zclaw_openfang/docs/knowledge-base/zclaw-technical-reference.md

ZCLAW Kernel 技术参考文档

文档版本: v2.0 更新日期: 2026-03-22 目标: 为 ZCLAW 内部 Kernel 架构提供技术参考

---

一、项目概述

1.1 基本信息

属性	值
项目名称	ZCLAW
架构	8 个 Rust Crates 模块化设计
定位	AI Agent 桌面客户端
许可	MIT

1.2 核心特性

┌─────────────────────────────────────────────────────────────────┐
│                    ZCLAW 核心特性                                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  🚀 内部 Kernel 架构                                            │
│  ├── 无外部进程依赖                                             │
│  ├── 单一安装包可运行                                           │
│  └── 即时启动                                                   │
│                                                                 │
│  🤖 多 LLM Provider 支持                                        │
│  ├── Kimi Code (kimi)                                          │
│  ├── 百炼/通义千问 (qwen)                                       │
│  ├── DeepSeek (deepseek)                                       │
│  ├── 智谱 GLM (zhipu)                                          │
│  ├── OpenAI (openai)                                           │
│  ├── Anthropic (anthropic)                                     │
│  └── 本地模型 (local/ollama)                                    │
│                                                                 │
│  🔒 UI 配置                                                     │
│  ├── 无需编辑配置文件                                           │
│  ├── "模型与 API"设置页面配置                                   │
│  └── 配置存储在 localStorage                                    │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

---

二、Crate 架构

2.1 模块结构

ZCLAW/
├── crates/                           # Rust Workspace
│   │
│   ├── zclaw-types/                  # L1: 基础类型（无依赖）
│   │   ├── AgentId, SessionId, Message
│   │   ├── Capability, Tool, Event
│   │   ├── Config, Error
│   │   └── 共享 trait 定义
│   │
│   ├── zclaw-memory/                 # L2: 存储层（依赖 types）
│   │   ├── KV Store（Agent 配置、状态）
│   │   ├── Session Manager（对话历史）
│   │   ├── Semantic Search（向量搜索）
│   │   └── SQLite 持久化
│   │
│   ├── zclaw-runtime/                # L3: 运行时（依赖types, memory）
│   │   ├── LLM Drivers（Anthropic, OpenAI, Gemini, Local）
│   │   ├── Tool Runner（23 个内置工具）
│   │   ├── Agent Loop（消息循环、流式处理）
│   │   ├── Loop Guard（防循环）
│   │   └── Session Compactor（上下文压缩）
│   │
│   ├── zclaw-kernel/                 # L4: 核心协调（依赖所有下层）
│   │   ├── Agent Registry（注册、生命周期）
│   │   ├── Scheduler（配额、调度）
│   │   ├── Capability Manager（权限）
│   │   ├── Event Bus（事件发布）
│   │   ├── Workflow Engine（工作流）
│   │   ├── Trigger Engine（触发器）
│   │   └── Supervisor（健康监控）
│   │
│   ├── zclaw-skills/                 # 技能系统
│   │   ├── Skill Loader（TOML/SKILL.md 解析）
│   │   ├── Skill Runner（Python/WASM/PromptOnly）
│   │   └── Bundled Skills（内置技能）
│   │
│   ├── zclaw-hands/                  # Hands 自主能力
│   │   ├── Hand/Trigger trait 定义
│   │   ├── HandRegistry/TriggerRegistry 实现
│   │   ├── Browser Hand
│   │   ├── Collector Hand
│   │   ├── Researcher Hand
│   │   └── 其他 Hands
│   │
│   ├── zclaw-channels/               # 通道适配器
│   │   ├── Channel Trait
│   │   ├── Telegram/Discord/Slack 等
│   │   └── Bridge Manager
│   │
│   └── zclaw-protocols/              # 协议支持
│       ├── MCP Client/Server
│       └── A2A Protocol
│
├── desktop/
│   ├── src-tauri/                    # Tauri 应用
│   │   ├── src/
│   │   │   ├── lib.rs                # 主入口，Kernel 初始化
│   │   │   ├── kernel_commands.rs    # Tauri 命令封装
│   │   │   └── state.rs              # 应用状态
│   │   └── Cargo.toml                # 依赖内部 crates
│   └── src/                          # React 前端
│
└── Cargo.toml                        # Workspace 定义

2.2 依赖关系

zclaw-types          (无依赖)
    ↑
zclaw-memory         (→ types)
    ↑
zclaw-runtime        (→ types, memory)
    ↑
zclaw-kernel         (→ types, memory, runtime)
    ↑
desktop/src-tauri    (→ kernel, skills, hands, channels, protocols)

---

三、zclaw-types

3.1 核心 ID 类型

pub struct AgentId(Uuid);
pub struct SessionId(Uuid);
pub struct ToolId(String);

3.2 消息类型

pub enum Message {
    User { content: String },
    Assistant { content: String, thinking: Option<String> },
    ToolUse { tool: ToolId, input: Value },
    ToolResult { tool: ToolId, output: Value, error: bool },
}

3.3 Agent 配置

pub struct AgentConfig {
    pub id: AgentId,
    pub name: String,
    pub model: ModelConfig,
    pub system_prompt: String,
    pub capabilities: Vec<Capability>,
    pub tools: Vec<ToolId>,
}

pub struct ModelConfig {
    pub provider: String,
    pub model: String,
    pub api_key_env: Option<String>,
    pub base_url: Option<String>,
}

3.4 统一错误类型

pub enum ZclawError {
    NotFound(String),
    PermissionDenied(String),
    LlmError(String),
    ToolError(String),
    StorageError(String),
    ConfigError(String),
}

---

四、zclaw-memory

4.1 MemoryStore

pub struct MemoryStore {
    db: Arc<Mutex<Connection>>,
}

impl MemoryStore {
    // Agent 配置持久化
    pub async fn save_agent(&self, agent: &AgentConfig) -> Result<()>;
    pub async fn load_agent(&self, id: &AgentId) -> Result<Option<AgentConfig>>;
    pub async fn list_agents(&self) -> Result<Vec<AgentConfig>>;

    // 会话管理
    pub async fn create_session(&self, agent_id: &AgentId) -> Result<SessionId>;
    pub async fn get_session(&self, id: &SessionId) -> Result<Option<Session>>;
    pub async fn append_message(&self, session: &SessionId, msg: Message) -> Result<()>;

    // KV 存储
    pub async fn store(&self, agent: &AgentId, key: &str, value: &Value) -> Result<()>;
    pub async fn recall(&self, agent: &AgentId, key: &str) -> Result<Option<Value>>;

    // 语义搜索
    pub async fn embed(&self, text: &str) -> Result<Vec<f32>>;
    pub async fn search_similar(&self, query: &str, limit: usize) -> Result<Vec<MemoryEntry>>;
}

4.2 数据库 Schema

-- Agent 配置表
CREATE TABLE agents (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    config JSON NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- 会话表
CREATE TABLE sessions (
    id TEXT PRIMARY KEY,
    agent_id TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (agent_id) REFERENCES agents(id)
);

-- 消息表
CREATE TABLE messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    session_id TEXT NOT NULL,
    role TEXT NOT NULL,
    content TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (session_id) REFERENCES sessions(id)
);

---

五、zclaw-runtime

5.1 LLM Driver Trait

#[async_trait]
pub trait LlmDriver: Send + Sync {
    async fn complete(&self, req: CompletionRequest) -> Result<CompletionResponse>;
    async fn stream(&self, req: CompletionRequest) -> Result<mpsc::Receiver<StreamEvent>>;
}

5.2 Driver 实现

// Anthropic Claude
pub struct AnthropicDriver {
    api_key: SecretString,
}

// OpenAI 兼容（支持 Kimi, Qwen, DeepSeek, Zhipu 等）
pub struct OpenAiDriver {
    api_key: SecretString,
    base_url: String,
}

impl OpenAiDriver {
    pub fn with_base_url(api_key: SecretString, base_url: String) -> Self {
        Self { api_key, base_url }
    }
}

// Google Gemini
pub struct GeminiDriver {
    api_key: SecretString,
}

// 本地模型（Ollama/LMStudio）
pub struct LocalDriver {
    base_url: String,
}

5.3 Agent Loop

pub struct AgentLoop {
    driver: Arc<dyn LlmDriver>,
    tools: Vec<Tool>,
    memory: Arc<MemoryStore>,
    loop_guard: LoopGuard,
    // 模型配置（2026-03-22 添加）
    model: String,
    system_prompt: Option<String>,
    max_tokens: u32,
    temperature: f32,
}

impl AgentLoop {
    pub fn new(...) -> Self;
    pub fn with_model(self, model: impl Into<String>) -> Self;
    pub fn with_system_prompt(self, prompt: impl Into<String>) -> Self;
    pub fn with_max_tokens(self, max_tokens: u32) -> Self;
    pub fn with_temperature(self, temperature: f32) -> Self;

    pub async fn run(&self, session: SessionId, input: String) -> Result<AgentLoopResult>;
    pub async fn run_streaming(&self, session: SessionId, input: String)
        -> Result<mpsc::Receiver<LoopEvent>>;
}

重要: AgentLoop 必须通过 builder 方法配置模型，否则将使用默认值。run() 方法会从 CompletionResponse.content 中提取 ContentBlock::Text 作为响应内容。

---

六、zclaw-kernel

6.1 Kernel 主结构

pub struct Kernel {
    registry: AgentRegistry,
    scheduler: Scheduler,
    capabilities: CapabilityManager,
    events: EventBus,
    workflows: WorkflowEngine,
    triggers: TriggerEngine,
    supervisor: Supervisor,
    memory: Arc<MemoryStore>,
    config: KernelConfig,
}

6.2 Kernel 方法

impl Kernel {
    /// 启动 Kernel
    pub async fn boot(config: KernelConfig) -> Result<Self>;

    /// 关闭 Kernel
    pub async fn shutdown(self) -> Result<()>;

    // Agent 生命周期
    pub async fn spawn_agent(&self, config: AgentConfig) -> Result<AgentId>;
    pub async fn kill_agent(&self, id: &AgentId) -> Result<()>;
    pub fn list_agents(&self) -> Vec<AgentInfo>;
    pub fn get_agent(&self, id: &AgentId) -> Option<AgentInfo>;

    // 消息处理
    pub async fn send_message(&self, agent: &AgentId, msg: String)
        -> Result<MessageResponse>;
    pub async fn send_message_stream(&self, agent: &AgentId, msg: String)
        -> Result<mpsc::Receiver<StreamEvent>>;
}

// send_message 实现：从 AgentConfig 获取模型配置传递给 AgentLoop
// 优先级：agent_config.model > kernel_config.default_model

消息处理流程:

send_message(agent_id, message)
    │
    ├── 获取 AgentConfig: registry.get(agent_id)
    │
    ├── 确定模型:
    │   if !agent_config.model.model.is_empty():
    │       model = agent_config.model.model
    │   else:
    │       model = kernel_config.default_model
    │
    ├── 创建 AgentLoop (使用 builder 模式):
    │   AgentLoop::new(...)
    │       .with_model(model)
    │       .with_max_tokens(agent_config.max_tokens || kernel_config.max_tokens)
    │       .with_temperature(agent_config.temperature || kernel_config.temperature)
    │       .with_system_prompt(agent_config.system_prompt) // 可选
    │
    └── 运行: loop_runner.run(session_id, message)
        │
        ├── 调用 LLM: driver.complete(request)
        │
        └── 提取响应: 从 ContentBlock::Text 获取内容

// 工作流
pub async fn run_workflow(&self, workflow_id: &str, input: Value) -> Result<Value>;

// 事件
pub fn subscribe(&self) -> broadcast::Receiver<Event>;

}

### 6.3 KernelConfig

```rust
pub struct KernelConfig {
    // 数据库
    pub database_url: String,

    // 默认 Provider 和模型
    pub default_provider: String,
    pub default_model: String,

    // API Keys（从 UI 传入）
    pub anthropic_api_key: Option<String>,
    pub openai_api_key: Option<String>,
    pub gemini_api_key: Option<String>,
    pub kimi_api_key: Option<String>,
    pub qwen_api_key: Option<String>,
    pub deepseek_api_key: Option<String>,
    pub zhipu_api_key: Option<String>,

    // Base URLs
    pub kimi_base_url: String,
    pub qwen_base_url: String,
    pub deepseek_base_url: String,
    pub zhipu_base_url: String,
    pub local_base_url: Option<String>,

    // 生成参数
    pub max_tokens: u32,
    pub temperature: f32,
}

6.4 create_driver 方法

impl KernelConfig {
    pub fn create_driver(&self) -> Result<Arc<dyn LlmDriver>> {
        match self.default_provider.as_str() {
            "anthropic" => {
                let key = self.anthropic_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("ANTHROPIC_API_KEY not set".into()))?;
                Ok(Arc::new(AnthropicDriver::new(SecretString::new(key))))
            }
            "kimi" => {
                let key = self.kimi_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("KIMI_API_KEY not set".into()))?;
                Ok(Arc::new(OpenAiDriver::with_base_url(
                    SecretString::new(key),
                    self.kimi_base_url.clone(),
                )))
            }
            "qwen" => {
                let key = self.qwen_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("QWEN_API_KEY not set".into()))?;
                Ok(Arc::new(OpenAiDriver::with_base_url(
                    SecretString::new(key),
                    self.qwen_base_url.clone(),
                )))
            }
            "deepseek" => {
                let key = self.deepseek_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("DEEPSEEK_API_KEY not set".into()))?;
                Ok(Arc::new(OpenAiDriver::with_base_url(
                    SecretString::new(key),
                    self.deepseek_base_url.clone(),
                )))
            }
            "zhipu" => {
                let key = self.zhipu_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("ZHIPU_API_KEY not set".into()))?;
                Ok(Arc::new(OpenAiDriver::with_base_url(
                    SecretString::new(key),
                    self.zhipu_base_url.clone(),
                )))
            }
            "openai" => {
                let key = self.openai_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("OPENAI_API_KEY not set".into()))?;
                Ok(Arc::new(OpenAiDriver::new(SecretString::new(key))))
            }
            "gemini" => {
                let key = self.gemini_api_key.clone()
                    .ok_or_else(|| ZclawError::ConfigError("GEMINI_API_KEY not set".into()))?;
                Ok(Arc::new(GeminiDriver::new(SecretString::new(key))))
            }
            "local" | "ollama" => {
                let base_url = self.local_base_url.clone()
                    .unwrap_or_else(|| "http://localhost:11434/v1".to_string());
                Ok(Arc::new(LocalDriver::new(base_url)))
            }
            _ => Err(ZclawError::ConfigError(
                format!("Unknown provider: {}", self.default_provider)
            )),
        }
    }
}

---

七、Tauri 集成

7.1 命令封装

// desktop/src-tauri/src/kernel_commands.rs

/// Kernel 状态包装器
pub type KernelState = Arc<Mutex<Option<Kernel>>>;

/// 初始化 Kernel
#[tauri::command]
pub async fn kernel_init(
    state: State<'_, KernelState>,
    config_request: Option<KernelConfigRequest>,
) -> Result<KernelStatusResponse, String> {
    // ...
}

/// 创建 Agent
#[tauri::command]
pub async fn agent_create(
    state: State<'_, KernelState>,
    request: CreateAgentRequest,
) -> Result<CreateAgentResponse, String> {
    // ...
}

/// 发送消息
#[tauri::command]
pub async fn agent_chat(
    state: State<'_, KernelState>,
    request: ChatRequest,
) -> Result<ChatResponse, String> {
    // ...
}

7.2 lib.rs 注册

// desktop/src-tauri/src/lib.rs

#[cfg_attr(mobile, tauri::mobile_entry_point)]
pub fn run() {
    tauri::Builder::default()
        .setup(|app| {
            app.manage(kernel_commands::create_kernel_state());
            Ok(())
        })
        .invoke_handler(tauri::generate_handler![
            kernel_commands::kernel_init,
            kernel_commands::kernel_status,
            kernel_commands::kernel_shutdown,
            kernel_commands::agent_create,
            kernel_commands::agent_list,
            kernel_commands::agent_get,
            kernel_commands::agent_delete,
            kernel_commands::agent_chat,
        ])
        .run(tauri::generate_context!())
}

---

八、前端集成

8.1 KernelClient

// desktop/src/lib/kernel-client.ts

export class KernelClient {
  private config: KernelConfig = {};
  private defaultAgentId: string = '';

  setConfig(config: KernelConfig): void {
    this.config = config;
  }

  async connect(): Promise<void> {
    if (!this.config.provider || !this.config.model || !this.config.apiKey) {
      throw new Error('请先在"模型与 API"设置页面配置模型');
    }

    const status = await invoke<KernelStatus>('kernel_init', {
      configRequest: {
        provider: this.config.provider,
        model: this.config.model,
        apiKey: this.config.apiKey,
        baseUrl: this.config.baseUrl || null,
      },
    });

    // 创建默认 Agent
    const agents = await this.listAgents();
    if (agents.length === 0) {
      const agent = await this.createAgent({
        name: 'Default Agent',
        provider: this.config.provider,
        model: this.config.model,
      });
      this.defaultAgentId = agent.id;
    }
  }

  async chat(message: string, opts?: ChatOptions): Promise<ChatResponse> {
    return invoke<ChatResponse>('agent_chat', {
      request: {
        agentId: opts?.agentId || this.defaultAgentId,
        message,
      },
    });
  }
}

8.2 ConnectionStore

// desktop/src/store/connectionStore.ts

connect: async (url?: string, token?: string) => {
  const useInternalKernel = isTauriRuntime();

  if (useInternalKernel) {
    const kernelClient = getKernelClient();
    const modelConfig = getDefaultModelConfig();

    if (!modelConfig) {
      throw new Error('请先在"模型与 API"设置页面添加自定义模型配置');
    }

    kernelClient.setConfig({
      provider: modelConfig.provider,
      model: modelConfig.model,
      apiKey: modelConfig.apiKey,
      baseUrl: modelConfig.baseUrl,
    });

    await kernelClient.connect();
    set({ client: kernelClient, gatewayVersion: '0.2.0-internal' });
    return;
  }

  // 非 Tauri 环境...
}

---

九、支持的 Provider

9.1 Provider 列表

Provider	Driver	Base URL	API 协议
kimi	OpenAiDriver	https://api.kimi.com/coding/v1	OpenAI 兼容
qwen	OpenAiDriver	https://dashscope.aliyuncs.com/compatible-mode/v1	OpenAI 兼容
deepseek	OpenAiDriver	https://api.deepseek.com/v1	OpenAI 兼容
zhipu	OpenAiDriver	https://open.bigmodel.cn/api/paas/v4	OpenAI 兼容
openai	OpenAiDriver	https://api.openai.com/v1	OpenAI
anthropic	AnthropicDriver	https://api.anthropic.com	Anthropic
gemini	GeminiDriver	https://generativelanguage.googleapis.com	Gemini
local	LocalDriver	http://localhost:11434/v1	OpenAI 兼容

9.2 配置示例

// Kimi Code 配置
{
  provider: 'kimi',
  model: 'kimi-k2-turbo',
  apiKey: 'your-kimi-api-key',
  baseUrl: 'https://api.kimi.com/coding/v1'
}

// 百炼 Qwen 配置
{
  provider: 'qwen',
  model: 'qwen-plus',
  apiKey: 'your-qwen-api-key',
  baseUrl: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
}

---

十、开发命令

10.1 构建命令

# 构建所有 crates
cargo build

# 构建生产版本
cargo build --release

# 运行测试
cargo test

# 启动开发环境
pnpm start:dev

10.2 常用命令

# 安装依赖
pnpm install

# 类型检查
pnpm tsc --noEmit

# 运行测试
pnpm vitest run

# E2E 测试
pnpm test:e2e

---

十一、参考资料

11.1 相关文档

• 快速启动指南
• 模型配置指南
• 通信层文档
• 后端集成文档

11.2 架构演进

版本	架构	说明
v1.x	外部 OpenFang	需要启动独立后端进程
v2.0	内部 Kernel	Kernel 集成在 Tauri 中，无需外部进程

---

文档版本: v2.0 | 更新日期: 2026-03-22