From c1ed2e43ab6e2b477fc8f053c97adf4cf4a49a4d Mon Sep 17 00:00:00 2001
From: vbarda <vadym@langchain.dev>
Date: Fri, 31 Jan 2025 15:51:47 -0500
Subject: [PATCH] Revert "docs: revamp streaming how-to guides (#3239)"

This reverts commit d5b0ad2cf6190c83b4b0520cfee4e47cd998139a.
---
 docs/_scripts/notebook_convert.py             |   2 -
 ...0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib |   1 +
 ...0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib |   1 +
 ...5-a489-47bf-b482-a744a54e2cc4.msgpack.zlib |   1 +
 ...0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib |   1 +
 ...9-4147-42e3-89fd-d942b2b49f6c.msgpack.zlib |   1 +
 ...4-62df-4855-8219-d5e1a1a09be9.msgpack.zlib |   1 -
 ...c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib |   1 -
 ...9-ba06-48ba-abe5-e72df24407af.msgpack.zlib |   1 +
 ...a-96e3-442f-9924-0c99f46baed8.msgpack.zlib |   1 +
 ...ming-from-final-node_68ac2c7f.msgpack.zlib |   1 +
 .../streaming-subgraphs_7.msgpack.zlib        |   1 +
 ...ns-without-langchain_d6ed3df5.msgpack.zlib |   1 +
 .../streaming-tokens_72785b66.msgpack.zlib    |   1 +
 .../streaming-tokens_96050fba.msgpack.zlib    |   2 +-
 ...4-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib |   1 -
 ...d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib |   1 -
 ...9-2625-403a-9253-418a0feeed77.msgpack.zlib |   1 -
 ...9-8922-46ea-bd5b-18264fcc523a.msgpack.zlib |   1 -
 docs/docs/concepts/high_level.md              |   2 +-
 docs/docs/concepts/streaming.md               |  12 +-
 docs/docs/how-tos/index.md                    |  11 +-
 docs/docs/how-tos/stream-updates.ipynb        | 186 ++++++
 docs/docs/how-tos/stream-values.ipynb         | 248 ++++++++
 docs/docs/how-tos/streaming-content.ipynb     | 346 +++++++++++
 ...-from-within-tools-without-langchain.ipynb | 372 ++++++++++++
 .../streaming-events-from-within-tools.ipynb  | 464 ++++-----------
 .../streaming-events-from-within-tools.md     |   2 -
 .../how-tos/streaming-from-final-node.ipynb   | 351 +++++++++++
 .../how-tos/streaming-specific-nodes.ipynb    | 211 -------
 docs/docs/how-tos/streaming-specific-nodes.md |   2 -
 docs/docs/how-tos/streaming-subgraphs.ipynb   | 327 ++++++++---
 .../streaming-tokens-without-langchain.ipynb  | 355 ++++++++++++
 docs/docs/how-tos/streaming-tokens.ipynb      | 534 ++++++++---------
 docs/docs/how-tos/streaming-tokens.md         |   2 -
 docs/docs/how-tos/streaming.ipynb             | 547 ------------------
 docs/docs/how-tos/streaming.md                |   2 -
 docs/mkdocs.yml                               |  18 +-
 libs/langgraph/langgraph/pregel/__init__.py   |   4 +-
 39 files changed, 2496 insertions(+), 1521 deletions(-)
 create mode 100644 docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
 create mode 100644 docs/cassettes/stream-updates_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
 create mode 100644 docs/cassettes/stream-values_c122bf15-a489-47bf-b482-a744a54e2cc4.msgpack.zlib
 create mode 100644 docs/cassettes/stream-values_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-events-from-within-tools-without-langchain_45c96a79-4147-42e3-89fd-d942b2b49f6c.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming-events-from-within-tools_2c30c7b4-62df-4855-8219-d5e1a1a09be9.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming-events-from-within-tools_8ae5051c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-from-final-node_2ab6d079-ba06-48ba-abe5-e72df24407af.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-subgraphs_7.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib
 create mode 100644 docs/cassettes/streaming-tokens_72785b66.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib
 delete mode 100644 docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib
 create mode 100644 docs/docs/how-tos/stream-updates.ipynb
 create mode 100644 docs/docs/how-tos/stream-values.ipynb
 create mode 100644 docs/docs/how-tos/streaming-content.ipynb
 create mode 100644 docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb
 delete mode 100644 docs/docs/how-tos/streaming-events-from-within-tools.md
 create mode 100644 docs/docs/how-tos/streaming-from-final-node.ipynb
 delete mode 100644 docs/docs/how-tos/streaming-specific-nodes.ipynb
 delete mode 100644 docs/docs/how-tos/streaming-specific-nodes.md
 create mode 100644 docs/docs/how-tos/streaming-tokens-without-langchain.ipynb
 delete mode 100644 docs/docs/how-tos/streaming-tokens.md
 delete mode 100644 docs/docs/how-tos/streaming.ipynb
 delete mode 100644 docs/docs/how-tos/streaming.md

diff --git a/docs/_scripts/notebook_convert.py b/docs/_scripts/notebook_convert.py
index 8a47c44cc1..6523372dcf 100644
--- a/docs/_scripts/notebook_convert.py
+++ b/docs/_scripts/notebook_convert.py
@@ -22,8 +22,6 @@ def preprocess_cell(self, cell, resources, cell_index):
             )
 
         elif cell.cell_type == "code":
-            # Remove noqa comments
-            cell.source = re.sub(r'#\s*noqa.*$', '', cell.source, flags=re.MULTILINE)
             # escape ``` in code
             cell.source = cell.source.replace("```", r"\`\`\`")
             # escape ``` in output
diff --git a/docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib b/docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
new file mode 100644
index 0000000000..686c9421cc
--- /dev/null
+++ b/docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/stream-updates_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib b/docs/cassettes/stream-updates_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
new file mode 100644
index 0000000000..354fa0926c
--- /dev/null
+++ b/docs/cassettes/stream-updates_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/stream-values_c122bf15-a489-47bf-b482-a744a54e2cc4.msgpack.zlib b/docs/cassettes/stream-values_c122bf15-a489-47bf-b482-a744a54e2cc4.msgpack.zlib
new file mode 100644
index 0000000000..3932052e7c
--- /dev/null
+++ b/docs/cassettes/stream-values_c122bf15-a489-47bf-b482-a744a54e2cc4.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/stream-values_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib b/docs/cassettes/stream-values_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
new file mode 100644
index 0000000000..75cdbb5360
--- /dev/null
+++ b/docs/cassettes/stream-values_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-events-from-within-tools-without-langchain_45c96a79-4147-42e3-89fd-d942b2b49f6c.msgpack.zlib b/docs/cassettes/streaming-events-from-within-tools-without-langchain_45c96a79-4147-42e3-89fd-d942b2b49f6c.msgpack.zlib
new file mode 100644
index 0000000000..108c689c48
--- /dev/null
+++ b/docs/cassettes/streaming-events-from-within-tools-without-langchain_45c96a79-4147-42e3-89fd-d942b2b49f6c.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-events-from-within-tools_2c30c7b4-62df-4855-8219-d5e1a1a09be9.msgpack.zlib b/docs/cassettes/streaming-events-from-within-tools_2c30c7b4-62df-4855-8219-d5e1a1a09be9.msgpack.zlib
deleted file mode 100644
index 1843d8e654..0000000000
--- a/docs/cassettes/streaming-events-from-within-tools_2c30c7b4-62df-4855-8219-d5e1a1a09be9.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-events-from-within-tools_8ae5051c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib b/docs/cassettes/streaming-events-from-within-tools_8ae5051c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib
deleted file mode 100644
index c306d31743..0000000000
--- a/docs/cassettes/streaming-events-from-within-tools_8ae5051c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-from-final-node_2ab6d079-ba06-48ba-abe5-e72df24407af.msgpack.zlib b/docs/cassettes/streaming-from-final-node_2ab6d079-ba06-48ba-abe5-e72df24407af.msgpack.zlib
new file mode 100644
index 0000000000..2568b28c0d
--- /dev/null
+++ b/docs/cassettes/streaming-from-final-node_2ab6d079-ba06-48ba-abe5-e72df24407af.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib b/docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib
new file mode 100644
index 0000000000..b0dd754569
--- /dev/null
+++ b/docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib b/docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib
new file mode 100644
index 0000000000..e34a76cf79
--- /dev/null
+++ b/docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-subgraphs_7.msgpack.zlib b/docs/cassettes/streaming-subgraphs_7.msgpack.zlib
new file mode 100644
index 0000000000..6756b11d43
--- /dev/null
+++ b/docs/cassettes/streaming-subgraphs_7.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib b/docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib
new file mode 100644
index 0000000000..835e4a8930
--- /dev/null
+++ b/docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-tokens_72785b66.msgpack.zlib b/docs/cassettes/streaming-tokens_72785b66.msgpack.zlib
new file mode 100644
index 0000000000..9d409b09b3
--- /dev/null
+++ b/docs/cassettes/streaming-tokens_72785b66.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/streaming-tokens_96050fba.msgpack.zlib b/docs/cassettes/streaming-tokens_96050fba.msgpack.zlib
index b9999a059f..cff0d098b6 100644
--- a/docs/cassettes/streaming-tokens_96050fba.msgpack.zlib
+++ b/docs/cassettes/streaming-tokens_96050fba.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib b/docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib
deleted file mode 100644
index ec6465345b..0000000000
--- a/docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/streaming-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib b/docs/cassettes/streaming-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib
deleted file mode 100644
index 0c05be40ab..0000000000
--- a/docs/cassettes/streaming-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
-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
\ No newline at end of file
diff --git a/docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib b/docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib
deleted file mode 100644
index fd77399049..0000000000
--- a/docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
-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
\ No newline at end of file
diff --git a/docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib b/docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib
deleted file mode 100644
index f09a9858b1..0000000000
--- a/docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
-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
\ No newline at end of file
diff --git a/docs/docs/concepts/high_level.md b/docs/docs/concepts/high_level.md
index 76c69a92af..3124e56dc6 100644
--- a/docs/docs/concepts/high_level.md
+++ b/docs/docs/concepts/high_level.md
@@ -19,7 +19,7 @@ LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/con
 
 ### Streaming
 
-LangGraph also provides support for [streaming](../how-tos/index.md#streaming) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](../how-tos/streaming.md#updates)) and [tokens from LLM calls](../how-tos/streaming-tokens.md) embedded in an application.
+LangGraph also provides support for [streaming](../how-tos/index.md#streaming) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](../how-tos/stream-updates.ipynb)) and [tokens from LLM calls](../how-tos/streaming-tokens.ipynb) embedded in an application.
 
 ### Debugging and Deployment
 
diff --git a/docs/docs/concepts/streaming.md b/docs/docs/concepts/streaming.md
index a97328cac2..4cff014971 100644
--- a/docs/docs/concepts/streaming.md
+++ b/docs/docs/concepts/streaming.md
@@ -7,11 +7,11 @@ LangGraph is built with first class support for streaming. There are several dif
 `.stream` and `.astream` are sync and async methods for streaming back outputs from a graph run.
 There are several different modes you can specify when calling these methods (e.g. `graph.stream(..., mode="...")):
 
-- [`"values"`](../how-tos/streaming.md#values): This streams the full value of the state after each step of the graph.
-- [`"updates"`](../how-tos/streaming.md#updates): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
-- [`"custom"`](../how-tos/streaming.md#custom): This streams custom data from inside your graph nodes.
-- [`"messages"`](../how-tos/streaming-tokens.md): This streams LLM tokens and metadata for the graph node where LLM is invoked.
-- [`"debug"`](../how-tos/streaming.md#debug): This streams as much information as possible throughout the execution of the graph.
+- [`"values"`](../how-tos/stream-values.ipynb): This streams the full value of the state after each step of the graph.
+- [`"updates"`](../how-tos/stream-updates.ipynb): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
+- [`"custom"`](../how-tos/streaming-content.ipynb): This streams custom data from inside your graph nodes.
+- [`"messages"`](../how-tos/streaming-tokens.ipynb): This streams LLM tokens and metadata for the graph node where LLM is invoked.
+- `"debug"`: This streams as much information as possible throughout the execution of the graph.
 
 You can also specify multiple streaming modes at the same time by passing them as a list. When you do this, the streamed outputs will be tuples `(stream_mode, data)`. For example:
 
@@ -145,7 +145,7 @@ guide for that [here](../how-tos/streaming-tokens.ipynb).
 
 
 !!! warning "ASYNC IN PYTHON<=3.10"
-    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-tokens.md) and [here](../how-tos/streaming-events-from-within-tools.md).
+    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).
 
 
 ## LangGraph Platform
diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md
index e1e966e8bd..e141f8bc3d 100644
--- a/docs/docs/how-tos/index.md
+++ b/docs/docs/how-tos/index.md
@@ -81,10 +81,15 @@ See the below guides for how-to implement human-in-the-loop workflows with the (
 
 [Streaming](../concepts/streaming.md) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
 
-- [How to stream](streaming.ipynb)
+- [How to stream full state of your graph](stream-values.ipynb)
+- [How to stream state updates of your graph](stream-updates.ipynb)
 - [How to stream LLM tokens](streaming-tokens.ipynb)
-- [How to stream LLM tokens from specific nodes](streaming-specific-nodes.ipynb)
-- [How to stream data from within a tool](streaming-events-from-within-tools.ipynb)
+- [How to stream LLM tokens without LangChain models](streaming-tokens-without-langchain.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)
+- [How to stream events from the final node](streaming-from-final-node.ipynb)
 - [How to stream from subgraphs](streaming-subgraphs.ipynb)
 - [How to disable streaming for models that don't support it](disable-streaming.ipynb)
 
diff --git a/docs/docs/how-tos/stream-updates.ipynb b/docs/docs/how-tos/stream-updates.ipynb
new file mode 100644
index 0000000000..a241b22821
--- /dev/null
+++ b/docs/docs/how-tos/stream-updates.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
+   "metadata": {},
+   "source": [
+    "# How to stream state updates of your graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
+   "metadata": {},
+   "source": [
+    "LangGraph supports multiple streaming modes. The main ones are:\n",
+    "\n",
+    "- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
+    "- `updates`: This streaming mode streams back updates to the graph. This is the **update to the state of the graph** after each node is called.\n",
+    "\n",
+    "This guide covers `stream_mode=\"updates\"`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required package and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc6c48fe",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e7777f9",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "We'll be using a simple ReAct agent for this guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85cf2e23-29f2-40cc-b302-5377b3b49da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
+    "graph = create_react_agent(model, tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956db549-5207-4be1-a823-78311738e3f8",
+   "metadata": {},
+   "source": [
+    "## Stream updates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Receiving update from node: 'agent'\n",
+      "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd68b3a0-86c3-4afa-9649-1b962a0dd062-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}\n",
+      "\n",
+      "\n",
+      "\n",
+      "Receiving update from node: 'tools'\n",
+      "{'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', tool_call_id='call_kc6cvcEkTAUGRlSHrP4PK9fn')]}\n",
+      "\n",
+      "\n",
+      "\n",
+      "Receiving update from node: 'agent'\n",
+      "{'messages': [AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-009d83c4-b874-4acc-9494-20aba43132b9-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"updates\"):\n",
+    "    for node, values in chunk.items():\n",
+    "        print(f\"Receiving update from node: '{node}'\")\n",
+    "        print(values)\n",
+    "        print(\"\\n\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/stream-values.ipynb b/docs/docs/how-tos/stream-values.ipynb
new file mode 100644
index 0000000000..74a7962407
--- /dev/null
+++ b/docs/docs/how-tos/stream-values.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
+   "metadata": {},
+   "source": [
+    "# How to stream full state of your graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
+   "metadata": {},
+   "source": [
+    "LangGraph supports multiple streaming modes. The main ones are:\n",
+    "\n",
+    "- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
+    "- `updates`: This streaming mode streams back updates to the graph. This is the **update to the state of the graph** after each node is called.\n",
+    "\n",
+    "This guide covers `stream_mode=\"values\"`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaaab1fc",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7939a3c5",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "We'll be using a simple ReAct agent for this guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ef5a3ec6-0cd0-4541-ab1b-d63ede22720e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
+    "graph = create_react_agent(model, tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "002a715b-e0be-4e89-8d42-f0098882586b",
+   "metadata": {},
+   "source": [
+    "## Stream values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "what's the weather in sf\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_weather (call_61VvIzqVGtyxcXi0z6knZkjZ)\n",
+      " Call ID: call_61VvIzqVGtyxcXi0z6knZkjZ\n",
+      "  Args:\n",
+      "    city: sf\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_weather\n",
+      "\n",
+      "It's always sunny in sf\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in San Francisco is currently sunny.\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
+    "    chunk[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d73de237-bf45-4fa7-93ef-6dae7eacffc0",
+   "metadata": {},
+   "source": [
+    "If we want to just get the final result, we can use the same method and just keep track of the last value we received"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c122bf15-a489-47bf-b482-a744a54e2cc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
+    "    final_result = chunk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "316022e5-4c65-48e4-9878-8d94a2425ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='54b39b6f-054b-4306-980b-86905e48a6bc'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_avoKnK8reERzTUSxrN9cgFxY', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5e6c71d4a8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-f2f43c89-2c96-45f4-975c-2d0f22d0d2d1-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_avoKnK8reERzTUSxrN9cgFxY'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n",
+       "  ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='fc18a798-c7b2-4f73-84fa-8ffdffb6ddcb', tool_call_id='call_avoKnK8reERzTUSxrN9cgFxY'),\n",
+       "  AIMessage(content='The weather in San Francisco is currently sunny. Enjoy the sunshine!', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5e6c71d4a8', 'finish_reason': 'stop', 'logprobs': None}, id='run-21418147-da8e-4738-a076-239377397c40-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0f64ebbe-535c-4b35-a95f-0a7490cfed90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in San Francisco is currently sunny. Enjoy the sunshine!\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_result[\"messages\"][-1].pretty_print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/streaming-content.ipynb b/docs/docs/how-tos/streaming-content.ipynb
new file mode 100644
index 0000000000..cc983fb5ff
--- /dev/null
+++ b/docs/docs/how-tos/streaming-content.ipynb
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15c4bd28",
+   "metadata": {},
+   "source": [
+    "# How to stream custom data\n",
+    "\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#astream_events\">\n",
+    "                    astream_events API\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
+    "\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.\n",
+    "\n",
+    "For example, if you have a long-running tool call, you can dispatch custom events between the steps and use these custom events to monitor progress. You could also surface these custom events to an end user of your application to show them how the current task is progressing.\n",
+    "\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 we'll see how to use both APIs.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's install our required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e1a20f31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12297071",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29814253-ca9b-4844-a8a5-d6b19fbdbdba",
+   "metadata": {},
+   "source": [
+    "## Stream custom data using `.stream / .astream`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b729644a-b65f-4e69-ad45-f2e88ffb4e9d",
+   "metadata": {},
+   "source": [
+    "### Define the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9731c40f-5ce7-460d-b2ad-33185529c99d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "from langgraph.graph import START, StateGraph, MessagesState, END\n",
+    "from langgraph.types import StreamWriter\n",
+    "\n",
+    "\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",
+    "        \"seven\",\n",
+    "        \"years\",\n",
+    "        \"ago\",\n",
+    "        \"our\",\n",
+    "        \"fathers\",\n",
+    "        \"...\",\n",
+    "    ]\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",
+    "\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": "ecd69eed-9624-4640-b0af-c9f82b190900",
+   "metadata": {},
+   "source": [
+    "### Stream content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "00a91b15-82c7-443c-acb6-a7406df15cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Four\n",
+      "score\n",
+      "and\n",
+      "seven\n",
+      "years\n",
+      "ago\n",
+      "our\n",
+      "fathers\n",
+      "...\n"
+     ]
+    }
+   ],
+   "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, flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7b9f1f0-c170-40dc-9c22-289483dfbc99",
+   "metadata": {},
+   "source": [
+    "You will likely need to use [multiple streaming modes](https://langchain-ai.github.io/langgraph/how-tos/stream-multiple/) as you will\n",
+    "want access to both the custom data and the state updates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f8ed22d4-6ce6-4b04-a68b-2ea516e3ab15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('custom', 'Four')\n",
+      "('custom', 'score')\n",
+      "('custom', 'and')\n",
+      "('custom', 'seven')\n",
+      "('custom', 'years')\n",
+      "('custom', 'ago')\n",
+      "('custom', 'our')\n",
+      "('custom', 'fathers')\n",
+      "('custom', '...')\n",
+      "('updates', {'model': {'messages': [AIMessage(content='Four score and seven years ago our fathers ...', additional_kwargs={}, response_metadata={})]}})\n"
+     ]
+    }
+   ],
+   "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\", \"updates\"]):\n",
+    "    print(chunk, flush=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ca976d6a-7c64-4603-8bb4-dee95428c33d",
+   "metadata": {},
+   "source": [
+    "## Stream custom data using `.astream_events`\n",
+    "\n",
+    "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`\n",
+    "\n",
+    "<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": 19,
+   "id": "486a01a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableConfig, RunnableLambda\n",
+    "from langchain_core.callbacks.manager import adispatch_custom_event\n",
+    "\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",
+    "\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": 20,
+   "id": "ce773a40",
+   "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 event in app.astream_events({\"messages\": inputs}, version=\"v2\"):\n",
+    "    tags = event.get(\"tags\", [])\n",
+    "    if event[\"event\"] == \"on_custom_event\" and event[\"name\"] == \"my_custom_event\":\n",
+    "        data = event[\"data\"]\n",
+    "        if data:\n",
+    "            print(data[\"chunk\"], end=\"|\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb b/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb
new file mode 100644
index 0000000000..face9feb23
--- /dev/null
+++ b/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "18e6e213-b398-4a7e-b342-ba225e97b424",
+   "metadata": {},
+   "source": [
+    "# How to stream events from within a tool (without LangChain LLMs / tools)\n",
+    "\n",
+    "\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#astream_events\">\n",
+    "                    astream_events API\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
+    "\n",
+    "In this guide, we will demonstrate how to stream tokens from tools used by a custom ReAct agent, without relying on LangChain’s chat models or tool-calling functionalities. \n",
+    "\n",
+    "We will use the OpenAI client library directly for the chat model interaction. The tool execution will be implemented from scratch.\n",
+    "\n",
+    "This showcases how LangGraph can be utilized independently of built-in LangChain components like chat models or tools.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8df7b58",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7d766c7d-34ea-455b-8bcb-f2f12d100e1d",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "### Define a node that will call OpenAI API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d59234f9-173e-469d-a725-c13e0979663e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import AsyncOpenAI\n",
+    "from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
+    "from langchain_core.messages import AIMessageChunk\n",
+    "from langchain_core.runnables.config import (\n",
+    "    ensure_config,\n",
+    "    get_callback_manager_for_config,\n",
+    ")\n",
+    "\n",
+    "openai_client = AsyncOpenAI()\n",
+    "# define tool schema for openai tool calling\n",
+    "\n",
+    "tool = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_items\",\n",
+    "        \"description\": \"Use this tool to look up which items are in the given place.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"place\": {\"type\": \"string\"}},\n",
+    "            \"required\": [\"place\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "async def call_model(state, config=None):\n",
+    "    config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
+    "    callback_manager = get_callback_manager_for_config(config)\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
+    "    response = await openai_client.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\", tools=[tool], stream=True\n",
+    "    )\n",
+    "\n",
+    "    response_content = \"\"\n",
+    "    role = None\n",
+    "\n",
+    "    tool_call_id = None\n",
+    "    tool_call_function_name = None\n",
+    "    tool_call_function_arguments = \"\"\n",
+    "    async for chunk in response:\n",
+    "        delta = chunk.choices[0].delta\n",
+    "        if delta.role is not None:\n",
+    "            role = delta.role\n",
+    "\n",
+    "        if delta.content:\n",
+    "            response_content += delta.content\n",
+    "            llm_run_manager.on_llm_new_token(delta.content)\n",
+    "\n",
+    "        if delta.tool_calls:\n",
+    "            # note: for simplicity we're only handling a single tool call here\n",
+    "            if delta.tool_calls[0].function.name is not None:\n",
+    "                tool_call_function_name = delta.tool_calls[0].function.name\n",
+    "                tool_call_id = delta.tool_calls[0].id\n",
+    "\n",
+    "            # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly\n",
+    "            tool_call_chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=\"\",\n",
+    "                    additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]},\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
+    "            tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
+    "\n",
+    "    if tool_call_function_name is not None:\n",
+    "        tool_calls = [\n",
+    "            {\n",
+    "                \"id\": tool_call_id,\n",
+    "                \"function\": {\n",
+    "                    \"name\": tool_call_function_name,\n",
+    "                    \"arguments\": tool_call_function_arguments,\n",
+    "                },\n",
+    "                \"type\": \"function\",\n",
+    "            }\n",
+    "        ]\n",
+    "    else:\n",
+    "        tool_calls = None\n",
+    "\n",
+    "    response_message = {\n",
+    "        \"role\": role,\n",
+    "        \"content\": response_content,\n",
+    "        \"tool_calls\": tool_calls,\n",
+    "    }\n",
+    "    return {\"messages\": [response_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
+   "metadata": {},
+   "source": [
+    "### Define our tools and a tool-calling node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b90941d8-afe4-42ec-9262-9c3b87c3b1ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from langchain_core.callbacks import adispatch_custom_event\n",
+    "\n",
+    "\n",
+    "async def get_items(place: str) -> str:\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "\n",
+    "    # this can be replaced with any actual streaming logic that you might have\n",
+    "    def stream(place: str):\n",
+    "        if \"bed\" in place:  # For under the bed\n",
+    "            yield from [\"socks\", \"shoes\", \"dust bunnies\"]\n",
+    "        elif \"shelf\" in place:  # For 'shelf'\n",
+    "            yield from [\"books\", \"penciles\", \"pictures\"]\n",
+    "        else:  # if the agent decides to ask about a different place\n",
+    "            yield \"cat snacks\"\n",
+    "\n",
+    "    tokens = []\n",
+    "    for token in stream(place):\n",
+    "        await adispatch_custom_event(\n",
+    "            # this will allow you to filter events by name\n",
+    "            \"tool_call_token_stream\",\n",
+    "            {\n",
+    "                \"function_name\": \"get_items\",\n",
+    "                \"arguments\": {\"place\": place},\n",
+    "                \"tool_output_token\": token,\n",
+    "            },\n",
+    "            # this will allow you to filter events by tags\n",
+    "            config={\"tags\": [\"tool_call\"]},\n",
+    "        )\n",
+    "        tokens.append(token)\n",
+    "\n",
+    "    return \", \".join(tokens)\n",
+    "\n",
+    "\n",
+    "# define mapping to look up functions when running tools\n",
+    "function_name_to_function = {\"get_items\": get_items}\n",
+    "\n",
+    "\n",
+    "async def call_tools(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    tool_call = messages[-1][\"tool_calls\"][0]\n",
+    "    function_name = tool_call[\"function\"][\"name\"]\n",
+    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
+    "    arguments = json.loads(function_arguments)\n",
+    "\n",
+    "    function_response = await function_name_to_function[function_name](**arguments)\n",
+    "    tool_message = {\n",
+    "        \"tool_call_id\": tool_call[\"id\"],\n",
+    "        \"role\": \"tool\",\n",
+    "        \"name\": function_name,\n",
+    "        \"content\": function_response,\n",
+    "    }\n",
+    "    return {\"messages\": [tool_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6685898c-9a1c-4803-a492-bd70574ebe38",
+   "metadata": {},
+   "source": [
+    "### Define our graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Literal\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph import StateGraph, END, START\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, operator.add]\n",
+    "\n",
+    "\n",
+    "def should_continue(state) -> Literal[\"tools\", END]:\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    if last_message[\"tool_calls\"]:\n",
+    "        return \"tools\"\n",
+    "    return END\n",
+    "\n",
+    "\n",
+    "workflow = StateGraph(State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)  # i.e. our \"agent\"\n",
+    "workflow.add_node(\"tools\", call_tools)\n",
+    "workflow.add_conditional_edges(\"model\", should_continue)\n",
+    "workflow.add_edge(\"tools\", \"model\")\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
+   "metadata": {},
+   "source": [
+    "## Stream tokens from within the tool\n",
+    "\n",
+    "Here, we'll use the `astream_events` API to stream back individual events. Please see [astream_events](https://python.langchain.com/docs/concepts/#astream_events) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tool token socks\n",
+      "Tool token shoes\n",
+      "Tool token dust bunnies\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for event in graph.astream_events(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, version=\"v2\"\n",
+    "):\n",
+    "    tags = event.get(\"tags\", [])\n",
+    "    if event[\"event\"] == \"on_custom_event\" and \"tool_call\" in tags:\n",
+    "        print(\"Tool token\", event[\"data\"][\"tool_output_token\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/streaming-events-from-within-tools.ipynb b/docs/docs/how-tos/streaming-events-from-within-tools.ipynb
index fca8586af2..522cc61f32 100644
--- a/docs/docs/how-tos/streaming-events-from-within-tools.ipynb
+++ b/docs/docs/how-tos/streaming-events-from-within-tools.ipynb
@@ -3,82 +3,50 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "695d935e-b4fe-45a6-a061-a66d32cb832b",
+   "id": "04b012ac-e0b5-483e-a645-d13d0e215aad",
    "metadata": {},
    "source": [
     "# How to stream data from within a tool\n",
     "\n",
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "    - [Tools](https://python.langchain.com/docs/concepts/tools/)\n",
-    "\n",
-    "If your graph calls tools that use LLMs or any other streaming APIs, you might want to surface partial results during the execution of the tool, especially if the tool takes a longer time to run.\n",
-    "\n",
-    "1. To stream **arbitrary** data from inside a tool you can use [`stream_mode=\"custom\"`](../streaming#custom) and `get_stream_writer()`:\n",
-    "\n",
-    "    ```python\n",
-    "    # highlight-next-line\n",
-    "    from langgraph.config import get_stream_writer\n",
-    "    \n",
-    "    def tool(tool_arg: str):\n",
-    "        writer = get_stream_writer()\n",
-    "        for chunk in custom_data_stream():\n",
-    "            # stream any arbitrary data\n",
-    "            # highlight-next-line\n",
-    "            writer(chunk)\n",
-    "        ...\n",
-    "    \n",
-    "    for chunk in graph.stream(\n",
-    "        inputs,\n",
-    "        # highlight-next-line\n",
-    "        stream_mode=\"custom\"\n",
-    "    ):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "2. To stream LLM tokens generated by a tool calling an LLM you can use [`stream_mode=\"messages\"`](../streaming#messages):\n",
-    "\n",
-    "    ```python\n",
-    "    from langgraph.graph import StateGraph, MessagesState\n",
-    "    from langchain_openai import ChatOpenAI\n",
-    "    \n",
-    "    model = ChatOpenAI()\n",
-    "    \n",
-    "    def tool(tool_arg: str):\n",
-    "        model.invoke(tool_arg)\n",
-    "        ...\n",
-    "    \n",
-    "    def call_tools(state: MessagesState):\n",
-    "        tool_call = get_tool_call(state)\n",
-    "        tool_result = tool(**tool_call[\"args\"])\n",
-    "        ...\n",
-    "    \n",
-    "    graph = (\n",
-    "        StateGraph(MessagesState)\n",
-    "        .add_node(call_tools)\n",
-    "        ...\n",
-    "        .compile()\n",
-    "    \n",
-    "    for msg, metadata in graph.stream(\n",
-    "        inputs,\n",
-    "        # highlight-next-line\n",
-    "        stream_mode=\"messages\"\n",
-    "    ):\n",
-    "        print(msg)\n",
-    "    ```\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig\">\n",
+    "                    RunnableConfig\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#runnable-interface\">\n",
+    "                    RunnableInterface\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
     "\n",
-    "!!! note \"Using without LangChain\"\n",
+    "If your graph involves tools that invoke LLMs (or any other LangChain `Runnable` objects like other graphs, `LCEL` chains, or retrievers), you might want to surface partial results during the execution of the tool, especially if the tool takes a longer time to run.\n",
     "\n",
-    "    If you need to stream data from inside tools **without using LangChain**, you can use [`stream_mode=\"custom\"`](../streaming/#custom). Check out the [example below](#example-without-langchain) to learn more.\n",
+    "A common scenario is streaming LLM tokens generated by a tool calling an LLM, though this applies to any use of Runnable objects. \n",
     "\n",
-    "!!! warning \"Async in Python < 3.11\"\n",
-    "    \n",
-    "    When using Python < 3.11 with async code, please ensure you manually pass the `RunnableConfig` through to the chat model when invoking it like so: `model.ainvoke(..., config)`.\n",
-    "    The stream method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via [contextvars](https://docs.python.org/3/library/contextvars.html); prior to 3.11, [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) lacked proper `contextvar` support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the `call_model` function below.\n",
+    "This guide shows how to stream data from within a tool using the `astream` API with `stream_mode=\"messages\"` and also the more granular `astream_events` API. The `astream` API should be sufficient for most use cases.\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -87,8 +55,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "b364dfe2-010b-4588-8489-fb4d8be1f200",
+   "execution_count": 3,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,18 +66,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_API_KEY:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -138,346 +98,156 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ddc3ff-5620-48de-82f0-03b9137410cf",
+   "id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
    "metadata": {},
    "source": [
-    "## Streaming custom data\n",
+    "## Define the graph\n",
     "\n",
-    "We'll use a [prebuilt ReAct agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] for this guide:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f1975577-a485-42bd-b0f1-d3e987faf52b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "from langgraph.config import get_stream_writer\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "async def get_items(place: str) -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # highlight-next-line\n",
-    "    writer = get_stream_writer()\n",
-    "\n",
-    "    # this can be replaced with any actual streaming logic that you might have\n",
-    "    items = [\"books\", \"penciles\", \"pictures\"]\n",
-    "    for chunk in items:\n",
-    "        # highlight-next-line\n",
-    "        writer({\"custom_tool_data\": chunk})\n",
-    "\n",
-    "    return \", \".join(items)\n",
-    "\n",
-    "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4o-mini\")\n",
-    "tools = [get_items]\n",
-    "# contains `agent` (tool-calling LLM) and `tools` (tool executor) nodes\n",
-    "agent = create_react_agent(llm, tools=tools)"
+    "We'll use a prebuilt ReAct agent for this guide"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa96d572-d15f-4f00-b629-cf25e0b4dece",
+   "id": "9378fd4a-69e4-49e2-b34c-a98a0505ea35",
    "metadata": {},
    "source": [
-    "Let's now invoke our agent with an input that requires a tool call:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8ae5051c-53b9-4c53-87b2-d7263cda3b7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'custom_tool_data': 'books'}\n",
-      "{'custom_tool_data': 'penciles'}\n",
-      "{'custom_tool_data': 'pictures'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = {\n",
-    "    \"messages\": [  # noqa\n",
-    "        {\"role\": \"user\", \"content\": \"what items are in the office?\"}\n",
-    "    ]\n",
-    "}\n",
-    "async for chunk in agent.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d8fa9fc-19af-47d6-9031-ee1720c51aa2",
-   "metadata": {},
-   "source": [
-    "## Streaming LLM tokens"
+    "<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>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "38eaf453-9773-424d-a110-9e1038a69805",
+   "id": "f1975577-a485-42bd-b0f1-d3e987faf52b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.messages import AIMessageChunk\n",
-    "from langchain_core.runnables import RunnableConfig\n",
+    "from langchain_core.callbacks import Callbacks\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "@tool\n",
     "async def get_items(\n",
     "    place: str,\n",
-    "    # Manually accept config (needed for Python <= 3.10)\n",
-    "    # highlight-next-line\n",
-    "    config: RunnableConfig,\n",
+    "    callbacks: Callbacks,  # <--- Manually accept callbacks (needed for Python <= 3.10)\n",
     ") -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # Attention: when using async, you should be invoking the LLM using ainvoke!\n",
-    "    # If you fail to do so, streaming will NOT work.\n",
-    "    response = await llm.ainvoke(\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "    # Attention when using async, you should be invoking the LLM using ainvoke!\n",
+    "    # If you fail to do so, streaming will not WORK.\n",
+    "    return await llm.ainvoke(\n",
     "        [\n",
     "            {\n",
     "                \"role\": \"user\",\n",
-    "                \"content\": (\n",
-    "                    f\"Can you tell me what kind of items i might find in the following place: '{place}'. \"\n",
-    "                    \"List at least 3 such items separating them by a comma. And include a brief description of each item.\"\n",
-    "                ),\n",
+    "                \"content\": f\"Can you tell me what kind of items i might find in the following place: '{place}'. \"\n",
+    "                \"List at least 3 such items separating them by a comma. And include a brief description of each item..\",\n",
     "            }\n",
     "        ],\n",
-    "        # highlight-next-line\n",
-    "        config,\n",
+    "        {\"callbacks\": callbacks},\n",
     "    )\n",
-    "    return response.content\n",
     "\n",
     "\n",
+    "llm = ChatOpenAI(model_name=\"gpt-4o\")\n",
     "tools = [get_items]\n",
-    "# contains `agent` (tool-calling LLM) and `tools` (tool executor) nodes\n",
     "agent = create_react_agent(llm, tools=tools)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "15cb55cc-b59d-4743-b6a3-13db75414d2c",
+   "metadata": {},
+   "source": [
+    "## Using stream_mode=\"messages\"\n",
+    "\n",
+    "Using `stream_mode=\"messages\"` is a good option if you don't have any complex LCEL logic inside of nodes (or you don't need super granular progress from within the LCEL chain)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "id": "4c9cdad3-3e9a-444f-9d9d-eae20b8d3486",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Certainly|!| Here| are| three| items| you| might| find| in| a| bedroom|:\n",
-      "\n",
-      "|1|.| **|Bed|**|:| The| central| piece| of| furniture| in| a| bedroom|,| typically| consisting| of| a| mattress| supported| by| a| frame|.| It| is| designed| for| sleeping| and| can| vary| in| size| from| twin| to| king|.| Beds| often| have| bedding|,| including| sheets|,| pillows|,| and| comfort|ers|,| to| enhance| comfort|.\n",
-      "\n",
-      "|2|.| **|D|resser|**|:| A| piece| of| furniture| with| drawers| used| for| storing| clothing| and| personal| items|.| Dress|ers| often| have| a| flat| surface| on| top|,| which| can| be| used| for| decorative| items|,| a| mirror|,| or| personal| accessories|.| They| help| keep| the| bedroom| organized| and| clutter|-free|.\n",
-      "\n",
-      "|3|.| **|Night|stand|**|:| A| small| table| or| cabinet| placed| beside| the| bed|,| used| for| holding| items| such| as| a| lamp|,| alarm| clock|,| books|,| or| personal| items|.| Night|stands| provide| convenience| for| easy| access| to| essentials| during| the| night|,| adding| functionality| and| style| to| the| bedroom| decor|.|"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "inputs = {\n",
-    "    \"messages\": [  # noqa\n",
-    "        {\"role\": \"user\", \"content\": \"what items are in the bedroom?\"}\n",
-    "    ]\n",
-    "}\n",
+    "final_message = \"\"\n",
     "async for msg, metadata in agent.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
+    "    {\"messages\": [(\"human\", \"what items are on the shelf?\")]}, stream_mode=\"messages\"\n",
     "):\n",
+    "    # Stream all messages from the tool node\n",
     "    if (\n",
-    "        isinstance(msg, AIMessageChunk)\n",
-    "        and msg.content\n",
-    "        # Stream all messages from the tool node\n",
-    "        # highlight-next-line\n",
+    "        msg.content\n",
+    "        and not isinstance(msg, HumanMessage)\n",
     "        and metadata[\"langgraph_node\"] == \"tools\"\n",
+    "        and not msg.name\n",
     "    ):\n",
-    "        print(msg.content, end=\"|\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d598d7e2-617d-4c06-bc9a-6a03d5f58499",
-   "metadata": {},
-   "source": [
-    "## Example without LangChain"
+    "        print(msg.content, end=\"|\", flush=True)\n",
+    "    # Final message should come from our agent\n",
+    "    if msg.content and metadata[\"langgraph_node\"] == \"agent\":\n",
+    "        final_message += msg.content"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "780ddcb6-63a7-4c83-a739-bafbe3cd135a",
-   "metadata": {},
-   "source": [
-    "You can also stream data from within tool invocations **without using LangChain**. Below example demonstrates how to do it for a graph with a single tool-executing node. We'll leave it as an exercise for the reader to [implement ReAct agent from scratch](../react-agent-from-scratch) without using LangChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3e8be67f-4bb8-4f14-9fdb-fc60340f3930",
+   "id": "81656193-1cbf-4721-a8df-0e316fd510e5",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import operator\n",
-    "import json\n",
-    "\n",
-    "from typing import TypedDict\n",
-    "from typing_extensions import Annotated\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "\n",
-    "from openai import AsyncOpenAI\n",
-    "\n",
-    "openai_client = AsyncOpenAI()\n",
-    "model_name = \"gpt-4o-mini\"\n",
-    "\n",
-    "\n",
-    "async def stream_tokens(model_name: str, messages: list[dict]):\n",
-    "    response = await openai_client.chat.completions.create(\n",
-    "        messages=messages, model=model_name, stream=True\n",
-    "    )\n",
-    "    role = None\n",
-    "    async for chunk in response:\n",
-    "        delta = chunk.choices[0].delta\n",
-    "\n",
-    "        if delta.role is not None:\n",
-    "            role = delta.role\n",
-    "\n",
-    "        if delta.content:\n",
-    "            yield {\"role\": role, \"content\": delta.content}\n",
-    "\n",
-    "\n",
-    "# this is our tool\n",
-    "async def get_items(place: str) -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # highlight-next-line\n",
-    "    writer = get_stream_writer()\n",
-    "    response = \"\"\n",
-    "    async for msg_chunk in stream_tokens(\n",
-    "        model_name,\n",
-    "        [\n",
-    "            {\n",
-    "                \"role\": \"user\",\n",
-    "                \"content\": (\n",
-    "                    \"Can you tell me what kind of items \"\n",
-    "                    f\"i might find in the following place: '{place}'. \"\n",
-    "                    \"List at least 3 such items separating them by a comma. \"\n",
-    "                    \"And include a brief description of each item.\"\n",
-    "                ),\n",
-    "            }\n",
-    "        ],\n",
-    "    ):\n",
-    "        response += msg_chunk[\"content\"]\n",
-    "        # highlight-next-line\n",
-    "        writer(msg_chunk)\n",
+    "## Using stream events API\n",
     "\n",
-    "    return response\n",
+    "For simplicity, the `get_items` tool doesn't use any complex LCEL logic inside it -- it only invokes an LLM.\n",
     "\n",
+    "However, if the tool were more complex (e.g., using a RAG chain inside it), and you wanted to see more granular events from within the chain, then you can use the astream events API.\n",
     "\n",
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list[dict], operator.add]\n",
+    "The example below only illustrates how to invoke the API.\n",
     "\n",
-    "\n",
-    "# this is the tool-calling graph node\n",
-    "async def call_tool(state: State):\n",
-    "    ai_message = state[\"messages\"][-1]\n",
-    "    tool_call = ai_message[\"tool_calls\"][-1]\n",
-    "\n",
-    "    function_name = tool_call[\"function\"][\"name\"]\n",
-    "    if function_name != \"get_items\":\n",
-    "        raise ValueError(f\"Tool {function_name} not supported\")\n",
-    "\n",
-    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
-    "    arguments = json.loads(function_arguments)\n",
-    "\n",
-    "    function_response = await get_items(**arguments)\n",
-    "    tool_message = {\n",
-    "        \"tool_call_id\": tool_call[\"id\"],\n",
-    "        \"role\": \"tool\",\n",
-    "        \"name\": function_name,\n",
-    "        \"content\": function_response,\n",
-    "    }\n",
-    "    return {\"messages\": [tool_message]}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)  # noqa\n",
-    "    .add_node(call_tool)\n",
-    "    .add_edge(START, \"call_tool\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e712d12-841c-4eac-a4d8-d01c73c86c8c",
-   "metadata": {},
-   "source": [
-    "Let's now invoke our graph with an AI message that contains a tool call:"
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">Use async for the astream events API</p>\n",
+    "    <p>\n",
+    "        You should generally be using `async` code (e.g., using `ainvoke` to invoke the llm) to be able to leverage the astream events API properly.\n",
+    "    </p>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "2c30c7b4-62df-4855-8219-d5e1a1a09be9",
+   "execution_count": 7,
+   "id": "c3acdec9-0a24-4348-921e-435c8ea6f9fe",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sure|!| Here| are| three| common| items| you| might| find| in| a| bedroom|:\n",
-      "\n",
-      "|1|.| **|Bed|**|:| The| focal| point| of| the| bedroom|,| a| bed| typically| consists| of| a| mattress| resting| on| a| frame|,| and| it| may| include| pillows| and| bedding|.| It| provides| a| comfortable| place| for| sleeping| and| resting|.\n",
+      "|In| a| bedroom|,| you| might| find| the| following| items|:\n",
       "\n",
-      "|2|.| **|D|resser|**|:| A| piece| of| furniture| with| multiple| drawers|,| a| dresser| is| used| for| storing| clothes|,| accessories|,| and| personal| items|.| It| often| has| a| flat| surface| that| may| be| used| to| display| decorative| items| or| a| mirror|.\n",
+      "|1|.| **|Bed|**|:| The| central| piece| of| furniture| in| a| bedroom|,| typically| consisting| of| a| mattress| on| a| frame|,| where| people| sleep|.| It| often| includes| bedding| such| as| sheets|,| blankets|,| and| pillows| for| comfort|.\n",
       "\n",
-      "|3|.| **|Night|stand|**|:| Also| known| as| a| bedside| table|,| a| night|stand| is| placed| next| to| the| bed| and| typically| holds| items| like| lamps|,| books|,| alarm| clocks|,| and| personal| belongings| for| convenience| during| the| night|.\n",
+      "|2|.| **|Ward|robe|**|:| A| large|,| tall| cupboard| or| fre|estanding| piece| of| furniture| used| for| storing| clothes|.| It| may| have| hanging| space|,| shelves|,| and| sometimes| drawers| for| organizing| garments| and| accessories|.\n",
       "\n",
-      "|These| items| contribute| to| the| functionality| and| comfort| of| the| bedroom| environment|.|"
+      "|3|.| **|Night|stand|**|:| A| small| table| or| cabinet| placed| beside| the| bed|,| used| for| holding| items| like| a| lamp|,| alarm| clock|,| books|,| or| personal| belongings| that| might| be| needed| during| the| night| or| early| morning|.||"
      ]
     }
    ],
    "source": [
-    "inputs = {\n",
-    "    \"messages\": [\n",
-    "        {\n",
-    "            \"content\": None,\n",
-    "            \"role\": \"assistant\",\n",
-    "            \"tool_calls\": [\n",
-    "                {\n",
-    "                    \"id\": \"1\",\n",
-    "                    \"function\": {\n",
-    "                        \"arguments\": '{\"place\":\"bedroom\"}',\n",
-    "                        \"name\": \"get_items\",\n",
-    "                    },\n",
-    "                    \"type\": \"function\",\n",
-    "                }\n",
-    "            ],\n",
-    "        }\n",
-    "    ]\n",
-    "}\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
-    "async for chunk in graph.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
+    "async for event in agent.astream_events(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom.\"}]}, version=\"v2\"\n",
     "):\n",
-    "    print(chunk[\"content\"], end=\"|\", flush=True)"
+    "    if (\n",
+    "        event[\"event\"] == \"on_chat_model_stream\"\n",
+    "        and event[\"metadata\"].get(\"langgraph_node\") == \"tools\"\n",
+    "    ):\n",
+    "        print(event[\"data\"][\"chunk\"].content, end=\"|\", flush=True)"
    ]
   }
  ],
@@ -497,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/streaming-events-from-within-tools.md b/docs/docs/how-tos/streaming-events-from-within-tools.md
deleted file mode 100644
index 57ba5d7276..0000000000
--- a/docs/docs/how-tos/streaming-events-from-within-tools.md
+++ /dev/null
@@ -1,2 +0,0 @@
-WARNING: DO NOT MODIFY/DELETE
-This is a dummy file needed for mkdocs-redirects, as it is expecting redirects to be markdown files
diff --git a/docs/docs/how-tos/streaming-from-final-node.ipynb b/docs/docs/how-tos/streaming-from-final-node.ipynb
new file mode 100644
index 0000000000..aef5d346a5
--- /dev/null
+++ b/docs/docs/how-tos/streaming-from-final-node.ipynb
@@ -0,0 +1,351 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15c4bd28",
+   "metadata": {},
+   "source": [
+    "# How to stream from the final node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "964686a6-8fed-4360-84d2-958c48186008",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div> \n",
+    "\n",
+    "A common use case when streaming from an agent is to stream LLM tokens from inside the final node. This guide demonstrates how you can do this.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c04a3f8e-0bc9-430b-85db-3edfa026d2cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c87e4a47-4099-4d1a-907c-a99fa857165a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb79e50b",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17f994ca-28e7-4379-a1c9-8c1682773b5f",
+   "metadata": {},
+   "source": [
+    "## Define model and tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5e62618d-0e0c-483c-acd3-40a26e61894a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "from langgraph.prebuilt import ToolNode\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "final_model = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "model = model.bind_tools(tools)\n",
+    "# NOTE: this is where we're adding a tag that we'll can use later to filter the model stream events to only the model called in the final node.\n",
+    "# This is not necessary if you call a single LLM but might be important in case you call multiple models within the node and want to filter events\n",
+    "# from only one of them.\n",
+    "final_model = final_model.with_config(tags=[\"final_node\"])\n",
+    "tool_node = ToolNode(tools=tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9acef997-5dd6-4108-baf1-c4d6be3e4999",
+   "metadata": {},
+   "source": [
+    "## Define graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8c7339d2-1835-4b5a-a99c-a60e150280af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph, START\n",
+    "from langgraph.graph.message import MessagesState\n",
+    "from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage\n",
+    "\n",
+    "\n",
+    "def should_continue(state: MessagesState) -> Literal[\"tools\", \"final\"]:\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    # If the LLM makes a tool call, then we route to the \"tools\" node\n",
+    "    if last_message.tool_calls:\n",
+    "        return \"tools\"\n",
+    "    # Otherwise, we stop (reply to the user)\n",
+    "    return \"final\"\n",
+    "\n",
+    "\n",
+    "def call_model(state: MessagesState):\n",
+    "    messages = state[\"messages\"]\n",
+    "    response = model.invoke(messages)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "def call_final_model(state: MessagesState):\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_ai_message = messages[-1]\n",
+    "    response = final_model.invoke(\n",
+    "        [\n",
+    "            SystemMessage(\"Rewrite this in the voice of Al Roker\"),\n",
+    "            HumanMessage(last_ai_message.content),\n",
+    "        ]\n",
+    "    )\n",
+    "    # overwrite the last AI message from the agent\n",
+    "    response.id = last_ai_message.id\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "builder = StateGraph(MessagesState)\n",
+    "\n",
+    "builder.add_node(\"agent\", call_model)\n",
+    "builder.add_node(\"tools\", tool_node)\n",
+    "# add a separate final node\n",
+    "builder.add_node(\"final\", call_final_model)\n",
+    "\n",
+    "builder.add_edge(START, \"agent\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"agent\",\n",
+    "    should_continue,\n",
+    ")\n",
+    "\n",
+    "builder.add_edge(\"tools\", \"agent\")\n",
+    "builder.add_edge(\"final\", END)\n",
+    "\n",
+    "graph = builder.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2ab6d079-ba06-48ba-abe5-e72df24407af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Image\n",
+    "\n",
+    "display(Image(graph.get_graph().draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "521adaef-dd2f-46d6-8f6a-5cc1d6e0aefc",
+   "metadata": {},
+   "source": [
+    "## Stream outputs from the final node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cfaeb64-5506-4546-96c0-4891e6288ad9",
+   "metadata": {},
+   "source": [
+    "### Filter on event metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f218a05d-1590-4d5c-b0b7-97d94c744efb",
+   "metadata": {},
+   "source": [
+    "First option to get the LLM events from within a specific node (`final` node in our case) is to filter on the `langgraph_node` field in the event metadata. This will be sufficient in case you need to stream events from ALL LLM calls inside the node. This means that if you have multiple different LLMs invoked inside the node, this filter will include events from all of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "68ac2c7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Well| folks|,| let| me| tell| you|,| the| weather| in| San| Francisco| is| always| sunny|!| That|'s| right|,| you| can| expect| clear| skies| and| plenty| of| sunshine| when| you|'re| in| the| City| by| the| Bay|.| So| grab| your| sunglasses| and| get| ready| to| enjoy| some| beautiful| weather| in| San| Francisco|!|"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
+    "for msg, metadata in graph.stream(inputs, stream_mode=\"messages\"):\n",
+    "    if (\n",
+    "        msg.content\n",
+    "        and not isinstance(msg, HumanMessage)\n",
+    "        and metadata[\"langgraph_node\"] == \"final\"\n",
+    "    ):\n",
+    "        print(msg.content, end=\"|\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0bb447a-6650-4166-b124-2d5b99a1f88b",
+   "metadata": {},
+   "source": [
+    "### Filter on custom tags"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea4db927-44b6-46ab-8b8d-f237edaf1438",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can add configuration with custom tags to your LLM, like we did in the beginning, by adding `final_model.with_config(tags=[\"final_node\"])`. This will allow us to more precisely filter the events to keep the ones only from this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "55d60dfa-96e3-442f-9924-0c99f46baed8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looks| like| we|'ve| got| some| clouds| roll|in|'| in| over| the| Big| Apple| today|,| folks|!| Keep| an| eye| out| for| some| over|cast| skies| in| NYC|.|"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [HumanMessage(content=\"what's the weather in nyc?\")]}\n",
+    "async for event in graph.astream_events(inputs, version=\"v2\"):\n",
+    "    kind = event[\"event\"]\n",
+    "    tags = event.get(\"tags\", [])\n",
+    "    # filter on the custom tag\n",
+    "    if kind == \"on_chat_model_stream\" and \"final_node\" in event.get(\"tags\", []):\n",
+    "        data = event[\"data\"]\n",
+    "        if data[\"chunk\"].content:\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[\"chunk\"].content, end=\"|\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/streaming-specific-nodes.ipynb b/docs/docs/how-tos/streaming-specific-nodes.ipynb
deleted file mode 100644
index fd9d8c6ee8..0000000000
--- a/docs/docs/how-tos/streaming-specific-nodes.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c5889ca0-6feb-4864-a630-e97ccc2c587e",
-   "metadata": {},
-   "source": [
-    "# How to stream LLM tokens from specific nodes\n",
-    "\n",
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "\n",
-    "A common use case when [streaming LLM tokens](../streaming-tokens) is to only stream them from specific nodes. To do so, you can use `stream_mode=\"messages\"` and filter the outputs by the `langgraph_node` field in the streamed metadata:\n",
-    "\n",
-    "```python\n",
-    "from langgraph.graph import StateGraph\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI()\n",
-    "\n",
-    "def node_a(state: State):\n",
-    "    model.invoke(...)\n",
-    "    ...\n",
-    "\n",
-    "def node_b(state: State):\n",
-    "    model.invoke(...)\n",
-    "    ...\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(node_a)\n",
-    "    .add_node(node_b)\n",
-    "    ...\n",
-    "    .compile()\n",
-    "    \n",
-    "for msg, metadata in graph.stream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\"\n",
-    "):\n",
-    "    # stream from 'node_a'\n",
-    "    # highlight-next-line\n",
-    "    if metadata[\"langgraph_node\"] == \"node_a\":\n",
-    "        print(msg)\n",
-    "```\n",
-    "\n",
-    "!!! note \"Streaming from a specific LLM invocation\"\n",
-    "\n",
-    "    If you need to instead filter streamed LLM tokens to a specific LLM invocation, check out [this guide](../streaming-tokens#filter-to-specific-llm-invocation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcff85bd-8a5d-409e-93d4-e9242b5e976d",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First we need to install the packages required"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "05157237-783c-49de-9f29-7dca3c285647",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "efd22cd2-3152-433b-ad50-65be8ace61d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0ce8c26-f38d-4bdb-89ff-b058e7560019",
-   "metadata": {},
-   "source": [
-    "## Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "419a3c71-7bf6-4656-99b8-b5d61f3f4bf1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import TypedDict\n",
-    "from langgraph.graph import START, StateGraph, MessagesState\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    topic: str\n",
-    "    joke: str\n",
-    "    poem: str\n",
-    "\n",
-    "\n",
-    "def write_joke(state: State):\n",
-    "    topic = state[\"topic\"]\n",
-    "    joke_response = model.invoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a joke about {topic}\"}]\n",
-    "    )\n",
-    "    return {\"joke\": joke_response.content}\n",
-    "\n",
-    "\n",
-    "def write_poem(state: State):\n",
-    "    topic = state[\"topic\"]\n",
-    "    poem_response = model.invoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a short poem about {topic}\"}]\n",
-    "    )\n",
-    "    return {\"poem\": poem_response.content}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(write_joke)\n",
-    "    .add_node(write_poem)\n",
-    "    # write both the joke and the poem concurrently\n",
-    "    .add_edge(START, \"write_joke\")\n",
-    "    .add_edge(START, \"write_poem\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fed84d5e-ba10-4324-a664-dca263951a33",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In| shadows| soft|,| they| quietly| creep|,|  \n",
-      "|Wh|isk|ered| wonders|,| in| dreams| they| leap|.|  \n",
-      "|With| eyes| like| lantern|s|,| bright| and| wide|,|  \n",
-      "|Myst|eries| linger| where| they| reside|.|  \n",
-      "\n",
-      "|P|aws| that| pat|ter| on| silent| floors|,|  \n",
-      "|Cur|led| in| sun|be|ams|,| they| seek| out| more|.|  \n",
-      "|A| flick| of| a| tail|,| a| leap|,| a| p|ounce|,|  \n",
-      "|In| their| playful| world|,| we| can't| help| but| bounce|.|  \n",
-      "\n",
-      "|Guard|ians| of| secrets|,| with| gentle| grace|,|  \n",
-      "|Each| little| me|ow|,| a| warm| embrace|.|  \n",
-      "|Oh|,| the| joy| that| they| bring|,| so| pure| and| true|,|  \n",
-      "|In| the| heart| of| a| cat|,| there's| magic| anew|.|  |"
-     ]
-    }
-   ],
-   "source": [
-    "for msg, metadata in graph.stream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    # highlight-next-line\n",
-    "    if msg.content and metadata[\"langgraph_node\"] == \"write_poem\":\n",
-    "        print(msg.content, end=\"|\", flush=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/docs/how-tos/streaming-specific-nodes.md b/docs/docs/how-tos/streaming-specific-nodes.md
deleted file mode 100644
index 57ba5d7276..0000000000
--- a/docs/docs/how-tos/streaming-specific-nodes.md
+++ /dev/null
@@ -1,2 +0,0 @@
-WARNING: DO NOT MODIFY/DELETE
-This is a dummy file needed for mkdocs-redirects, as it is expecting redirects to be markdown files
diff --git a/docs/docs/how-tos/streaming-subgraphs.ipynb b/docs/docs/how-tos/streaming-subgraphs.ipynb
index d21fc4ccaf..4bf593ffee 100644
--- a/docs/docs/how-tos/streaming-subgraphs.ipynb
+++ b/docs/docs/how-tos/streaming-subgraphs.ipynb
@@ -6,38 +6,39 @@
    "source": [
     "# How to stream from subgraphs\n",
     "\n",
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Subgraphs](../../concepts/low_level/#subgraphs)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "\n",
-    "If you have created a graph with [subgraphs](../subgraph), you may wish to stream outputs from those subgraphs. To do so, you can specify `subgraphs=True` in parent graph's `.stream()` method:\n",
-    "\n",
-    "\n",
-    "```python\n",
-    "for chunk in parent_graph.stream(\n",
-    "    {\"foo\": \"foo\"},\n",
-    "    # highlight-next-line\n",
-    "    subgraphs=True\n",
-    "):\n",
-    "    print(chunk)\n",
-    "```\n",
+    "If you have created a graph with subgraphs you may wish to stream things occurring inside those subgraphs (or you may not!). This guide will walk through how you can control the information that is streamed back from subgraphs.\n",
     "\n",
     "## Setup\n",
     "\n",
-    "First let's install the required packages"
+    "First let's install the required packages and set our API keys"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langgraph"
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
    ]
   },
   {
@@ -56,129 +57,307 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example"
+    "## Define subgraphs\n",
+    "\n",
+    "We are going to use the same subgraph from [this how-to](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, Annotated\n",
+    "from typing_extensions import TypedDict\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import StateGraph, START, END\n",
+    "\n",
+    "\n",
+    "# The structure of the logs\n",
+    "class Logs(TypedDict):\n",
+    "    id: str\n",
+    "    question: str\n",
+    "    answer: str\n",
+    "    grade: Optional[int]\n",
+    "    feedback: Optional[str]\n",
+    "\n",
+    "\n",
+    "# Define custom reducer (see more on this in the \"Custom reducer\" section below)\n",
+    "def add_logs(left: list[Logs], right: list[Logs]) -> list[Logs]:\n",
+    "    if not left:\n",
+    "        left = []\n",
+    "\n",
+    "    if not right:\n",
+    "        right = []\n",
+    "\n",
+    "    logs = left.copy()\n",
+    "    left_id_to_idx = {log[\"id\"]: idx for idx, log in enumerate(logs)}\n",
+    "    # update if the new logs are already in the state, otherwise append\n",
+    "    for log in right:\n",
+    "        idx = left_id_to_idx.get(log[\"id\"])\n",
+    "        if idx is not None:\n",
+    "            logs[idx] = log\n",
+    "        else:\n",
+    "            logs.append(log)\n",
+    "    return logs\n",
+    "\n",
+    "\n",
+    "# Failure Analysis Subgraph\n",
+    "class FailureAnalysisState(TypedDict):\n",
+    "    # keys shared with the parent graph (EntryGraphState)\n",
+    "    logs: Annotated[list[Logs], add_logs]\n",
+    "    failure_report: str\n",
+    "    # subgraph key\n",
+    "    failures: list[Logs]\n",
+    "\n",
+    "\n",
+    "def get_failures(state: FailureAnalysisState):\n",
+    "    failures = [log for log in state[\"logs\"] if log[\"grade\"] == 0]\n",
+    "    return {\"failures\": failures}\n",
+    "\n",
+    "\n",
+    "def generate_summary(state: FailureAnalysisState):\n",
+    "    failures = state[\"failures\"]\n",
+    "    # NOTE: you can implement custom summarization logic here\n",
+    "    failure_ids = [log[\"id\"] for log in failures]\n",
+    "    fa_summary = f\"Poor quality of retrieval for document IDs: {', '.join(failure_ids)}\"\n",
+    "    return {\"failure_report\": fa_summary}\n",
+    "\n",
+    "\n",
+    "fa_builder = StateGraph(FailureAnalysisState)\n",
+    "fa_builder.add_node(\"get_failures\", get_failures)\n",
+    "fa_builder.add_node(\"generate_summary\", generate_summary)\n",
+    "fa_builder.add_edge(START, \"get_failures\")\n",
+    "fa_builder.add_edge(\"get_failures\", \"generate_summary\")\n",
+    "fa_builder.add_edge(\"generate_summary\", END)\n",
+    "\n",
+    "\n",
+    "# Summarization subgraph\n",
+    "class QuestionSummarizationState(TypedDict):\n",
+    "    # keys that are shared with the parent graph (EntryGraphState)\n",
+    "    summary_report: str\n",
+    "    logs: Annotated[list[Logs], add_logs]\n",
+    "    # subgraph keys\n",
+    "    summary: str\n",
+    "\n",
+    "\n",
+    "def generate_summary(state: QuestionSummarizationState):\n",
+    "    docs = state[\"logs\"]\n",
+    "    # NOTE: you can implement custom summarization logic here\n",
+    "    summary = \"Questions focused on usage of ChatOllama and Chroma vector store.\"\n",
+    "    return {\"summary\": summary}\n",
+    "\n",
+    "\n",
+    "def send_to_slack(state: QuestionSummarizationState):\n",
+    "    summary = state[\"summary\"]\n",
+    "    # NOTE: you can implement custom logic here, for example sending the summary generated in the previous step to Slack\n",
+    "    return {\"summary_report\": summary}\n",
+    "\n",
+    "\n",
+    "qs_builder = StateGraph(QuestionSummarizationState)\n",
+    "qs_builder.add_node(\"generate_summary\", generate_summary)\n",
+    "qs_builder.add_node(\"send_to_slack\", send_to_slack)\n",
+    "qs_builder.add_edge(START, \"generate_summary\")\n",
+    "qs_builder.add_edge(\"generate_summary\", \"send_to_slack\")\n",
+    "qs_builder.add_edge(\"send_to_slack\", END)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define a simple example:"
+    "## Define parent graph"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "from langgraph.graph import START, StateGraph\n",
-    "from typing import TypedDict\n",
+    "# Entry Graph\n",
+    "class EntryGraphState(TypedDict):\n",
+    "    raw_logs: Annotated[list[Logs], add_logs]\n",
+    "    logs: Annotated[list[Logs], add_logs]  # This will be used in subgraphs\n",
+    "    failure_report: str  # This will be generated in the FA subgraph\n",
+    "    summary_report: str  # This will be generated in the QS subgraph\n",
     "\n",
     "\n",
-    "# Define subgraph\n",
-    "class SubgraphState(TypedDict):\n",
-    "    foo: str  # note that this key is shared with the parent graph state\n",
-    "    bar: str\n",
+    "def select_logs(state):\n",
+    "    return {\"logs\": [log for log in state[\"raw_logs\"] if \"grade\" in log]}\n",
     "\n",
     "\n",
-    "def subgraph_node_1(state: SubgraphState):\n",
-    "    return {\"bar\": \"bar\"}\n",
+    "entry_builder = StateGraph(EntryGraphState)\n",
+    "entry_builder.add_node(\"select_logs\", select_logs)\n",
+    "entry_builder.add_node(\"question_summarization\", qs_builder.compile())\n",
+    "entry_builder.add_node(\"failure_analysis\", fa_builder.compile())\n",
     "\n",
+    "entry_builder.add_edge(START, \"select_logs\")\n",
+    "entry_builder.add_edge(\"select_logs\", \"failure_analysis\")\n",
+    "entry_builder.add_edge(\"select_logs\", \"question_summarization\")\n",
+    "entry_builder.add_edge(\"failure_analysis\", END)\n",
+    "entry_builder.add_edge(\"question_summarization\", END)\n",
     "\n",
-    "def subgraph_node_2(state: SubgraphState):\n",
-    "    return {\"foo\": state[\"foo\"] + state[\"bar\"]}\n",
+    "graph = entry_builder.compile()\n",
     "\n",
+    "from IPython.display import Image, display\n",
     "\n",
-    "subgraph_builder = StateGraph(SubgraphState)\n",
-    "subgraph_builder.add_node(subgraph_node_1)\n",
-    "subgraph_builder.add_node(subgraph_node_2)\n",
-    "subgraph_builder.add_edge(START, \"subgraph_node_1\")\n",
-    "subgraph_builder.add_edge(\"subgraph_node_1\", \"subgraph_node_2\")\n",
-    "subgraph = subgraph_builder.compile()\n",
-    "\n",
-    "\n",
-    "# Define parent graph\n",
-    "class ParentState(TypedDict):\n",
-    "    foo: str\n",
+    "# Setting xray to 1 will show the internal structure of the nested graph\n",
+    "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream\n",
     "\n",
+    "Now let's see how we can stream from our graph!\n",
     "\n",
-    "def node_1(state: ParentState):\n",
-    "    return {\"foo\": \"hi! \" + state[\"foo\"]}\n",
+    "### Define input\n",
     "\n",
+    "First, let's define the input we will use for the rest of the notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dummy logs\n",
+    "dummy_logs = [\n",
+    "    Logs(\n",
+    "        id=\"1\",\n",
+    "        question=\"How can I import ChatOllama?\",\n",
+    "        grade=1,\n",
+    "        answer=\"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\",\n",
+    "    ),\n",
+    "    Logs(\n",
+    "        id=\"2\",\n",
+    "        question=\"How can I use Chroma vector store?\",\n",
+    "        answer=\"To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).\",\n",
+    "        grade=0,\n",
+    "        feedback=\"The retrieved documents discuss vector stores in general, but not Chroma specifically\",\n",
+    "    ),\n",
+    "    Logs(\n",
+    "        id=\"3\",\n",
+    "        question=\"How do I create react agent in langgraph?\",\n",
+    "        answer=\"from langgraph.prebuilt import create_react_agent\",\n",
+    "    ),\n",
+    "]\n",
     "\n",
-    "builder = StateGraph(ParentState)\n",
-    "builder.add_node(\"node_1\", node_1)\n",
-    "builder.add_node(\"node_2\", subgraph)\n",
-    "builder.add_edge(START, \"node_1\")\n",
-    "builder.add_edge(\"node_1\", \"node_2\")\n",
-    "graph = builder.compile()"
+    "input = {\"raw_logs\": dummy_logs}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's now stream the outputs from the graph:"
+    "### Stream normally\n",
+    "\n",
+    "First let us examine the output of streaming normally:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'node_1': {'foo': 'hi! foo'}}\n",
-      "{'node_2': {'foo': 'hi! foobar'}}\n"
+      "---------- Update from node select_logs ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}]}\n",
+      "---------- Update from node failure_analysis ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}], 'failure_report': 'Poor quality of retrieval for document IDs: 2'}\n",
+      "---------- Update from node question_summarization ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}], 'summary_report': 'Questions focused on usage of ChatOllama and Chroma vector store.'}\n"
      ]
     }
    ],
    "source": [
-    "for chunk in graph.stream({\"foo\": \"foo\"}, stream_mode=\"updates\"):\n",
-    "    print(chunk)"
+    "for chunk in graph.stream(input, stream_mode=\"updates\"):\n",
+    "    node_name = list(chunk.keys())[0]\n",
+    "    print(f\"---------- Update from node {node_name} ---------\")\n",
+    "    print(chunk[node_name])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can see that we're only emitting the updates from the parent graph nodes (`node_1` and `node_2`). To emit the updates from the _subgraph_ nodes you can specify `subgraphs=True`:"
+    "As you can see there are only 3 updates made to our overall graph state. The first one is by the `select_logs` node, and then we receive one update from each subgraph (note if you don't want to see the `log` update from each subgraph that you can set the [output schema](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/) to exclude it). What we do not see however, is the updates occurring *inside* each subgraph. The next section will explain how to do that.\n",
+    "\n",
+    "### Stream subgraph \n",
+    "\n",
+    "To show the updates occurring inside of each subgraph, we can simply set `subgraphs=True` to the streaming call:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "((), {'node_1': {'foo': 'hi! foo'}})\n",
-      "(('node_2:b692b345-cfb3-b709-628c-f0ba9608f72e',), {'subgraph_node_1': {'bar': 'bar'}})\n",
-      "(('node_2:b692b345-cfb3-b709-628c-f0ba9608f72e',), {'subgraph_node_2': {'foo': 'hi! foobar'}})\n",
-      "((), {'node_2': {'foo': 'hi! foobar'}})\n"
+      "---------- Update from node select_logs in parent graph ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}]}\n",
+      "---------- Update from node get_failures in failure_analysis subgraph ---------\n",
+      "{'failures': [{'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}]}\n",
+      "---------- Update from node generate_summary in failure_analysis subgraph ---------\n",
+      "{'failure_report': 'Poor quality of retrieval for document IDs: 2'}\n",
+      "---------- Update from node failure_analysis in parent graph ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}], 'failure_report': 'Poor quality of retrieval for document IDs: 2'}\n",
+      "---------- Update from node generate_summary in question_summarization subgraph ---------\n",
+      "{'summary': 'Questions focused on usage of ChatOllama and Chroma vector store.'}\n",
+      "---------- Update from node send_to_slack in question_summarization subgraph ---------\n",
+      "{'summary_report': 'Questions focused on usage of ChatOllama and Chroma vector store.'}\n",
+      "---------- Update from node question_summarization in parent graph ---------\n",
+      "{'logs': [{'id': '1', 'question': 'How can I import ChatOllama?', 'grade': 1, 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"}, {'id': '2', 'question': 'How can I use Chroma vector store?', 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).', 'grade': 0, 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}], 'summary_report': 'Questions focused on usage of ChatOllama and Chroma vector store.'}\n"
      ]
     }
    ],
    "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"foo\": \"foo\"},\n",
-    "    stream_mode=\"updates\",\n",
-    "    # highlight-next-line\n",
-    "    subgraphs=True,\n",
-    "):\n",
-    "    print(chunk)"
+    "# Format the namespace slightly nicer\n",
+    "def format_namespace(namespace):\n",
+    "    return (\n",
+    "        namespace[-1].split(\":\")[0] + \" subgraph\"\n",
+    "        if len(namespace) > 0\n",
+    "        else \"parent graph\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "for namespace, chunk in graph.stream(input, stream_mode=\"updates\", subgraphs=True):\n",
+    "    node_name = list(chunk.keys())[0]\n",
+    "    print(\n",
+    "        f\"---------- Update from node {node_name} in {format_namespace(namespace)} ---------\"\n",
+    "    )\n",
+    "    print(chunk[node_name])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Voila! The streamed outputs now contain updates from both the parent graph and the subgraph. **Note** that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from."
+    "The first thing you will notice as different is that we are no longer just receiving chunks, but we also receive namespaces which tell us what subgraph we are currently inside of.\n",
+    "\n",
+    "If you look carefully at the logs you can see we are now receiving the updates made by nodes inside of each subgraph, for instance we now see updates to the `summary_report` state channel from the `get_failure` node which lives in the `failure_analysis` subgraph. When we didn't set `subgraphs=True` all we saw was the overall update made by the subgraph `failure_analysis`."
    ]
   }
  ],
@@ -198,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb b/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb
new file mode 100644
index 0000000000..697def4e78
--- /dev/null
+++ b/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b23ced4e-dc29-43be-9f94-0c36bb181b8a",
+   "metadata": {},
+   "source": [
+    "# How to stream LLM tokens (without LangChain LLMs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7044eeb8-4074-4f9c-8a62-962488744557",
+   "metadata": {},
+   "source": [
+    "In this example we will stream tokens from the language model powering an agent. We'll be using OpenAI client library directly, without using LangChain chat models. We will also use a ReAct agent as an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a37f60af-43ea-4aa6-847a-df8cc47065f5",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5bc618",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
+   "metadata": {},
+   "source": [
+    "## Define model, tools and graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba684f1-d46b-42e4-95cf-9685209a5992",
+   "metadata": {},
+   "source": [
+    "### Define a node that will call OpenAI API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d59234f9-173e-469d-a725-c13e0979663e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import AsyncOpenAI\n",
+    "from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
+    "from langchain_core.messages import AIMessageChunk\n",
+    "from langchain_core.runnables.config import (\n",
+    "    ensure_config,\n",
+    "    get_callback_manager_for_config,\n",
+    ")\n",
+    "\n",
+    "openai_client = AsyncOpenAI()\n",
+    "# define tool schema for openai tool calling\n",
+    "\n",
+    "tool = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_items\",\n",
+    "        \"description\": \"Use this tool to look up which items are in the given place.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"place\": {\"type\": \"string\"}},\n",
+    "            \"required\": [\"place\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "async def call_model(state, config=None):\n",
+    "    config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
+    "    callback_manager = get_callback_manager_for_config(config)\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
+    "    response = await openai_client.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\", tools=[tool], stream=True\n",
+    "    )\n",
+    "\n",
+    "    response_content = \"\"\n",
+    "    role = None\n",
+    "\n",
+    "    tool_call_id = None\n",
+    "    tool_call_function_name = None\n",
+    "    tool_call_function_arguments = \"\"\n",
+    "    async for chunk in response:\n",
+    "        delta = chunk.choices[0].delta\n",
+    "        if delta.role is not None:\n",
+    "            role = delta.role\n",
+    "\n",
+    "        if delta.content:\n",
+    "            response_content += delta.content\n",
+    "            # note: we're wrapping the response in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
+    "            chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=delta.content,\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(delta.content, chunk=chunk)\n",
+    "\n",
+    "        if delta.tool_calls:\n",
+    "            # note: for simplicity we're only handling a single tool call here\n",
+    "            if delta.tool_calls[0].function.name is not None:\n",
+    "                tool_call_function_name = delta.tool_calls[0].function.name\n",
+    "                tool_call_id = delta.tool_calls[0].id\n",
+    "\n",
+    "            # note: we're wrapping the tools calls in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
+    "            tool_call_chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=\"\",\n",
+    "                    additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]},\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
+    "            tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
+    "\n",
+    "    if tool_call_function_name is not None:\n",
+    "        tool_calls = [\n",
+    "            {\n",
+    "                \"id\": tool_call_id,\n",
+    "                \"function\": {\n",
+    "                    \"name\": tool_call_function_name,\n",
+    "                    \"arguments\": tool_call_function_arguments,\n",
+    "                },\n",
+    "                \"type\": \"function\",\n",
+    "            }\n",
+    "        ]\n",
+    "    else:\n",
+    "        tool_calls = None\n",
+    "\n",
+    "    response_message = {\n",
+    "        \"role\": role,\n",
+    "        \"content\": response_content,\n",
+    "        \"tool_calls\": tool_calls,\n",
+    "    }\n",
+    "    return {\"messages\": [response_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
+   "metadata": {},
+   "source": [
+    "### Define our tools and a tool-calling node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b756ea32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "\n",
+    "async def get_items(place: str) -> str:\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "    if \"bed\" in place:  # For under the bed\n",
+    "        return \"socks, shoes and dust bunnies\"\n",
+    "    if \"shelf\" in place:  # For 'shelf'\n",
+    "        return \"books, penciles and pictures\"\n",
+    "    else:  # if the agent decides to ask about a different place\n",
+    "        return \"cat snacks\"\n",
+    "\n",
+    "\n",
+    "# define mapping to look up functions when running tools\n",
+    "function_name_to_function = {\"get_items\": get_items}\n",
+    "\n",
+    "\n",
+    "async def call_tools(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    tool_call = messages[-1][\"tool_calls\"][0]\n",
+    "    function_name = tool_call[\"function\"][\"name\"]\n",
+    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
+    "    arguments = json.loads(function_arguments)\n",
+    "\n",
+    "    function_response = await function_name_to_function[function_name](**arguments)\n",
+    "    tool_message = {\n",
+    "        \"tool_call_id\": tool_call[\"id\"],\n",
+    "        \"role\": \"tool\",\n",
+    "        \"name\": function_name,\n",
+    "        \"content\": function_response,\n",
+    "    }\n",
+    "    return {\"messages\": [tool_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6685898c-9a1c-4803-a492-bd70574ebe38",
+   "metadata": {},
+   "source": [
+    "### Define our graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Literal\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph import StateGraph, END, START\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, operator.add]\n",
+    "\n",
+    "\n",
+    "def should_continue(state) -> Literal[\"tools\", END]:\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    if last_message[\"tool_calls\"]:\n",
+    "        return \"tools\"\n",
+    "    return END\n",
+    "\n",
+    "\n",
+    "workflow = StateGraph(State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)  # i.e. our \"agent\"\n",
+    "workflow.add_node(\"tools\", call_tools)\n",
+    "workflow.add_conditional_edges(\"model\", should_continue)\n",
+    "workflow.add_edge(\"tools\", \"model\")\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
+   "metadata": {},
+   "source": [
+    "## Stream tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d6ed3df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': ''}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bed'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "In| the| bedroom|,| you| have| socks|,| shoes|,| and| some| dust| b|unn|ies|.|"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessageChunk\n",
+    "\n",
+    "first = True\n",
+    "async for msg, metadata in graph.astream(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]},\n",
+    "    stream_mode=\"messages\",\n",
+    "):\n",
+    "    if msg.content:\n",
+    "        print(msg.content, end=\"|\", flush=True)\n",
+    "\n",
+    "    if isinstance(msg, AIMessageChunk):\n",
+    "        if first:\n",
+    "            gathered = msg\n",
+    "            first = False\n",
+    "        else:\n",
+    "            gathered = gathered + msg\n",
+    "\n",
+    "        if msg.tool_call_chunks:\n",
+    "            print(gathered.tool_calls)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how-tos/streaming-tokens.ipynb b/docs/docs/how-tos/streaming-tokens.ipynb
index 57812a4600..edde2d3a50 100644
--- a/docs/docs/how-tos/streaming-tokens.ipynb
+++ b/docs/docs/how-tos/streaming-tokens.ipynb
@@ -7,47 +7,24 @@
    "source": [
     "# How to stream LLM tokens from your graph\n",
     "\n",
-    "!!! info \"Prerequisites\"\n",
+    "In this example we will stream tokens from the language model powering an agent. We will use a ReAct agent as an example.\n",
     "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
+    "This how-to guide closely follows the others in this directory, so we will call out differences with the **STREAMING** tag below (if you just want to search for those).\n",
     "\n",
-    "When building LLM applications with LangGraph, you might want to stream individual LLM tokens from the LLM calls inside LangGraph nodes. You can do so via `graph.stream(..., stream_mode=\"messages\")`:\n",
-    "\n",
-    "```python\n",
-    "from langgraph.graph import StateGraph\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI()\n",
-    "def call_model(state: State):\n",
-    "    model.invoke(...)\n",
-    "    ...\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(call_model)\n",
-    "    ...\n",
-    "    .compile()\n",
-    "    \n",
-    "for msg, metadata in graph.stream(inputs, stream_mode=\"messages\"):\n",
-    "    print(msg)\n",
-    "```\n",
-    "\n",
-    "The streamed outputs will be tuples of `(message chunk, metadata)`:\n",
-    "\n",
-    "* message chunk is the token streamed by the LLM\n",
-    "* metadata is a dictionary with information about the graph node where the LLM was called as well as the LLM invocation metadata\n",
-    "\n",
-    "!!! note \"Using without LangChain\"\n",
-    "\n",
-    "    If you need to stream LLM tokens **without using LangChain**, you can use [`stream_mode=\"custom\"`](../streaming/#custom) to stream the outputs from LLM provider clients directly. Check out the [example below](#example-without-langchain) to learn more.\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Note</p>\n",
+    "    <p>\n",
+    "        In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the <code>create_react_agent(model, tools=tool)</code> (<a href=\"https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent\">API doc</a>) constructor. This may be more appropriate if you are used to LangChain’s <a href=\"https://python.langchain.com/docs/how_to/agent_executor/#concepts\">AgentExecutor</a> class.\n",
+    "    </p>\n",
+    "</div>\n",
     "\n",
-    "!!! warning \"Async in Python < 3.11\"\n",
-    "    \n",
-    "    When using Python < 3.11 with async code, please ensure you manually pass the `RunnableConfig` through to the chat model when invoking it like so: `model.ainvoke(..., config)`.\n",
-    "    The stream method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via [contextvars](https://docs.python.org/3/library/contextvars.html); prior to 3.11, [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) lacked proper `contextvar` support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the `call_model` function below."
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">Note on Python < 3.11</p>\n",
+    "    <p>\n",
+    "        When using python 3.8, 3.9, or 3.10, please ensure you manually pass the RunnableConfig through to the llm when invoking it like so: <code>llm.ainvoke(..., config)</code>.\n",
+    "        The stream method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via <a href=\"https://docs.python.org/3/library/contextvars.html\">contextvar</a>'s; prior to 3.11, <a href=\"https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task\">asyncio's tasks</a> lacked proper contextvar support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the <code>call_model</code> method below.\n",
+    "    </p>\n",
+    "</div>"
    ]
   },
   {
@@ -62,13 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 26,
    "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
+    "%pip install --quiet -U langgraph langchain_openai langsmith"
    ]
   },
   {
@@ -81,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a372be6f",
    "metadata": {},
    "outputs": [],
@@ -113,389 +90,342 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
+   "id": "cd420984",
    "metadata": {},
    "source": [
-    "!!! note Manual Callback Propagation\n",
+    "## Set up the state\n",
     "\n",
-    "    Note that in `call_model(state: State, config: RunnableConfig):` below, we a) accept the [`RunnableConfig`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig) in the node function and b) pass it in as the second arg for `model.ainvoke(..., config)`. This is optional for python >= 3.11."
+    "The main type of graph in `langgraph` is the [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph).\n",
+    "This graph is parameterized by a `State` object that it passes around to each node.\n",
+    "Each node then returns operations the graph uses to `update` that state.\n",
+    "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n",
+    "Whether to set or add is denoted by annotating the `State` object you use to construct the graph.\n",
+    "\n",
+    "For this example, the state we will track will just be a list of messages.\n",
+    "We want each node to just add messages to that list.\n",
+    "Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is \"append-only\"."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "ad2c85b6-28f8-4c7f-843a-c05cb7fd7187",
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "17ef4967",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Example"
+    "from typing import Annotated\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph.message import add_messages\n",
+    "\n",
+    "# Add messages essentially does this with more\n",
+    "# robust handling\n",
+    "# def add_messages(left: list, right: list):\n",
+    "#     return left + right\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, add_messages]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "afcbdd41-dff8-4118-8901-a619f91f3feb",
+   "id": "81ed4e9c",
    "metadata": {},
    "source": [
-    "Below we demonstrate an example with two LLM calls in a single node."
+    "## Set up the tools\n",
+    "\n",
+    "We will first define the tools we want to use.\n",
+    "For this simple example, we will use create a placeholder search engine.\n",
+    "It is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/how_to/custom_tools) on how to do that.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "7cc5905f-df82-4b31-84ad-2054f463aee8",
+   "execution_count": 2,
+   "id": "9a8bc61e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import TypedDict\n",
-    "from langgraph.graph import START, StateGraph, MessagesState\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_core.tools import tool\n",
     "\n",
     "\n",
-    "# Note: we're adding the tags here to be able to filter the model outputs down the line\n",
-    "joke_model = ChatOpenAI(model=\"gpt-4o-mini\", tags=[\"joke\"])\n",
-    "poem_model = ChatOpenAI(model=\"gpt-4o-mini\", tags=[\"poem\"])\n",
+    "@tool\n",
+    "def search(query: str):\n",
+    "    \"\"\"Call to surf the web.\"\"\"\n",
+    "    # This is a placeholder, but don't tell the LLM that...\n",
+    "    return [\"Cloudy with a chance of hail.\"]\n",
     "\n",
     "\n",
-    "class State(TypedDict):\n",
-    "    topic: str\n",
-    "    joke: str\n",
-    "    poem: str\n",
-    "\n",
-    "\n",
-    "# highlight-next-line\n",
-    "async def call_model(state, config):\n",
-    "    topic = state[\"topic\"]\n",
-    "    print(\"Writing joke...\")\n",
-    "    # Note: Passing the config through explicitly is required for python < 3.11\n",
-    "    # Since context var support wasn't added before then: https://docs.python.org/3/library/asyncio-task.html#creating-tasks\n",
-    "    joke_response = await joke_model.ainvoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a joke about {topic}\"}],\n",
-    "        # highlight-next-line\n",
-    "        config,\n",
-    "    )\n",
-    "    print(\"\\n\\nWriting poem...\")\n",
-    "    poem_response = await poem_model.ainvoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a short poem about {topic}\"}],\n",
-    "        # highlight-next-line\n",
-    "        config,\n",
-    "    )\n",
-    "    return {\"joke\": joke_response.content, \"poem\": poem_response.content}\n",
-    "\n",
-    "\n",
-    "graph = StateGraph(State).add_node(call_model).add_edge(START, \"call_model\").compile()"
+    "tools = [search]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "96050fba",
+   "cell_type": "markdown",
+   "id": "b0aa12b9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing joke...\n",
-      "Why| was| the| cat| sitting| on| the| computer|?\n",
-      "\n",
-      "|Because| it| wanted| to| keep| an| eye| on| the| mouse|!|\n",
-      "\n",
-      "Writing poem...\n",
-      "In| sun|lit| patches|,| sleek| and| sly|,|  \n",
-      "|Wh|isk|ers| twitch| as| shadows| fly|.|  \n",
-      "|With| velvet| paws| and| eyes| so| bright|,|  \n",
-      "|They| dance| through| dreams|,| both| day| and| night|.|  \n",
-      "\n",
-      "|A| playful| p|ounce|,| a| gentle| p|urr|,|  \n",
-      "|In| every| leap|,| a| soft| allure|.|  \n",
-      "|Cur|led| in| warmth|,| a| silent| grace|,|  \n",
-      "|Each| furry| friend|,| a| warm| embrace|.|  \n",
-      "\n",
-      "|Myst|ery| wrapped| in| fur| and| charm|,|  \n",
-      "|A| soothing| presence|,| a| gentle| balm|.|  \n",
-      "|In| their| gaze|,| the| world| slows| down|,|  \n",
-      "|For| in| their| realm|,| we're| all| ren|own|.|"
-     ]
-    }
-   ],
    "source": [
-    "async for msg, metadata in graph.astream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    if msg.content:\n",
-    "        print(msg.content, end=\"|\", flush=True)"
+    "We can now wrap these tools in a simple [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode).\n",
+    "This is  a simple class that takes in a list of messages containing an [AIMessages with tool_calls](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls), runs the tools, and returns the output as [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html#langchain_core.messages.tool.ToolMessage)s.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "bcdf561d-a5cd-4197-9c65-9ab8af85941f",
+   "execution_count": 3,
+   "id": "4d6ac180",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'langgraph_step': 1,\n",
-       " 'langgraph_node': 'call_model',\n",
-       " 'langgraph_triggers': ['start:call_model'],\n",
-       " 'langgraph_path': ('__pregel_pull', 'call_model'),\n",
-       " 'langgraph_checkpoint_ns': 'call_model:6ddc5f0f-1dd0-325d-3014-f949286ce595',\n",
-       " 'checkpoint_ns': 'call_model:6ddc5f0f-1dd0-325d-3014-f949286ce595',\n",
-       " 'ls_provider': 'openai',\n",
-       " 'ls_model_name': 'gpt-4o-mini',\n",
-       " 'ls_model_type': 'chat',\n",
-       " 'ls_temperature': 0.7,\n",
-       " 'tags': ['poem']}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "metadata"
+    "from langgraph.prebuilt import ToolNode\n",
+    "\n",
+    "tool_node = ToolNode(tools)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7db91f8d-3e17-47f4-b45e-c72bbbcbb5ed",
+   "id": "4f13e0a5",
    "metadata": {},
    "source": [
-    "### Filter to specific LLM invocation"
+    "## Set up the model\n",
+    "\n",
+    "Now we need to load the chat model we want to use.\n",
+    "This should satisfy two criteria:\n",
+    "\n",
+    "1. It should work with messages, since our state is primarily a list of messages (chat history).\n",
+    "2. It should work with tool calling, since we are using a prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)\n",
+    "\n",
+    "**Note:** these model requirements are not requirements for using LangGraph - they are just requirements for this particular example.\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "a3a72acd-98cc-43f6-9dbb-0e97d03d211b",
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "42c0af37",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "You can see that we're streaming tokens from all of the LLM invocations. Let's now filter the streamed tokens to include only a specific LLM invocation. We can use the streamed metadata and filter events using the tags we've added to the LLMs previously:"
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c9e0df34-6020-445e-8ecd-ca4239e9b22b",
+   "cell_type": "markdown",
+   "id": "8a592001",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing joke...\n",
-      "Why| was| the| cat| sitting| on| the| computer|?\n",
-      "\n",
-      "|Because| it| wanted| to| keep| an| eye| on| the| mouse|!|\n",
-      "\n",
-      "Writing poem...\n"
-     ]
-    }
-   ],
    "source": [
-    "async for msg, metadata in graph.astream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    # highlight-next-line\n",
-    "    if msg.content and \"joke\" in metadata.get(\"tags\", []):\n",
-    "        print(msg.content, end=\"|\", flush=True)"
+    "\n",
+    "After we've done this, we should make sure the model knows that it has these tools available to call.\n",
+    "We can do this by converting the LangChain tools into the format for function calling, and then bind them to the model class.\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "be8fd3d7-a227-41ad-bd08-7ef994ab291b",
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2bbdd3bc",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Example without LangChain"
+    "model = model.bind_tools(tools)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "699b3bab-9da7-4f2a-8006-93289350d89d",
+   "cell_type": "markdown",
+   "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from openai import AsyncOpenAI\n",
+    "## Define the nodes\n",
     "\n",
-    "openai_client = AsyncOpenAI()\n",
-    "model_name = \"gpt-4o-mini\"\n",
+    "We now need to define a few different nodes in our graph.\n",
+    "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel).\n",
+    "There are two main nodes we need for this:\n",
     "\n",
+    "1. The agent: responsible for deciding what (if any) actions to take.\n",
+    "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
     "\n",
-    "async def stream_tokens(model_name: str, messages: list[dict]):\n",
-    "    response = await openai_client.chat.completions.create(\n",
-    "        messages=messages, model=model_name, stream=True\n",
-    "    )\n",
+    "We will also need to define some edges.\n",
+    "Some of these edges may be conditional.\n",
+    "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
+    "The path that is taken is not known until that node is run (the LLM decides).\n",
     "\n",
-    "    role = None\n",
-    "    async for chunk in response:\n",
-    "        delta = chunk.choices[0].delta\n",
+    "1. Conditional Edge: after the agent is called, we should either:\n",
+    "   a. If the agent said to take an action, then the function to invoke tools should be called\n",
+    "   b. If the agent said that it was finished, then it should finish\n",
+    "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
     "\n",
-    "        if delta.role is not None:\n",
-    "            role = delta.role\n",
+    "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n",
     "\n",
-    "        if delta.content:\n",
-    "            yield {\"role\": role, \"content\": delta.content}\n",
+    "**STREAMING**\n",
     "\n",
+    "We define each node as an async function.\n",
     "\n",
-    "# highlight-next-line\n",
-    "async def call_model(state, config, writer):\n",
-    "    topic = state[\"topic\"]\n",
-    "    joke = \"\"\n",
-    "    poem = \"\"\n",
+    "<div class=\"admonition note\">\n",
+    "    <p class=\"admonition-title\">Manual Callback Propagation</p>\n",
+    "    <p>\n",
+    "        Note that in <code>call_model(state: State, config: RunnableConfig):</code> below, we a) accept the <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig\">RunnableConfig</a> in the node and b) pass this in as the second arg for <code>llm.ainvoke(..., config)</code>. This is optional for python 3.11 and later.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "\n",
+    "from langchain_core.runnables import RunnableConfig\n",
     "\n",
-    "    print(\"Writing joke...\")\n",
-    "    async for msg_chunk in stream_tokens(\n",
-    "        model_name, [{\"role\": \"user\", \"content\": f\"Write a joke about {topic}\"}]\n",
-    "    ):\n",
-    "        joke += msg_chunk[\"content\"]\n",
-    "        metadata = {**config[\"metadata\"], \"tags\": [\"joke\"]}\n",
-    "        chunk_to_stream = (msg_chunk, metadata)\n",
-    "        # highlight-next-line\n",
-    "        writer(chunk_to_stream)\n",
+    "from langgraph.graph import END, START, StateGraph\n",
     "\n",
-    "    print(\"\\n\\nWriting poem...\")\n",
-    "    async for msg_chunk in stream_tokens(\n",
-    "        model_name, [{\"role\": \"user\", \"content\": f\"Write a short poem about {topic}\"}]\n",
-    "    ):\n",
-    "        poem += msg_chunk[\"content\"]\n",
-    "        metadata = {**config[\"metadata\"], \"tags\": [\"poem\"]}\n",
-    "        chunk_to_stream = (msg_chunk, metadata)\n",
-    "        # highlight-next-line\n",
-    "        writer(chunk_to_stream)\n",
     "\n",
-    "    return {\"joke\": joke, \"poem\": poem}\n",
+    "# Define the function that determines whether to continue or not\n",
+    "def should_continue(state: State):\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    # If there is no function call, then we finish\n",
+    "    if not last_message.tool_calls:\n",
+    "        return END\n",
+    "    # Otherwise if there is, we continue\n",
+    "    else:\n",
+    "        return \"tools\"\n",
     "\n",
     "\n",
-    "graph = StateGraph(State).add_node(call_model).add_edge(START, \"call_model\").compile()"
+    "# Define the function that calls the model\n",
+    "async def call_model(state: State, config: RunnableConfig):\n",
+    "    messages = state[\"messages\"]\n",
+    "    # Note: Passing the config through explicitly is required for python < 3.11\n",
+    "    # Since context var support wasn't added before then: https://docs.python.org/3/library/asyncio-task.html#creating-tasks\n",
+    "    response = await model.ainvoke(messages, config)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": response}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8af13d73-a0ea-44c0-a92e-28676cd164dd",
+   "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
    "metadata": {},
    "source": [
-    "!!! note \"stream_mode=\"custom\"\"\n",
+    "## Define the graph\n",
     "\n",
-    "    When streaming LLM tokens without LangChain, we recommend using [`stream_mode=\"custom\"`](../streaming/#stream-modecustom). This allows you to explicitly control which data from the LLM provider APIs to include in LangGraph streamed outputs, including any additional metadata."
+    "We can now put it all together and define the graph!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "e977406d-7be6-4c9f-9185-5e5551f848f3",
+   "execution_count": 7,
+   "id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing joke...\n",
-      "Why| was| the| cat| sitting| on| the| computer|?\n",
-      "\n",
-      "|Because| it| wanted| to| keep| an| eye| on| the|\n",
-      "\n",
-      "Writing poem...\n",
-      " mouse|!|In| sun|lit| patches|,| they| stretch| and| y|awn|,|  \n",
-      "|With| whispered| paws| at| the| break| of| dawn|.|  \n",
-      "|Wh|isk|ers| twitch| in| the| morning| light|,|  \n",
-      "|Sil|ken| shadows|,| a| graceful| sight|.|  \n",
-      "\n",
-      "|The| gentle| p|urr|s|,| a| soothing| song|,|  \n",
-      "|In| a| world| of| comfort|,| where| they| belong|.|  \n",
-      "|M|yster|ious| hearts| wrapped| in| soft|est| fur|,|  \n",
-      "|F|eline| whispers| in| every| p|urr|.|  \n",
-      "\n",
-      "|Ch|asing| dreams| on| a| moon|lit| chase|,|  \n",
-      "|With| a| flick| of| a| tail|,| they| glide| with| grace|.|  \n",
-      "|Oh|,| playful| spirits| of| whisk|ered| cheer|,|  \n",
-      "|In| your| quiet| company|,| the| world| feels| near|.|  |"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "async for msg, metadata in graph.astream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    print(msg[\"content\"], end=\"|\", flush=True)"
+    "# Define a new graph\n",
+    "workflow = StateGraph(State)\n",
+    "\n",
+    "# Define the two nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", call_model)\n",
+    "workflow.add_node(\"tools\", tool_node)\n",
+    "\n",
+    "# Set the entrypoint as `agent`\n",
+    "# This means that this node is the first one called\n",
+    "workflow.add_edge(START, \"agent\")\n",
+    "\n",
+    "# We now add a conditional edge\n",
+    "workflow.add_conditional_edges(\n",
+    "    # First, we define the start node. We use `agent`.\n",
+    "    # This means these are the edges taken after the `agent` node is called.\n",
+    "    \"agent\",\n",
+    "    # Next, we pass in the function that will determine which node is called next.\n",
+    "    should_continue,\n",
+    "    # Next we pass in the path map - all the nodes this edge could go to\n",
+    "    [\"tools\", END],\n",
+    ")\n",
+    "\n",
+    "workflow.add_edge(\"tools\", \"agent\")\n",
+    "\n",
+    "# Finally, we compile it!\n",
+    "# This compiles it into a LangChain Runnable,\n",
+    "# meaning you can use it as you would any other runnable\n",
+    "app = workflow.compile()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "0bdc1635-f424-4a5f-95db-e993bb16adb2",
+   "execution_count": 8,
+   "id": "72785b66",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/jpeg": 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",
       "text/plain": [
-       "{'langgraph_step': 1,\n",
-       " 'langgraph_node': 'call_model',\n",
-       " 'langgraph_triggers': ['start:call_model'],\n",
-       " 'langgraph_path': ('__pregel_pull', 'call_model'),\n",
-       " 'langgraph_checkpoint_ns': 'call_model:3fa3fbe1-39d8-5209-dd77-0da38d4cc1c9',\n",
-       " 'tags': ['poem']}"
+       "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "metadata"
+    "from IPython.display import Image, display\n",
+    "\n",
+    "display(Image(app.get_graph().draw_mermaid_png()))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a3afbbee-fab8-4c7f-ad26-094f8c8f4dd9",
+   "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
    "metadata": {},
    "source": [
-    "To filter to the specific LLM invocation, you can use the streamed metadata:"
+    "## Streaming LLM Tokens\n",
+    "\n",
+    "You can access the LLM tokens as they are produced by each node. \n",
+    "In this case only the \"agent\" node produces LLM tokens.\n",
+    "In order for this to work properly, you must be using an LLM that supports streaming as well as have set it when constructing the LLM (e.g. `ChatOpenAI(model=\"gpt-3.5-turbo-1106\", streaming=True)`)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "fdeee9d9-2625-403a-9253-418a0feeed77",
+   "execution_count": 9,
+   "id": "96050fba",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing joke...\n",
-      "\n",
-      "\n",
-      "Writing poem...\n",
-      "In| shadows| soft|,| they| weave| and| play|,|  \n",
-      "|With| whispered| paws|,| they| greet| the| day|.|  \n",
-      "|Eyes| like| lantern|s|,| bright| and| keen|,|  \n",
-      "|Guard|ians| of| secrets|,| unseen|,| serene|.|  \n",
-      "\n",
-      "|They| twist| and| stretch| in| sun|lit| beams|,|  \n",
-      "|Ch|asing| the| echoes| of| half|-|formed| dreams|.|  \n",
-      "|With| p|urring| songs| that| soothe| the| night|,|  \n",
-      "|F|eline| spirits|,| pure| delight|.|  \n",
-      "\n",
-      "|On| windows|ills|,| they| perch| and| stare|,|  \n",
-      "|Ad|vent|urers| bold| with| a| graceful| flair|.|  \n",
-      "|In| every| leap| and| playful| bound|,|  \n",
-      "|The| magic| of| cats|—|where| love| is| found|.|"
+      "[{'name': 'search', 'args': {}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': ''}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': 'weather'}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': 'weather in'}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': 'weather in San'}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_lfwgOci165GXplBjSDBeD4sE', 'type': 'tool_call'}]\n",
+      "[\"Cloudy with a chance of hail.\"]|The| weather| in| San| Francisco| is| currently| cloudy| with| a| chance| of| hail|.|"
      ]
     }
    ],
    "source": [
-    "async for msg, metadata in graph.astream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    # highlight-next-line\n",
-    "    if \"poem\" in metadata.get(\"tags\", []):\n",
-    "        print(msg[\"content\"], end=\"|\", flush=True)"
+    "from langchain_core.messages import AIMessageChunk, HumanMessage\n",
+    "\n",
+    "inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
+    "first = True\n",
+    "async for msg, metadata in app.astream({\"messages\": inputs}, stream_mode=\"messages\"):\n",
+    "    if msg.content and not isinstance(msg, HumanMessage):\n",
+    "        print(msg.content, end=\"|\", flush=True)\n",
+    "\n",
+    "    if isinstance(msg, AIMessageChunk):\n",
+    "        if first:\n",
+    "            gathered = msg\n",
+    "            first = False\n",
+    "        else:\n",
+    "            gathered = gathered + msg\n",
+    "\n",
+    "        if msg.tool_call_chunks:\n",
+    "            print(gathered.tool_calls)"
    ]
   }
  ],
@@ -515,7 +445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/streaming-tokens.md b/docs/docs/how-tos/streaming-tokens.md
deleted file mode 100644
index 57ba5d7276..0000000000
--- a/docs/docs/how-tos/streaming-tokens.md
+++ /dev/null
@@ -1,2 +0,0 @@
-WARNING: DO NOT MODIFY/DELETE
-This is a dummy file needed for mkdocs-redirects, as it is expecting redirects to be markdown files
diff --git a/docs/docs/how-tos/streaming.ipynb b/docs/docs/how-tos/streaming.ipynb
deleted file mode 100644
index 86fbb79eb3..0000000000
--- a/docs/docs/how-tos/streaming.ipynb
+++ /dev/null
@@ -1,547 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "76c4b04f-0c03-4321-9d40-38d12c59d088",
-   "metadata": {},
-   "source": [
-    "# How to stream"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15403cdb-441d-43af-a29f-fc15abe03dcc",
-   "metadata": {},
-   "source": [
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "\n",
-    "Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.\n",
-    "\n",
-    "LangGraph is built with first class support for streaming. There are several different ways to stream back outputs from a graph run:\n",
-    "\n",
-    "- `\"values\"`: Emit all values in the state after each step.\n",
-    "- `\"updates\"`: Emit only the node names and updates returned by the nodes after each step.\n",
-    "    If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.\n",
-    "- `\"custom\"`: Emit custom data from inside nodes using `StreamWriter`.\n",
-    "- [`\"messages\"`](../streaming-tokens): Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes.\n",
-    "- `\"debug\"`: Emit debug events with as much information as possible for each step.\n",
-    "\n",
-    "You can stream outputs from the graph by using `graph.stream(..., stream_mode=<stream_mode>)` method, e.g.:\n",
-    "\n",
-    "=== \"Sync\"\n",
-    "\n",
-    "    ```python\n",
-    "    for chunk in graph.stream(inputs, stream_mode=\"updates\"):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "=== \"Async\"\n",
-    "\n",
-    "    ```python\n",
-    "    async for chunk in graph.astream(inputs, stream_mode=\"updates\"):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "You can also combine multiple streaming mode by providing a list to `stream_mode` parameter:\n",
-    "\n",
-    "=== \"Sync\"\n",
-    "\n",
-    "    ```python\n",
-    "    for chunk in graph.stream(inputs, stream_mode=[\"updates\", \"custom\"]):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "=== \"Async\"\n",
-    "\n",
-    "    ```python\n",
-    "    async for chunk in graph.astream(inputs, stream_mode=[\"updates\", \"custom\"]):\n",
-    "        print(chunk)\n",
-    "    ```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9723cf76-6fe4-4b52-829f-3f28712ddcb7",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "427f8f66-7404-4c7d-a642-af5053b8b28f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "03310ce6-e21f-4378-93bf-dd273fdb3e9a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_API_KEY:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80399508-bad8-43b7-8ec9-4c06ad1774cc",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
-    "    <p style=\"padding-top: 5px;\">\n",
-    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4adbb2-61e8-4bb7-942d-b4dc27ba71ac",
-   "metadata": {},
-   "source": [
-    "Let's define a simple graph with two nodes:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6d4c513-1006-4179-bba9-d858fc952169",
-   "metadata": {},
-   "source": [
-    "## Define graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "faeb5ce8-d383-4277-b0a8-322e713638e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import TypedDict\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    topic: str\n",
-    "    joke: str\n",
-    "\n",
-    "\n",
-    "def refine_topic(state: State):\n",
-    "    return {\"topic\": state[\"topic\"] + \" and cats\"}\n",
-    "\n",
-    "\n",
-    "def generate_joke(state: State):\n",
-    "    return {\"joke\": f\"This is a joke about {state['topic']}\"}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9b90850-85bf-4391-b6b7-22ad45edaa3b",
-   "metadata": {},
-   "source": [
-    "## Stream all values in the state (stream_mode=\"values\") {#values}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ed60d4-cf78-4d4d-a660-6879539e168f",
-   "metadata": {},
-   "source": [
-    "Use this to stream **all values** in the state after each step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3daca06a-369b-41e5-8e4e-6edc4d4af3a7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'topic': 'ice cream'}\n",
-      "{'topic': 'ice cream and cats'}\n",
-      "{'topic': 'ice cream and cats', 'joke': 'This is a joke about ice cream and cats'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"values\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adcb1bdb-f9fa-4d42-87ce-8e25d4290883",
-   "metadata": {},
-   "source": [
-    "## Stream state updates from the nodes (stream_mode=\"updates\") {#updates}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44c55326-d077-4583-ae5b-396f45daf21c",
-   "metadata": {},
-   "source": [
-    "Use this to stream only the **state updates** returned by the nodes after each step. The streamed outputs include the name of the node as well as the update."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eed7d401-37d1-4d15-b6dd-88956fff89e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'refine_topic': {'topic': 'ice cream and cats'}}\n",
-      "{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"updates\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9ed9c68-b7c5-4420-945d-84fa33fcf88f",
-   "metadata": {},
-   "source": [
-    "## Stream debug events (stream_mode=\"debug\") {#debug}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94690715-f86c-42f6-be2d-4df82f6f9a96",
-   "metadata": {},
-   "source": [
-    "Use this to stream **debug events** with as much information as possible for each step. Includes information about tasks that were scheduled to be executed as well as the results of the task executions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cc6354f6-0c39-49cf-a529-b9c6c8713d7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'type': 'task', 'timestamp': '2025-01-28T22:06:34.789803+00:00', 'step': 1, 'payload': {'id': 'eb305d74-3460-9510-d516-beed71a63414', 'name': 'refine_topic', 'input': {'topic': 'ice cream'}, 'triggers': ['start:refine_topic']}}\n",
-      "{'type': 'task_result', 'timestamp': '2025-01-28T22:06:34.790013+00:00', 'step': 1, 'payload': {'id': 'eb305d74-3460-9510-d516-beed71a63414', 'name': 'refine_topic', 'error': None, 'result': [('topic', 'ice cream and cats')], 'interrupts': []}}\n",
-      "{'type': 'task', 'timestamp': '2025-01-28T22:06:34.790165+00:00', 'step': 2, 'payload': {'id': '74355cb8-6284-25e0-579f-430493c1bdab', 'name': 'generate_joke', 'input': {'topic': 'ice cream and cats'}, 'triggers': ['refine_topic']}}\n",
-      "{'type': 'task_result', 'timestamp': '2025-01-28T22:06:34.790337+00:00', 'step': 2, 'payload': {'id': '74355cb8-6284-25e0-579f-430493c1bdab', 'name': 'generate_joke', 'error': None, 'result': [('joke', 'This is a joke about ice cream and cats')], 'interrupts': []}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"debug\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6791da60-0513-43e6-b445-788dd81683bb",
-   "metadata": {},
-   "source": [
-    "## Stream LLM tokens ([stream_mode=\"messages\"](../streaming-tokens)) {#messages}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f45d68b-f7ca-4012-96cc-d276a143f571",
-   "metadata": {},
-   "source": [
-    "Use this to stream **LLM messages token-by-token** together with metadata for any LLM invocations inside nodes or tasks. Let's modify the above example to include LLM calls:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "efa787e1-be4d-433b-a1af-46a9c99ad8f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "\n",
-    "def generate_joke(state: State):\n",
-    "    # highlight-next-line\n",
-    "    llm_response = llm.invoke(\n",
-    "        # highlight-next-line\n",
-    "        [\n",
-    "            # highlight-next-line\n",
-    "            {\"role\": \"user\", \"content\": f\"Generate a joke about {state['topic']}\"}\n",
-    "            # highlight-next-line\n",
-    "        ]\n",
-    "        # highlight-next-line\n",
-    "    )\n",
-    "    return {\"joke\": llm_response.content}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c251f809-8922-46ea-bd5b-18264fcc523a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Why| did| the| cat| sit| on| the| ice| cream| cone|?\n",
-      "\n",
-      "|Because| it| wanted| to| be| a| \"|p|urr|-f|ect|\"| scoop|!| 🍦|🐱|"
-     ]
-    }
-   ],
-   "source": [
-    "for message_chunk, metadata in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    if message_chunk.content:\n",
-    "        print(message_chunk.content, end=\"|\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b1912d72-7b68-4810-8b98-d7f3c35fbb6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'langgraph_step': 2,\n",
-       " 'langgraph_node': 'generate_joke',\n",
-       " 'langgraph_triggers': ['refine_topic'],\n",
-       " 'langgraph_path': ('__pregel_pull', 'generate_joke'),\n",
-       " 'langgraph_checkpoint_ns': 'generate_joke:568879bc-8800-2b0d-a5b5-059526a4bebf',\n",
-       " 'checkpoint_ns': 'generate_joke:568879bc-8800-2b0d-a5b5-059526a4bebf',\n",
-       " 'ls_provider': 'openai',\n",
-       " 'ls_model_name': 'gpt-4o-mini',\n",
-       " 'ls_model_type': 'chat',\n",
-       " 'ls_temperature': 0.7}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d1ebeda-4498-40e0-a30a-0844cb491425",
-   "metadata": {},
-   "source": [
-    "## Stream custom data (stream_mode=\"custom\") {#custom}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9ca56cc-d36e-4061-b1f6-9ade4e3e00a0",
-   "metadata": {},
-   "source": [
-    "Use this to stream custom data from inside nodes using [`StreamWriter`][langgraph.types.StreamWriter]."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e3bf6a2b-afe3-4bd3-8474-57cccd994f23",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.types import StreamWriter\n",
-    "\n",
-    "\n",
-    "# highlight-next-line\n",
-    "def generate_joke(state: State, writer: StreamWriter):\n",
-    "    # highlight-next-line\n",
-    "    writer({\"custom_key\": \"Writing custom data while generating a joke\"})\n",
-    "    return {\"joke\": f\"This is a joke about {state['topic']}\"}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2ecfb0b0-3311-46f5-9dc8-6c7853373792",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'custom_key': 'Writing custom data while generating a joke'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28e67f4d-fcab-46a8-93e2-b7bee30336c1",
-   "metadata": {},
-   "source": [
-    "## Configure multiple streaming modes (stream_mode=\"custom\") {#multiple}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01ff946a-f38d-42ad-bc71-a2621fab1b6c",
-   "metadata": {},
-   "source": [
-    "Use this to combine multiple streaming modes. The outputs are streamed as tuples `(stream_mode, streamed_output)`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "bf4cab4b-356c-4276-9035-26974abe1efe",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stream mode: updates\n",
-      "{'refine_topic': {'topic': 'ice cream and cats'}}\n",
-      "\n",
-      "\n",
-      "Stream mode: custom\n",
-      "{'custom_key': 'Writing custom data while generating a joke'}\n",
-      "\n",
-      "\n",
-      "Stream mode: updates\n",
-      "{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for stream_mode, chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=[\"updates\", \"custom\"],\n",
-    "):\n",
-    "    print(f\"Stream mode: {stream_mode}\")\n",
-    "    print(chunk)\n",
-    "    print(\"\\n\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/docs/how-tos/streaming.md b/docs/docs/how-tos/streaming.md
deleted file mode 100644
index 57ba5d7276..0000000000
--- a/docs/docs/how-tos/streaming.md
+++ /dev/null
@@ -1,2 +0,0 @@
-WARNING: DO NOT MODIFY/DELETE
-This is a dummy file needed for mkdocs-redirects, as it is expecting redirects to be markdown files
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 7ecc54c2e8..760139a54a 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -58,15 +58,6 @@ plugins:
   - autorefs
   - redirects:
       redirect_maps:
-        # lib redirects
-        'how-tos/stream-values.md': 'how-tos/streaming.md#values'
-        'how-tos/stream-updates.md': 'how-tos/streaming.md#updates'
-        'how-tos/streaming-content.md': 'how-tos/streaming.md#custom'
-        'how-tos/stream-multiple.md': 'how-tos/streaming.md#multiple'
-        'how-tos/streaming-tokens-without-langchain.md': 'how-tos/streaming-tokens.md#example-without-langchain'
-        'how-tos/streaming-from-final-node.md': 'how-tos/streaming-specific-nodes.md'
-        'how-tos/streaming-events-from-within-tools-without-langchain.md': 'how-tos/streaming-events-from-within-tools.md#example-without-langchain'
-        # cloud redirects
         'cloud/index.md': 'concepts/index.md#langgraph-platform'
         'cloud/how-tos/index.md': 'how-tos/index.md#langgraph-platform'
         'cloud/concepts/api.md': 'concepts/langgraph_server.md'
@@ -147,10 +138,15 @@ nav:
               - how-tos/review-tool-calls-functional.ipynb
           - Streaming:
               - Streaming: how-tos#streaming
-              - how-tos/streaming.ipynb
+              - how-tos/stream-values.ipynb
+              - how-tos/stream-updates.ipynb
               - how-tos/streaming-tokens.ipynb
-              - how-tos/streaming-specific-nodes.ipynb
+              - how-tos/streaming-tokens-without-langchain.ipynb
+              - how-tos/streaming-content.ipynb
+              - how-tos/stream-multiple.ipynb
               - how-tos/streaming-events-from-within-tools.ipynb
+              - how-tos/streaming-events-from-within-tools-without-langchain.ipynb
+              - how-tos/streaming-from-final-node.ipynb
               - how-tos/streaming-subgraphs.ipynb
               - how-tos/disable-streaming.ipynb
           - Tool calling:
diff --git a/libs/langgraph/langgraph/pregel/__init__.py b/libs/langgraph/langgraph/pregel/__init__.py
index 257815de98..327373bcb7 100644
--- a/libs/langgraph/langgraph/pregel/__init__.py
+++ b/libs/langgraph/langgraph/pregel/__init__.py
@@ -1496,7 +1496,7 @@ def stream(
                     When used with functional API, values are emitted once at the end of the workflow.
                 - `"updates"`: Emit only the node or task names and updates returned by the nodes or tasks after each step.
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
-                - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
+                - `"custom"`: Emit custom data using from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
                 - `"debug"`: Emit debug events with as much information as possible for each step.
             output_keys: The keys to stream, defaults to all non-context channels.
@@ -1772,7 +1772,7 @@ async def astream(
                     When used with functional API, values are emitted once at the end of the workflow.
                 - `"updates"`: Emit only the node or task names and updates returned by the nodes or tasks after each step.
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
-                - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
+                - `"custom"`: Emit custom data using from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
                 - `"debug"`: Emit debug events with as much information as possible for each step.
             output_keys: The keys to stream, defaults to all non-context channels.