diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md
index 518064348..fc1f14de5 100644
--- a/docs/docs/how-tos/index.md
+++ b/docs/docs/how-tos/index.md
@@ -50,7 +50,7 @@ These guides show how to use different streaming modes.
 - [How to stream state updates of your graph](stream-updates.ipynb)
 - [How to stream LLM tokens](streaming-tokens.ipynb)
 - [How to stream LLM tokens without LangChain models](streaming-tokens-without-langchain.ipynb)
-- [How to stream arbitrarily nested content](streaming-content.ipynb)
+- [How to stream custom data](streaming-content.ipynb)
 - [How to configure multiple streaming modes at the same time](stream-multiple.ipynb)
 - [How to stream events from within a tool](streaming-events-from-within-tools.ipynb)
 - [How to stream events from within a tool without LangChain models](streaming-events-from-within-tools-without-langchain.ipynb)
diff --git a/docs/docs/how-tos/streaming-content.ipynb b/docs/docs/how-tos/streaming-content.ipynb
index 84f60a19f..2f7b70a4a 100644
--- a/docs/docs/how-tos/streaming-content.ipynb
+++ b/docs/docs/how-tos/streaming-content.ipynb
@@ -5,13 +5,15 @@
    "id": "15c4bd28",
    "metadata": {},
    "source": [
-    "# How to stream arbitrary nested content\n",
+    "# How to stream custom data\n",
     "\n",
-    "The most common use case for streaming from inside a node is to stream LLM tokens, but you may have other long-running streaming functions you wish to render for the user. While individual nodes in LangGraph cannot return generators (since they are executed to completion for each [superstep](https://langchain-ai.github.io/langgraph/concepts/low_level)), we can still stream arbitrary custom functions from within a node using a similar tact and calling `astream_events` on the graph.\n",
+    "The most common use case for streaming from inside a node is to stream LLM tokens, but you may also want to stream custom data. For example, you might have some long-running streaming functions you may wish to render for the user. \n",
     "\n",
-    "We do so using a [RunnableGenerator](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableGenerator.html#langchain-core-runnables-base-runnablegenerator) (which your function will automatically behave as if wrapped as a [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda)).\n",
+    "You can do so in two ways:\n",
+    "* using graph's `.stream` / `.astream` methods with `stream_mode=\"custom\"`\n",
+    "* emitting custom events using [adispatch_custom_events](https://python.langchain.com/docs/how_to/callbacks_custom_events/).\n",
     "\n",
-    "Below is a simple toy example.\n",
+    "Below is a simple toy example that shows both.\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -20,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "e1a20f31",
    "metadata": {},
    "outputs": [],
@@ -44,46 +46,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95301021-1db9-426f-807c-ec5b37bd5a9d",
+   "id": "29814253-ca9b-4844-a8a5-d6b19fbdbdba",
    "metadata": {},
    "source": [
-    "<div class=\"admonition warning\">\n",
-    "    <p class=\"admonition-title\">ASYNC IN PYTHON<=3.10</p>\n",
-    "    <p>\n",
-    "Any Langchain RunnableLambda, a RunnableGenerator, or Tool that invokes other runnables and is running async in python<=3.10, will have to propagate callbacks to child objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case.\n",
-    "    \n",
-    "This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
-    "    </p>\n",
-    "</div>"
+    "## Stream custom data using `.stream / .astream`"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "07d5779e",
+   "id": "b729644a-b65f-4e69-ad45-f2e88ffb4e9d",
    "metadata": {},
    "source": [
-    "## Define the graph"
+    "### Define the graph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "486a01a0",
+   "execution_count": 2,
+   "id": "9731c40f-5ce7-460d-b2ad-33185529c99d",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.messages import AIMessage\n",
-    "from langchain_core.runnables import RunnableGenerator\n",
-    "from langchain_core.runnables import RunnableConfig\n",
-    "\n",
     "from langgraph.graph import START, StateGraph, MessagesState, END\n",
+    "from langgraph.types import StreamWriter\n",
     "\n",
-    "# Define a new graph\n",
-    "workflow = StateGraph(MessagesState)\n",
-    "\n",
-    "\n",
-    "async def my_generator(state: MessagesState):\n",
-    "    messages = [\n",
+    "async def my_node(\n",
+    "    state: MessagesState, \n",
+    "    writer: StreamWriter  # <-- provide StreamWriter to write chunks to be streamed\n",
+    "):\n",
+    "    chunks = [\n",
     "        \"Four\",\n",
     "        \"score\",\n",
     "        \"and\",\n",
@@ -94,42 +86,146 @@
     "        \"fathers\",\n",
     "        \"...\",\n",
     "    ]\n",
-    "    for message in messages:\n",
-    "        yield message\n",
+    "    for chunk in chunks:\n",
+    "        # write the chunk to be streamed using stream_mode=custom \n",
+    "        writer(chunk)\n",
     "\n",
+    "    return {\"messages\": [AIMessage(content=\" \".join(chunks))]}\n",
     "\n",
-    "async def my_node(state: MessagesState, config: RunnableConfig):\n",
-    "    messages = []\n",
-    "    # Tagging a node makes it easy to filter out which events to include in your stream\n",
-    "    # It's completely optional, but useful if you have many functions with similar names\n",
-    "    gen = RunnableGenerator(my_generator).with_config(\n",
-    "        tags=[\"should_stream\"],\n",
-    "        callbacks=config.get(\n",
-    "            \"callbacks\", []\n",
-    "        ),  # <-- Propagate callbacks (Python <= 3.10)\n",
-    "    )\n",
-    "    async for message in gen.astream(state):\n",
-    "        messages.append(message)\n",
-    "    return {\"messages\": [AIMessage(content=\" \".join(messages))]}\n",
-    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(MessagesState)\n",
     "\n",
     "workflow.add_node(\"model\", my_node)\n",
     "workflow.add_edge(START, \"model\")\n",
     "workflow.add_edge(\"model\", END)\n",
+    "\n",
     "app = workflow.compile()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2af9e94e",
+   "id": "ecd69eed-9624-4640-b0af-c9f82b190900",
    "metadata": {},
    "source": [
-    "## Stream arbitrarily nested content"
+    "### Stream content"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "id": "00a91b15-82c7-443c-acb6-a7406df15cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Four|score|and|seven|years|ago|our|fathers|...|"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = [HumanMessage(content=\"What are you thinking about?\")]\n",
+    "async for chunk in app.astream({\"messages\": inputs}, stream_mode=\"custom\"):\n",
+    "    print(chunk, end=\"|\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29035302-3111-45bf-ac69-50ab940f8cb4",
+   "metadata": {},
+   "source": [
+    "## Stream custom data using `.astream_events`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "822e91c3-03be-4778-9fa5-a6ec57be3e52",
+   "metadata": {},
+   "source": [
+    "If you are already using graph's `.astream_events` method in your workflow, you can also stream custom data by emitting custom events using `adispatch_custom_event`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fb6c3e5-7377-4f93-a8c6-44582ee3bc1a",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">ASYNC IN PYTHON<=3.10</p>\n",
+    "    <p>\n",
+    "\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running async code in python<=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "\n",
+    "If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the node below with `adispatch_custom_event`.\n",
+    "\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b390a9fe-2d5f-4e82-a1ea-c7c0186b8559",
+   "metadata": {},
+   "source": [
+    "### Define the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "486a01a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableConfig, RunnableLambda\n",
+    "from langchain_core.callbacks.manager import adispatch_custom_event\n",
+    "\n",
+    "async def my_node(state: MessagesState, config: RunnableConfig):\n",
+    "    chunks = [\n",
+    "        \"Four\",\n",
+    "        \"score\",\n",
+    "        \"and\",\n",
+    "        \"seven\",\n",
+    "        \"years\",\n",
+    "        \"ago\",\n",
+    "        \"our\",\n",
+    "        \"fathers\",\n",
+    "        \"...\",\n",
+    "    ]\n",
+    "    for chunk in chunks:\n",
+    "        await adispatch_custom_event(\n",
+    "            \"my_custom_event\",\n",
+    "            {\"chunk\": chunk},\n",
+    "            config=config  # <-- propagate config\n",
+    "        )\n",
+    "\n",
+    "    return {\"messages\": [AIMessage(content=\" \".join(chunks))]}\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(MessagesState)\n",
+    "\n",
+    "workflow.add_node(\"model\", my_node)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_edge(\"model\", END)\n",
+    "\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dcded03-6776-405e-afae-005a3212d3e4",
+   "metadata": {},
+   "source": [
+    "### Stream content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "ce773a40",
    "metadata": {},
    "outputs": [
@@ -137,7 +233,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'chunk': 'Four'}|{'chunk': 'score'}|{'chunk': 'and'}|{'chunk': 'seven'}|{'chunk': 'years'}|{'chunk': 'ago'}|{'chunk': 'our'}|{'chunk': 'fathers'}|{'chunk': '...'}|"
+      "Four|score|and|seven|years|ago|our|fathers|...|"
      ]
     }
    ],
@@ -146,15 +242,11 @@
     "\n",
     "inputs = [HumanMessage(content=\"What are you thinking about?\")]\n",
     "async for event in app.astream_events({\"messages\": inputs}, version=\"v2\"):\n",
-    "    kind = event[\"event\"]\n",
     "    tags = event.get(\"tags\", [])\n",
-    "    if kind == \"on_chain_stream\" and \"should_stream\" in tags:\n",
+    "    if event[\"event\"] == \"on_custom_event\" and event[\"name\"] == \"my_custom_event\":\n",
     "        data = event[\"data\"]\n",
     "        if data:\n",
-    "            # Empty content in the context of OpenAI or Anthropic usually means\n",
-    "            # that the model is asking for a tool to be invoked.\n",
-    "            # So we only print non-empty content\n",
-    "            print(data, end=\"|\")"
+    "            print(data[\"chunk\"], end=\"|\", flush=True)"
    ]
   }
  ],
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 4745bd491..b91d8034a 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -153,7 +153,7 @@ nav:
           - Stream state updates: how-tos/stream-updates.ipynb
           - Stream LLM tokens: how-tos/streaming-tokens.ipynb
           - Stream LLM tokens without LangChain models: how-tos/streaming-tokens-without-langchain.ipynb
-          - Stream arbitrarily nested content: how-tos/streaming-content.ipynb
+          - Stream custom data: how-tos/streaming-content.ipynb
           - Configure multiple streaming modes: how-tos/stream-multiple.ipynb
           - Stream events from within tools: how-tos/streaming-events-from-within-tools.ipynb
           - Stream events from within tools without LangChain models: how-tos/streaming-events-from-within-tools-without-langchain.ipynb