diff --git a/docs/core_docs/docs/tutorials/rag.ipynb b/docs/core_docs/docs/tutorials/rag.ipynb
index ffb49c120726..5de1dd38681f 100644
--- a/docs/core_docs/docs/tutorials/rag.ipynb
+++ b/docs/core_docs/docs/tutorials/rag.ipynb
@@ -252,9 +252,7 @@
     "\n",
     "\n",
     "// Compile application and test\n",
-    "const graph = new StateGraph({\n",
-    "  stateSchema: StateAnnotation,\n",
-    "})\n",
+    "const graph = new StateGraph(StateAnnotation)\n",
     "  .addNode(\"retrieve\", retrieve)\n",
     "  .addNode(\"generate\", generate)\n",
     "  .addEdge(\"__start__\", \"retrieve\")\n",
@@ -592,19 +590,11 @@
     "};\n",
     "\n",
     "\n",
-    "// Below we stream the output from the LLM. This is to support token-by-token\n",
-    "// streaming for older versions of @langchain/core. For\n",
-    "// @langchain/core >= 0.2.3, we can use `llm.invoke` instead.\n",
-    "// Read more: https://langchain-ai.github.io/langgraphjs/how-tos/stream-tokens/\n",
     "const generate = async (state: typeof StateAnnotation.State) => {\n",
     "  const docsContent = state.context.map(doc => doc.pageContent).join(\"\\n\");\n",
     "  const messages = await promptTemplate.invoke({ question: state.question, context: docsContent });\n",
-    "  const stream = await llm.stream(messages);\n",
-    "  let gathered = undefined;\n",
-    "  for await (const chunk of stream) {\n",
-    "    gathered = gathered !== undefined ? concat(gathered, chunk) : chunk;\n",
-    "  }\n",
-    "  return { answer: gathered.content };\n",
+    "  const response = await llm.invoke(messages);\n",
+    "  return { answer: response.content };\n",
     "};"
    ]
   },
@@ -629,9 +619,7 @@
    "source": [
     "import { StateGraph } from \"@langchain/langgraph\";\n",
     "\n",
-    "const graph = new StateGraph({\n",
-    "  stateSchema: StateAnnotation,\n",
-    "})\n",
+    "const graph = new StateGraph(StateAnnotation)\n",
     "  .addNode(\"retrieve\", retrieve)\n",
     "  .addNode(\"generate\", generate)\n",
     "  .addEdge(\"__start__\", \"retrieve\")\n",
@@ -810,20 +798,20 @@
    "id": "f860142d-d50b-4526-a03f-a59a763117fe",
    "metadata": {},
    "source": [
-    "Stream [tokens](/docs/concepts/tokens/):"
+    "Stream [tokens](/docs/concepts/tokens/) (requires `@langchain/core` >= 0.3.24 and `@langchain/langgraph` >= 0.2.34 with above implementation):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "28625cc3-0f77-4143-af51-ce0fd6682120",
+   "id": "acb80ba0-d5d6-4425-9683-aaeab7081e6c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| This| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|),| which| guides| models| to| think| step| by| step|,| or| by| using| specific| prompts| and| human| inputs|.| The| Tree| of| Thoughts| approach| further| enhances| this| by| exploring| multiple| reasoning| possibilities| at| each| step|,| creating| a| structured| tree| of| thoughts|.||"
+      "|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| This| can| be| achieved| through| various| methods|,| including| prompting| large| language| models| (|LL|Ms|)| to| outline| steps| or| using| task|-specific| instructions|.| Techniques| like| Chain| of| Thought| (|Co|T|)| and| Tree| of| Thoughts| further| enhance| this| process| by| struct|uring| reasoning| and| exploring| multiple| possibilities| at| each| step|.||"
      ]
     }
    ],
@@ -838,6 +826,20 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5aeb45ad-9bd5-4ee4-8356-9dca9ece76c5",
+   "metadata": {},
+   "source": [
+    "```{=mdx}\n",
+    ":::note\n",
+    "\n",
+    "Streaming tokens with the current implementation, using `.invoke` in the `generate` step, requires `@langchain/core` >= 0.3.24 and `@langchain/langgraph` >= 0.2.34. See details [here](https://langchain-ai.github.io/langgraphjs/how-tos/stream-tokens/).\n",
+    "\n",
+    ":::\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "406534d4-66a3-4c27-b277-2bd2f5930cf5",
@@ -946,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "5daf62fd-4086-49ad-8b3a-514c4fa214ea",
    "metadata": {},
    "outputs": [],
@@ -967,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "id": "7b4864dd-172a-441f-8224-0661b156ed29",
    "metadata": {},
    "outputs": [],
@@ -993,7 +995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "id": "afdbe4e7-b1e1-41ff-9c9e-0194ebd73049",
    "metadata": {},
    "outputs": [],
@@ -1038,11 +1040,7 @@
     "\n",
     "\n",
     "\n",
-    "const graphQA = new StateGraph({\n",
-    "  input: InputStateAnnotation,\n",
-    "  output: StateAnnotationQA,\n",
-    "  stateSchema: StateAnnotationQA,\n",
-    "})\n",
+    "const graphQA = new StateGraph(StateAnnotationQA)\n",
     "  .addNode(\"analyzeQuery\", analyzeQuery)\n",
     "  .addNode(\"retrieveQA\", retrieveQA)\n",
     "  .addNode(\"generateQA\", generateQA)\n",
@@ -1062,7 +1060,7 @@
     "// Note: tslab only works inside a jupyter notebook. Don't worry about running this code yourself!\n",
     "import * as tslab from \"tslab\";\n",
     "\n",
-    "const image = await graph.getGraph().drawMermaidPng();\n",
+    "const image = await graphQA.getGraph().drawMermaidPng();\n",
     "const arrayBuffer = await image.arrayBuffer();\n",
     "\n",
     "await tslab.display.png(new Uint8Array(arrayBuffer));\n",