diff --git a/README.md b/README.md
index 27acedf5..7b5e4061 100644
--- a/README.md
+++ b/README.md
@@ -66,15 +66,15 @@ working_dir/
 [ReMeLight](reme/reme_light.py) is the core class of this memory system, providing complete memory management
 capabilities for AI Agents:
 
-| Method                 | Function                           | Key Components                                                                                                                                              |
-|------------------------|------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `start`                | 🚀 Start memory system             | Initialize file store, file watcher, Embedding cache; clean up expired tool result files                                                                    |
-| `close`                | 📕 Close and clean up              | Clean tool result files, stop file watcher, save Embedding cache                                                                                            |
-| `compact_memory`       | 📦 Compact history to summary      | [Compactor](reme/memory/file_based/compactor.py) — ReActAgent generates structured context checkpoint                                                       |
-| `summary_memory`       | 📝 Write important memory to files | [Summarizer](reme/memory/file_based/summarizer.py) — ReActAgent + file tools (read / write / edit)                                                          |
-| `compact_tool_result`  | ✂️ Compact oversized tool output   | [ToolResultCompactor](reme/memory/file_based/tool_result_compactor.py) — Truncate and save to `tool_result/`, keep file reference in message                |
-| `pre_reasoning_hook`   | 🔄 Pre-reasoning hook              | Auto compact tool results + generate summary + async trigger memory summarization task                                                                       |
-| `memory_search`        | 🔍 Semantic memory search          | [MemorySearch](reme/memory/tools/chunk/memory_search.py) — Vector + BM25 hybrid retrieval                                                                   |
+| Method                 | Function                           | Key Components                                                                                                                                                              |
+|------------------------|------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `start`                | 🚀 Start memory system             | Initialize file store, file watcher, Embedding cache; clean up expired tool result files                                                                                    |
+| `close`                | 📕 Close and clean up              | Clean tool result files, stop file watcher, save Embedding cache                                                                                                            |
+| `compact_memory`       | 📦 Compact history to summary      | [Compactor](reme/memory/file_based/compactor.py) — ReActAgent generates structured context checkpoint                                                                       |
+| `summary_memory`       | 📝 Write important memory to files | [Summarizer](reme/memory/file_based/summarizer.py) — ReActAgent + file tools (read / write / edit)                                                                          |
+| `compact_tool_result`  | ✂️ Compact oversized tool output   | [ToolResultCompactor](reme/memory/file_based/tool_result_compactor.py) — Truncate and save to `tool_result/`, keep file reference in message                                |
+| `pre_reasoning_hook`   | 🔄 Pre-reasoning hook              | Auto compact tool results + generate summary + async trigger memory summarization task                                                                                      |
+| `memory_search`        | 🔍 Semantic memory search          | [MemorySearch](reme/memory/tools/chunk/memory_search.py) — Vector + BM25 hybrid retrieval                                                                                   |
 | `get_in_memory_memory` | 🗂️ Create in-memory instance      | [ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) — Token-aware memory management, supports compression summary and state serialization (static method) |
 
 ---
@@ -125,9 +125,9 @@ async def main():
     summary = await reme.compact_memory(
         messages=messages,
         previous_summary="",
-        max_input_length=128000,      # Model context window (tokens)
-        compact_ratio=0.7,            # Trigger compaction when reaching max_input_length * 0.7
-        language="zh",                # Summary language (zh / "")
+        max_input_length=128000,  # Model context window (tokens)
+        compact_ratio=0.7,  # Trigger compaction when reaching max_input_length * 0.7
+        language="zh",  # Summary language (zh / "")
     )
 
     # 3. Submit async summary task in background (non-blocking, writes to memory/YYYY-MM-DD.md)
@@ -169,7 +169,8 @@ if __name__ == "__main__":
 ```
 
 > 📂 Full example code: [test_reme_light.py](tests/light/test_reme_light.py)
-> 📋 Example output: [test_reme_light.log](tests/light/test_reme_light.log) (223,838 tokens → 1,105 tokens, 99.5% compression ratio)
+> 📋 Example output: [test_reme_light.log](tests/light/test_reme_light_log.txt) (223,838 tokens → 1,105 tokens, 99.5%
+> compression ratio)
 
 ### File-Based ReMeLight Memory System Architecture
 
diff --git a/README_ZH.md b/README_ZH.md
index ded1d8d1..3c379a2b 100644
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -97,7 +97,6 @@ pip install -e ".[light]"
 ```python
 import asyncio
 
-from agentscope.message import Msg
 from reme.reme_light import ReMeLight
 
 
@@ -119,9 +118,9 @@ async def main():
     summary = await reme.compact_memory(
         messages=messages,
         previous_summary="",
-        max_input_length=128000,      # 模型上下文窗口（tokens）
-        compact_ratio=0.7,            # 达到 max_input_length * 0.7 时触发压缩
-        language="zh",                # 摘要语言（zh / ""）
+        max_input_length=128000,  # 模型上下文窗口（tokens）
+        compact_ratio=0.7,  # 达到 max_input_length * 0.7 时触发压缩
+        language="zh",  # 摘要语言（zh / ""）
     )
 
     # 3. 后台异步提交摘要任务（不阻塞对话，摘要写入 memory/YYYY-MM-DD.md）
@@ -163,7 +162,7 @@ if __name__ == "__main__":
 ```
 
 > 📂 完整示例代码：[test_reme_light.py](tests/light/test_reme_light.py)
-> 📋 运行结果示例：[test_reme_light.log](tests/light/test_reme_light.log)（223,838 tokens → 1,105 tokens，压缩率 99.5%）
+> 📋 运行结果示例：[test_reme_light.log](tests/light/test_reme_light_log.txt)（223,838 tokens → 1,105 tokens，压缩率 99.5%）
 
 ### 基于文件的 ReMeLight 记忆系统架构
 
diff --git a/tests/light/test_reme_light_log.txt b/tests/light/test_reme_light_log.txt
new file mode 100644
index 00000000..5a0c71a5
--- /dev/null
+++ b/tests/light/test_reme_light_log.txt
@@ -0,0 +1,84 @@
+======================================================================
+ReMeLight 已启动
+======================================================================
+
+[原始消息]: 18 条, 223,838 tokens
+  目标阈值: 128K = 131,072 tokens
+  超出阈值: True
+
+======================================================================
+[步骤 1] compact_tool_result - 压缩超长工具输出
+======================================================================
+  消息数量: 18 → 18
+  📊 Token 统计: 223,838 → 1,107 (变化: -222,731, -99.5%)
+
+======================================================================
+[步骤 2] compact_memory - 生成结构化压缩摘要
+======================================================================
+  输入消息 tokens: 223,838
+  压缩摘要长度: 1032 字符, 493 tokens
+  压缩比: 0.2%
+
+======================================================================
+[步骤 3] summary_memory - 生成完整摘要并写入文件
+======================================================================
+
+reme_summarizer: [SILENT]
+  输入消息 tokens: 223,838
+  摘要结果长度: 8 字符
+  摘要: [SILENT]
+
+======================================================================
+[步骤 4] pre_reasoning_hook - 推理前预处理
+======================================================================
+  消息数量: 18 → 18
+  📊 Token 统计: 223,838 → 1,105 (变化: -222,733, -99.5%)
+  压缩摘要: 0 字符, 0 tokens
+  总上下文: 1,105 tokens
+
+======================================================================
+[步骤 5] memory_search - 语义搜索记忆
+======================================================================
+  搜索结果: [{'type': 'text', 'text': '[\n  {\n    "path": "/Users...
+
+======================================================================
+[步骤 6] ReMeInMemoryMemory - 会话内存管理
+======================================================================
+  已添加 18 条原始消息到内存
+
+[6.1] estimate_tokens - 估算 Token 使用:
+  - 总消息数: 18
+  - 消息 Token 数: 223,838
+  - 压缩摘要 Token 数: 0
+  - 预估总 Token 数: 223,838
+  - 最大输入长度: 128,000
+  - 上下文使用率: 174.87%
+
+[6.2] get_history_str - 格式化历史记录:
+**Conversation History**
+
+- Total messages: 18
+- Estimated tokens: 223838
+- Max input length: 128000
+- Context usage: 174.9%
+- Compressed summary tokens: 0
+...
+
+======================================================================
+[步骤 7] 等待后台任务完成
+======================================================================
+  后台任务完成，结果长度: 0 字符
+
+======================================================================
+📊 Token 变化总结
+======================================================================
+  原始消息: 223,838 tokens
+  Step 1 compact_tool_result 后: 1,107 tokens
+  Step 2 compact_memory 摘要: 493 tokens
+  Step 4 pre_reasoning_hook 后: 1,105 tokens + 摘要 0 tokens = 1,105 tokens
+  最大节省: 222,733 tokens (99.5%)
+  目标阈值: 131,072 tokens
+
+======================================================================
+ReMeLight 已关闭
+======================================================================