diff --git a/metagpt/memory/brain_memory.py b/metagpt/memory/brain_memory.py
index 7eda9c601..fea3b2512 100644
--- a/metagpt/memory/brain_memory.py
+++ b/metagpt/memory/brain_memory.py
@@ -178,13 +178,16 @@ class BrainMemory(pydantic.BaseModel):
         self.is_dirty = True
         return self.historical_summary
 
-    async def get_summary(self, text: str, llm, max_words=200, keep_language: bool = False, **kwargs):
+    async def summerize(self, llm, max_words=200, keep_language: bool = False, **kwargs):
         max_token_count = DEFAULT_MAX_TOKENS
         max_count = 100
+        text = self.history_text
         text_length = len(text)
+        summary = ""
         while max_count > 0:
             if text_length < max_token_count:
-                return await self._get_summary(text=text, llm=llm, max_words=max_words, keep_language=keep_language)
+                summary = await self._get_summary(text=text, llm=llm, max_words=max_words, keep_language=keep_language)
+                break
 
             padding_size = 20 if max_token_count > 20 else 0
             text_windows = self.split_texts(text, window_size=max_token_count - padding_size)
@@ -194,13 +197,18 @@ class BrainMemory(pydantic.BaseModel):
                 response = await self._get_summary(text=ws, max_words=part_max_words, keep_language=keep_language)
                 summaries.append(response)
             if len(summaries) == 1:
-                return summaries[0]
+                summary = summaries[0]
+                break
 
             # Merged and retry
             text = "\n".join(summaries)
             text_length = len(text)
 
             max_count -= 1  # safeguard
+        if not summary:
+            await self.set_history_summary(history_summary=summary, redis_key=CONFIG.REDIS_KEY, redis_conf=CONFIG.REDIS)
+            return summary
+
         raise openai.error.InvalidRequestError("text too long")
 
     async def _get_summary(self, text: str, llm, max_words=20, keep_language: bool = False):
diff --git a/metagpt/roles/assistant.py b/metagpt/roles/assistant.py
index d5467cafb..26711486f 100644
--- a/metagpt/roles/assistant.py
+++ b/metagpt/roles/assistant.py
@@ -45,7 +45,7 @@ class Assistant(Role):
             name=name, profile=profile, goal=goal, constraints=constraints, desc=desc, *args, **kwargs
         )
         brain_memory = CONFIG.BRAIN_MEMORY
-        self.memory = BrainMemory(**brain_memory) if brain_memory else BrainMemory()
+        self.memory = BrainMemory(**brain_memory) if brain_memory else BrainMemory(llm_type=CONFIG.LLM_TYPE)
         skill_path = Path(CONFIG.SKILL_PATH) if CONFIG.SKILL_PATH else None
         self.skills = SkillLoader(skill_yaml_file_name=skill_path)
 
@@ -83,7 +83,7 @@ class Assistant(Role):
         self.memory.add_talk(Message(content=text))
 
     async def _plan(self, rsp: str, **kwargs) -> bool:
-        skill, text = Assistant.extract_info(input_string=rsp)
+        skill, text = BrainMemory.extract_info(input_string=rsp)
         handlers = {
             MessageType.Talk.value: self.talk_handler,
             MessageType.Skill.value: self.skill_handler,
@@ -121,24 +121,14 @@ class Assistant(Role):
             return None
         if history_text == "":
             return last_talk
-        history_summary = await self.memory.get_summary(
-            text=history_text, max_words=800, keep_language=True, llm=self._llm
-        )
-        # await self.memory.set_history_summary(
-        #     history_summary=history_summary, redis_key=CONFIG.REDIS_KEY, redis_conf=CONFIG.REDIS
-        # )
-        if last_talk and await self.memory.is_related(
-            text1=last_talk, text2=history_summary, llm=self._llm
-        ):  # Merge relevant content.
+        history_summary = await self.memory.summerize(max_words=800, keep_language=True, llm=self._llm)
+        if last_talk and await self.memory.is_related(text1=last_talk, text2=history_summary, llm=self._llm):
+            # Merge relevant content.
             last_talk = await self.memory.rewrite(sentence=last_talk, context=history_text, llm=self._llm)
             return last_talk
 
         return last_talk
 
-    @staticmethod
-    def extract_info(input_string):
-        return BrainMemory.extract_info(input_string)
-
     def get_memory(self) -> str:
         return self.memory.json()