diff --git a/code/chapter1/FirstAgentTest.py b/code/chapter1/FirstAgentTest.py
index 43098741..1b2f8ce5 100644
--- a/code/chapter1/FirstAgentTest.py
+++ b/code/chapter1/FirstAgentTest.py
@@ -28,10 +28,11 @@
 
 def get_weather(city: str) -> str:
     """
-    通过调用 wttr.in API 查询真实的天气信息。
+    通过调用 WeatherAPI 查询真实的天气信息。
     """
+    weather_api_key='YOUR_API_KEY'
     # API端点，我们请求JSON格式的数据
-    url = f"https://wttr.in/{city}?format=j1"
+    url = f"https://api.weatherapi.com/v1/current.json?key={weather_api_key}&q={city}&aqi=no"
     
     try:
         # 发起网络请求
@@ -42,9 +43,9 @@ def get_weather(city: str) -> str:
         data = response.json()
         
         # 提取当前天气状况
-        current_condition = data['current_condition'][0]
-        weather_desc = current_condition['weatherDesc'][0]['value']
-        temp_c = current_condition['temp_C']
+        current_condition = data['current']
+        weather_desc = current_condition['condition']['text']
+        temp_c = current_condition['temp_c']
         
         # 格式化成自然语言返回
         return f"{city}当前天气：{weather_desc}，气温{temp_c}摄氏度"
diff --git a/code/chapter2/ELIZA.py b/code/chapter2/ELIZA.py
index 5d161c18..b9ac158e 100644
--- a/code/chapter2/ELIZA.py
+++ b/code/chapter2/ELIZA.py
@@ -42,7 +42,7 @@
 
 # 定义代词转换规则
 pronoun_swap = {
-    "i": "you", "you": "i", "me": "you", "my": "your",
+    "i": "you", "you": "i", "me": "you", "my": "your", "your": "my",
     "am": "are", "are": "am", "was": "were", "i'd": "you would",
     "i've": "you have", "i'll": "you will", "yours": "mine",
     "mine": "yours"
diff --git a/code/chapter3/BPE.py b/code/chapter3/BPE.py
index cee0e589..20c3509b 100644
--- a/code/chapter3/BPE.py
+++ b/code/chapter3/BPE.py
@@ -17,7 +17,8 @@ def merge_vocab(pair, v_in):
     for word in v_in:
         w_out = p.sub(''.join(pair), word)
         v_out[w_out] = v_in[word]
-    return v_out
+    return v_outs
+    
 
 # 准备语料库，每个词末尾加上</w>表示结束，并切分好字符
 vocab = {'h u g </w>': 1, 'p u g </w>': 1, 'p u n </w>': 1, 'b u n </w>': 1}
diff --git a/code/chapter4/.env copy b/code/chapter4/.env copy
deleted file mode 100644
index 26b81a57..00000000
--- a/code/chapter4/.env copy	
+++ /dev/null
@@ -1,4 +0,0 @@
-LLM_MODEL_ID="YOUR-MODEL"
-LLM_API_KEY="YOUR-API-KEY"
-LLM_BASE_URL="YOUR-URL"
-SERPAPI_API_KEY="YOUR_SERPAPI_API_KEY"
\ No newline at end of file
diff --git a/code/chapter4/Reflection.py b/code/chapter4/Reflection.py
index fdf0d402..1a2ae82d 100644
--- a/code/chapter4/Reflection.py
+++ b/code/chapter4/Reflection.py
@@ -107,6 +107,7 @@ def run(self, task: str):
         print("\n--- 正在进行初始尝试 ---")
         initial_prompt = INITIAL_PROMPT_TEMPLATE.format(task=task)
         initial_code = self._get_llm_response(initial_prompt)
+        print(f"✅ 初始尝试完成，生成的代码:\n{initial_code}")
         self.memory.add_record("execution", initial_code)
 
         # --- 2. 迭代循环：反思与优化 ---
@@ -118,6 +119,7 @@ def run(self, task: str):
             last_code = self.memory.get_last_execution()
             reflect_prompt = REFLECT_PROMPT_TEMPLATE.format(task=task, code=last_code)
             feedback = self._get_llm_response(reflect_prompt)
+            print(f"✅ 反思完成，反馈: {feedback}")
             self.memory.add_record("reflection", feedback)
 
             # b. 检查是否需要停止
@@ -133,6 +135,7 @@ def run(self, task: str):
                 feedback=feedback
             )
             refined_code = self._get_llm_response(refine_prompt)
+            print(f"✅ 优化完成，生成的代码:\n{refined_code}")
             self.memory.add_record("execution", refined_code)
         
         final_code = self.memory.get_last_execution()
diff --git a/code/chapter4/llm_client.py b/code/chapter4/llm_client.py
index b7e2d0a3..104b2678 100644
--- a/code/chapter4/llm_client.py
+++ b/code/chapter4/llm_client.py
@@ -43,9 +43,9 @@ def think(self, messages: List[Dict[str, str]], temperature: float = 0) -> str:
             collected_content = []
             for chunk in response:
                 content = chunk.choices[0].delta.content or ""
-                print(content, end="", flush=True)
+                # print(content, end="", flush=True)
                 collected_content.append(content)
-            print()  # 在流式输出结束后换行
+            # print()  # 在流式输出结束后换行
             return "".join(collected_content)
 
         except Exception as e:
diff --git a/code/chapter6/AgentScopeDemo/main_cn.py b/code/chapter6/AgentScopeDemo/main_cn.py
index 2cd6aa3f..af6df490 100644
--- a/code/chapter6/AgentScopeDemo/main_cn.py
+++ b/code/chapter6/AgentScopeDemo/main_cn.py
@@ -33,6 +33,8 @@
     MAX_DISCUSSION_ROUND,
 )
 
+os.environ["DASHSCOPE_API_KEY"] = "sk-fbd837c733ba4ce5a15d4a5555c27f41"
+
 
 class ThreeKingdomsWerewolfGame:
     """三国狼人杀游戏主类"""
diff --git a/code/chapter6/AutoGenDemo/.env copy b/code/chapter6/AutoGenDemo/.env copy
deleted file mode 100644
index 57b59999..00000000
--- a/code/chapter6/AutoGenDemo/.env copy	
+++ /dev/null
@@ -1,4 +0,0 @@
-LLM_MODEL_ID="YOUR-MODEL"
-LLM_API_KEY="YOUR-API-KEY"
-LLM_BASE_URL="YOUR-URL"
-LLM_TIMEOUT=60
\ No newline at end of file
diff --git a/code/chapter6/AutoGenDemo/autogen_software_team.py b/code/chapter6/AutoGenDemo/autogen_software_team.py
index 1dd5f9d0..d21abb3d 100644
--- a/code/chapter6/AutoGenDemo/autogen_software_team.py
+++ b/code/chapter6/AutoGenDemo/autogen_software_team.py
@@ -106,11 +106,32 @@ def create_user_proxy():
         name="UserProxy",
         description="""用户代理，负责以下职责：
 1. 代表用户提出开发需求
-2. 执行最终的代码实现
-3. 验证功能是否符合预期
-4. 提供用户反馈和建议
-
-完成测试后请回复 TERMINATE。""",
+2. 在代码审查完成后执行测试验证
+3. 验证功能是否符合原始需求
+4. 对工程师提供的代码进行实际验证和测试
+
+你刚刚收到的代码是工程师为黄金价格显示应用开发的完整实现，包含：
+- 完整的app.py文件代码
+- Streamlit应用的所有功能
+- 数据获取、UI展示、错误处理等完整模块
+
+测试验证步骤：
+1. **代码语法检查**：分析代码是否有语法错误或导入问题
+2. **核心功能验证**：检查以下功能是否实现：
+   - 实时黄金价格显示（美元/盎司）
+   - 24小时价格趋势图表
+   - 手动刷新按钮功能
+   - 错误处理和加载状态
+3. **用户体验评估**：评估界面是否简洁美观、用户友好
+4. **运行环境检查**：确认代码依赖（streamlit, requests, pandas, altair等）是否合理
+5. **安全性检查**：确认API密钥通过环境变量注入，前端不可见
+
+请基于工程师提供的完整代码进行上述测试验证，然后提供具体的测试结果反馈，包括：
+- 哪些功能已经正确实现
+- 是否存在任何问题或改进建议
+- 代码的整体质量评估
+
+完成测试验证后，请总结测试结果并回复 TERMINATE。""",
     )
 
 async def run_software_development_team():
@@ -145,10 +166,10 @@ async def run_software_development_team():
     )
     
     # 定义开发任务
-    task = """我们需要开发一个比特币价格显示应用，具体要求如下：
+    task = """我们需要开发一个黄金价格显示应用，具体要求如下：
 
 核心功能：
-- 实时显示比特币当前价格（USD）
+- 实时显示黄金当前价格（美元/盎司）
 - 显示24小时价格变化趋势（涨跌幅和涨跌额）
 - 提供价格刷新功能
 
@@ -188,6 +209,3 @@ async def run_software_development_team():
         print(f"❌ 运行错误：{e}")
         import traceback
         traceback.print_exc()
-
-
-
diff --git a/code/chapter6/CAMEL/DigitalBookWriting.py b/code/chapter6/CAMEL/DigitalBookWriting.py
index 1ef23d38..0ab80083 100644
--- a/code/chapter6/CAMEL/DigitalBookWriting.py
+++ b/code/chapter6/CAMEL/DigitalBookWriting.py
@@ -6,22 +6,21 @@
 from dotenv import load_dotenv
 import os
 
-load_dotenv()
-LLM_API_KEY = os.getenv("LLM_API_KEY")
-LLM_BASE_URL = os.getenv("LLM_BASE_URL")
-LLM_MODEL = os.getenv("LLM_MODEL")
+LLM_MODEL_ID="coding-glm-4.7-free"
+LLM_API_KEY="sk-Yfh9jNQSWoNgoZAC23Cb8d1a924d43E286Cd81Dc17Fa0b89"
+LLM_BASE_URL="https://aihubmix.com/v1"
 
 #创建模型,在这里以Qwen为例,调用的百炼大模型平台API
 model = ModelFactory.create(
-    model_platform=ModelPlatformType.QWEN,
-    model_type=LLM_MODEL,
+    model_platform=ModelPlatformType.ZHIPU,
+    model_type=LLM_MODEL_ID,
     url=LLM_BASE_URL,
     api_key=LLM_API_KEY
 )
 
 # 定义协作任务
 task_prompt = """
-创作一本关于"拖延症心理学"的短篇电子书，目标读者是对心理学感兴趣的普通大众。
+创作一本关于"人类移民"的短篇电子书，目标读者是对科幻题材感兴趣的普通大众。
 要求：
 1. 内容科学严谨，基于实证研究
 2. 语言通俗易懂，避免过多专业术语
@@ -34,7 +33,7 @@
 
 # 初始化角色扮演会话
 role_play_session = RolePlaying(
-    assistant_role_name="心理学家", 
+    assistant_role_name="量子力学学家", 
     user_role_name="作家", 
     task_prompt=task_prompt,
     model=model
diff --git a/code/chapter6/Langgraph/Dialogue_System.py b/code/chapter6/Langgraph/Dialogue_System.py
index 281868a4..35ff832c 100644
--- a/code/chapter6/Langgraph/Dialogue_System.py
+++ b/code/chapter6/Langgraph/Dialogue_System.py
@@ -31,13 +31,13 @@ class SearchState(TypedDict):
 # 初始化模型和Tavily客户端
 llm = ChatOpenAI(
     model=os.getenv("LLM_MODEL_ID", "gpt-4o-mini"),
-    api_key=os.getenv("LLM_API_KEY"),
+    api_key=os.getenv("LLM_API_KEY", ""),
     base_url=os.getenv("LLM_BASE_URL", "https://api.openai.com/v1"),
     temperature=0.7
 )
 
 # 初始化Tavily客户端
-tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY", "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"))
 
 def understand_query_node(state: SearchState) -> SearchState:
     """步骤1：理解用户查询并生成搜索关键词"""
@@ -195,11 +195,7 @@ def create_search_assistant():
 
 async def main():
     """主函数：运行智能搜索助手"""
-    
-    # 检查API密钥
-    if not os.getenv("TAVILY_API_KEY"):
-        print("❌ 错误：请在.env文件中配置TAVILY_API_KEY")
-        return
+
     
     app = create_search_assistant()
     
diff --git a/docs/chapter1/Chapter1-Introduction-to-Agents.md b/docs/chapter1/Chapter1-Introduction-to-Agents.md
index 8fe22d43..2ff2196e 100644
--- a/docs/chapter1/Chapter1-Introduction-to-Agents.md
+++ b/docs/chapter1/Chapter1-Introduction-to-Agents.md
@@ -258,17 +258,18 @@ Let's begin!
 
 (2) Tool 1: Query Real Weather
 
-We will use the free weather query service `wttr.in`, which can return weather data for a specified city in JSON format. Here is the code to implement this tool:
+We will use the free weather query service `weatherapi`(You need to register an account at https://www.weatherapi.com to obtain the weather_api_key), which can return weather data for a specified city in JSON format. Here is the code to implement this tool:
 
 ```python
 import requests
 
 def get_weather(city: str) -> str:
     """
-    Query real weather information by calling the wttr.in API.
+    Query real weather information by calling the WeatherAPI.
     """
+    weather_api_key='YOUR_API_KEY'
     # API endpoint, we request data in JSON format
-    url = f"https://wttr.in/{city}?format=j1"
+    url = f"https://api.weatherapi.com/v1/current.json?key={weather_api_key}&q={city}&aqi=no"
 
     try:
         # Make network request
@@ -279,9 +280,9 @@ def get_weather(city: str) -> str:
         data = response.json()
 
         # Extract current weather conditions
-        current_condition = data['current_condition'][0]
-        weather_desc = current_condition['weatherDesc'][0]['value']
-        temp_c = current_condition['temp_C']
+        current_condition = data['current']
+        weather_desc = current_condition['condition']['text']
+        temp_c = current_condition['temp_c']
 
         # Format as natural language return
         return f"{city} current weather: {weather_desc}, temperature {temp_c} degrees Celsius"
@@ -296,7 +297,7 @@ def get_weather(city: str) -> str:
 
 (3) Tool 2: Search and Recommend Tourist Attractions
 
-We will define a new tool `search_attraction` that searches the internet for suitable attractions based on city and weather conditions:
+We will define a new tool `get_attraction` that searches the internet for suitable attractions based on city and weather conditions:
 
 ```python
 import os
diff --git "a/docs/chapter1/\347\254\254\344\270\200\347\253\240 \345\210\235\350\257\206\346\231\272\350\203\275\344\275\223.md" "b/docs/chapter1/\347\254\254\344\270\200\347\253\240 \345\210\235\350\257\206\346\231\272\350\203\275\344\275\223.md"
index cf8625eb..71ffcae3 100644
--- "a/docs/chapter1/\347\254\254\344\270\200\347\253\240 \345\210\235\350\257\206\346\231\272\350\203\275\344\275\223.md"	
+++ "b/docs/chapter1/\347\254\254\344\270\200\347\253\240 \345\210\235\350\257\206\346\231\272\350\203\275\344\275\223.md"	
@@ -262,17 +262,18 @@ Action的格式必须是以下之一：
 
 （2）工具 1：查询真实天气
 
-我们将使用免费的天气查询服务 `wttr.in`，它能以 JSON 格式返回指定城市的天气数据。下面是实现该工具的代码：
+我们将使用免费的天气查询服务 `weatherapi`(需要去https://www.weatherapi.com注册账户以获取weather_api_key)，它能以 JSON 格式返回指定城市的天气数据。下面是实现该工具的代码：
 
 ```python
 import requests
 
 def get_weather(city: str) -> str:
     """
-    通过调用 wttr.in API 查询真实的天气信息。
+    通过调用 WeatherAPI 查询真实的天气信息。
     """
+    weather_api_key='YOUR_API_KEY'
     # API端点，我们请求JSON格式的数据
-    url = f"https://wttr.in/{city}?format=j1"
+    url = f"https://api.weatherapi.com/v1/current.json?key={weather_api_key}&q={city}&aqi=no"
     
     try:
         # 发起网络请求
@@ -283,9 +284,9 @@ def get_weather(city: str) -> str:
         data = response.json()
         
         # 提取当前天气状况
-        current_condition = data['current_condition'][0]
-        weather_desc = current_condition['weatherDesc'][0]['value']
-        temp_c = current_condition['temp_C']
+        current_condition = data['current']
+        weather_desc = current_condition['condition']['text']
+        temp_c = current_condition['temp_c']
         
         # 格式化成自然语言返回
         return f"{city}当前天气:{weather_desc}，气温{temp_c}摄氏度"
@@ -300,7 +301,7 @@ def get_weather(city: str) -> str:
 
 （3）工具 2：搜索并推荐旅游景点
 
-我们将定义一个新工具 `search_attraction`，它会根据城市和天气状况，互联网上搜索合适的景点：
+我们将定义一个新工具 `get_attraction`，它会根据城市和天气状况，互联网上搜索合适的景点：
 
 ```python
 import os