OpenClaw 深度研究报告
技术原理与企业级 AI Agent 系统中的应用

# 1. 消息接收
async def receive_message(channel: MessageChannel, raw_data: dict):
    """从各渠道接收原始消息"""
    message = await channel.parse(raw_data)
    return message

# 2. 消息标准化
async def normalize_message(message: RawMessage) -> StandardMessage:
    """将不同渠道消息格式统一为标准格式"""
    return StandardMessage(
        id=message.id,
        channel=message.channel,
        sender=message.sender,
        content=message.content,
        timestamp=message.timestamp
    )

# 3. 意图识别
async def recognize_intent(message: StandardMessage) -> Intent:
    """分析用户意图，提取关键参数"""
    intent = await llm.analyze(message.content)
    context = await memory.retrieve(message.sender)
    return Intent(type=intent.type, params=intent.params, context=context)

# 4. 任务规划
async def plan_task(intent: Intent) -> TaskPlan:
    """制定执行计划，分配给相应 Agent"""
    plan = await planner.create(intent)
    agents = await agent_registry.select(plan.required_skills)
    return TaskPlan(steps=plan.steps, agents=agents)

# 5. 工具调用
async def execute_tools(plan: TaskPlan) -> List[Result]:
    """安全执行所需工具和操作"""
    results = []
    for step in plan.steps:
        result = await sandbox.execute(step.tool, step.params)
        results.append(result)
    return results

# 6. 结果聚合
async def aggregate_results(results: List[Result]) -> Response:
    """聚合各 Agent 的执行结果"""
    response = await aggregator.combine(results)
    return response

# 7. 响应生成
async def generate_response(response: Response, channel: MessageChannel):
    """生成自然语言响应，通过原渠道返回"""
    text = await llm.generate(response)
    await channel.send(text)

class BrowserController:
    """浏览器控制器"""
    
    async def navigate(self, url: str):
        """导航到指定 URL"""
        await self.page.goto(url, wait_until="networkidle")
    
    async def click(self, selector: str):
        """模拟用户点击操作"""
        await self.page.click(selector)
    
    async def input_text(self, selector: str, text: str):
        """在网页表单中输入文本"""
        await self.page.fill(selector, text)
    
    async def screenshot(self, path: str):
        """截取网页截图"""
        await self.page.screenshot(path=path)
    
    async def extract_data(self, selectors: dict) -> dict:
        """提取网页结构化数据"""
        data = {}
        for key, selector in selectors.items():
            element = await self.page.query_selector(selector)
            data[key] = await element.inner_text()
        return data

class MemoryManager:
    """记忆管理器"""
    
    def __init__(self, db_path: str, embedding_model: str):
        self.db = sqlite3.connect(db_path)
        self.embedding = load_embedding_model(embedding_model)
        self.vector_store = ChromaDB()
    
    async def create(self, content: str, tags: list, ttl: int = None) -> Memory:
        """创建记忆"""
        embedding = self.embedding.encode(content)
        memory = Memory(
            id=uuid.uuid4(),
            content=content,
            tags=tags,
            embedding=embedding,
            ttl=ttl,
            created_at=datetime.now()
        )
        await self.vector_store.add(memory)
        return memory
    
    async def search(self, query: str, limit: int = 5, threshold: float = 0.7) -> list:
        """语义搜索记忆"""
        query_embedding = self.embedding.encode(query)
        results = await self.vector_store.search(
            query_embedding,
            limit=limit,
            threshold=threshold
        )
        return results
    
    async def retrieve_context(self, user_id: str, window: int = 10) -> list:
        """检索用户上下文"""
        memories = self.db.execute(
            "SELECT * FROM memories WHERE user_id = ? ORDER BY created_at DESC LIMIT ?",
            (user_id, window)
        ).fetchall()
        return memories

class TaskScheduler:
    """任务调度器"""
    
    async def schedule_cron(self, cron_expr: str, task: Callable):
        """定时任务（Cron 表达式）"""
        schedule = CronSchedule(cron_expr)
        await self.scheduler.add(task, schedule)
    
    async def schedule_event(self, event: Event, task: Callable):
        """事件触发任务"""
        await self.event_bus.subscribe(event, task)
    
    async def schedule_priority(self, task: Task, priority: int):
        """优先级调度"""
        await self.priority_queue.push(task, priority)
    
    async def schedule_dag(self, tasks: List[Task], dependencies: dict):
        """DAG 依赖调度"""
        dag = DAG(tasks, dependencies)
        await self.dag_executor.run(dag)

# config.yaml - OpenClaw 与 AI Agent 集成配置
openclaw:
  gateway:
    host: localhost
    port: 8080
    api_key: ${OPENCLAW_API_KEY}
  
  channels:
    - type: wechat
      enabled: true
      config:
        app_id: ${WECHAT_APP_ID}
        app_secret: ${WECHAT_APP_SECRET}
    
    - type: dingtalk
      enabled: true
      config:
        app_key: ${DINGTALK_APP_KEY}
        app_secret: ${DINGTALK_APP_SECRET}
  
  agents:
    coordinator:
      model: gpt-4
      temperature: 0.7
    
    researcher:
      model: gpt-4
      tools: [web_search, browser]
    
    coder:
      model: gpt-4
      sandbox: docker
      timeout: 300
  
  memory:
    type: sqlite
    path: ./data/memory.db
    embedding_model: text-embedding-ada-002
  
  security:
    sandbox_enabled: true
    command_whitelist: [python, node, curl, git]
    file_whitelist: [/workspace, /tmp]

from openclaw import Client

# 初始化客户端
client = Client(
    api_key="your-api-key",
    base_url="http://localhost:8080"
)

# 发送消息
async def send_message():
    response = await client.messages.create(
        channel="wechat",
        recipient="user123",
        content="请帮我分析今天的销售数据"
    )
    return response

# 执行任务
async def execute_task():
    task = await client.tasks.create(
        type="data_analysis",
        params={
            "source": "sales_db",
            "date": "2025-02-27",
            "metrics": ["revenue", "orders", "customers"]
        },
        priority="high"
    )
    result = await task.wait()
    return result

# 文件操作
async def file_operations():
    # 读取文件
    content = await client.files.read("/workspace/data.csv")
    
    # 写入文件
    await client.files.write(
        "/workspace/report.md",
        content="# Sales Report\n\n..."
    )
    
    # 搜索文件
    files = await client.files.find("*.csv", max_depth=3)
    return files

workflow: office_automation
triggers:
  - type: email_received
    filter:
      from: "*@company.com"
      subject_contains: [报表，报告，总结]

steps:
  - name: extract_attachment
    action: email.get_attachments
    params:
      file_types: [.xlsx, .pdf, .docx]
  
  - name: analyze_data
    action: coder.execute
    params:
      language: python
      script: analyze_report.py
  
  - name: generate_summary
    action: writer.generate
    params:
      template: report_summary.md
      data: ${analyze_data.result}
  
  - name: send_notification
    action: message.send
    params:
      channel: dingtalk
      recipient: manager_group
      content: ${generate_summary.output}