# OpenClaw智能體示例代碼
import openclaw
# 創(chuàng)建智能體實(shí)例
agent = openclaw.Agent(
    name="我的第一個(gè)龍蝦智能體",
    version="1.0.0",
    config={
        "debug": True,
        "log_level": "INFO"
    }
)
# 查看智能體基本信息
print(f"智能體名稱: {agent.name}")
print(f"版本號(hào): {agent.version}")
print(f"配置信息: {agent.config}")

class OpenClawAgent:
    """OpenClaw智能體核心類"""
    
    def __init__(self, name, config=None):
        """
        初始化智能體
        
        Args:
            name: 智能體名稱
            config: 配置參數(shù)字典
        """
        self.name = name
        self.config = config or {}
        self.memory = []
        self.skills = {}
        
    def perceive(self, input_data):
        """感知環(huán)境信息"""
        # 處理輸入數(shù)據(jù)
        processed_data = self._process_input(input_data)
        return processed_data
    
    def decide(self, context):
        """決策下一步行動(dòng)"""
        # 分析上下文，做出決策
        action = self._analyze_context(context)
        return action
    
    def execute(self, action):
        """執(zhí)行決策"""
        # 執(zhí)行具體動(dòng)作
        result = self._perform_action(action)
        return result
    
    def learn(self, experience):
        """從經(jīng)驗(yàn)中學(xué)習(xí)"""
        self.memory.append(experience)
        # 更新技能庫(kù)
        self._update_skills(experience)

# 使用示例
agent = OpenClawAgent("龍蝦助手")
print(f"智能體 {agent.name} 已創(chuàng)建成功！")

# 使用pip安裝
pip install openclaw
# 或使用conda安裝
conda install -c openclaw openclaw
# 驗(yàn)證安裝
python -c "import openclaw; print(openclaw.__version__)"

# 創(chuàng)建配置文件
config_content = """
agent:
  name: 我的龍蝦智能體
  version: 1.0.0
  
logging:
  level: INFO
  file: agent.log
  
skills:
  - data_processing
  - web_crawling
  - text_analysis
"""

# 保存配置文件
with open("config.yaml", "w") as f:
    f.write(config_content)

print("? 配置文件創(chuàng)建成功！")

from openclaw import Agent

# 創(chuàng)建智能體
agent = Agent(name="HelloWorld")

# 定義任務(wù)
@agent.task
def say_hello(name):
    """打招呼任務(wù)"""
    return f"你好，{name}！我是{agent.name}智能體。"

# 執(zhí)行任務(wù)
result = agent.run("say_hello", name="小龍蝦")
print(result)
# 輸出：你好，小龍蝦！我是HelloWorld智能體。

from openclaw import Agent
from openclaw.skills import DataProcessor

# 創(chuàng)建帶數(shù)據(jù)處理能力的智能體
agent = Agent(
    name="數(shù)據(jù)處理專家",
    skills=[DataProcessor]
)

# 準(zhǔn)備數(shù)據(jù)
data = [
    {"name": "張三", "age": 25, "city": "北京"},
    {"name": "李四", "age": 30, "city": "上海"},
    {"name": "王五", "age": 28, "city": "廣州"},
]

# 執(zhí)行數(shù)據(jù)處理
result = agent.process(
    data=data,
    operations=["filter", "sort", "aggregate"]
)

print(f"處理結(jié)果: {result}")

from openclaw import Agent
from openclaw.skills import WebCrawler, TextAnalyzer
import asyncio

class AdvancedAgent(Agent):
    """高級(jí)智能體示例"""
    
    def __init__(self):
        super().__init__(
            name="高級(jí)龍蝦智能體",
            skills=[WebCrawler, TextAnalyzer]
        )
    
    async def crawl_and_analyze(self, url):
        """爬取網(wǎng)頁(yè)并分析內(nèi)容"""
        # 爬取網(wǎng)頁(yè)
        content = await self.crawl(url)
        
        # 分析文本
        analysis = await self.analyze(content)
        
        return {
            "url": url,
            "content_length": len(content),
            "keywords": analysis.keywords,
            "sentiment": analysis.sentiment
        }

# 使用示例
async def main():
    agent = AdvancedAgent()
    result = await agent.crawl_and_analyze("https://example.com")
    print(f"分析結(jié)果: {result}")

# 運(yùn)行
asyncio.run(main())

# 更新pip
python -m pip install --upgrade pip
# 使用國(guó)內(nèi)鏡像
pip install openclaw -i https://pypi.tuna.tsinghua.edu.cn/simple

# 創(chuàng)建新的虛擬環(huán)境
python -m venv openclaw_env
source openclaw_env/bin/activate  # Linux/Mac
# 或 openclaw_env\Scripts\activate  # Windows
# 重新安裝
pip install openclaw

# 使用生成器處理大數(shù)據(jù)
def process_large_data(data_stream):
    for chunk in data_stream:
        result = process_chunk(chunk)
        yield result  # 使用生成器，避免一次性加載

# 定期清理緩存
agent.clear_cache()

# 使用異步處理
import asyncio

async def parallel_process(tasks):
    results = await asyncio.gather(*tasks)
    return results

# 使用緩存
from functools import lru_cache

@lru_cache(maxsize=1000)
def expensive_computation(key):
    # 耗時(shí)計(jì)算
    return result

# 1. 使用有意義的變量名
agent_name = "數(shù)據(jù)處理智能體"  # ? 好
a = "數(shù)據(jù)處理智能體"  # ? 不好

# 2. 添加文檔字符串
def process_data(data):
    """
    處理輸入數(shù)據(jù)
    
    Args:
        data: 輸入數(shù)據(jù)列表
    
    Returns:
        處理后的結(jié)果
    """
    pass

# 3. 使用類型注解
def analyze(text: str) -> dict:
    return {"keywords": [], "sentiment": "neutral"}

# 4. 異常處理
try:
    result = agent.run(task)
except AgentError as e:
    logger.error(f"智能體執(zhí)行失敗: {e}")
    raise