Replace 'execute' with 'run' in memory_tool calls

Refactor memory_tool examples for consistency

Updated memory_tool examples to use 'run' method.

Replace execute() with run() and clarify add operation

Updated the execute interface to run() in hello_agents.tools and clarified the add operation's role in the memory system.

Update Chapter8-Memory-and-Retrieval.md

Update Chapter8-Memory-and-Retrieval.md

docs/chapter8/Chapter8-Memory-and-Retrieval.md

@@ -340,25 +340,25 @@ agent.tool_registry = tool_registry
 print("=== Adding Multiple Memories ===")
 
 # Add first memory
-result1 = memory_tool.execute("add", content="User Zhang San is a Python developer focusing on machine learning and data analysis", memory_type="semantic", importance=0.8)
+result1 = memory_tool.run("add", content="User Zhang San is a Python developer focusing on machine learning and data analysis", memory_type="semantic", importance=0.8)
 print(f"Memory 1: {result1}")
 
 # Add second memory
-result2 = memory_tool.execute("add", content="Li Si is a frontend engineer skilled in React and Vue.js development", memory_type="semantic", importance=0.7)
+result2 = memory_tool.run("add", content="Li Si is a frontend engineer skilled in React and Vue.js development", memory_type="semantic", importance=0.7)
 print(f"Memory 2: {result2}")
 
 # Add third memory
-result3 = memory_tool.execute("add", content="Wang Wu is a product manager responsible for user experience design and requirements analysis", memory_type="semantic", importance=0.6)
+result3 = memory_tool.run("add", content="Wang Wu is a product manager responsible for user experience design and requirements analysis", memory_type="semantic", importance=0.6)
 print(f"Memory 3: {result3}")
 
 print("\n=== Searching Specific Memories ===")
 # Search for frontend-related memories
 print("🔍 Searching 'frontend engineer':")
-result = memory_tool.execute("search", query="frontend engineer", limit=3)
+result = memory_tool.run("search", query="frontend engineer", limit=3)
 print(result)
 
 print("\n=== Memory Summary ===")
-result = memory_tool.execute("summary")
+result = memory_tool.run("summary")
 print(result)
 ```
 
@@ -445,14 +445,14 @@ For each memory type, we provide different usage examples:
 
 ```python
 # 1. Working Memory - Temporary information, limited capacity
-memory_tool.execute("add",
+memory_tool.run("add",
     content="User just asked a question about Python functions",
     memory_type="working",
     importance=0.6
 )
 
 # 2. Episodic Memory - Specific events and experiences
-memory_tool.execute("add",
+memory_tool.run("add",
     content="On March 15, 2024, user Zhang San completed their first Python project",
     memory_type="episodic",
     importance=0.8,
@@ -461,7 +461,7 @@ memory_tool.execute("add",
 )
 
 # 3. Semantic Memory - Abstract knowledge and concepts
-memory_tool.execute("add",
+memory_tool.run("add",
     content="Python is an interpreted, object-oriented programming language",
     memory_type="semantic",
     importance=0.9,
@@ -469,7 +469,7 @@ memory_tool.execute("add",
 )
 
 # 4. Perceptual Memory - Multimodal information
-memory_tool.execute("add",
+memory_tool.run("add",
     content="User uploaded a Python code screenshot containing function definitions",
     memory_type="perceptual",
     importance=0.7,

docs/chapter8/第八章 记忆与检索.md

@@ -341,25 +341,25 @@ agent.tool_registry = tool_registry
 print("=== 添加多个记忆 ===")
 
 # 添加第一个记忆
-result1 = memory_tool.execute("add", content="用户张三是一名Python开发者，专注于机器学习和数据分析", memory_type="semantic", importance=0.8)
+result1 = memory_tool.run("add", content="用户张三是一名Python开发者，专注于机器学习和数据分析", memory_type="semantic", importance=0.8)
 print(f"记忆1: {result1}")
 
 # 添加第二个记忆
-result2 = memory_tool.execute("add", content="李四是前端工程师，擅长React和Vue.js开发", memory_type="semantic", importance=0.7)
+result2 = memory_tool.run("add", content="李四是前端工程师，擅长React和Vue.js开发", memory_type="semantic", importance=0.7)
 print(f"记忆2: {result2}")
 
 # 添加第三个记忆
-result3 = memory_tool.execute("add", content="王五是产品经理，负责用户体验设计和需求分析", memory_type="semantic", importance=0.6)
+result3 = memory_tool.run("add", content="王五是产品经理，负责用户体验设计和需求分析", memory_type="semantic", importance=0.6)
 print(f"记忆3: {result3}")
 
 print("\n=== 搜索特定记忆 ===")
 # 搜索前端相关的记忆
 print("🔍 搜索 '前端工程师':")
-result = memory_tool.execute("search", query="前端工程师", limit=3)
+result = memory_tool.run("search", query="前端工程师", limit=3)
 print(result)
 
 print("\n=== 记忆摘要 ===")
-result = memory_tool.execute("summary")
+result = memory_tool.run("summary")
 print(result)
 ```
 
@@ -446,14 +446,14 @@ def _add_memory(
 
 ```python
 # 1. 工作记忆 - 临时信息，容量有限
-memory_tool.execute("add",
+memory_tool.run("add",
     content="用户刚才问了关于Python函数的问题",
     memory_type="working",
     importance=0.6
 )
 
 # 2. 情景记忆 - 具体事件和经历
-memory_tool.execute("add",
+memory_tool.run("add",
     content="2024年3月15日，用户张三完成了第一个Python项目",
     memory_type="episodic",
     importance=0.8,
@@ -462,7 +462,7 @@ memory_tool.execute("add",
 )
 
 # 3. 语义记忆 - 抽象知识和概念
-memory_tool.execute("add",
+memory_tool.run("add",
     content="Python是一种解释型、面向对象的编程语言",
     memory_type="semantic",
     importance=0.9,
@@ -470,7 +470,7 @@ memory_tool.execute("add",
 )
 
 # 4. 感知记忆 - 多模态信息
-memory_tool.execute("add",
+memory_tool.run("add",
     content="用户上传了一张Python代码截图，包含函数定义",
     memory_type="perceptual",
     importance=0.7,