diff --git a/docs/modules/memory/getting_started.ipynb b/docs/modules/memory/getting_started.ipynb
index 326d187f..b9fb83b9 100644
--- a/docs/modules/memory/getting_started.ipynb
+++ b/docs/modules/memory/getting_started.ipynb
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "356e7c73",
+   "id": "87235cf1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "54f90bd7",
+   "id": "4404d509",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "3884ff81",
+   "id": "78c1a67b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b48d5844",
+   "id": "525ce606",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "495dcff0",
+   "id": "be030822",
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46196aa3",
+   "id": "2c0328fb",
    "metadata": {},
    "source": [
     "## ConversationBufferMemory\n",
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3bac84f3",
+   "id": "a382b160",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "cef35e7f",
+   "id": "a280d337",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +122,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2c9b39af",
+   "id": "1b739c0a",
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "567f7c16",
+   "id": "989e9425",
    "metadata": {},
    "source": [
     "We can also get the history as a list of messages"
@@ -151,7 +151,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a481a415",
+   "id": "798ceb1c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "86a56348",
+   "id": "698688fd",
    "metadata": {},
    "outputs": [
     {
@@ -187,6 +187,7 @@
    "id": "d051c1da",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt)."
    ]
   },
@@ -332,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd0146c2",
+   "id": "7826c210",
    "metadata": {},
    "source": [
     "And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all"
@@ -341,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "447c138d",
+   "id": "3dd37d93",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/docs/modules/memory/types/buffer.ipynb b/docs/modules/memory/types/buffer.ipynb
index 47e9800f..e48c7de5 100644
--- a/docs/modules/memory/types/buffer.ipynb
+++ b/docs/modules/memory/types/buffer.ipynb
@@ -100,6 +100,7 @@
    "id": "d051c1da",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt)."
    ]
   },
diff --git a/docs/modules/memory/types/buffer_window.ipynb b/docs/modules/memory/types/buffer_window.ipynb
index 32fbdfc1..b93ab954 100644
--- a/docs/modules/memory/types/buffer_window.ipynb
+++ b/docs/modules/memory/types/buffer_window.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "716c8cab",
+   "id": "a20c4e38",
    "metadata": {},
    "source": [
     "## ConversationBufferWindowMemory\n",
@@ -15,7 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "dc9d10b0",
+   "id": "1196da3f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "40664f10",
+   "id": "0c034a90",
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1932b49",
+   "id": "8c5cce1d",
    "metadata": {},
    "source": [
     "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "5fd077d5",
+   "id": "9b15b427",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b94b750f",
+   "id": "3bb47191",
    "metadata": {},
    "outputs": [
     {
@@ -99,9 +99,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac59a682",
+   "id": "a95af04c",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Let's walk through an example, again setting `verbose=True` so we can see the prompt."
    ]
   },
diff --git a/docs/modules/memory/types/entity_summary_memory.ipynb b/docs/modules/memory/types/entity_summary_memory.ipynb
index 35dfe49e..fbc1f038 100644
--- a/docs/modules/memory/types/entity_summary_memory.ipynb
+++ b/docs/modules/memory/types/entity_summary_memory.ipynb
@@ -14,7 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7b165e71",
+   "id": "1bea1181",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "25354d39",
+   "id": "34425079",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "71c75295",
+   "id": "b425642c",
    "metadata": {},
    "outputs": [
     {
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "bb8a7943",
+   "id": "3bf89b46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "a37ac236",
+   "id": "3e37d126",
    "metadata": {},
    "outputs": [
     {
@@ -102,9 +102,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "655ab2c4",
+   "id": "ee5ad043",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Let's now use it in a chain!"
    ]
   },
@@ -189,7 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "dc1c0d5e",
+   "id": "0269f513",
    "metadata": {},
    "outputs": [
     {
diff --git a/docs/modules/memory/types/kg.ipynb b/docs/modules/memory/types/kg.ipynb
index 5e614a98..1890a2e6 100644
--- a/docs/modules/memory/types/kg.ipynb
+++ b/docs/modules/memory/types/kg.ipynb
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "1d9a5b18",
+   "id": "2f4a3c85",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "3f5da288",
+   "id": "72283b4f",
    "metadata": {},
    "outputs": [
     {
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ab23035",
+   "id": "0c8ff11e",
    "metadata": {},
    "source": [
     "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "43dbbc43",
+   "id": "44df43af",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ef6a4f60",
+   "id": "4726b1c8",
    "metadata": {},
    "outputs": [
     {
@@ -100,9 +100,68 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ba0dde9",
+   "id": "dc956b0e",
    "metadata": {},
    "source": [
+    "We can also more modularly get current entities from a new message (will use previous messages as context.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "36331ca5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Sam']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.get_current_entities(\"what's Sams favorite color?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8749134",
+   "metadata": {},
+   "source": [
+    "We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b02d44db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.get_knowledge_triplets(\"her favorite color is red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7a02ef3",
+   "metadata": {},
+   "source": [
+    "## Using in a chain\n",
     "Let's now use this in a chain!"
    ]
   },
diff --git a/docs/modules/memory/types/summary.ipynb b/docs/modules/memory/types/summary.ipynb
index 8a8bf2bd..7d7bc6e1 100644
--- a/docs/modules/memory/types/summary.ipynb
+++ b/docs/modules/memory/types/summary.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5d3f2f0f",
+   "id": "1674bfd6",
    "metadata": {},
    "source": [
     "## ConversationSummaryMemory\n",
@@ -14,7 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a36c34d6",
+   "id": "c5565e5c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "89646c34",
+   "id": "61621239",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "dea210aa",
+   "id": "3bcb8b02",
    "metadata": {},
    "outputs": [
     {
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3838fe93",
+   "id": "dedf0698",
    "metadata": {},
    "source": [
     "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "114dbef6",
+   "id": "6cb06b22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,13 +76,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "39c8c106",
+   "id": "47b03ed7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'history': [SystemMessage(content='\\nThe human greets the AI and the AI responds with a casual greeting.', additional_kwargs={})]}"
+       "{'history': [SystemMessage(content='\\nThe human greets the AI, to which the AI responds.', additional_kwargs={})]}"
       ]
      },
      "execution_count": 5,
@@ -94,11 +94,43 @@
     "memory.load_memory_variables({})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9ec0a0ee",
+   "metadata": {},
+   "source": [
+    "We can also utilize the `predict_new_summary` method directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9c4dafb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe human greets the AI, to which the AI responds.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = memory.chat_memory.messages\n",
+    "previous_summary = \"\"\n",
+    "memory.predict_new_summary(messages, previous_summary)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4fad9448",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Let's walk through an example of using this in a chain, again setting `verbose=True` so we can see the prompt."
    ]
   },
diff --git a/docs/modules/memory/types/summary_buffer.ipynb b/docs/modules/memory/types/summary_buffer.ipynb
index 0672e4de..158577bc 100644
--- a/docs/modules/memory/types/summary_buffer.ipynb
+++ b/docs/modules/memory/types/summary_buffer.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2e90817b",
+   "id": "ff4be5f3",
    "metadata": {},
    "source": [
     "## ConversationSummaryBufferMemory\n",
@@ -14,8 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "b8aec547",
+   "execution_count": 1,
+   "id": "da3384db",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "2594c8f1",
+   "execution_count": 2,
+   "id": "e00d4938",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,17 +38,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "a25087e0",
+   "execution_count": 3,
+   "id": "2fe28a28",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'history': 'System: \\nThe human greets the AI, to which the AI responds inquiring what is up.\\nHuman: not much you\\nAI: not much'}"
+       "{'history': 'System: \\nThe human says \"hi\", and the AI responds with \"whats up\".\\nHuman: not much you\\nAI: not much'}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a6e7905",
+   "id": "cf57b97a",
    "metadata": {},
    "source": [
     "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
@@ -67,8 +67,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "e439451f",
+   "execution_count": 4,
+   "id": "3422a3a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,17 +77,49 @@
     "memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a1dcaaee",
+   "metadata": {},
+   "source": [
+    "We can also utilize the `predict_new_summary` method directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd7d7d6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe human and AI state that they are not doing much.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = memory.chat_memory.messages\n",
+    "previous_summary = \"\"\n",
+    "memory.predict_new_summary(messages, previous_summary)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a6d2569f",
    "metadata": {},
    "source": [
+    "## Using in a chain\n",
     "Let's walk through an example, again setting `verbose=True` so we can see the prompt."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "ebd68c10",
    "metadata": {},
    "outputs": [
@@ -112,10 +144,10 @@
     {
      "data": {
       "text/plain": [
-       "\" Hi there! I'm doing great. I'm spending some time learning about the latest developments in AI technology. How about you?\""
+       "\" Hi there! I'm doing great. I'm learning about the latest advances in artificial intelligence. What about you?\""
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/langchain/memory/kg.py b/langchain/memory/kg.py
index 63157944..f8036de0 100644
--- a/langchain/memory/kg.py
+++ b/langchain/memory/kg.py
@@ -4,7 +4,7 @@ from pydantic import BaseModel, Field
 
 from langchain.chains.llm import LLMChain
 from langchain.graphs import NetworkxEntityGraph
-from langchain.graphs.networkx_graph import get_entities, parse_triples
+from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
 from langchain.llms.base import BaseLLM
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import (
@@ -78,9 +78,7 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
             return list(outputs.keys())[0]
         return self.output_key
 
-    def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
-        """Get the current entities in the conversation."""
-        prompt_input_key = self._get_prompt_input_key(inputs)
+    def get_current_entities(self, input_string: str) -> List[str]:
         chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
         buffer_string = get_buffer_string(
             self.chat_memory.messages[-self.k * 2 :],
@@ -89,14 +87,17 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
         )
         output = chain.predict(
             history=buffer_string,
-            input=inputs[prompt_input_key],
+            input=input_string,
         )
         return get_entities(output)
 
-    def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
-        """Get and update knowledge graph from the conversation history."""
-        chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
+    def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
+        """Get the current entities in the conversation."""
         prompt_input_key = self._get_prompt_input_key(inputs)
+        return self.get_current_entities(inputs[prompt_input_key])
+
+    def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]:
+        chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
         buffer_string = get_buffer_string(
             self.chat_memory.messages[-self.k * 2 :],
             human_prefix=self.human_prefix,
@@ -104,10 +105,16 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
         )
         output = chain.predict(
             history=buffer_string,
-            input=inputs[prompt_input_key],
+            input=input_string,
             verbose=True,
         )
         knowledge = parse_triples(output)
+        return knowledge
+
+    def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
+        """Get and update knowledge graph from the conversation history."""
+        prompt_input_key = self._get_prompt_input_key(inputs)
+        knowledge = self.get_knowledge_triplets(inputs[prompt_input_key])
         for triple in knowledge:
             self.kg.add_triple(triple)
 
diff --git a/langchain/memory/summary.py b/langchain/memory/summary.py
index 09279697..7056274f 100644
--- a/langchain/memory/summary.py
+++ b/langchain/memory/summary.py
@@ -8,17 +8,32 @@ from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.memory.utils import get_buffer_string
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import SystemMessage
+from langchain.schema import BaseMessage, SystemMessage
 
 
-class ConversationSummaryMemory(BaseChatMemory, BaseModel):
-    """Conversation summarizer to memory."""
-
-    buffer: str = ""
+class SummarizerMixin(BaseModel):
     human_prefix: str = "Human"
     ai_prefix: str = "AI"
     llm: BaseLLM
     prompt: BasePromptTemplate = SUMMARY_PROMPT
+
+    def predict_new_summary(
+        self, messages: List[BaseMessage], existing_summary: str
+    ) -> str:
+        new_lines = get_buffer_string(
+            messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+        chain = LLMChain(llm=self.llm, prompt=self.prompt)
+        return chain.predict(summary=existing_summary, new_lines=new_lines)
+
+
+class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin, BaseModel):
+    """Conversation summarizer to memory."""
+
+    buffer: str = ""
     memory_key: str = "history"  #: :meta private:
 
     @property
@@ -52,15 +67,10 @@ class ConversationSummaryMemory(BaseChatMemory, BaseModel):
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         """Save context from this conversation to buffer."""
         super().save_context(inputs, outputs)
-        new_lines = get_buffer_string(
-            self.chat_memory.messages[-2:],
-            human_prefix=self.human_prefix,
-            ai_prefix=self.ai_prefix,
+        self.buffer = self.predict_new_summary(
+            self.chat_memory.messages[-2:], self.buffer
         )
 
-        chain = LLMChain(llm=self.llm, prompt=self.prompt)
-        self.buffer = chain.predict(summary=self.buffer, new_lines=new_lines)
-
     def clear(self) -> None:
         """Clear memory contents."""
         super().clear()
diff --git a/langchain/memory/summary_buffer.py b/langchain/memory/summary_buffer.py
index e023155f..44049b9d 100644
--- a/langchain/memory/summary_buffer.py
+++ b/langchain/memory/summary_buffer.py
@@ -2,24 +2,17 @@ from typing import Any, Dict, List
 
 from pydantic import BaseModel, root_validator
 
-from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.memory.chat_memory import BaseChatMemory
-from langchain.memory.prompt import SUMMARY_PROMPT
+from langchain.memory.summary import SummarizerMixin
 from langchain.memory.utils import get_buffer_string
-from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import BaseMessage, SystemMessage
 
 
-class ConversationSummaryBufferMemory(BaseChatMemory, BaseModel):
+class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin, BaseModel):
     """Buffer with summarizer for storing conversation memory."""
 
     max_token_limit: int = 2000
-    human_prefix: str = "Human"
-    ai_prefix: str = "AI"
     moving_summary_buffer: str = ""
-    llm: BaseLLM
-    prompt: BasePromptTemplate = SUMMARY_PROMPT
     memory_key: str = "history"
 
     @property
@@ -77,16 +70,8 @@ class ConversationSummaryBufferMemory(BaseChatMemory, BaseModel):
             while curr_buffer_length > self.max_token_limit:
                 pruned_memory.append(buffer.pop(0))
                 curr_buffer_length = sum(self.get_num_tokens_list(buffer))
-            chain = LLMChain(llm=self.llm, prompt=self.prompt)
-            self.moving_summary_buffer = chain.predict(
-                summary=self.moving_summary_buffer,
-                new_lines=(
-                    get_buffer_string(
-                        pruned_memory,
-                        human_prefix=self.human_prefix,
-                        ai_prefix=self.ai_prefix,
-                    )
-                ),
+            self.moving_summary_buffer = self.predict_new_summary(
+                pruned_memory, self.moving_summary_buffer
             )
 
     def clear(self) -> None: