v1.0.1

What's Changed

• feat: add checkRunning parameter to control concurrent agent calls by @AlbumenJ in #111
• fix: installation docs by @AlbumenJ in #117
• feat: add ChatUsage to Msg metadata for token usage tracking by @AlbumenJ in #118
• refactor: reuse ObjectMapper to avoid repeated construction overhead. by @yangl in #121
• feat: enhance GenerateOptions with topK, seed and additional HTTP params by @AlbumenJ in #119
• feat: add build-in file operationg tool by @fang-tech in #89
• refactor: split examples by @AlbumenJ in #122
• refactor: move embedding to rag-simple by @AlbumenJ in #133
• feat(session): add support redis session storage by @jianjun159 in #142
• fix modifications to PostActingEvent in Hook not taking effect as expected by @LearningGp in #154
• fix: all-in-one should shade extensions by @AlbumenJ in #153

New Contributors

• @yangl made their first contribution in #121
• @jianjun159 made their first contribution in #142

Full Changelog: v1.0.0...v1.0.1

v1.0.0

The first stable release of AgentScope Java - an agent-oriented programming framework for building LLM applications.

Installation

Requires JDK 17+

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

Highlights

• Multi-Model Support - DashScope, OpenAI, Anthropic, Gemini out of the box
• ReAct Agent - Built-in reasoning and acting loop with tool calling
• Annotation-based Tools - Define tools with simple @Tool annotations
• MCP Protocol - Native Model Context Protocol support
• Multimodal - Text, image, audio, and video content handling
• RAG - Knowledge retrieval with local vector store or Bailian integration
• Long-term Memory - Cross-session memory via Mem0 extension
• Multi-Agent Pipelines - Sequential, fanout, and message hub patterns
• Streaming & Hooks - Real-time output with execution lifecycle hooks
• AgentScope Studio - Visual debugging interface

Extensions

• agentscope-extensions-mem0 - Long-term memory
• agentscope-extensions-rag-simple - Local RAG
• agentscope-extensions-rag-bailian - Bailian knowledge base
• agentscope-extensions-studio - Visual debugger

v0.2.1

AgentScope Studio Integration

• Real-time Visualization: Added AgentScope Studio integration with real-time visualization capabilities for monitoring agent execution (#57)

RAG Support

• Comprehensive RAG Framework: Introduced full-featured RAG (Retrieval-Augmented Generation) support including:
  • Embedding model integration
  • Vector store implementations
  • Document retrieval capabilities (#55)

Performance

• Non-blocking I/O: Improved model execution performance by using boundedElastic scheduler to prevent blocking I/O threads (#56)

v0.2.0

We're excited to announce the release of AgentScope Java v0.2.0, a major milestone that brings production-ready features, comprehensive multi-agent capabilities, and full alignment with the Python version.

Multi-Agent System

• MsgHub & Pipeline (#39, #36): Message broadcasting and orchestration for multi-agent collaboration
• PlanNotebook (#40): Structured multi-step task planning and execution
• Werewolf Game Example (#51): Complete multi-agent game with i18n support demonstrating advanced agent interactions

Advanced Tool System

• MCP (Model Context Protocol) Integration (#16): Connect to external tools and services
• Tool Streaming (#14): Real-time progress updates during tool execution
• Meta Tools & Tool Groups (#17, #42): Hierarchical tool organization with access control
• Parallel Tool Execution (#11): Concurrent tool calls for improved performance

Model Capabilities

• Multimodal Support (#25): Vision (Image) and Audio support for DashScope and OpenAI models
• Thinking Mode (#22, #24): Support for o1-style reasoning with ThinkingBlock
• Structured Output (#26, #37, #45, #46, #47): JSON schema-based response formatting with tool choice integration
• Timeout & Retry (#33, #35): Three-layer timeout architecture with exponential backoff retry

Agent Enhancements

• Agent Interruption (#15): Graceful agent interruption mechanism
• Context Summarization (#27): Auto-summarize when max iterations reached
• Stream API (#32): Reactive event streaming for real-time agent monitoring
• UserAgent Refactoring (#29): Comprehensive user interaction agent with builder pattern

Architecture Improvements

• Fully Reactive Execution (#28): Async-first architecture using Project Reactor
• Event-Driven Hooks (#13, #20, #41): Unified hook system aligned with Python implementation
• Sealed ContentBlock (#30): Type-safe message content using Java sealed classes
• SessionManager (#50): Automatic component naming and session management
• Formatter Restructuring (#45): Cleaner separation between DashScope and OpenAI formatters

v0.1.0

Please note that this is an early version, so the APIs and dependencies in the current version do not guarantee long-term stability. Future versions might bring breaking changes.

🚀 Quickstart

Installation

AgentScope Java requires jdk 17 or higher.

<dependency>
    <groupId>io.agentscope</groupId>
    <artifactId>agentscope-core</artifactId>
    <version>0.1.0</version>
</dependency>

Hello AgentScope!

Start with a basic ReActAgent that replies to user queries!

public static void main(String[] args) {
    Model model = DashScopeChatModel.builder()
		.apiKey(System.getenv("DASHSCOPE_API_KEY"))
		.modelName("qwen-max")
		.build();

    ReActAgent agent = ReActAgent.builder()
    .name("hello-world-agent")
    .sysPrompt("You are a helpful AI assistant. Be concise and friendly. " +
               "When thinking through problems, use <thinking>...</thinking> tags to show your reasoning.")
    .model(model)
    .memory(new InMemoryMemory())
    .formatter(new DashScopeChatFormatter())
    .build();

    Msg userMessage = Msg.builder()
        .role(MsgRole.USER)
        .textContent("Hello, please introduce yourself.")
        .build();
    Msg response = agent.reply(userMessage).block();

    System.out.println("Agent Response: " + response.getTextContent());
}