diff --git a/agentboard/agents/summarize.py b/agentboard/agents/summarize.py
index ebca5a8..1ef3d0f 100644
--- a/agentboard/agents/summarize.py
+++ b/agentboard/agents/summarize.py
@@ -1,53 +1,127 @@
+"""
+TrajectorySummarizer：HiAgent 论文 §3.3 (Observation Summarization) 模块的最小可用复刻。
+
+背景：HiAgent 官方仓库 (HiAgent2024/HiAgent) 的 agentboard/agents/ 目录下缺失 summarize.py，
+issue #3 与 #4 均反映该模块缺失但作者尚未修复。本文件依据论文 §3.3 给出的 prompt 模板
+以及 cme_final.py 中对 TrajectorySummarizer 的调用（cme_final.py:160-182）复刻。
+
+调用（来源：cme_final.py）：
+    summarizer = TrajectorySummarizer(self.llm_model)
+    summary = summarizer.generate_summary([trajectory], [subgoal])[0]
+
+其中：
+- trajectory 是双重列表：List[List[(role, content)]]，每个内层 list 通常是
+  [('Action', ...), ('Observation', ...)]，调用前已剔除 "check valid actions" 项。
+- subgoal 是 ('Subgoal', '<子目标文本>') 形式的元组。
+- 返回值是与输入等长的字符串列表，每个元素是该 (trajectory, subgoal) 的浓缩摘要，
+  会被填入 cme_final.py 中作为压缩后的 Observation。
+
+LLM 接口签名（来源：agentboard/llm/openai_gpt.py:74 等）：
+    llm_model.generate(system_message: str, prompt: str) -> Tuple[bool, str]
+"""
+
+from typing import List, Sequence, Tuple
+
+
+# 论文 §3.3 给出的 prompt 模板。原文中存在 {example} few-shot 占位符，但论文与官方仓库均未
+# 提供具体的示例内容，此处保持 zero-shot 模式，不渲染该占位符。
+_PROMPT_TEMPLATE = """You are an advanced AI system tasked with summarizing and analyzing a series of action-observation pairs (trajectories) and determining whether a specific subgoal has been met.
+
+Your goal is to create a summary that captures all essential information, decisions, and outcomes from the given trajectories, and indicate whether the subgoal has been met based on the summarized observations.
+If there are no valid actions taken, you need to analyze the reason.
+
+### Instructions:
+1. Provide a summarized observation related to the subgoal in a concise manner.
+2. Determine whether the subgoal has been met.
+3. Do not output anything except whether summary and subgoal are met. Your output should be only one line. Do not output things like '##Summary', '##Summary and Analysis'.
+
+##Trajectory
+{formatted_trajectory}
+
+##Subgoal
+{subgoal}
+
+###Output:"""
+
+
+_SYSTEM_MESSAGE = "You are a helpful assistant."
+
+
 class TrajectorySummarizer:
+    """对一个已完成子目标对应的 action-observation 轨迹进行 LLM 摘要。"""
+
     def __init__(self, llm_model):
         self.llm_model = llm_model
 
-    def generate_summary(self, trajectories, subgoals):
+    @staticmethod
+    def _format_trajectory(trajectory: Sequence[Sequence[Tuple[str, str]]]) -> str:
+        """把 cme_final.py 中的双重列表轨迹格式化成 'Action: xxx\nObservation: yyy' 的字符串。
+
+        与 cme_final.py:vanilla_serialize_history 中的拼接逻辑保持一致，确保 LLM 看到的
+        轨迹格式与 working memory 中其他位置一致。
         """
-        Summarize trajectories for completed subgoals to save context length.
+        lines = []
+        for chunk in trajectory:
+            for role, content in chunk:
+                lines.append(f"{role}: {content}")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _format_subgoal(subgoal) -> str:
+        """从 ('Subgoal', '<text>') 元组中取出子目标文本；对异常输入做兜底处理。"""
+        if isinstance(subgoal, tuple) and len(subgoal) >= 2:
+            text = subgoal[1]
+            return text.strip() if isinstance(text, str) else str(text)
+        return str(subgoal)
+
+    @staticmethod
+    def _last_observation(trajectory: Sequence[Sequence[Tuple[str, str]]]) -> str:
+        """LLM 调用失败时的降级摘要：取轨迹中的最后一个 Observation 作为代替。"""
+        for chunk in reversed(trajectory):
+            for role, content in reversed(chunk):
+                if role == "Observation":
+                    return content
+        return ""
+
+    def generate_summary(
+        self,
+        trajectories: Sequence[Sequence[Sequence[Tuple[str, str]]]],
+        subgoals: Sequence[Tuple[str, str]],
+    ) -> List[str]:
+        """对一批 (trajectory, subgoal) 生成压缩摘要。
 
         Args:
-            trajectories: list of trajectories, where each trajectory is a list of
-                          action-observation pairs, e.g. [[("Action", "go east"), ("Observation", "You see...")], ...]
-            subgoals: list of subgoal tuples, e.g. [("Subgoal", "Navigate to the kitchen")]
+            trajectories: 轨迹列表，每个轨迹是 List[List[(role, content)]]。
+            subgoals: 与 trajectories 等长的子目标元组列表。
 
         Returns:
-            list of summary strings, one per trajectory
+            字符串列表，与输入等长，每项是单行的浓缩摘要。
         """
-        summaries = []
+        summaries: List[str] = []
         for trajectory, subgoal in zip(trajectories, subgoals):
-            if not trajectory:
-                summaries.append("Subgoal completed.")
-                continue
-            prompt = self._build_prompt(trajectory, subgoal)
-            success, summary = self.llm_model.generate(
-                "You are a helpful assistant that summarizes agent trajectories concisely.",
-                prompt,
+            formatted_trajectory = self._format_trajectory(trajectory)
+            subgoal_text = self._format_subgoal(subgoal)
+            prompt = _PROMPT_TEMPLATE.format(
+                formatted_trajectory=formatted_trajectory,
+                subgoal=subgoal_text,
             )
-            if success:
-                summaries.append(summary.strip())
-            else:
-                summaries.append(self._fallback_summary(trajectory))
-        return summaries
 
-    def _build_prompt(self, trajectory, subgoal):
-        subgoal_text = subgoal[1] if isinstance(subgoal, tuple) else str(subgoal)
-        lines = [f"Subgoal: {subgoal_text}", "Trajectory:"]
-        for pair in trajectory:
-            for key, value in pair:
-                lines.append(f"  {key}: {value}")
-        lines.append(
-            "\nPlease provide a concise summary (1-2 sentences) of what happened "
-            "during this trajectory and the outcome. Focus on the key actions taken "
-            "and the final result."
-        )
-        return "\n".join(lines)
+            success, completion = self.llm_model.generate(_SYSTEM_MESSAGE, prompt)
 
-    @staticmethod
-    def _fallback_summary(trajectory):
-        """Return the last observation as a fallback when LLM summarization fails."""
-        for pair in reversed(trajectory):
-            for key, value in pair:
-                if key == "Observation":
-                    return value
-        return "Subgoal completed."
+            if success and completion:
+                # 论文 instruction 3 要求输出仅一行；此处取第一行非空内容作为最终摘要。
+                summary = ""
+                for line in completion.strip().splitlines():
+                    line = line.strip()
+                    if line:
+                        summary = line
+                        break
+                if not summary:
+                    summary = self._last_observation(trajectory)
+            else:
+                # LLM 调用失败：退化为「最后一个 Observation」，与 cme_final.py 中
+                # summarization=False 分支的行为对齐，避免训练流程中断。
+                summary = self._last_observation(trajectory)
+
+            summaries.append(summary)
+        return summaries