AgentScope Harness

0.环境

0.1 依赖

<parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.3.6</version>
        <relativePath/>
    </parent>

    <properties>
        <java.version>17</java.version>
        <agentscope.version>1.1.0-RC2</agentscope.version>
    </properties>

        <dependency>
            <groupId>io.agentscope</groupId>
            <artifactId>agentscope-harness</artifactId>
            <version>${agentscope.version}</version>
        </dependency>

        <dependency>
            <groupId>io.agentscope</groupId>
            <artifactId>agentscope-core</artifactId>
            <version>${agentscope.version}</version>
        </dependency>

0.2 QuickStart

public class QuickstartExample {

    static final String BASE_URL = "https://api.minimaxi.com/anthropic";
    static final String MODEL_NAME = "MiniMax-M2.5";

    public static void main(String[] args) throws Exception {
        // 1. 准备工作区：第一次运行生成 AGENTS.md，后续运行复用
        Path workspace = Paths.get(".agentscope/workspace");
        initWorkspaceIfAbsent(workspace);

        // 2. 构建模型
        Model model = AnthropicChatModel.builder()
                .apiKey(API_KEY)
                .modelName(MODEL_NAME)
                .baseUrl(BASE_URL)
                .stream(true)
                .build();

        // 3. 构建 HarnessAgent：工作区注入、会话持久化、追踪日志默认开启；
        //    这里显式启用对话压缩
        // name纬度区别文件
        HarnessAgent agent = HarnessAgent.builder()
                .name("quickstart-agent")
                .sysPrompt("你是一个帮助用户做笔记的助手。")
                .model(model)
                .workspace(workspace)
                .compaction(CompactionConfig.builder()
                        .triggerMessages(30)
                        .keepMessages(10)
                        .flushBeforeCompact(true)   // 压缩前把事实提取到日流水账
                        .build())
                .build();

        // 4. 同一个 RuntimeContext 发起两轮对话
        //    sessionId 相同 → 第二轮自动从 Session 恢复第一轮的状态
        RuntimeContext ctx = RuntimeContext.builder()
                .sessionId("demo-session")
                .userId("alice")
                .build();

        Msg turn1 = agent.call(
                Msg.builder().role(MsgRole.USER)
                        .textContent("我叫天宇,今天准备一个关于 ReAct 的技术分享。")
                        .build(),
                ctx).block();
        System.out.println("[turn1] " + turn1.getTextContent());

        Msg turn2 = agent.call(
                Msg.builder().role(MsgRole.USER)
                        .textContent("我叫什么?我今天要干什么?")
                        .build(),
                ctx).block();
        System.out.println("[turn2] " + turn2.getTextContent());
    }

    private static void initWorkspaceIfAbsent(Path workspace) throws Exception {
        Files.createDirectories(workspace);
        Path agentsMd = workspace.resolve("AGENTS.md");
        if (Files.exists(agentsMd)) return;
        Files.writeString(agentsMd, """
                # 笔记助手

                你是一个帮助用户整理笔记和知识的助手。

                ## 行为约定
                - 主动记录用户提到的关键事实(姓名、计划、偏好等)
                - 回答用简洁中文,必要时给出要点列表
                - 对不确定的内容要主动说明,不要臆造
                """);
    }
}

• 工作区生成的文件
  

2.架构

3.Skill

3.1 workspace自动注入

3.2 显示注入

• Windows 无 sh 需要 disableShellTool()

public static void main(String[] args) throws Exception {
        String pythonDir = "D:\\Code\\Python";
        String currentPath = System.getenv("Path");
        if (currentPath == null || !currentPath.contains(pythonDir)) {
            ProcessBuilder pb = new ProcessBuilder();
            pb.environment().put("Path", pythonDir + ";" + pythonDir + "\\Scripts;" + (currentPath != null ? currentPath : ""));
            System.out.println("[Env] 已将 Python 加入进程 PATH: " + pythonDir);
        }

        Path workspace = Paths.get(".agentscope/workspace").toAbsolutePath();
        Files.createDirectories(workspace);

        Model model = AnthropicChatModel.builder()
                .apiKey(API_KEY)
                .modelName(MODEL_NAME)
                .baseUrl(BASE_URL)
                .stream(true)
                .build();

        Toolkit toolkit = new Toolkit();

        SkillBox skillBox = new SkillBox(toolkit);

        Path skillsPath = workspace.resolve("skills");
        if (Files.exists(skillsPath) && Files.isDirectory(skillsPath)) {
            AgentSkillRepository skillRepo = new FileSystemSkillRepository(skillsPath);
            for (AgentSkill skill : skillRepo.getAllSkills()) {
                skillBox.registerSkill(skill);
                System.out.println("[Skill] 已注册: " + skill.getName() + " - " + skill.getDescription());
            }
        } else {
            System.out.println("[Skill] workspace/skills/ 目录不存在，跳过技能加载");
        }

        ShellCommandTool shellTool = new ShellCommandTool(
                null,
                Set.of("python", "py", "pip", "jshell", "java", "javac", "dir", "type", "echo", "where", "cmd"),
                command -> true
        );

        skillBox.codeExecution()
                .workDir(workspace.toString())
                .withShell(shellTool)
                .withWrite()
                .enable();
        System.out.println("[CodeExecution] 已启用, workDir=" + skillBox.getCodeExecutionWorkDir());

        HarnessAgent agent = HarnessAgent.builder()
                .name("demo-agent")
                .sysPrompt("你是一个功能全面的智能助手，能够帮助用户完成各种任务。使用简洁中文回答。当需要执行计算或脚本时，使用 python-runner 技能。当前环境有 Python 3.9 可用，执行命令为 python，脚本后缀为 .py。重要：写脚本请用 write_text_file 工具，执行脚本请用 execute_shell_command 工具，两者共享同一工作目录。")
                .model(model)
                .workspace(workspace)
                .toolkit(toolkit)
                .skillRepository(new FileSystemSkillRepository(skillsPath))
                .disableShellTool()
                .disableFilesystemTools()
                .compaction(CompactionConfig.builder()
                        .triggerMessages(50)
                        .keepMessages(20)
                        .build())
                .build();

        System.out.println("=== HarnessAgent Python Runner Demo ===\n");

        RuntimeContext ctx = RuntimeContext.builder()
                .sessionId("python-demo-session")
                .userId("demo-user")
                .build();

        System.out.println("--- 第1轮: 用Python执行计算 ---");
        Msg msg1 = Msg.builder().role(MsgRole.USER).textContent("执行一下lwx-test").build();
        Msg reply1 = agent.call(msg1, ctx).block();
        System.out.println("[助手] " + (reply1 != null ? reply1.getTextContent() : "无响应"));

        System.out.println("\n=== Demo 完成 ===");
    }

• SKILL.md

---
name: lwx-test
description: 当用户指明使用lwx-test SKILL时，才使用该SKILL
---

# 功能描述

执行 write_log.py
``
python D:\WorkSpace\AI\MyHarness\.agentscope\workspace\skills\lwx-test\write_log.py
``