai-fusion-video --skill agentscope

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AgentScope addresses the complexity of building reactive, multi-agent AI systems in Java by providing a modular framework that unifies messaging, memory, hooks, tools, and LLM integration under a reactive core.

Core Features & Use Cases

Reactive agents built on Project Reactor for scalable, non-blocking reasoning and conversation flows
Agent orchestration and pipelines to coordinate multiple agents across tasks
Tool integration and hook system for extensible, production-grade automation
Memory models (InMemory, long-term options) and MCP integration for context sharing
Real-world use: build assistants that reason, invoke tools, and collaborate across agents in a single workflow

Quick Start

Install the AgentScope runtime for Java and load a sample agent to begin building a reactive, multi-agent solution.

Frequently Asked Questions about agentscope

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build reactive multi-agent systems in Java with non-blocking LLM workflows?▼

Reactive multi-agent systems in Java use Project Reactor to provide scalable, non-blocking reasoning and conversation flows for cooperative agents. AgentScope unifies messaging, memory, hooks, tools, and LLM integration under a reactive core to orchestrate these workflows.

What is the best way to orchestrate multiple agents and share context across tasks in Java?▼

Agent orchestration and context sharing in Java is achieved through modular pipelines coordinating multiple agents. AgentScope uses memory models like InMemory and MCP integration to manage context sharing and cross-agent communication efficiently.

Can I integrate external tools and customize agent pipelines using hooks in a Java framework?▼

External tools and pipeline customization in Java are supported through an extensible tool integration and hook system. AgentScope enables production-grade automation by allowing plugin-like tools and MCP-compatible tool contexts within agent pipelines.

Does AgentScope require Java 17 and Project Reactor for building LLM-powered agents?▼

Building LLM-powered agents with AgentScope requires strict Java 17+ compatibility and Project Reactor. These dependencies ensure the framework maintains its non-blocking architecture and production-ready safety patterns for reactive reasoning.

When should I use a reactive Java framework over standard synchronous agent orchestration?▼

A reactive Java framework is necessary when your project demands non-blocking architecture, scalable LLM-powered reasoning, and cooperative multi-agent communication. AgentScope satisfies these requirements using Project Reactor for strict production-ready safety patterns.