Co-Pilot
Updated a month ago

agentscope-java

Aagentscope-ai
1.1k
agentscope-ai/agentscope-java
82
Agent Score

💡 Summary

AgentScope Java is a reactive framework for building multi-agent systems using Java 17+ and Project Reactor.

🎯 Target Audience

Java developers interested in reactive programmingAI engineers working with multi-agent systemsSoftware architects designing agent-based applicationsData scientists integrating LLMs into applicationsDevOps professionals deploying reactive applications

🤖 AI Roast:Powerful, but the setup might scare off the impatient.

Security AnalysisMedium Risk

Risk: Medium. Review: outbound network access (SSRF, data egress); API keys/tokens handling and storage; filesystem read/write scope and path traversal. Run with least privilege and audit before enabling in production.


name: agentscope-java description: Expert Java developer skill for AgentScope Java framework - a reactive, message-driven multi-agent system built on Project Reactor. Use when working with reactive programming, LLM integration, agent orchestration, multi-agent systems, or when the user mentions AgentScope, ReActAgent, Mono/Flux, Project Reactor, or Java agent development. Specializes in non-blocking code, tool integration, hooks, pipelines, and production-ready agent applications. license: Apache-2.0 compatibility: Designed for Claude Code and Cursor. Requires Java 17+, Maven/Gradle, and familiarity with reactive programming concepts. metadata: framework: AgentScope Java language: Java 17+ paradigm: Reactive Programming core-library: Project Reactor version: "1.0"

When the user asks you to write AgentScope Java code, follow these instructions carefully.

CRITICAL RULES - NEVER VIOLATE THESE

🚫 ABSOLUTELY FORBIDDEN:

  1. NEVER use .block() in example code - This is the #1 mistake. Only use .block() in main() methods or test code when explicitly creating a runnable example.
  2. NEVER use Thread.sleep() - Use Mono.delay() instead.
  3. NEVER use ThreadLocal - Use Reactor Context with Mono.deferContextual().
  4. NEVER hardcode API keys - Always use System.getenv().
  5. NEVER ignore errors silently - Always log errors and provide fallback values.
  6. NEVER use wrong import paths - All models are in io.agentscope.core.model.*, NOT io.agentscope.model.*.

✅ ALWAYS DO:

  1. Use Mono and Flux for all asynchronous operations.
  2. Chain operations with .map(), .flatMap(), .then().
  3. Use Builder pattern for creating agents, models, and messages.
  4. Include error handling with .onErrorResume() or .onErrorReturn().
  5. Add logging with SLF4J for important operations.
  6. Use correct imports: import io.agentscope.core.model.DashScopeChatModel;
  7. Use correct APIs (many methods don't exist or have changed):
    • toolkit.registerTool() NOT registerObject()
    • toolkit.getToolNames() NOT getTools()
    • event.getToolUse().getName() NOT getToolName()
    • result.getOutput() NOT getContent() (ToolResultBlock)
    • event.getToolResult() NOT getResult() (PostActingEvent)
    • toolUse.getInput() NOT getArguments() (ToolUseBlock)
    • Model builder: NO temperature() method, use defaultOptions(GenerateOptions.builder()...)
    • Hook events: NO getMessages(), getResponse(), getIterationCount(), getThinkingBlock() methods
    • ToolResultBlock: NO getToolUseName() method, use event.getToolUse().getName() instead
    • ToolResultBlock.getOutput() returns List<ContentBlock> NOT String, need to convert
    • @ToolParam format: MUST use @ToolParam(name = "x", description = "y") NOT @ToolParam(name="x")

WHEN GENERATING CODE

FIRST: Identify the context

  • Is this a main() method or test code? → .block() is allowed (but add a warning comment)
  • Is this agent logic, service method, or library code? → .block() is FORBIDDEN

For every code example you provide:

  1. Check: Does it use .block()? → If yes in non-main/non-test code, REWRITE IT.
  2. Check: Are all operations non-blocking? → If no, FIX IT.
  3. Check: Does it have error handling? → If no, ADD IT.
  4. Check: Are API keys from environment? → If no, CHANGE IT.
  5. Check: Are imports correct? → If using io.agentscope.model.*, FIX TO io.agentscope.core.model.*.

Default code structure for agent logic:

// ✅ CORRECT - Non-blocking, reactive (use this pattern by default) return model.generate(messages, null, null) .map(response -> processResponse(response)) .onErrorResume(e -> { log.error("Operation failed", e); return Mono.just(fallbackValue); }); // ❌ WRONG - Never generate this in agent logic String result = model.generate(messages, null, null).block(); // DON'T DO THIS

Only for main() methods (add warning comment):

public static void main(String[] args) { // ⚠️ .block() is ONLY allowed here because this is a main() method Msg response = agent.call(userMsg).block(); System.out.println(response.getTextContent()); }

PROJECT SETUP

When creating a new AgentScope project, use the correct Maven dependencies:

Maven Configuration (pom.xml)

For production use (recommended):

<properties> <java.version>17</java.version> </properties> <dependencies> <!-- Use the latest stable release from Maven Central --> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope</artifactId> <version>1.0.7</version> </dependency> </dependencies>

For local development (if working with source code):

<properties> <agentscope.version>1.0.7</agentscope.version> <java.version>17</java.version> </properties> <dependencies> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-core</artifactId> <version>${agentscope.version}</version> </dependency> </dependencies>

⚠️ IMPORTANT: Version Selection

  • Use agentscope:1.0.7 for production (stable, from Maven Central)
  • Use agentscope-core:1.0.7 only if you're developing AgentScope itself
  • NEVER use version 0.1.0-SNAPSHOT - this version doesn't exist

⚠️ CRITICAL: Common Dependency Mistakes

❌ WRONG - These artifacts don't exist:

<!-- DON'T use these - they don't exist --> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-model-dashscope</artifactId> <!-- ❌ WRONG --> </dependency> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-model-openai</artifactId> <!-- ❌ WRONG --> </dependency>

❌ WRONG - These versions don't exist:

<dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-core</artifactId> <version>0.1.0-SNAPSHOT</version> <!-- ❌ WRONG - doesn't exist --> </dependency> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope</artifactId> <version>0.1.0</version> <!-- ❌ WRONG - doesn't exist --> </dependency>

✅ CORRECT - Use the stable release:

<!-- For production: use the stable release from Maven Central --> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope</artifactId> <version>1.0.7</version> <!-- ✅ CORRECT --> </dependency>

Available Model Classes (all in agentscope-core)

// DashScope (Alibaba Cloud) import io.agentscope.core.model.DashScopeChatModel; // OpenAI import io.agentscope.core.model.OpenAIChatModel; // Gemini (Google) import io.agentscope.core.model.GeminiChatModel; // Anthropic (Claude) import io.agentscope.core.model.AnthropicChatModel; // Ollama (Local models) import io.agentscope.core.model.OllamaChatModel;

Optional Extensions

<!-- Long-term memory with Mem0 --> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-extensions-mem0</artifactId> <version>${agentscope.version}</version> </dependency> <!-- RAG with Dify --> <dependency> <groupId>io.agentscope</groupId> <artifactId>agentscope-extensions-rag-dify</artifactId> <version>${agentscope.version}</version> </dependency>

PROJECT OVERVIEW & ARCHITECTURE

AgentScope Java is a reactive, message-driven multi-agent framework built on Project Reactor and Java 17+.

Core Abstractions

  • Agent: The fundamental unit of execution. Most agents extend AgentBase.
  • Msg: The message object exchanged between agents.
  • Memory: Stores conversation history (InMemoryMemory, LongTermMemory).
  • Toolkit & AgentTool: Defines capabilities the agent can use.
  • Model: Interfaces with LLMs (OpenAI, DashScope, Gemini, Anthropic, etc.).
  • Hook: Intercepts and modifies agent execution at various lifecycle points.
  • Pipeline: Orchestrates multiple agents in sequential or parallel patterns.

Reactive Nature

Almost all operations (agent calls, model inference, tool execution) return Mono<T> or Flux<T>.

Key Design Principles

  1. Non-blocking: All I/O operations are asynchronous
  2. Message-driven: Agents communicate via immutable Msg objects
  3. Composable: Agents and pipelines can be nested and combined
  4. Extensible: Hooks and custom tools allow deep customization

CODING STANDARDS & BEST PRACTICES

2.1 Java Version & Style

Target Java 17 (LTS) for maximum compatibility:

  • Use Java 17 features (Records, Switch expressions, Pattern Matching for instanceof, var, Sealed classes)
  • AVOID Java 21+ preview features (pattern matching in switch, record patterns)
  • Follow standard Java conventions (PascalCase for classes, camelCase for methods/variables)
  • Use Lombok where appropriate (@Data, @Builder for DTOs/Messages)
  • Prefer immutability for data classes
  • Use meaningful names that reflect domain concepts

⚠️ CRITICAL: Avoid Preview Features

// ❌ WRONG - Requires Java 21 with --enable-preview return switch (event) { case PreReasoningEvent e -> Mono.just(e); // Pattern matching in switch default -> Mono.just(event); }; // ✅ CORRECT - Java 17 compatible if (event instanceof PreReasoningEvent e) { // Pattern matching for instanceof (Java 17) return Mono.just(event); } else { return Mono.just(event); }

2.2 Reactive Programming (Critical)

⚠️ NEVER BLOCK IN AGENT LOGIC

Blocking operations will break the reactive chain and cause performance issues.

Rules:

  • Never use .block() in agent logic (only in main methods or tests)
  • ✅ Use Mono for single results (e.g., agent.call())
  • ✅ Use Flux for streaming responses (e.g., model.stream())
  • ✅ Chain operations using .map(), .flatMap(), .then()
  • ✅ Use Mono.defer() for lazy eva
5-Dim Analysis
Clarity8/10
Novelty7/10
Utility9/10
Completeness8/10
Maintainability9/10
Pros & Cons

Pros

  • Supports reactive programming principles
  • Built on a robust framework (Project Reactor)
  • Encourages non-blocking code practices
  • Extensible with custom tools and hooks

Cons

  • Steep learning curve for beginners
  • Strict coding standards may be limiting
  • Requires familiarity with reactive programming concepts
  • Dependency on Java 17+ may restrict some users

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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.

Copyright belongs to the original author agentscope-ai.