diff --git a/02-Basic/01-LangGraph-Models.ipynb b/02-Basic/01-LangGraph-Models.ipynb
index 6263773..ebecfe7 100644
--- a/02-Basic/01-LangGraph-Models.ipynb
+++ b/02-Basic/01-LangGraph-Models.ipynb
@@ -21,7 +21,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 환경 설정\n\nLangGraph 튜토리얼을 시작하기 전에 필요한 환경을 설정합니다. `dotenv`를 사용하여 API 키를 로드하고, `langchain_teddynote`의 로깅 기능을 활성화하여 LangSmith에서 실행 추적을 확인할 수 있도록 합니다.\n\nLangSmith 추적을 활성화하면 모델 호출 과정을 시각적으로 디버깅할 수 있어, 개발 및 문제 해결에 큰 도움이 됩니다.\n\n아래 코드는 환경 변수를 로드하고 LangSmith 프로젝트를 설정합니다."
+   "source": [
+    "## 환경 설정\n",
+    "\n",
+    "LangGraph 튜토리얼을 시작하기 전에 필요한 환경을 설정합니다. `dotenv`를 사용하여 API 키를 로드하고, `langchain_teddynote`의 로깅 기능을 활성화하여 LangSmith에서 실행 추적을 확인할 수 있도록 합니다.\n",
+    "\n",
+    "LangSmith 추적을 활성화하면 모델 호출 과정을 시각적으로 디버깅할 수 있어, 개발 및 문제 해결에 큰 도움이 됩니다.\n",
+    "\n",
+    "아래 코드는 환경 변수를 로드하고 LangSmith 프로젝트를 설정합니다."
+   ]
   },
   {
    "cell_type": "code",
@@ -51,7 +59,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 모델 (Model)\n\nLLM은 에이전트의 추론 엔진으로서, LangGraph 애플리케이션의 핵심 구성 요소입니다. LangChain은 다양한 모델 제공자를 통합하여 일관된 인터페이스를 제공하며, 간단하게 `provider:model` 형식의 문자열로 모델을 지정할 수 있습니다.\n\n`init_chat_model` 함수를 사용하면 제공자와 모델명만으로 손쉽게 LLM 인스턴스를 생성할 수 있습니다. 이 방식은 빠른 프로토타이핑에 적합합니다.\n\n아래 코드는 OpenAI의 gpt-4.1-mini 모델을 초기화합니다."
+   "source": [
+    "## 모델 (Model)\n",
+    "\n",
+    "LLM은 에이전트의 추론 엔진으로서, LangGraph 애플리케이션의 핵심 구성 요소입니다. LangChain은 다양한 모델 제공자를 통합하여 일관된 인터페이스를 제공하며, 간단하게 `provider:model` 형식의 문자열로 모델을 지정할 수 있습니다.\n",
+    "\n",
+    "`init_chat_model` 함수를 사용하면 제공자와 모델명만으로 손쉽게 LLM 인스턴스를 생성할 수 있습니다. 이 방식은 빠른 프로토타이핑에 적합합니다.\n",
+    "\n",
+    "아래 코드는 Anthropic의 Claude Sonnet 모델을 초기화합니다."
+   ]
   },
   {
    "cell_type": "code",
@@ -61,14 +77,31 @@
    "source": [
     "from langchain.chat_models import init_chat_model\n",
     "\n",
-    "# 모델 식별자 문자열을 사용한 간단한 방법\n",
-    "model = init_chat_model(\"openai:gpt-4.1-mini\")"
+    "# When using an OpenAI key, change to models like gpt-5.2, gpt-4.1-mini etc.\n",
+    "model = init_chat_model(\"claude-sonnet-4-5\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 모델 세부 설정\n\n더 세밀한 제어가 필요한 경우, 모델 클래스를 직접 인스턴스화하여 다양한 옵션을 설정할 수 있습니다. `temperature`는 응답의 무작위성을, `max_tokens`는 생성할 최대 토큰 수를 제어합니다.\n\n**주요 초기화 인자**\n\n| 파라미터 | 설명 |\n|:---|:---|\n| `model` | 사용할 OpenAI 모델 이름 |\n| `temperature` | Sampling temperature (0에 가까울수록 결정적, 높을수록 창의적) |\n| `max_tokens` | 생성할 최대 token 수 |\n| `logprobs` | logprobs 반환 여부 |\n| `stream_options` | Streaming 출력 설정 (예: `{\"include_usage\": True}`로 token 사용량 포함) |\n| `timeout` | 요청 timeout (초 단위) |\n| `max_retries` | 최대 재시도 횟수 |\n| `api_key` | OpenAI API key (미지정 시 환경변수 `OPENAI_API_KEY`에서 읽음) |\n| `base_url` | API 요청 base URL (proxy 또는 service emulator 사용 시에만 지정) |\n\n아래 코드는 ChatOpenAI 클래스를 사용하여 세부 옵션을 설정한 모델을 생성합니다."
+   "source": [
+    "### 모델 세부 설정\n",
+    "\n",
+    "더 세밀한 제어가 필요한 경우, 모델 클래스를 직접 인스턴스화하여 다양한 옵션을 설정할 수 있습니다. `temperature`는 응답의 무작위성을, `max_tokens`는 생성할 최대 토큰 수를 제어합니다.\n",
+    "\n",
+    "**주요 초기화 인자**\n",
+    "\n",
+    "| 파라미터 | 설명 |\n",
+    "|:---|:---|\n",
+    "| `model` | 사용할 Anthropic 모델 이름 |\n",
+    "| `temperature` | Sampling temperature (0에 가까울수록 결정적, 높을수록 창의적) |\n",
+    "| `max_tokens` | 생성할 최대 token 수 |\n",
+    "| `timeout` | 요청 timeout (초 단위) |\n",
+    "| `max_retries` | 최대 재시도 횟수 |\n",
+    "| `api_key` | Anthropic API key (미지정 시 환경변수 `ANTHROPIC_API_KEY`에서 읽음) |\n",
+    "\n",
+    "아래 코드는 ChatAnthropic 클래스를 사용하여 세부 옵션을 설정한 모델을 생성합니다."
+   ]
   },
   {
    "cell_type": "code",
@@ -76,11 +109,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_anthropic import ChatAnthropic\n",
     "\n",
     "# 모델 인스턴스를 직접 초기화하여 더 세밀한 제어\n",
-    "model = ChatOpenAI(\n",
-    "    model=\"gpt-4.1-mini\",\n",
+    "model = ChatAnthropic(\n",
+    "    model=\"claude-sonnet-4-5\",\n",
     "    temperature=0.1,  # 응답의 무작위성 제어\n",
     "    max_tokens=1000,  # 최대 생성 토큰 수\n",
     "    timeout=30,  # 요청 타임아웃(초)\n",
@@ -90,17 +123,27 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Anthropic 모델 사용\n\nAnthropic의 Claude 모델도 동일한 방식으로 사용할 수 있습니다. 환경 변수에 API 키가 설정되어 있어야 합니다.\n\n```\nANTHROPIC_API_KEY=\"sk-ant-api03-...\"\n```\n\n아래 코드는 Anthropic의 Claude Sonnet 모델을 초기화합니다."
+   "source": [
+    "### OpenAI 모델 사용\n",
+    "\n",
+    "OpenAI의 GPT 모델도 동일한 방식으로 사용할 수 있습니다. 환경 변수에 API 키가 설정되어 있어야 합니다.\n",
+    "\n",
+    "```\n",
+    "OPENAI_API_KEY=\"sk-proj-...\"\n",
+    "```\n",
+    "\n",
+    "아래 코드는 OpenAI의 GPT 모델을 초기화합니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.chat_models import init_chat_model\n",
     "\n",
-    "model = init_chat_model(\"anthropic:claude-sonnet-4-5\")"
+    "model = init_chat_model(\"openai:gpt-4.1-mini\")"
    ]
   },
   {
@@ -137,18 +180,26 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 답변 출력: invoke()\n\n`invoke()` 메서드는 모델에 메시지를 전달하고 응답을 받는 가장 기본적인 방법입니다. 이 메서드는 동기(synchronous) 방식으로 동작하며, 전체 응답이 생성될 때까지 기다렸다가 한 번에 결과를 반환합니다.\n\n메시지는 튜플 형식 `(role, content)` 또는 메시지 객체로 전달할 수 있습니다. `system` 역할은 모델의 행동을 지시하고, `human` 역할은 사용자 입력을 나타냅니다.\n\n아래 코드는 간단한 질문을 모델에 전달하고 응답을 출력합니다."
+   "source": [
+    "## 답변 출력: invoke()\n",
+    "\n",
+    "`invoke()` 메서드는 모델에 메시지를 전달하고 응답을 받는 가장 기본적인 방법입니다. 이 메서드는 동기(synchronous) 방식으로 동작하며, 전체 응답이 생성될 때까지 기다렸다가 한 번에 결과를 반환합니다.\n",
+    "\n",
+    "메시지는 튜플 형식 `(role, content)` 또는 메시지 객체로 전달할 수 있습니다. `system` 역할은 모델의 행동을 지시하고, `human` 역할은 사용자 입력을 나타냅니다.\n",
+    "\n",
+    "아래 코드는 간단한 질문을 모델에 전달하고 응답을 출력합니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='대한민국의 수도는 서울입니다.' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 25, 'total_tokens': 33, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_4c2851f862', 'id': 'chatcmpl-CeNPOEJrSaedT8jJuWxn3ImO9qrlc', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='lc_run--9163477d-88a5-407f-be3e-b7773449e975-0' usage_metadata={'input_tokens': 25, 'output_tokens': 8, 'total_tokens': 33, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n"
+      "content='대한민국의 수도는 서울입니다. 도움이 필요하시면 언제든지 말씀해 주세요!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 25, 'total_tokens': 44, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_376a7ccef1', 'id': 'chatcmpl-CyDQQ43lWuRoOhx1zJo8CwLVY8UPC', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='lc_run--019bc0dd-d608-7471-818a-dc3bec781777-0' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 25, 'output_tokens': 19, 'total_tokens': 44, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n"
      ]
     }
    ],
@@ -166,20 +217,24 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 응답 구조\n\n모델의 응답은 `AIMessage` 객체로 반환됩니다. `content` 속성에는 모델이 생성한 텍스트 답변이 포함되어 있습니다."
+   "source": [
+    "### 응답 구조\n",
+    "\n",
+    "모델의 응답은 `AIMessage` 객체로 반환됩니다. `content` 속성에는 모델이 생성한 텍스트 답변이 포함되어 있습니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'대한민국의 수도는 서울입니다.'"
+       "'대한민국의 수도는 서울입니다. 도움이 필요하시면 언제든지 말씀해 주세요!'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,24 +246,28 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 토큰 사용량 정보\n\n`usage_metadata` 속성에는 이번 호출에서 사용된 토큰 수가 포함됩니다. 입력 토큰(`input_tokens`), 출력 토큰(`output_tokens`), 총 토큰(`total_tokens`) 정보를 확인할 수 있습니다."
+   "source": [
+    "### 토큰 사용량 정보\n",
+    "\n",
+    "`usage_metadata` 속성에는 이번 호출에서 사용된 토큰 수가 포함됩니다. 입력 토큰(`input_tokens`), 출력 토큰(`output_tokens`), 총 토큰(`total_tokens`) 정보를 확인할 수 있습니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'input_tokens': 25,\n",
-       " 'output_tokens': 8,\n",
-       " 'total_tokens': 33,\n",
+       " 'output_tokens': 19,\n",
+       " 'total_tokens': 44,\n",
        " 'input_token_details': {'audio': 0, 'cache_read': 0},\n",
        " 'output_token_details': {'audio': 0, 'reasoning': 0}}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,19 +279,23 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 응답 메타데이터\n\n`response_metadata` 속성에는 모델 제공자로부터 받은 상세 응답 정보가 포함됩니다. 모델명, 종료 사유(`finish_reason`), 서비스 티어 등의 정보를 확인할 수 있습니다."
+   "source": [
+    "### 응답 메타데이터\n",
+    "\n",
+    "`response_metadata` 속성에는 모델 제공자로부터 받은 상세 응답 정보가 포함됩니다. 모델명, 종료 사유(`finish_reason`), 서비스 티어 등의 정보를 확인할 수 있습니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'token_usage': {'completion_tokens': 8,\n",
+       "{'token_usage': {'completion_tokens': 19,\n",
        "  'prompt_tokens': 25,\n",
-       "  'total_tokens': 33,\n",
+       "  'total_tokens': 44,\n",
        "  'completion_tokens_details': {'accepted_prediction_tokens': 0,\n",
        "   'audio_tokens': 0,\n",
        "   'reasoning_tokens': 0,\n",
@@ -240,14 +303,14 @@
        "  'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}},\n",
        " 'model_provider': 'openai',\n",
        " 'model_name': 'gpt-4.1-mini-2025-04-14',\n",
-       " 'system_fingerprint': 'fp_4c2851f862',\n",
-       " 'id': 'chatcmpl-CeNPOEJrSaedT8jJuWxn3ImO9qrlc',\n",
+       " 'system_fingerprint': 'fp_376a7ccef1',\n",
+       " 'id': 'chatcmpl-CyDQQ43lWuRoOhx1zJo8CwLVY8UPC',\n",
        " 'service_tier': 'default',\n",
        " 'finish_reason': 'stop',\n",
        " 'logprobs': None}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -259,7 +322,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 답변 스트리밍: stream()\n\n`stream()` 메서드는 모델의 응답을 실시간으로 받아볼 수 있는 스트리밍 방식입니다. 전체 응답을 기다리지 않고 토큰이 생성되는 즉시 출력되므로, 사용자 경험을 크게 향상시킬 수 있습니다.\n\n`stream()` 메서드는 generator 객체를 반환하며, `for` 루프를 통해 각 청크(chunk)를 순회하면서 실시간으로 응답을 출력할 수 있습니다.\n\n아래 코드는 스트리밍 방식으로 모델 응답을 출력합니다."
+   "source": [
+    "## 답변 스트리밍: stream()\n",
+    "\n",
+    "`stream()` 메서드는 모델의 응답을 실시간으로 받아볼 수 있는 스트리밍 방식입니다. 전체 응답을 기다리지 않고 토큰이 생성되는 즉시 출력되므로, 사용자 경험을 크게 향상시킬 수 있습니다.\n",
+    "\n",
+    "`stream()` 메서드는 generator 객체를 반환하며, `for` 루프를 통해 각 청크(chunk)를 순회하면서 실시간으로 응답을 출력할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 스트리밍 방식으로 모델 응답을 출력합니다."
+   ]
   },
   {
    "cell_type": "code",
@@ -282,11 +353,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### stream_response 헬퍼 함수\n\n`langchain_teddynote` 패키지의 `stream_response` 함수를 사용하면 스트리밍 출력을 더 간단하게 처리할 수 있습니다. 이 함수는 내부적으로 스트림을 순회하며 응답을 깔끔하게 출력합니다.\n\n아래 코드는 `stream_response` 함수를 사용한 스트리밍 예시입니다."
+   "source": [
+    "### stream_response 헬퍼 함수\n",
+    "\n",
+    "`langchain_teddynote` 패키지의 `stream_response` 함수를 사용하면 스트리밍 출력을 더 간단하게 처리할 수 있습니다. 이 함수는 내부적으로 스트림을 순회하며 응답을 깔끔하게 출력합니다.\n",
+    "\n",
+    "아래 코드는 `stream_response` 함수를 사용한 스트리밍 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -306,20 +383,28 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 비동기 처리: ainvoke()\n\n`ainvoke()` 메서드는 비동기(asynchronous) 방식으로 모델을 호출합니다. Python의 `async/await` 구문과 함께 사용하며, 여러 모델 호출을 동시에 처리하거나 I/O 바운드 작업과 병렬로 실행할 때 유용합니다.\n\n비동기 호출은 `await` 키워드를 사용하여 결과를 기다립니다. Jupyter 노트북에서는 최상위 수준에서 `await`를 직접 사용할 수 있습니다.\n\n아래 코드는 비동기 방식으로 모델을 호출하는 예시입니다."
+   "source": [
+    "## 비동기 처리: ainvoke()\n",
+    "\n",
+    "`ainvoke()` 메서드는 비동기(asynchronous) 방식으로 모델을 호출합니다. Python의 `async/await` 구문과 함께 사용하며, 여러 모델 호출을 동시에 처리하거나 I/O 바운드 작업과 병렬로 실행할 때 유용합니다.\n",
+    "\n",
+    "비동기 호출은 `await` 키워드를 사용하여 결과를 기다립니다. Jupyter 노트북에서는 최상위 수준에서 `await`를 직접 사용할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 비동기 방식으로 모델을 호출하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='대한민국의 수도는 서울입니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 25, 'total_tokens': 33, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_4c2851f862', 'id': 'chatcmpl-CeNRozdY58oa9lS9uVfpqQu3UslxU', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--ba392b88-4d0f-46e4-af06-f9fbd7969a94-0', usage_metadata={'input_tokens': 25, 'output_tokens': 8, 'total_tokens': 33, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})"
+       "AIMessage(content='대한민국의 수도는 서울입니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 25, 'total_tokens': 33, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_376a7ccef1', 'id': 'chatcmpl-CyDRVsbuva3Ygtc8hFfiyhCHbbCgV', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019bc0de-dafe-7f70-986e-b495d15a2540-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 25, 'output_tokens': 8, 'total_tokens': 33, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,18 +420,26 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 비동기 스트리밍: astream()\n\n`astream()` 메서드는 비동기 스트리밍을 지원합니다. `async for` 구문을 사용하여 비동기적으로 스트림을 순회하며, 각 청크가 도착할 때마다 처리할 수 있습니다.\n\n비동기 스트리밍은 여러 스트림을 동시에 처리하거나, 스트리밍 중에 다른 비동기 작업을 병렬로 수행할 때 유용합니다.\n\n아래 코드는 비동기 스트리밍 방식으로 모델 응답을 출력합니다."
+   "source": [
+    "## 비동기 스트리밍: astream()\n",
+    "\n",
+    "`astream()` 메서드는 비동기 스트리밍을 지원합니다. `async for` 구문을 사용하여 비동기적으로 스트림을 순회하며, 각 청크가 도착할 때마다 처리할 수 있습니다.\n",
+    "\n",
+    "비동기 스트리밍은 여러 스트림을 동시에 처리하거나, 스트리밍 중에 다른 비동기 작업을 병렬로 수행할 때 유용합니다.\n",
+    "\n",
+    "아래 코드는 비동기 스트리밍 방식으로 모델 응답을 출력합니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "대한민국의 수도는 서울입니다."
+      "대한민국의 수도는 서울특별시입니다."
      ]
     }
    ],
@@ -359,7 +452,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 배치 요청: batch()\n\n`batch()` 메서드는 여러 개의 독립적인 요청을 한 번에 처리합니다. 내부적으로 요청들이 병렬로 실행되므로, 다량의 데이터를 처리해야 할 때 순차 호출보다 훨씬 효율적입니다.\n\n배치 처리는 대량의 텍스트 분류, 번역, 요약 등의 작업에서 처리 시간을 크게 단축할 수 있습니다.\n\n아래 코드는 세 개의 질문을 동시에 처리하는 배치 요청 예시입니다."
+   "source": [
+    "## 배치 요청: batch()\n",
+    "\n",
+    "`batch()` 메서드는 여러 개의 독립적인 요청을 한 번에 처리합니다. 내부적으로 요청들이 병렬로 실행되므로, 다량의 데이터를 처리해야 할 때 순차 호출보다 훨씬 효율적입니다.\n",
+    "\n",
+    "배치 처리는 대량의 텍스트 분류, 번역, 요약 등의 작업에서 처리 시간을 크게 단축할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 세 개의 질문을 동시에 처리하는 배치 요청 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
@@ -397,18 +498,26 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Tool Calling\n\nTool Calling은 LLM이 외부 도구(함수)를 호출할 수 있게 해주는 기능입니다. `bind_tools()` 메서드를 사용하여 모델에 도구를 바인딩하면, 모델은 사용자 질문에 따라 적절한 도구를 선택하고 필요한 인자를 생성합니다.\n\n도구는 Pydantic 모델로 정의하며, 도구의 설명(`docstring`)과 필드 설명(`Field`)이 모델이 도구를 이해하는 데 중요한 역할을 합니다.\n\n아래 코드는 날씨 조회 도구를 정의하고 모델에 바인딩하는 예시입니다."
+   "source": [
+    "## Tool Calling\n",
+    "\n",
+    "Tool Calling은 LLM이 외부 도구(함수)를 호출할 수 있게 해주는 기능입니다. `bind_tools()` 메서드를 사용하여 모델에 도구를 바인딩하면, 모델은 사용자 질문에 따라 적절한 도구를 선택하고 필요한 인자를 생성합니다.\n",
+    "\n",
+    "도구는 Pydantic 모델로 정의하며, 도구의 설명(`docstring`)과 필드 설명(`Field`)이 모델이 도구를 이해하는 데 중요한 역할을 합니다.\n",
+    "\n",
+    "아래 코드는 날씨 조회 도구를 정의하고 모델에 바인딩하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 67, 'total_tokens': 84, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_4c2851f862', 'id': 'chatcmpl-CeNZPIG2UbGSJVWe0WrI3N8TKan4I', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None} id='lc_run--e2a1dc16-bb86-4ffe-9e95-536d88360297-0' tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Seoul, Korea'}, 'id': 'call_VtFX9A26HYm2IhXQM6BfgE9I', 'type': 'tool_call'}] usage_metadata={'input_tokens': 67, 'output_tokens': 17, 'total_tokens': 84, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n"
+      "content='' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 67, 'total_tokens': 84, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4.1-mini-2025-04-14', 'system_fingerprint': 'fp_376a7ccef1', 'id': 'chatcmpl-CyDRbSHdA7jNLat4L29aJDl7dQJdt', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None} id='lc_run--019bc0de-f189-7081-b92b-23545880508a-0' tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Seoul, Korea'}, 'id': 'call_40D22aOz1TkOahtzzehubGYV', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 67, 'output_tokens': 17, 'total_tokens': 84, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n"
      ]
     }
    ],
@@ -430,11 +539,23 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Tool Call 응답 구조\n\n도구 호출이 발생하면 응답의 `content`는 빈 문자열이 됩니다. 대신 `tool_calls` 속성에 호출할 도구 정보가 포함됩니다.\n\n`tool_calls`는 리스트 형태로, 각 항목에는 다음 정보가 포함됩니다:\n- `name`: 호출할 도구 이름\n- `args`: 도구에 전달할 인자\n- `id`: 도구 호출의 고유 식별자 (ToolMessage 응답 시 필요)\n- `type`: 호출 유형 (`tool_call`)\n\n아래에서 도구 호출 정보를 확인할 수 있습니다."
+   "source": [
+    "### Tool Call 응답 구조\n",
+    "\n",
+    "도구 호출이 발생하면 응답의 `content`는 빈 문자열이 됩니다. 대신 `tool_calls` 속성에 호출할 도구 정보가 포함됩니다.\n",
+    "\n",
+    "`tool_calls`는 리스트 형태로, 각 항목에는 다음 정보가 포함됩니다:\n",
+    "- `name`: 호출할 도구 이름\n",
+    "- `args`: 도구에 전달할 인자\n",
+    "- `id`: 도구 호출의 고유 식별자 (ToolMessage 응답 시 필요)\n",
+    "- `type`: 호출 유형 (`tool_call`)\n",
+    "\n",
+    "아래에서 도구 호출 정보를 확인할 수 있습니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -442,11 +563,11 @@
       "text/plain": [
        "[{'name': 'GetWeather',\n",
        "  'args': {'location': 'Seoul, Korea'},\n",
-       "  'id': 'call_VtFX9A26HYm2IhXQM6BfgE9I',\n",
+       "  'id': 'call_40D22aOz1TkOahtzzehubGYV',\n",
        "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,11 +580,19 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 정형화된 답변 출력 (Structured Output)\n\n`with_structured_output()` 메서드를 사용하면 모델의 응답을 미리 정의한 스키마에 맞게 구조화할 수 있습니다. Pydantic 모델을 스키마로 전달하면, 모델은 해당 구조에 맞는 응답만 생성합니다.\n\n이 기능은 정보 추출, 데이터 파싱, API 응답 생성 등 일관된 형식의 출력이 필요한 경우에 매우 유용합니다. 모델이 스키마를 벗어난 응답을 생성하지 않으므로 후처리가 간단해집니다.\n\n아래 코드는 사용자 정보를 추출하여 구조화된 형태로 반환하는 예시입니다."
+   "source": [
+    "## 정형화된 답변 출력 (Structured Output)\n",
+    "\n",
+    "`with_structured_output()` 메서드를 사용하면 모델의 응답을 미리 정의한 스키마에 맞게 구조화할 수 있습니다. Pydantic 모델을 스키마로 전달하면, 모델은 해당 구조에 맞는 응답만 생성합니다.\n",
+    "\n",
+    "이 기능은 정보 추출, 데이터 파싱, API 응답 생성 등 일관된 형식의 출력이 필요한 경우에 매우 유용합니다. 모델이 스키마를 벗어난 응답을 생성하지 않으므로 후처리가 간단해집니다.\n",
+    "\n",
+    "아래 코드는 사용자 정보를 추출하여 구조화된 형태로 반환하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +601,7 @@
        "ResponseFormat(name='테디', email='teddy@example.com', phone='010-1234-5678')"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,29 +628,41 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 토큰 사용량 추적\n\n대규모 애플리케이션에서는 비용 관리를 위해 토큰 사용량을 추적하는 것이 중요합니다. `UsageMetadataCallbackHandler`를 사용하면 여러 모델 호출에 걸친 누적 토큰 사용량을 쉽게 추적할 수 있습니다.\n\n콜백 핸들러는 `config` 매개변수를 통해 런타임에 주입되며, 호출이 완료될 때마다 사용량 정보가 자동으로 누적됩니다. 모델별로 구분된 사용량 통계를 확인할 수 있습니다.\n\n아래 코드는 두 개의 서로 다른 모델 호출에서 토큰 사용량을 추적하는 예시입니다."
+   "source": [
+    "## 토큰 사용량 추적\n",
+    "\n",
+    "대규모 애플리케이션에서는 비용 관리를 위해 토큰 사용량을 추적하는 것이 중요합니다. `UsageMetadataCallbackHandler`를 사용하면 여러 모델 호출에 걸친 누적 토큰 사용량을 쉽게 추적할 수 있습니다.\n",
+    "\n",
+    "콜백 핸들러는 `config` 매개변수를 통해 런타임에 주입되며, 호출이 완료될 때마다 사용량 정보가 자동으로 누적됩니다. 모델별로 구분된 사용량 통계를 확인할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 두 개의 서로 다른 모델 호출에서 토큰 사용량을 추적하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'gpt-4.1-mini-2025-04-14': {'input_tokens': 8,\n",
-       "  'output_tokens': 9,\n",
-       "  'total_tokens': 17,\n",
-       "  'input_token_details': {'audio': 0, 'cache_read': 0},\n",
-       "  'output_token_details': {'audio': 0, 'reasoning': 0}},\n",
-       " 'gpt-4.1-nano-2025-04-14': {'input_tokens': 8,\n",
-       "  'output_tokens': 9,\n",
-       "  'total_tokens': 17,\n",
-       "  'input_token_details': {'audio': 0, 'cache_read': 0},\n",
-       "  'output_token_details': {'audio': 0, 'reasoning': 0}}}"
+       "{'claude-sonnet-4-5-20250929': {'input_tokens': 8,\n",
+       "  'output_tokens': 12,\n",
+       "  'total_tokens': 20,\n",
+       "  'input_token_details': {'cache_read': 0,\n",
+       "   'cache_creation': 0,\n",
+       "   'ephemeral_5m_input_tokens': 0,\n",
+       "   'ephemeral_1h_input_tokens': 0}},\n",
+       " 'claude-haiku-4-5-20251001': {'input_tokens': 8,\n",
+       "  'output_tokens': 16,\n",
+       "  'total_tokens': 24,\n",
+       "  'input_token_details': {'cache_read': 0,\n",
+       "   'cache_creation': 0,\n",
+       "   'ephemeral_5m_input_tokens': 0,\n",
+       "   'ephemeral_1h_input_tokens': 0}}}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,8 +671,9 @@
     "from langchain.chat_models import init_chat_model\n",
     "from langchain_core.callbacks import UsageMetadataCallbackHandler\n",
     "\n",
-    "model_1 = init_chat_model(model=\"openai:gpt-4.1-mini\")\n",
-    "model_2 = init_chat_model(model=\"openai:gpt-4.1-nano\")\n",
+    "# When using an OpenAI key, change to models like gpt-5.2, gpt-4.1-mini etc.\n",
+    "model_1 = init_chat_model(model=\"claude-sonnet-4-5\")\n",
+    "model_2 = init_chat_model(model=\"claude-haiku-4-5\")\n",
     "\n",
     "# 콜백 핸들러를 사용하여 토큰 사용량 추적\n",
     "callback = UsageMetadataCallbackHandler()\n",
@@ -545,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -553,11 +695,11 @@
      "output_type": "stream",
      "text": [
       "토큰 사용량은 callback.usage_metadata에 누적됩니다.\n",
-      "{'gpt-4.1-mini-2025-04-14': {'input_tokens': 16, 'output_tokens': 18, 'total_tokens': 34, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'gpt-4.1-nano-2025-04-14': {'input_tokens': 16, 'output_tokens': 18, 'total_tokens': 34, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}\n",
+      "{'claude-sonnet-4-5-20250929': {'output_tokens': 24, 'total_tokens': 40, 'input_tokens': 16, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}, 'claude-haiku-4-5-20251001': {'output_tokens': 32, 'total_tokens': 48, 'input_tokens': 16, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}}\n",
       "토큰 사용량은 callback.usage_metadata에 누적됩니다.\n",
-      "{'gpt-4.1-mini-2025-04-14': {'input_tokens': 24, 'output_tokens': 27, 'total_tokens': 51, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'gpt-4.1-nano-2025-04-14': {'input_tokens': 24, 'output_tokens': 27, 'total_tokens': 51, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}\n",
+      "{'claude-sonnet-4-5-20250929': {'output_tokens': 36, 'total_tokens': 60, 'input_tokens': 24, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}, 'claude-haiku-4-5-20251001': {'output_tokens': 48, 'total_tokens': 72, 'input_tokens': 24, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}}\n",
       "토큰 사용량은 callback.usage_metadata에 누적됩니다.\n",
-      "{'gpt-4.1-mini-2025-04-14': {'input_tokens': 32, 'output_tokens': 36, 'total_tokens': 68, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'gpt-4.1-nano-2025-04-14': {'input_tokens': 32, 'output_tokens': 36, 'total_tokens': 68, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}\n"
+      "{'claude-sonnet-4-5-20250929': {'output_tokens': 48, 'total_tokens': 80, 'input_tokens': 32, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}, 'claude-haiku-4-5-20251001': {'output_tokens': 64, 'total_tokens': 96, 'input_tokens': 32, 'input_token_details': {'ephemeral_5m_input_tokens': 0, 'cache_read': 0, 'cache_creation': 0, 'ephemeral_1h_input_tokens': 0}}}\n"
      ]
     }
    ],
@@ -573,11 +715,22 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 호출 시 config 전달\n\n모델을 호출할 때 `config` 매개변수를 통해 추가 구성을 전달할 수 있습니다. 이를 통해 실행 동작, 콜백 및 메타데이터 추적을 런타임에 제어할 수 있습니다.\n\n**주요 config 옵션:**\n- `run_name`: 실행의 커스텀 이름 (LangSmith에서 식별용)\n- `tags`: 분류 및 필터링을 위한 태그 목록\n- `metadata`: 커스텀 메타데이터 (사용자 ID, 세션 정보 등)\n\n아래 코드는 config를 사용하여 메타데이터와 함께 모델을 호출하는 예시입니다."
+   "source": [
+    "### 호출 시 config 전달\n",
+    "\n",
+    "모델을 호출할 때 `config` 매개변수를 통해 추가 구성을 전달할 수 있습니다. 이를 통해 실행 동작, 콜백 및 메타데이터 추적을 런타임에 제어할 수 있습니다.\n",
+    "\n",
+    "**주요 config 옵션:**\n",
+    "- `run_name`: 실행의 커스텀 이름 (LangSmith에서 식별용)\n",
+    "- `tags`: 분류 및 필터링을 위한 태그 목록\n",
+    "- `metadata`: 커스텀 메타데이터 (사용자 ID, 세션 정보 등)\n",
+    "\n",
+    "아래 코드는 config를 사용하여 메타데이터와 함께 모델을 호출하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -606,23 +759,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Trace 링크: https://smith.langchain.com/public/d610868a-266b-46d2-929d-5a09677c56ce/r"
+    "Config 값 Langsmith에서 확인\n",
+    "![](./assets/LangGraph-Models-Config-Langsmith.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 추론 강도 조절: reasoning_effort\n\nOpenAI의 최신 모델(gpt-5 등)은 `reasoning_effort` 옵션을 통해 추론의 깊이를 조절할 수 있습니다. 복잡한 문제에는 더 깊은 추론을, 간단한 질문에는 빠른 응답을 설정할 수 있어 비용과 품질 사이의 균형을 맞출 수 있습니다.\n\n**reasoning_effort 옵션:**\n- `minimal`: 최소한의 추론 (가장 빠름)\n- `low`: 낮은 수준의 추론\n- `medium`: 중간 수준의 추론 (균형)\n- `high`: 깊은 추론 (가장 정확)\n\n아래 코드는 medium 수준의 추론으로 모델을 설정하는 예시입니다."
+   "source": [
+    "## 추론 강도 조절: reasoning_effort\n",
+    "\n",
+    "OpenAI의 최신 모델(gpt-5 등)은 `reasoning_effort` 옵션을 통해 추론의 깊이를 조절할 수 있습니다. 복잡한 문제에는 더 깊은 추론을, 간단한 질문에는 빠른 응답을 설정할 수 있어 비용과 품질 사이의 균형을 맞출 수 있습니다.\n",
+    "\n",
+    "**reasoning_effort 옵션:**\n",
+    "- `minimal`: 최소한의 추론 (가장 빠름)\n",
+    "- `low`: 낮은 수준의 추론\n",
+    "- `medium`: 중간 수준의 추론 (균형)\n",
+    "- `high`: 깊은 추론 (가장 정확)\n",
+    "\n",
+    "아래 코드는 medium 수준의 추론으로 모델을 설정하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "# 모델 식별자 문자열을 사용한 간단한 방법\n",
+    "# reasoning_effort는 OpenAI의 gpt-5 모델에서 지원됩니다\n",
     "model = ChatOpenAI(\n",
     "    model=\"gpt-5\",\n",
     "    temperature=0.1,\n",
@@ -632,24 +798,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "지구가 둥근 이유는 크게 “중력”과 “규모” 때문입니다.\n",
-      "\n",
-      "- 중력이 모든 방향으로 고르게 당긴다: 물체가 충분히 크면 자기중력에 의해 물질이 중심을 향해 끌려가고, 전체 에너지가 가장 낮아지는 모양(동일한 부피에서 표면적이 최소인 모양)이 구형에 가깝습니다. 모서리나 돌출부는 중력이 더 많이 작용해 시간이 지나며 눌려 평탄해집니다.\n",
-      "\n",
-      "- 초기 지구는 뜨겁고 유동적이었다: 형성 초기에 충돌과 방사성 붕괴로 거의 “녹은” 상태였고, 이때 중력이 모양을 빠르게 둥글게 만들었습니다. 지금은 단단해 보이지만, 지구 전체 규모에서는 바위도 매우 느리게 흐르는(점성 유동) 성질이 있어 장구한 시간에 걸쳐 둥근 형태를 유지합니다.\n",
-      "\n",
-      "- 크기가 충분히 커야 한다: 천체가 작으면(지름 수백 km 이하) 암석의 강도가 중력보다 커서 감자처럼 울퉁불퉁할 수 있습니다. 하지만 지구처럼 큰 천체는 자중(자기 무게)이 암석 강도를 이겨 “정역학적 평형(둥근 상태)”에 도달합니다.\n",
+      "지구가 둥근 이유는 중력이 지구 전체 물질을 중심으로 끌어당겨, 에너지가 가장 낮은(안정한) 형태인 거의 구형으로 만들기 때문입니다. 핵심은 다음과 같습니다.\n",
       "\n",
-      "- 완벽한 구는 아니다: 자전 때문에 적도가 약간 부풀어 올라 “편평한 회전타원체”입니다(적도 반지름이 극보다 약 21 km 큼). 또 내부 밀도 불균질과 지형·해양 분포 때문에 중력이 미세하게 달라져, 평균 해수면을 기준으로 한 실제 형태는 ‘지오이드’라는 울퉁불퉁한 구형에 가깝습니다.\n",
+      "- 중력과 유체처럼 행동하는 지구: 지구는 단단해 보이지만, 긴 지질학적 시간(scale)에서는 맨틀이 점성이 큰 유체처럼 천천히 흐를 수 있습니다. 그러면 튀어나온 부분은 가라앉고 꺼진 부분은 메워지며, 중력 퍼텐셜 에너지를 최소로 하는 구형(정확히는 회전타원체)에 가까워집니다.\n",
+      "- 형성 당시의 고온·용융 상태: 지구는 형성 초기에 충돌과 방사성 붕괴로 매우 뜨거워 부분적으로 녹아 있었고, 그 상태에서 스스로 둥글어지기가 쉬웠습니다. 이후 식었어도 깊은 내부는 여전히 흐를 수 있어 둥근 형태를 유지합니다.\n",
+      "- 완벽한 구가 아닌 이유: 지구는 자전 때문에 적도가 약간 불룩한 ‘편평한 구(타원체)’입니다. 적도 반지름이 극 반지름보다 약 21 km 큽니다. 산맥·해구 등 지형 차이는 수 km 수준이라 지구 반지름(약 6371 km)에 비하면 매우 작습니다. 또 질량 분포 차이로 해수면 기준면(지오이드)도 수십~수백 m 요철이 있습니다.\n",
+      "- 왜 작은 천체는 울퉁불퉁한가: 충분히 큰 질량이 있어야 자체 중력으로 스스로를 둥글게 만들 수 있습니다. 암석 천체는 대략 지름 수백 km(대략 300~600 km 전후) 이상이면 둥글어지기 쉽고, 그보다 작은 소행성은 불규칙한 모양을 유지합니다. 얼음 천체는 임계 크기가 더 작습니다.\n",
       "\n",
-      "요약하면, 지구는 거대한 질량을 가진 유동적(지질학적 시간척도에서) 천체라서 중력이 모양을 거의 구로 만들고, 자전과 내부 구조가 그 구형을 약간 변형시킵니다."
+      "요약하면, 지구는 충분히 크고 뜨거운 상태를 거치면서 중력에 의해 장기적으로 흐르고 정렬되어, 자전의 영향이 더해진 ‘거의 둥근’ 형태가 된 것입니다."
      ]
     }
    ],
@@ -662,20 +825,30 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 멀티모달 LLM\n\n최신 LLM들은 텍스트뿐만 아니라 이미지도 입력으로 받을 수 있습니다. `langchain_teddynote`의 `MultiModal` 클래스를 사용하면 이미지와 텍스트를 함께 처리하는 작업을 간단하게 수행할 수 있습니다.\n\n멀티모달 기능은 이미지 분석, 차트 해석, 문서 이해 등 다양한 작업에 활용됩니다. 이미지는 URL 또는 로컬 파일 경로로 제공할 수 있습니다.\n\n아래 코드는 이미지 인식이 가능한 모델로 MultiModal 객체를 생성합니다."
+   "source": [
+    "## 멀티모달 LLM\n",
+    "\n",
+    "최신 LLM들은 텍스트뿐만 아니라 이미지도 입력으로 받을 수 있습니다. `langchain_teddynote`의 `MultiModal` 클래스를 사용하면 이미지와 텍스트를 함께 처리하는 작업을 간단하게 수행할 수 있습니다.\n",
+    "\n",
+    "멀티모달 기능은 이미지 분석, 차트 해석, 문서 이해 등 다양한 작업에 활용됩니다. 이미지는 URL 또는 로컬 파일 경로로 제공할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 이미지 인식이 가능한 모델로 MultiModal 객체를 생성합니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_teddynote.models import MultiModal\n",
     "from langchain_teddynote.messages import stream_response\n",
+    "from langchain.chat_models import init_chat_model\n",
     "\n",
-    "llm = ChatOpenAI(\n",
+    "# When using an OpenAI key, change to models like gpt-5.2, gpt-4.1 etc.\n",
+    "llm = init_chat_model(\n",
+    "    model=\"claude-sonnet-4-5\",\n",
     "    temperature=0.1,\n",
-    "    model=\"gpt-4.1\",  # 이미지 인식이 가능한 모델\n",
     ")\n",
     "\n",
     "# 멀티모달(이미지 + 텍스트 처리) 객체 생성\n",
@@ -684,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -703,54 +876,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Certainly! This image presents three bar charts summarizing key financial metrics for a company over four years (2019–2022). The metrics are labeled in Korean, and the units are in billions of Korean Won (십억원).\n",
-      "\n",
-      "Let's break down each chart:\n",
+      "# Analysis of Financial Performance Charts\n",
       "\n",
-      "---\n",
-      "\n",
-      "### 1. **매출액 (Sales Revenue)**\n",
-      "- **2019:** 1,766.6\n",
-      "- **2020:** 1,222.0\n",
-      "- **2021:** 1,562.4\n",
-      "- **2022:** 2,147.0\n",
-      "\n",
-      "**Analysis:**\n",
-      "- There was a significant drop in sales revenue from 2019 to 2020.\n",
-      "- Revenue rebounded in 2021 and saw a substantial increase in 2022, reaching its highest point in the four-year period.\n",
-      "\n",
-      "---\n",
+      "This image displays three separate bar charts showing financial metrics over a four-year period (2019-2022), all measured in Korean Won (단위: 십억원 / Unit: Billions of Won).\n",
       "\n",
-      "### 2. **영업 이익 (Operating Profit)**\n",
-      "- **2019:** -142.4 (loss)\n",
-      "- **2020:** -71.7 (loss)\n",
-      "- **2021:** 205.5 (profit)\n",
-      "- **2022:** 212.7 (profit)\n",
+      "## Left Chart - 매출액 (Revenue)\n",
+      "- **2019**: 1,766.6 billion won (gray bar)\n",
+      "- **2020**: 1,222.0 billion won (gray bar) - showing a decline\n",
+      "- **2021**: 1,562.4 billion won (gray bar) - showing recovery\n",
+      "- **2022**: 2,147.0 billion won (red bar) - significant increase\n",
       "\n",
-      "**Analysis:**\n",
-      "- The company experienced operating losses in 2019 and 2020.\n",
-      "- There was a turnaround in 2021, with a significant operating profit, which further increased in 2022.\n",
-      "- This indicates improved operational efficiency or increased profitability in recent years.\n",
+      "The revenue shows volatility with a dip in 2020, followed by recovery and strong growth in 2022.\n",
       "\n",
-      "---\n",
-      "\n",
-      "### 3. **자산 총계 (Total Assets)**\n",
-      "- **2019:** 3,796.7\n",
-      "- **2020:** 3,369.0\n",
-      "- **2021:** 3,523.6\n",
-      "- **2022:** 3,776.9\n",
+      "## Middle Chart - 영업 이익 (Operating Profit)\n",
+      "- **2019**: -142.4 billion won (negative, shown below baseline)\n",
+      "- **2020**: -71.7 billion won (negative, shown below baseline)\n",
+      "- **2021**: 205.5 billion won (gray bar) - return to profitability\n",
+      "- **2022**: 212.7 billion won (red bar) - continued profitability\n",
       "\n",
-      "**Analysis:**\n",
-      "- Total assets decreased from 2019 to 2020, then gradually increased in 2021 and 2022.\n",
-      "- By 2022, total assets nearly returned to the 2019 level, indicating recovery or expansion.\n",
+      "This chart shows a dramatic turnaround from operating losses in 2019-2020 to solid profits in 2021-2022.\n",
       "\n",
-      "---\n",
+      "## Right Chart - 자산 총계 (Total Assets)\n",
+      "- **2019**: 3,796.7 billion won (gray bar)\n",
+      "- **2020**: 3,369.0 billion won (gray bar) - decrease\n",
+      "- **2021**: 3,523.6 billion won (gray bar) - slight recovery\n",
+      "- **2022**: 3,776.9 billion won (red bar) - growth\n",
       "\n",
-      "### **Overall Insights**\n",
-      "- **2022** stands out as a year of strong recovery and growth, with the highest sales revenue, operating profit, and a rebound in total assets.\n",
-      "- The company overcame losses and asset declines seen in 2019 and 2020, showing a positive trend in financial health.\n",
+      "Total assets show relative stability with a temporary decline in 2020, returning to near-2019 levels by 2022.\n",
       "\n",
-      "If you need further analysis or context (such as industry or company background), please let me know!"
+      "## Key Observations:\n",
+      "- The red bars consistently represent 2022 data across all three charts\n",
+      "- 2022 shows the strongest performance across all metrics\n",
+      "- The company experienced challenges in 2019-2020 but demonstrated strong recovery by 2021-2022"
      ]
     }
    ],
@@ -767,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -813,40 +970,55 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "제공해주신 재무상태표(대차대조표)를 바탕으로 주요 재무 지표와 트렌드를 분석해드리겠습니다.\n",
+      "# 재무상태표 분석 결과\n",
+      "\n",
+      "## 주요 발견사항\n",
       "\n",
-      "### 1. **자산 구성 및 변화**\n",
-      "- **유동자산**\n",
-      "  - 2017년: 8,377,679백만원\n",
-      "  - 2018년: 8,602,837백만원\n",
-      "  - 2019년: 8,349,633백만원\n",
-      "  - **분석:** 2018년에 소폭 증가했다가 2019년에 다시 감소했습니다. 전반적으로 큰 변동은 없으나, 2019년에는 현금 및 현금성 자산이 크게 감소(1,690,862 → 1,002,263백만원)한 점이 눈에 띕니다.\n",
+      "### 1. **자산 총계 증가 추세** 📈\n",
+      "- 제19기(2019): 18,677백만원\n",
+      "- 제18기(2018): 15,128백만원\n",
+      "- 제17기(2017): 12,770백만원\n",
+      "- **3개년 연속 성장**: 약 46% 증가 (2017→2019)\n",
       "\n",
-      "- **비유동자산**\n",
-      "  - 2017년: 12,770,457백만원\n",
-      "  - 2018년: 15,127,741백만원\n",
-      "  - 2019년: 18,677,453백만원\n",
-      "  - **분석:** 비유동자산이 꾸준히 증가하고 있습니다. 특히 2019년에 큰 폭으로 증가(약 3.5조원)하였으며, 이는 장기적 투자나 설비 확장, 혹은 무형자산 증가 등으로 해석될 수 있습니다.\n",
+      "### 2. **유동자산 안정세** 💰\n",
+      "- 제19기: 8,350백만원\n",
+      "- 제18기: 8,603백만원  \n",
+      "- 제17기: 8,378백만원\n",
+      "- 비교적 안정적인 수준 유지\n",
       "\n",
-      "### 2. **세부 항목별 주요 변화**\n",
-      "- **현금 및 현금성 자산**\n",
-      "  - 2018년 대비 2019년에 약 40% 감소(1,690,862 → 1,002,263백만원)하였습니다. 이는 투자, 부채 상환, 대규모 지출 등이 있었을 가능성을 시사합니다.\n",
-      "- **매출채권**\n",
-      "  - 2018년 대비 2019년에 소폭 감소(4,004,920 → 3,981,935백만원)하였으나, 전반적으로 안정적인 수준을 유지하고 있습니다.\n",
-      "- **재고자산**\n",
-      "  - 2017년부터 2019년까지 꾸준히 증가(2,074,555 → 2,670,294백만원)하고 있습니다. 이는 매출 증가에 따른 재고 확충, 혹은 재고 회전율 저하의 신호일 수 있습니다.\n",
+      "### 3. **매출채권 변동** ⚠️\n",
+      "- 제19기: 3,982백만원\n",
+      "- 제18기: 4,005백만원\n",
+      "- 제17기: 3,781백만원\n",
+      "- 최근 소폭 감소 추세 (회수 개선 가능성)\n",
       "\n",
-      "### 3. **종합 분석 및 인사이트**\n",
-      "- **비유동자산의 급격한 증가**는 회사가 장기적 성장에 투자하고 있음을 보여줍니다. 설비 투자, 장기 금융자산, 무형자산 등이 늘어난 것으로 추정됩니다.\n",
-      "- **현금성 자산의 감소**는 유동성 관리에 주의가 필요함을 시사합니다. 현금 유출이 많았던 해로, 투자 대비 단기 유동성 위험을 점검할 필요가 있습니다.\n",
-      "- **재고자산 증가**는 수요 증가에 대응한 것일 수 있으나, 재고 회전율 등 추가적인 지표 확인이 필요합니다.\n",
+      "### 4. **기타수취채권 급감** 📉\n",
+      "- 제19기: 366백만원\n",
+      "- 제18기: 322백만원\n",
+      "- 제17기: 724백만원\n",
+      "- **2017년 대비 50% 감소** - 채권 정리 또는 회수 진행\n",
       "\n",
-      "### 4. **의사결정에 유용한 포인트**\n",
-      "- **장기 투자 확대**에 따른 성장 기대감이 있으나, 단기 유동성(현금성 자산 감소)에 대한 리스크 관리가 필요합니다.\n",
-      "- **재고 관리** 및 **매출채권 회수** 등 운전자본 효율성 점검이 필요합니다.\n",
-      "- **비유동자산 증가**의 구체적 내역(설비, 무형자산, 투자 등)을 추가로 확인하면 더 정확한 전략 수립이 가능합니다.\n",
+      "### 5. **비유동자산 급증** 🚀\n",
+      "- 제19기: 18,677백만원\n",
+      "- 제18기: 15,128백만원\n",
+      "- 제17기: 12,770백만원\n",
+      "- **지속적인 설비투자 또는 장기자산 확대**\n",
       "\n",
-      "추가적으로 부채 및 자본 구조, 손익계산서 등과 연계해 분석하면 더 깊이 있는 인사이트를 얻을 수 있습니다. 필요하시면 추가 자료도 분석해드릴 수 있습니다!"
+      "### 6. **재고자산 증가** 📦\n",
+      "- 제19기: 2,670백만원\n",
+      "- 제18기: 2,426백만원\n",
+      "- 제17기: 2,075백만원\n",
+      "- **29% 증가** - 매출 확대 준비 또는 재고 관리 필요\n",
+      "\n",
+      "## 비즈니스 인사이트\n",
+      "\n",
+      "✅ **긍정적 신호:**\n",
+      "- 총자산 지속 성장으로 사업 확장 중\n",
+      "- 유동자산 안정적 유지로 단기 유동성 양호\n",
+      "\n",
+      "⚠️ **주의 필요:**\n",
+      "- 재고자산 증가율이 높아 재고 회전율 모니터링 필요\n",
+      "- 비유동자산 비중 증가 추세 확인 필요"
      ]
     }
    ],
@@ -864,20 +1036,29 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 토큰 확률 분포: Logprobs\n\n`logprobs` 옵션을 활성화하면 모델이 각 토큰을 생성할 때의 확률 분포를 확인할 수 있습니다. 이는 모델의 확신도를 측정하거나 불확실성을 분석하는 데 유용합니다.\n\n`langchain_teddynote`의 `extract_token_probabilities` 함수를 사용하면 로그 확률을 백분율로 변환하여 직관적으로 확인할 수 있습니다.\n\n아래 코드는 예/아니오 질문에 대한 모델의 확신도를 확인하는 예시입니다."
+   "source": [
+    "## 토큰 확률 분포: Logprobs\n",
+    "\n",
+    "`logprobs` 옵션을 활성화하면 모델이 각 토큰을 생성할 때의 확률 분포를 확인할 수 있습니다. 이는 모델의 확신도를 측정하거나 불확실성을 분석하는 데 유용합니다.\n",
+    "\n",
+    "`langchain_teddynote`의 `extract_token_probabilities` 함수를 사용하면 로그 확률을 백분율로 변환하여 직관적으로 확인할 수 있습니다.\n",
+    "\n",
+    "아래 코드는 예/아니오 질문에 대한 모델의 확신도를 확인하는 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "from langchain_teddynote.messages import extract_token_probabilities\n",
     "\n",
+    "# logprobs는 OpenAI 모델에서만 지원됩니다\n",
     "llm = ChatOpenAI(\n",
     "    temperature=0.1,\n",
-    "    model=\"gpt-4.1\",  # 이미지 인식이 가능한 모델\n",
+    "    model=\"gpt-4.1\",\n",
     ")\n",
     "\n",
     "logprobs_model = llm.bind(logprobs=True)\n",
@@ -888,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -904,7 +1085,7 @@
        "{'tokens': ['0'], 'probabilities': [100.0]}"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -916,11 +1097,19 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## OpenAI 호환 API 사용\n\nLangChain의 `ChatOpenAI` 클래스는 OpenAI API 형식을 따르는 다양한 서비스와 호환됩니다. `base_url`을 변경하여 LM Studio, vLLM, Ollama 등의 로컬 서버나 다른 호환 서비스에 연결할 수 있습니다.\n\n이 기능을 활용하면 동일한 코드로 다양한 모델 제공자를 사용할 수 있어, 개발 및 테스트 환경에서 유연성을 확보할 수 있습니다.\n\n아래는 LM Studio와 vLLM에 연결하는 예시 코드입니다."
+   "source": [
+    "## OpenAI 호환 API 사용\n",
+    "\n",
+    "LangChain의 `ChatOpenAI` 클래스는 OpenAI API 형식을 따르는 다양한 서비스와 호환됩니다. `base_url`을 변경하여 LM Studio, vLLM, Ollama 등의 로컬 서버나 다른 호환 서비스에 연결할 수 있습니다.\n",
+    "\n",
+    "이 기능을 활용하면 동일한 코드로 다양한 모델 제공자를 사용할 수 있어, 개발 및 테스트 환경에서 유연성을 확보할 수 있습니다.\n",
+    "\n",
+    "아래는 LM Studio와 vLLM에 연결하는 예시 코드입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -944,11 +1133,27 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### 파라미터 구분: model_kwargs vs extra_body\n\nOpenAI 호환 API를 사용할 때, 파라미터 전달 방식에 따라 두 가지 옵션을 사용합니다.\n\n**`model_kwargs` 사용:**\n- 표준 OpenAI API 파라미터\n- 최상위 요청 payload에 병합되는 파라미터\n- 예: `stream_options`, `max_completion_tokens`\n\n**`extra_body` 사용:**\n- OpenAI 호환 provider의 커스텀 파라미터\n- 제공자별 고유 기능을 활성화할 때 사용\n- 예: vLLM의 `use_beam_search`, `best_of`\n\n아래는 각 방식의 사용 예시입니다."
+   "source": [
+    "### 파라미터 구분: model_kwargs vs extra_body\n",
+    "\n",
+    "OpenAI 호환 API를 사용할 때, 파라미터 전달 방식에 따라 두 가지 옵션을 사용합니다.\n",
+    "\n",
+    "**`model_kwargs` 사용:**\n",
+    "- 표준 OpenAI API 파라미터\n",
+    "- 최상위 요청 payload에 병합되는 파라미터\n",
+    "- 예: `stream_options`, `max_completion_tokens`\n",
+    "\n",
+    "**`extra_body` 사용:**\n",
+    "- OpenAI 호환 provider의 커스텀 파라미터\n",
+    "- 제공자별 고유 기능을 활성화할 때 사용\n",
+    "- 예: vLLM의 `use_beam_search`, `best_of`\n",
+    "\n",
+    "아래는 각 방식의 사용 예시입니다."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -974,9 +1179,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "langgraph_v1",
    "language": "python",
-   "name": "python3"
+   "name": "langgraph_v1"
   },
   "language_info": {
    "codemirror_mode": {
@@ -988,9 +1193,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/02-Basic/assets/LangGraph-Models-Config-Langsmith.png b/02-Basic/assets/LangGraph-Models-Config-Langsmith.png
new file mode 100644
index 0000000..00dccd9
Binary files /dev/null and b/02-Basic/assets/LangGraph-Models-Config-Langsmith.png differ
diff --git a/pyproject.toml b/pyproject.toml
index 517ded3..78af993 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "mcp>=1.21.0",
     "notebook>=7.4.7",
     "pandas>=2.3.3",
-    "psycopg2>=2.9.11",
+    "psycopg2-binary>=2.9.11",
     "psycopg[binary,pool]>=3.2.12",
     "python-dotenv>=1.2.1",
     "scikit-learn>=1.7.2",
diff --git a/uv.lock b/uv.lock
index 822216f..292f0de 100644
--- a/uv.lock
+++ b/uv.lock
@@ -8,15 +8,6 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
-[[package]]
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