diff --git a/integrations/llm/langchain/notebooks/Comet_with_Langchain.ipynb b/integrations/llm/langchain/notebooks/Comet_with_Langchain.ipynb
index 783a2607..55afa5e0 100644
--- a/integrations/llm/langchain/notebooks/Comet_with_Langchain.ipynb
+++ b/integrations/llm/langchain/notebooks/Comet_with_Langchain.ipynb
@@ -18,13 +18,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook).  \n",
+    "In this guide we will demonstrate how to track your Langchain prompts, Chains, and Agents with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook).\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/llm/langchain/notebooks/Comet_with_Langchain.ipynb\">\n",
-    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
     "\n",
-    "**Example Project:** [Comet with LangChain](https://www.comet.com/examples/comet-example-langchain-notebook/prompts?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook)"
+    "**Example Project:** [Comet with LangChain](https://www.comet.com/examples/comet-example-langchain-llm-notebook/prompts?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook)"
    ]
   },
   {
@@ -47,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U comet_llm \"langchain>=0.0.346\" openai numexpr"
+    "%pip install -U comet_llm \"langchain>=0.1.3\" \"langchain-openai\" openai numexpr"
    ]
   },
   {
@@ -57,13 +54,6 @@
     "### Initialize Comet and Set your Credentials"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can grab your [Comet API Key here](https://www.comet.com/signup?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook) or click the link after intializing Comet"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -72,7 +62,7 @@
    "source": [
     "import comet_llm\n",
     "\n",
-    "comet_llm.init(project=\"comet-example-langchain-notebook\")"
+    "comet_llm.init(project=\"comet-example-langchain-llm-notebook\")\n",
    ]
   },
   {
@@ -97,10 +87,32 @@
    "source": [
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
     "# os.environ[\"OPENAI_ORGANIZATION\"] = \"...\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tracing with Comet\n",
+    "\n",
+    "There are two ways to trace your LangChains executions with Comet:\n",
+    "\n",
+    "1. Setting the `LANGCHAIN_COMET_TRACING` environment variable to \"true\". This is the recommended way.\n",
+    "2. Import the `CometTracer` manually and pass it explicitely."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"LANGCHAIN_COMET_TRACING\"] = \"true\"\n",
+    "from langchain.callbacks.tracers.comet import CometTracer"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -116,17 +128,13 @@
    "source": [
     "from datetime import datetime\n",
     "\n",
-    "from langchain.callbacks.tracers.comet import CometTracer\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
-    "comet_callback = CometTracer()\n",
     "llm = OpenAI(temperature=0.9, verbose=True)\n",
-    "llm_result = llm.generate(\n",
-    "    [\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3,\n",
-    "    callbacks=[comet_callback],\n",
-    ")\n",
-    "print(\"LLM result\", llm_result)\n",
-    "comet_callback.flush()"
+    "\n",
+    "for prompt in [\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"]:\n",
+    "    llm_result = llm.generate([prompt])\n",
+    "    print(\"LLM result\", llm_result)"
    ]
   },
   {
@@ -142,25 +150,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.callbacks.tracers.comet import CometTracer\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "\n",
-    "comet_callback = CometTracer()\n",
-    "callbacks = [comet_callback]\n",
-    "\n",
     "llm = OpenAI(temperature=0.9, verbose=True)\n",
     "\n",
     "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)\n",
     "\n",
     "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
-    "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
-    "comet_callback.flush()"
+    "print(synopsis_chain.apply(test_prompts))"
    ]
   },
   {
@@ -176,27 +179,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain.callbacks.tracers.comet import CometTracer\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
-    "comet_callback = CometTracer()\n",
-    "callbacks = [comet_callback]\n",
+    "llm = OpenAI(temperature=0.9)\n",
     "\n",
-    "llm = OpenAI(temperature=0.9, verbose=True)\n",
-    "\n",
-    "tools = load_tools([\"llm-math\"], llm=llm, verbose=True)\n",
+    "tools = load_tools([\"llm-math\"], llm=llm)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
+    "    agent=\"zero-shot-react-description\",\n",
     ")\n",
     "agent.run(\n",
     "    \"What is 2 raised to .123243 power?\",\n",
-    "    callbacks=callbacks,\n",
-    ")\n",
-    "comet_callback.flush()"
+    ")"
    ]
   }
  ],
@@ -216,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,