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Tauri 2.0 Development Skill

Tauri 2.0 开发技能

Architecture Overview

架构概述

LocalCowork uses Tauri 2.0 with a three-layer architecture. This skill covers the Rust backend (middle layer) and its integration with both the React frontend (top layer) and the MCP servers + inference backend (bottom layer).
React Frontend ←──Tauri IPC──→ Rust Backend ←──JSON-RPC/stdio──→ MCP Servers
                                    └──OpenAI API──→ Local LLM (Ollama/llama.cpp)
LocalCowork采用Tauri 2.0的三层架构。本技能涵盖Rust后端(中间层)及其与React前端(上层)、MCP服务器+推理后端(下层)的集成。
React Frontend ←──Tauri IPC──→ Rust Backend ←──JSON-RPC/stdio──→ MCP Servers
                                    └──OpenAI API──→ Local LLM (Ollama/llama.cpp)

Key References

关键参考文档

  • docs/architecture-decisions/001-tauri-over-electron.md
    — why Tauri
  • docs/architecture-decisions/003-model-abstraction-layer.md
    — the OpenAI API contract
  • docs/patterns/human-in-the-loop.md
    — confirmation/undo flow
  • docs/patterns/context-window-management.md
    — 32k token budget
  • docs/architecture-decisions/001-tauri-over-electron.md
    — 选择Tauri而非Electron的原因
  • docs/architecture-decisions/003-model-abstraction-layer.md
    — OpenAI API契约
  • docs/patterns/human-in-the-loop.md
    — 确认/撤销流程
  • docs/patterns/context-window-management.md
    — 32k令牌预算

Rust Backend Modules

Rust后端模块

agent_core/conversation.rs — ConversationManager

agent_core/conversation.rs — ConversationManager(对话管理器)

Manages conversation state, history, and persistence.
rust
pub struct ConversationManager {
    db: SqlitePool,           // Conversation history stored in SQLite
    current_session: Session,
    context_manager: ContextWindowManager,
}

impl ConversationManager {
    /// Create a new conversation session
    pub async fn new_session(&mut self) -> Result<SessionId>;

    /// Add a user message and get the model's response
    pub async fn send_message(&mut self, message: &str) -> Result<MessageStream>;

    /// Get conversation history for context window
    pub fn get_history(&self, max_tokens: usize) -> Vec<Message>;

    /// Persist a message to SQLite
    async fn persist_message(&self, message: &Message) -> Result<()>;
}
管理对话状态、历史记录与持久化。
rust
pub struct ConversationManager {
    db: SqlitePool,           // Conversation history stored in SQLite
    current_session: Session,
    context_manager: ContextWindowManager,
}

impl ConversationManager {
    /// Create a new conversation session
    pub async fn new_session(&mut self) -> Result<SessionId>;

    /// Add a user message and get the model's response
    pub async fn send_message(&mut self, message: &str) -> Result<MessageStream>;

    /// Get conversation history for context window
    pub fn get_history(&self, max_tokens: usize) -> Vec<Message>;

    /// Persist a message to SQLite
    async fn persist_message(&self, message: &Message) -> Result<()>;
}

agent_core/tool_router.rs — ToolRouter

agent_core/tool_router.rs — ToolRouter(工具路由器)

Routes model tool calls to the appropriate MCP server.
rust
pub struct ToolRouter {
    mcp_client: MCPClient,
    audit_logger: AuditLogger,
}

impl ToolRouter {
    /// Process a tool call from the model
    pub async fn dispatch(&self, tool_call: ToolCall) -> Result<ToolResult> {
        // 1. Look up tool in the MCP registry
        // 2. Check if confirmation is required
        // 3. If confirmed (or not required): send JSON-RPC call to server
        // 4. Log to audit trail
        // 5. If undo supported: push to undo stack
        // 6. Return result
    }

    /// Check if a tool requires user confirmation
    fn requires_confirmation(&self, tool_name: &str) -> bool;

    /// Push to undo stack for reversible actions
    async fn push_undo(&self, tool_call: &ToolCall, result: &ToolResult) -> Result<()>;
}
将模型的工具调用路由到对应的MCP服务器。
rust
pub struct ToolRouter {
    mcp_client: MCPClient,
    audit_logger: AuditLogger,
}

impl ToolRouter {
    /// Process a tool call from the model
    pub async fn dispatch(&self, tool_call: ToolCall) -> Result<ToolResult> {
        // 1. Look up tool in the MCP registry
        // 2. Check if confirmation is required
        // 3. If confirmed (or not required): send JSON-RPC call to server
        // 4. Log to audit trail
        // 5. If undo supported: push to undo stack
        // 6. Return result
    }

    /// Check if a tool requires user confirmation
    fn requires_confirmation(&self, tool_name: &str) -> bool;

    /// Push to undo stack for reversible actions
    async fn push_undo(&self, tool_call: &ToolCall, result: &ToolResult) -> Result<()>;
}

agent_core/context_window.rs — ContextWindowManager

agent_core/context_window.rs — ContextWindowManager(上下文窗口管理器)

Manages the 32k token budget. See
docs/patterns/context-window-management.md
.
rust
pub struct ContextWindowManager {
    max_tokens: usize,       // 32,768
    tokenizer: Tokenizer,    // tiktoken-rs
    system_prompt: String,
    tool_definitions: String,
}

impl ContextWindowManager {
    /// Build the full prompt for the model
    pub fn build_prompt(
        &self,
        history: &[Message],
        active_context: Option<&str>,
    ) -> Result<Vec<ChatMessage>>;

    /// Count tokens for a string
    pub fn count_tokens(&self, text: &str) -> usize;

    /// Evict old messages when context is tight
    fn evict_oldest(&mut self, history: &mut Vec<Message>);
}
管理32k令牌预算。详情见
docs/patterns/context-window-management.md
rust
pub struct ContextWindowManager {
    max_tokens: usize,       // 32,768
    tokenizer: Tokenizer,    // tiktoken-rs
    system_prompt: String,
    tool_definitions: String,
}

impl ContextWindowManager {
    /// Build the full prompt for the model
    pub fn build_prompt(
        &self,
        history: &[Message],
        active_context: Option<&str>,
    ) -> Result<Vec<ChatMessage>>;

    /// Count tokens for a string
    pub fn count_tokens(&self, text: &str) -> usize;

    /// Evict old messages when context is tight
    fn evict_oldest(&mut self, history: &mut Vec<Message>);
}

mcp_client/ — MCP Client

mcp_client/ — MCP Client(MCP客户端)

Manages MCP server processes and JSON-RPC communication.
rust
pub struct MCPClient {
    servers: HashMap<String, MCPServerProcess>,
    tool_registry: ToolRegistry,
}

impl MCPClient {
    /// Start all configured MCP servers
    pub async fn start_servers(&mut self, config: &MCPConfig) -> Result<()>;

    /// Get the aggregated tool definitions for the LLM
    pub fn get_tool_definitions(&self) -> Vec<ToolDefinition>;

    /// Send a tool call to the appropriate server
    pub async fn call_tool(&self, name: &str, args: Value) -> Result<Value>;

    /// Gracefully shutdown all servers
    pub async fn shutdown(&mut self) -> Result<()>;
}

struct MCPServerProcess {
    child: Child,        // tokio::process::Child
    stdin: ChildStdin,
    stdout: BufReader<ChildStdout>,
    tools: Vec<ToolDefinition>,
}
管理MCP服务器进程与JSON-RPC通信。
rust
pub struct MCPClient {
    servers: HashMap<String, MCPServerProcess>,
    tool_registry: ToolRegistry,
}

impl MCPClient {
    /// Start all configured MCP servers
    pub async fn start_servers(&mut self, config: &MCPConfig) -> Result<()>;

    /// Get the aggregated tool definitions for the LLM
    pub fn get_tool_definitions(&self) -> Vec<ToolDefinition>;

    /// Send a tool call to the appropriate server
    pub async fn call_tool(&self, name: &str, args: Value) -> Result<Value>;

    /// Gracefully shutdown all servers
    pub async fn shutdown(&mut self) -> Result<()>;
}

struct MCPServerProcess {
    child: Child,        // tokio::process::Child
    stdin: ChildStdin,
    stdout: BufReader<ChildStdout>,
    tools: Vec<ToolDefinition>,
}

inference/ — Inference Client

inference/ — Inference Client(推理客户端)

OpenAI-compatible API client for the local LLM.
rust
pub struct InferenceClient {
    base_url: String,        // e.g., "http://localhost:11434/v1"
    model: String,           // e.g., "qwen2.5:32b-instruct"
    http_client: reqwest::Client,
}

impl InferenceClient {
    /// Send a chat completion request (streaming)
    pub async fn chat_completion(
        &self,
        messages: Vec<ChatMessage>,
        tools: Vec<ToolDefinition>,
    ) -> Result<impl Stream<Item = StreamChunk>>;

    /// Parse tool calls from model response
    fn parse_tool_calls(response: &str) -> Result<Vec<ToolCall>>;
}
兼容OpenAI API的本地LLM客户端。
rust
pub struct InferenceClient {
    base_url: String,        // e.g., "http://localhost:11434/v1"
    model: String,           // e.g., "qwen2.5:32b-instruct"
    http_client: reqwest::Client,
}

impl InferenceClient {
    /// Send a chat completion request (streaming)
    pub async fn chat_completion(
        &self,
        messages: Vec<ChatMessage>,
        tools: Vec<ToolDefinition>,
    ) -> Result<impl Stream<Item = StreamChunk>>;

    /// Parse tool calls from model response
    fn parse_tool_calls(response: &str) -> Result<Vec<ToolCall>>;
}

Tauri IPC Commands

Tauri IPC命令

The frontend communicates with the Rust backend via Tauri commands.
rust
// src-tauri/src/commands/chat.rs
#[tauri::command]
async fn send_message(
    state: tauri::State<'_, AppState>,
    message: String,
) -> Result<String, String> {
    let mut conv = state.conversation_manager.lock().await;
    let response = conv.send_message(&message).await
        .map_err(|e| e.to_string())?;
    Ok(response)
}

#[tauri::command]
async fn confirm_action(
    state: tauri::State<'_, AppState>,
    action_id: String,
    confirmed: bool,
) -> Result<(), String> {
    let router = state.tool_router.lock().await;
    if confirmed {
        router.execute_confirmed(&action_id).await.map_err(|e| e.to_string())?;
    } else {
        router.reject_action(&action_id).await.map_err(|e| e.to_string())?;
    }
    Ok(())
}

#[tauri::command]
async fn undo_last_action(
    state: tauri::State<'_, AppState>,
) -> Result<String, String> {
    let router = state.tool_router.lock().await;
    router.undo_last().await.map_err(|e| e.to_string())
}
Frontend invocation:
typescript
import { invoke } from '@tauri-apps/api/core';

const response = await invoke<string>('send_message', { message: userInput });
await invoke('confirm_action', { actionId: 'act-001', confirmed: true });
await invoke('undo_last_action');
前端通过Tauri命令与Rust后端通信。
rust
// src-tauri/src/commands/chat.rs
#[tauri::command]
async fn send_message(
    state: tauri::State<'_, AppState>,
    message: String,
) -> Result<String, String> {
    let mut conv = state.conversation_manager.lock().await;
    let response = conv.send_message(&message).await
        .map_err(|e| e.to_string())?;
    Ok(response)
}

#[tauri::command]
async fn confirm_action(
    state: tauri::State<'_, AppState>,
    action_id: String,
    confirmed: bool,
) -> Result<(), String> {
    let router = state.tool_router.lock().await;
    if confirmed {
        router.execute_confirmed(&action_id).await.map_err(|e| e.to_string())?;
    } else {
        router.reject_action(&action_id).await.map_err(|e| e.to_string())?;
    }
    Ok(())
}

#[tauri::command]
async fn undo_last_action(
    state: tauri::State<'_, AppState>,
) -> Result<String, String> {
    let router = state.tool_router.lock().await;
    router.undo_last().await.map_err(|e| e.to_string())
}
前端调用示例:
typescript
import { invoke } from '@tauri-apps/api/core';

const response = await invoke<string>('send_message', { message: userInput });
await invoke('confirm_action', { actionId: 'act-001', confirmed: true });
await invoke('undo_last_action');

Tauri Permissions (tauri.conf.json)

Tauri权限配置(tauri.conf.json)

Each capability is granted explicitly:
json
{
  "app": {
    "security": {
      "capabilities": [
        {
          "identifier": "filesystem-access",
          "description": "Access user-granted directories",
          "permissions": [
            "fs:allow-read",
            "fs:allow-write",
            "fs:scope-$DOCUMENTS",
            "fs:scope-$DOWNLOADS"
          ]
        },
        {
          "identifier": "process-management",
          "description": "Manage MCP server child processes",
          "permissions": [
            "shell:allow-spawn",
            "shell:allow-kill"
          ]
        },
        {
          "identifier": "clipboard-access",
          "permissions": ["clipboard-manager:allow-read", "clipboard-manager:allow-write"]
        }
      ]
    }
  }
}
每个能力都需要显式授权:
json
{
  "app": {
    "security": {
      "capabilities": [
        {
          "identifier": "filesystem-access",
          "description": "Access user-granted directories",
          "permissions": [
            "fs:allow-read",
            "fs:allow-write",
            "fs:scope-$DOCUMENTS",
            "fs:scope-$DOWNLOADS"
          ]
        },
        {
          "identifier": "process-management",
          "description": "Manage MCP server child processes",
          "permissions": [
            "shell:allow-spawn",
            "shell:allow-kill"
          ]
        },
        {
          "identifier": "clipboard-access",
          "permissions": ["clipboard-manager:allow-read", "clipboard-manager:allow-write"]
        }
      ]
    }
  }
}

Coding Standards (Rust)

Rust编码规范

  • Edition 2021
  • cargo clippy -- -D warnings
    must pass (zero warnings)
  • All public functions have doc comments (
    ///
    )
  • Error handling:
    thiserror
    for custom errors,
    anyhow
    for application errors
  • Async:
    tokio
    runtime (multi-threaded)
  • Max 300 lines per file — extract to submodules when approaching
  • Use
    tracing
    crate for structured logging (integrates with shared Logger)
  • No
    unwrap()
    in production code — use
    ?
    operator or explicit error handling
  • 使用Rust 2021版本
  • 必须通过
    cargo clippy -- -D warnings
    (零警告)
  • 所有公共函数需包含文档注释(
    ///
  • 错误处理:自定义错误使用
    thiserror
    ,应用错误使用
    anyhow
  • 异步处理:使用
    tokio
    多线程运行时
  • 单个文件最大不超过300行——接近上限时拆分到子模块
  • 使用
    tracing
    crate进行结构化日志(与共享Logger集成)
  • 生产代码中禁止使用
    unwrap()
    ——使用
    ?
    运算符或显式错误处理

Dependencies (Cargo.toml)

依赖项(Cargo.toml)

Key crates:
toml
[dependencies]
tauri = { version = "2", features = ["shell-open"] }
tokio = { version = "1", features = ["full"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
sqlx = { version = "0.7", features = ["runtime-tokio", "sqlite"] }
reqwest = { version = "0.12", features = ["json", "stream"] }
tiktoken-rs = "0.5"
thiserror = "1"
anyhow = "1"
tracing = "0.1"
tracing-subscriber = "0.3"
核心依赖 crate:
toml
[dependencies]
tauri = { version = "2", features = ["shell-open"] }
tokio = { version = "1", features = ["full"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
sqlx = { version = "0.7", features = ["runtime-tokio", "sqlite"] }
reqwest = { version = "0.12", features = ["json", "stream"] }
tiktoken-rs = "0.5"
thiserror = "1"
anyhow = "1"
tracing = "0.1"
tracing-subscriber = "0.3"