cli: release 0.1.71 (#3342)

* Convert graph paths to POSIX for docker.
* Will set up windows build in a separate PR (some issue w/ pulling down
the image) https://github.com/langchain-ai/langgraph/pull/3271
* Fix for https://github.com/langchain-ai/langgraph/issues/2061

.github/workflows/codespell.yml

@@ -9,7 +9,11 @@
     
     permissions:
       contents: read
-    
+
+    defaults:
+      run:
+        working-directory: docs
+
     jobs:
       codespell:
         name: (Check for spelling errors)
@@ -21,12 +25,12 @@
     
           - name: Install Dependencies
             run: |
-              pip install toml codespell jupytext
+              pip install toml codespell==2.3.0 jupytext
     
           - name: Extract Ignore Words List
             run: |
               # Use a Python script to extract the ignore words list from pyproject.toml
-              python .github/workflows/extract_ignored_words_list.py
+              python ../.github/workflows/extract_ignored_words_list.py
             id: extract_ignore_words
     
           - name: Codespell

.github/workflows/deploy_docs.yml

@@ -21,6 +21,10 @@ concurrency:
   group: "pages"
   cancel-in-progress: false
 
+defaults:
+  run:
+    working-directory: docs
+
 jobs:
   get-changed-files:
     runs-on: ubuntu-latest
@@ -73,11 +77,12 @@ jobs:
         # This step lints the docs using the existing linting set up.
         # It should be very fast and should not require any external services.
         run: make lint-docs
+      - name: Build llms-text
+        run: make llms-text
       - name: Build site
         run: make build-docs
         env:
           MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
-
       - name: Check links in notebooks
         env:
           LANGCHAIN_API_KEY: test
@@ -85,7 +90,8 @@ jobs:
           if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
-              --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "http://localhost:8123/.*" \
@@ -95,18 +101,19 @@ jobs:
               --check-links-ignore "https://python\.langchain\.com/.*" \
               --check-links-ignore "https://openai\.com/.*" \
               --check-links-ignore "https://pepy\.tech/.*" \
-              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
+              --check-links $(find site -name "index.html" | grep -v 'storm/index.html')
               
           else
             echo "Fetching changes from origin/main..."
             git fetch origin main
             echo "Checking for changed notebook files..."
-            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
+            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
             echo "Changed files: ${CHANGED_FILES}"
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
-                --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://academy\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "http://localhost:2024.*" \
                 --check-links-ignore "http://127.0.0.1:.*" \

.github/workflows/extract_ignored_words_list.py

@@ -1,6 +1,6 @@
 import toml
 
-pyproject_toml = toml.load("libs/langgraph/pyproject.toml")
+pyproject_toml = toml.load("../libs/langgraph/pyproject.toml")
 
 # Extract the ignore words list (adjust the key as per your TOML structure)
 ignore_words_list = (

.github/workflows/run_notebooks.yml

@@ -11,6 +11,10 @@ on:
   schedule:
     - cron: '0 13 * * *'
 
+defaults:
+  run:
+    working-directory: docs
+
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -39,14 +43,14 @@ jobs:
 
       - name: Pre-download tiktoken files
         run: |
-          poetry run python docs/_scripts/download_tiktoken.py
+          poetry run python _scripts/download_tiktoken.py
 
       - name: Prepare notebooks
         run: |
           if [ "${{ matrix.lib-version }}" = "development" ]; then
-            poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
+            poetry run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
           else
-            poetry run python docs/_scripts/prepare_notebooks_for_ci.py
+            poetry run python _scripts/prepare_notebooks_for_ci.py
           fi
 
       - name: Run notebooks
@@ -63,12 +67,12 @@ jobs:
         run: |
           if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
             echo "Running all notebooks"
-            ./docs/_scripts/execute_notebooks.sh
+            ./_scripts/execute_notebooks.sh
           else
-            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
+            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
             if [ -n "$CHANGED_FILES" ]; then
               echo "Running changed notebooks: $CHANGED_FILES"
-              ./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
+              ./_scripts/execute_notebooks.sh $CHANGED_FILES
             else
               echo "No notebook files changed, skipping execution"
             fi

.gitignore

@@ -178,4 +178,4 @@ Untitled*.ipynb
 
 Chinook.db
 
-libs/langgraph/out
+.vercel

Makefile

@@ -1,39 +0,0 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
-
-build-typedoc:
-	cd libs/sdk-js && yarn install --include-dev && yarn typedoc
-	cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
-	#  Add links to the monorepo
-	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-build-docs: build-typedoc
-	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
-
-serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
-
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph  -w ./libs/checkpoint -w ./libs/sdk-py --dirty
-
-clean-docs:
-	find ./docs/docs -name "*.ipynb" -type f -delete
-	rm -rf docs/site
-
-## Run format against the project documentation.
-format-docs:
-	poetry run ruff format docs/docs
-	poetry run ruff check --fix docs/docs
-
-# Check the docs for linting violations
-lint-docs:
-	poetry run ruff format --check docs/docs
-	poetry run ruff check docs/docs
-
-codespell:
-	./docs/codespell_notebooks.sh .
-
-start-services:
-	docker compose -f docs/test-compose.yml up -V --force-recreate --wait --remove-orphans
-
-stop-services:
-	docker compose -f docs/test-compose.yml down

README.md

@@ -12,25 +12,45 @@
 
 ## Overview
 
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
-[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+### Why use LangGraph?
 
-To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
 
-### Key Features
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
 
-- **Cycles and Branching**: Implement loops and conditionals in your apps.
-- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
-- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
-- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### LangGraph Platform
 
-LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
@@ -47,9 +67,7 @@ pip install -U langgraph
 
 ## Example
 
-One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
-
-Let's take a look at a simple example of an agent that can use a search tool.
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
 pip install langchain-anthropic
@@ -66,10 +84,72 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 
-```python
-from typing import Annotated, Literal, TypedDict
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +171,7 @@ tools = [search]
 
 tool_node = ToolNode(tools)
 
-model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,92 +225,102 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
 
-# Use the Runnable
+# Use the agent
 final_state = app.invoke(
-    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
     config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 
-```
-"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
-```
+<b>Step-by-step Breakdown</b>:
 
-Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
+  </li>
+</ul>
+</details>
 
-```python
-final_state = app.invoke(
-    {"messages": [HumanMessage(content="what about ny")]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+<details>
+<summary>Initialize graph with state.</summary>
 
-```
-"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
-```
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
 
-### Step-by-step Breakdown
+<details>
+<summary>Define graph nodes.</summary>
 
-1. <details>
-    <summary>Initialize the model and tools.</summary>
+There are two main nodes we need:
 
-    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
-    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-   </details>
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
 
-2. <details>
-    <summary>Initialize graph with state.</summary>
+<details>
+<summary>Define entry point and graph edges.</summary>
 
-    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
-   </details>
+First, we need to set the entry point for graph execution - <code>agent</code> node.
 
-3. <details>
-    <summary>Define graph nodes.</summary>
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
 
-    There are two main nodes we need:
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
 
-      - The `agent` node: responsible for deciding what (if any) actions to take.
-      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
-   </details>
+<details>
+<summary>Compile the graph.</summary>
 
-4. <details>
-    <summary>Define entry point and graph edges.</summary>
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
 
-      First, we need to set the entry point for graph execution - `agent` node.
+<details>
+<summary>Execute the graph.</summary>
 
-      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
-
-      - Conditional edge: after the agent is called, we should either:
-        - a. Run tools if the agent said to take an action, OR
-        - b. Finish (respond to the user) if the agent did not ask to run tools
-      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-   </details>
-
-5. <details>
-    <summary>Compile the graph.</summary>
-
-    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
-    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
-    </details>
-
-6. <details>
-   <summary>Execute the graph.</summary>
-
-    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
-    2. The `"agent"` node executes, invoking the chat model.
-    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
-    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
-
-        - If `AIMessage` has `tool_calls`, `"tools"` node executes
-        - The `"agent"` node executes again and returns `AIMessage`
-
-    5. Execution progresses to the special `END` value and outputs the final state.
-    And as a result, we get a list of all our chat messages as output.
-   </details>
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
 
+</details>
 
 ## Documentation
 
@@ -240,6 +330,10 @@ final_state["messages"][-1].content
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
 * [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
+## Resources
+
+* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+
 ## Contributing
 
 For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

docs/.gitignore

@@ -1,2 +1,4 @@
 site/
 docs/cloud/reference/sdk/js_ts_sdk_ref.md
+
+.vercel

docs/Makefile

@@ -0,0 +1,50 @@
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text
+
+build-typedoc:
+	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
+
+build-docs: build-typedoc
+	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
+
+llms-text:
+	poetry run python _scripts/generate_llms_text.py docs/llms-full.txt
+
+install-vercel-deps:
+	curl -sSL https://install.python-poetry.org | python3 -
+	poetry self update 1.8.5
+
+vercel-build-docs: install-vercel-deps
+	poetry install
+	make build-docs
+
+serve-clean-docs: clean-docs
+	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
+
+serve-docs: build-typedoc
+	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
+
+clean-docs:
+	find ./docs -name "*.ipynb" -type f -delete
+	rm -rf site
+
+## Run format against the project documentation.
+format-docs:
+	poetry run ruff format docs
+	poetry run ruff check --fix docs
+
+# Check the docs for linting violations
+lint-docs:
+	poetry run ruff format --check docs
+	poetry run ruff check docs
+
+codespell:
+	./codespell_notebooks.sh .
+
+start-services:
+	docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
+
+stop-services:
+	docker compose -f test-compose.yml down

docs/README.md

@@ -19,23 +19,21 @@ make serve-docs
 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
 
 ```bash
-python docs/_scripts/prepare_notebooks_for_ci.py
-./docs/_scripts/execute_notebooks.sh
+python _scripts/prepare_notebooks_for_ci.py
+./_scripts/execute_notebooks.sh
 ```
 
 **Note**: if you want to run the notebooks without `%pip install` cells, you can run:
 
 ```bash
-python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
-./docs/_scripts/execute_notebooks.sh
+python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
+./_scripts/execute_notebooks.sh
 ```
 
 `prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
 * when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
 * when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
 
-**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
-
 ## Adding new notebooks
 
 If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
@@ -48,14 +46,14 @@ Then, run
 jupyter execute <path_to_notebook>
 ```
 
-Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
+Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
 
 ## Updating existing notebooks
 
-If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
+If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
 
 To delete cassettes for a notebook, you can run:
 
 ```bash
-rm docs/cassettes/<notebook_name>*
+rm cassettes/<notebook_name>*
 ```

docs/_scripts/_patch.py

@@ -31,7 +31,7 @@ def request(self, method, url, body=None, headers=None):
         The result of calling the parent request method.
     """
     # Update the inner socket's timeout value to send the request.
-    # This only triggers if the connection is re-used.
+    # This only triggers if the connection is reused.
     if getattr(self, "sock", None) is not None:
         self.sock.settimeout(self.timeout)
 
@@ -90,4 +90,4 @@ def patch_urllib3():
         return request(self, *args, **kwargs)
 
     connection.HTTPConnection.request = new_request
-    _PATCHED = True
+    _PATCHED = True

docs/_scripts/execute_notebooks.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Read the list of notebooks to skip from the JSON file
-SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
+SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
 
 # Function to execute a single notebook
 execute_notebook() {
@@ -27,7 +27,7 @@ if [ $# -gt 0 ]; then
     notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
 else
     # Find all notebooks and filter out those in the skip list
-    notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+    notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
 fi
 
 # Execute notebooks sequentially

docs/_scripts/generate_api_reference_links.py

@@ -1,17 +1,11 @@
 import importlib
 import inspect
 import logging
-import os
 import re
-from typing import List, Literal, Optional
-from typing_extensions import TypedDict
-
-
 from functools import lru_cache
+from typing import List, Literal, Optional
 
-import nbformat
-from nbconvert.preprocessors import Preprocessor
-
+from typing_extensions import TypedDict
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -52,6 +46,8 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
     (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "Command", "types"),
+    (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
+    (["langgraph.func"], "langgraph.func", "task", "func"),
     ([], "langgraph.types", "RetryPolicy", "types"),
     ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
     ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
@@ -88,8 +84,6 @@ _IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
 _IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
 
 
-
-
 @lru_cache(maxsize=10_000)
 def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
     """Get full module name using inspect, with LRU cache to memoize results."""
@@ -109,6 +103,7 @@ def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
         logger.warning(f"API Reference: Failed to load for class {class_name}, {e}")
         return None
 
+
 def _get_doc_title(data: str, file_name: str) -> str:
     try:
         return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
@@ -287,4 +282,4 @@ def update_markdown_with_imports(markdown: str) -> str:
 
     # Apply the replace_code_block function to all matches in the markdown
     updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
-    return updated_markdown
+    return updated_markdown

docs/_scripts/generate_llms_text.py

@@ -0,0 +1,91 @@
+"""Experimental script to generate consolidated llms text from the docs."""
+
+import glob
+import os
+import pathlib
+
+from mkdocs.structure.files import File
+from mkdocs.structure.pages import Page
+
+from notebook_hooks import _on_page_markdown_with_config
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+# Get source directory (parent of HERE / docs)
+SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
+
+
+def _make_llms_text(output_file: str) -> str:
+    """Generate a consolidated text file from markdown/notebook files for LLM training.
+
+    Args:
+        output_file: Path to output the consolidated text file
+    """
+    # Collect all markdown and notebook files
+    relative_paths = [
+        # Files relative to docs/docs/
+        "tutorials/introduction.ipynb",
+    ]
+    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
+
+    all_files.extend(
+        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
+    )
+    all_files.extend(
+        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
+    )
+    # Add all concepts
+    all_files.extend(
+        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
+    )
+    all_files.extend(
+        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.ipynb"), recursive=True)
+    )
+
+    all_content = []
+
+    # Process each file
+    for file_path in all_files:
+        print(f"Processing {file_path}")
+        rel_path = os.path.relpath(file_path, SOURCE_DIR)
+
+        # Create File and Page objects to match mkdocs structure
+        file_obj = File(
+            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
+        )
+        page = Page(
+            title="",
+            file=file_obj,
+            config={},
+        )
+
+        # Read raw content
+        with open(file_path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Convert to markdown without logic to resolve API references
+        processed_content = _on_page_markdown_with_config(
+            content, page, add_api_references=False, remove_base64_images=True
+        )
+        if processed_content:
+            # Add file name
+            all_content.append(f"---\n{rel_path}\n---")
+            # Add content
+            all_content.append(processed_content)
+
+    # Write consolidated output
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write("\n\n".join(all_content))
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description=(
+            "Generate consolidated text file from markdown/notebook files for LLMs."
+        )
+    )
+    parser.add_argument("output_file", help="Path to output the consolidated text file")
+
+    args = parser.parse_args()
+    _make_llms_text(args.output_file)

docs/_scripts/notebook_convert.py

@@ -22,6 +22,8 @@ class EscapePreprocessor(Preprocessor):
             )
 
         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

docs/_scripts/notebook_hooks.py

@@ -5,9 +5,10 @@ from typing import Any, Dict
 
 from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
+import posixpath
 
-from notebook_convert import convert_notebook
 from generate_api_reference_links import update_markdown_with_imports
+from notebook_convert import convert_notebook
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -15,6 +16,24 @@ logger.setLevel(logging.INFO)
 DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
 
 
+REDIRECT_MAP = {
+    # lib redirects
+    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
+    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
+    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
+    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
+    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
+    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
+    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#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",
+    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
+    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+}
+
+
 class NotebookFile(File):
     def is_documentation_page(self):
         return True
@@ -106,7 +125,14 @@ def _highlight_code_blocks(markdown: str) -> str:
     return markdown
 
 
-def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
+def _on_page_markdown_with_config(
+    markdown: str,
+    page: Page,
+    *,
+    add_api_references: bool = True,
+    remove_base64_images: bool = False,
+    **kwargs: Any,
+) -> str:
     if DISABLED:
         return markdown
     if page.file.src_path.endswith(".ipynb"):
@@ -114,7 +140,76 @@ def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
         markdown = convert_notebook(page.file.abs_src_path)
 
     # Append API reference links to code blocks
-    markdown = update_markdown_with_imports(markdown)
+    if add_api_references:
+        markdown = update_markdown_with_imports(markdown)
     # Apply highlight comments to code blocks
     markdown = _highlight_code_blocks(markdown)
+
+    if remove_base64_images:
+        # Remove base64 encoded images from markdown
+        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^\)]+\)", "", markdown)
+
     return markdown
+
+
+def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
+    return _on_page_markdown_with_config(
+        markdown,
+        page,
+        add_api_references=True,
+        **kwargs,
+    )
+
+# redirects
+
+HTML_TEMPLATE = """
+<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting...</title>
+    <link rel="canonical" href="{url}">
+    <meta name="robots" content="noindex">
+    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
+    <meta http-equiv="refresh" content="0; url={url}">
+</head>
+<body>
+Redirecting...
+</body>
+</html>
+"""
+
+
+def write_html(site_dir, old_path, new_path):
+    """Write an HTML file in the site_dir with a meta redirect to the new page"""
+    # Determine all relevant paths
+    old_path_abs = os.path.join(site_dir, old_path)
+    old_dir_abs = os.path.dirname(old_path_abs)
+
+    # Create parent directories if they don't exist
+    if not os.path.exists(old_dir_abs):
+        os.makedirs(old_dir_abs)
+
+    # Write the HTML redirect file in place of the old file
+    content = HTML_TEMPLATE.format(url=new_path)
+    with open(old_path_abs, "w", encoding="utf-8") as f:
+        f.write(content)
+
+
+# Create HTML files for redirects after site dir has been built
+def on_post_build(config):
+    use_directory_urls = config.get("use_directory_urls")
+    for page_old, page_new in REDIRECT_MAP.items():
+        page_old = page_old.replace(".ipynb", ".md")
+        page_new = page_new.replace(".ipynb", ".md")
+        page_new_before_hash, hash, suffix = page_new.partition("#")
+        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
+            os.sep, "/"
+        )
+        new_html_path = File(page_new_before_hash, "", "", True).url
+        new_html_path = (
+            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
+            + hash
+            + suffix
+        )
+        write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -7,7 +7,7 @@ import click
 import nbformat
 
 logger = logging.getLogger(__name__)
-NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
+NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
 DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
 
@@ -19,36 +19,37 @@ BLOCKLIST_COMMANDS = (
 )
 
 NOTEBOOKS_NO_CASSETTES = (
-    "docs/docs/how-tos/visualization.ipynb",
-    "docs/docs/how-tos/many-tools.ipynb"
+    "docs/how-tos/visualization.ipynb",
+    "docs/how-tos/many-tools.ipynb"
 )
 
 NOTEBOOKS_NO_EXECUTION = [
     # this uses a user provided project name for langsmith
-    "docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
+    "docs/tutorials/tnt-llm/tnt-llm.ipynb",
     # this uses langsmith datasets
-    "docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
+    "docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
     # this uses browser APIs
-    "docs/docs/tutorials/web-navigation/web_voyager.ipynb",
+    "docs/tutorials/web-navigation/web_voyager.ipynb",
     # these RAG guides use an ollama model
-    "docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
-    "docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
-    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
+    "docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
+    "docs/tutorials/rag/langgraph_crag_local.ipynb",
+    "docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
-    "docs/docs/tutorials/usaco/usaco.ipynb",
+    "docs/tutorials/usaco/usaco.ipynb",
     # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
-    "docs/docs/how-tos/autogen-integration.ipynb",
+    "docs/how-tos/autogen-integration.ipynb",
+    "docs/how-tos/autogen-integration-functional.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
-    "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
-    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
-    "docs/docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
-    "docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
-    "docs/docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
-    "docs/docs/tutorials/tot/tot.ipynb",
-    "docs/docs/how-tos/visualization.ipynb",
-    "docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
+    "docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
+    "docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
+    "docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
+    "docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
+    "docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
+    "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
+    "docs/tutorials/tot/tot.ipynb",
+    "docs/how-tos/visualization.ipynb",
+    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
 ]
 
 
@@ -216,7 +217,7 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                 except Exception as e:
                     logger.error(f"Error processing {notebook_path}: {e}")
     
-    with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
+    with open("notebooks_no_execution.json", "w") as f:
         json.dump(NOTEBOOKS_NO_EXECUTION, f)
 
 

docs/_scripts/third_party_page/create_third_party_page.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python
+"""Create the third party page for the documentation."""
+
+import argparse
+from typing import List
+from typing import TypedDict
+
+import yaml
+
+MARKDOWN = """\
+[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
+# 🚀 Prebuilt Libraries
+
+LangGraph includes a prebuilt React agent. For more information on how to use it, 
+check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
+
+If you’re looking for other prebuilt libraries, explore the community-built options 
+below. These libraries can extend LangGraph's functionality in various ways.
+
+## 📚 Available Libraries
+
+{library_list}
+
+## ✨ Contributing Your Library
+
+Have you built an awesome open-source library using LangGraph? We'd love to feature 
+your project on the official LangGraph documentation pages! 🏆
+
+To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
+
+**Guidelines**
+
+- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
+  for JavaScript/TypeScript, etc.) 📦
+- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
+  the Functional API (exposing an `entrypoint`).
+- The package must include documentation (e.g., a `README.md` or docs site) 
+  explaining how to use it.
+  
+We'll review your contribution and merge it in!
+
+Thanks for contributing! 🚀
+"""
+
+
+class ResolvedPackage(TypedDict):
+    name: str
+    """The name of the package."""
+    repo: str
+    """Repository ID within github. Format is: [orgname]/[repo_name]."""
+    weekly_downloads: int | None
+    """The weekly download count of the package."""
+    description: str
+    """A brief description of what the package does."""
+
+
+def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
+    """Generate the markdown content for the third party page.
+
+    Args:
+        resolved_packages: A list of resolved package information.
+        language: str
+
+    Returns:
+        The markdown content as a string.
+    """
+    # Update the URL to the actual file once the initial version is merged
+    if language == "python":
+        langgraph_url = (
+            "https://github.com/langchain-ai/langgraph/blob/main/docs"
+            "/_scripts/third_party_page/packages.yml"
+        )
+    elif language == "js":
+        langgraph_url = (
+            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
+            "/_scripts/third_party/packages.yml"
+        )
+    else:
+        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
+
+    sorted_packages = sorted(
+        resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
+    )
+    rows = [
+        "| Name | GitHub URL | Description | Weekly Downloads |",
+        "| --- | --- | --- | --- |",
+    ]
+    for package in sorted_packages:
+        name = f"**{package['name']}**"
+        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
+        downloads = package["weekly_downloads"] or 0
+        row = f"| {name} | {repo_url} | {package['description']} | {downloads} |"
+        rows.append(row)
+    markdown_content = MARKDOWN.format(
+        library_list="\n".join(rows), langgraph_url=langgraph_url
+    )
+    return markdown_content
+
+
+def main(input_file: str, output_file: str, language: str) -> None:
+    """Main function to create the third party page.
+
+    Args:
+        input_file: Path to the input YAML file containing resolved package information.
+        output_file: Path to the output file for the third party page.
+        language: The language for which to generate the third party page.
+    """
+    # Parse the input YAML file
+    with open(input_file, "r") as f:
+        resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
+
+    markdown_content = generate_markdown(resolved_packages, language)
+
+    # Write the markdown content to the output file
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write(markdown_content)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Create the third party page.")
+    parser.add_argument(
+        "input_file",
+        help="Path to the input YAML file containing resolved package information.",
+    )
+    parser.add_argument(
+        "output_file", help="Path to the output file for the third party page."
+    )
+    parser.add_argument(
+        "--language",
+        choices=["python", "js"],
+        default="python",
+        help="The language for which to generate the third party page. Defaults to 'python'.",
+    )
+    args = parser.parse_args()
+
+    main(args.input_file, args.output_file, args.language)

docs/_scripts/third_party_page/get_download_stats.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+"""Retrieve download count for a list of Python packages from PyPI."""
+
+import argparse
+from datetime import datetime
+from typing import TypedDict
+import pathlib
+
+import requests
+import yaml
+
+
+class Package(TypedDict):
+    """A TypedDict representing a package"""
+
+    name: str
+    """The name of the package."""
+    repo: str
+    """Repository ID within github. Format is: [orgname]/[repo_name]."""
+    description: str
+    """A brief description of what the package does."""
+
+
+class ResolvedPackage(Package):
+    weekly_downloads: int | None
+
+
+HERE = pathlib.Path(__file__).parent
+PACKAGES_FILE = HERE / "packages.yml"
+PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
+
+
+def _get_weekly_downloads(packages: list[Package]) -> list[ResolvedPackage]:
+    """Retrieve the monthly download count for a list of packages from PyPIStats."""
+    resolved_packages: list[ResolvedPackage] = []
+
+    for package in packages:
+        url = f"https://pypistats.org/api/packages/{package['name']}/overall"
+
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+
+        sorted_data = sorted(
+            data["data"],
+            key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
+            reverse=True,
+        )
+
+        # Sum the last 7 days of downloads
+        num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
+
+        resolved_packages.append(
+            {
+                "name": package["name"],
+                "repo": package["repo"],
+                "weekly_downloads": num_downloads,
+                "description": package["description"],
+            }
+        )
+
+    return resolved_packages
+
+
+
+def main(output_file: str) -> None:
+    """Main function to generate package download information.
+
+    Args:
+        output_file: Path to the output YAML file.
+    """
+    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES)
+
+    if not output_file.endswith(".yml"):
+        raise ValueError("Output file must have a .yml extension")
+
+    with open(output_file, "w") as f:
+        f.write("# This file is auto-generated. Do not edit.\n")
+        yaml.dump(resolved_packages, f)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate package download information."
+    )
+    parser.add_argument(
+        "output_file",
+        help=(
+            "Path to the output YAML file. Example: python generate_downloads.py "
+            "downloads.yml"
+        ),
+    )
+    args = parser.parse_args()
+
+    main(args.output_file)

docs/_scripts/third_party_page/packages.yml

@@ -0,0 +1,5 @@
+#A list of third-party packages to surface on the third-party page.
+packages:
+  - name: "trustcall"
+    repo: "hinthornw/trustcall"
+    description: "Tenacious tool calling built on LangGraph"

docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/introduction_11d5b934-6d8b-4f52-a3bc-b3daa7207e00.msgpack.zlib

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docs/cassettes/introduction_6b32914d-4d60-491f-8e11-1e6867e38ffd.msgpack.zlib

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docs/cassettes/react-agent-from-scratch-functional_18.msgpack.zlib

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docs/cassettes/react-agent-from-scratch-functional_20.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_16.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_17.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_20.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_21.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_25.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_26.msgpack.zlib

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docs/cassettes/review-tool-calls-functional_28.msgpack.zlib

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docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib

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docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib

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docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib

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docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib

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docs/cassettes/streaming-tokens_96050fba.msgpack.zlib

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docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib

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docs/cassettes/streaming-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib

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docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib

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docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib

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docs/cassettes/wait-user-input-functional_25.msgpack.zlib

@@ -0,0 +1 @@
+eNqdVnlwE9cZBwxJaNqktBCnlGMRpGmJV97VaclxAcuAD4RsZA47gFi9fZIW78UelmSggOvCJDDAchXS0GBbWOAYc9iFcBgyDUkg9dQmDGlNgZS0Q5OhoU2YztBAQ9+upGAP/JFWM5L2vff7zt/3fW/rk7VQkhmBH9zG8AqUKKCghazVJyW4TIWy0tDCQSUi0Ilyn7+yWZWYvskRRRFld24uJTJmQYQ8xZiBwOXWkrkgQim56FlkoaEmERTo+OUhjctNHJRlKgxlkxt7abkJCMgWr6CFyUPxWFxQMQlSIIIJqoKFBAmLqBzap2SZkRWKB9CNRZFqTI4IKktjJVgIQhrj4higlCmYH4qURCmQjeegjZQ6EIGgBlMiEItCCv1JGMNjfnQ4Q0L6GBkIU0w5mEkSWKh7ocpQMq1chHY4gYasvhUWFdwm4BzDMzpSVpCHHDoIUawM0YYiCGw6HCUuGlpCKm+kT8d//ezGlpt4ijMAYagE0v7oGBrKQGLENAylgmUxRcAQaoDnIUngMAqTRQiYEAMwVkBhIxmzrkMPHTGESDQsiRJiRFIYmFpmoMYi4yYKheHDppUr9QQglhkJ0nogD9B6IjJoIbgUAgWhEfx/CNVgMPCAwUfEOydVYf1ozoRqCH+D8JC8FP9msaWgjw5s0cpkBFI0MrIpERFkRWsfWNoHKQAgKgfIA4FG6rUD4TpGzMFoGGJR3bWicuahkQKttQZCEadYpha2pKS0Q5Qoskwqs7lLZYFvS5c/rnvy8HGrXow4ahZe0Tp9yIlpJbnlcdSDPEaa7S6z81AMR+lieBb1FM5SyJ8W0Tg/2f9ApEANUoKn+1trSQm398cIsrbXSwGff4BKSgIRbS8lcQ5bR/99SeUVhoNa0lP+sLn04QNzVjNJmp2HByiW4zzQ9hoddGyAMFSkOA4EpENrJNoz+WEhH1YiWrPDad0nQVlEEwX+vAWJKapcn0BcwO5zyfRkafKVZUi8Nig7UYR40brmQzoHs1iwUtT4FsJixyxWN+ly2xzYTG9lmydtpvKRNByuRJNCDiEqpmdoT4KIytdAutXzSMK7dMJRNLr7aLDgMCYKMsTTXmltC/B0xeMlRR2p6sIFKUzxTJ1hVusymI/WxaI0UGk6UhvlCFedzcoEoQpCnWkR1AO6GeQQzslagiSslvb0USb5rShYAicJnCBPxHBjNjIcgxJq/KYnO5K1EwTx5sMARaiB6A5I2gjjc7o/QoIcYk03/kCNzeVynXo0KKPKiiAuu+XEQJQM+3tDWjj5zYcBaRVNhNwWy6Bxhtb6JqFFwOIk7cBOBEOAdACKCkIr4QqSdpeDIp3BICCP6xMBIC06m6IgKbgMAbrGlLjWl8NRMb3RCqyk3epAkeajWwKwKg39arBI0GOQ8zFRgqxA0Qc9M3APuqMg7jcKUEsWVc2e5i3xtPqRkx5BqGHglsuDswIBEAoEuQIn4Zyulk33Or1WT4W1qqxo6TwfPa/cSXqoeOmMCiVqMVewap2nkqrCSafVac9zuvIcOGkmzKSZxBkmwJLVc4nSksLYMlYKEFygECyz5s2vjYV50lVZsczGiVXT4Nxaycl7ZIoGswMl4XK5GISANEcRKpc5yOkMWShU+uNFdEnI6lIFbxhFg+6Ugtx8DBUnmo1yQbpFcNQieKpBbJkGycdoIwcF5oHjMB8rRm8APp6N56O7FyUTon80pf2MAgtmCzzs24ZyoNYydAHpLSoWi/NkNba0yh+qqVxaMctqLwpHC2lbMa/6ikpnKvbyKBNS6qL9kuC0OXEinQcHYcszqvCB6/+nV0cX4P07HveJqVedJC/IPBMKtfihhBpIawWsoNJoskuwBXE+Z1qV1ukiHFab3UbTQeQiTTrxQjQzM9q+ng8J/VpIUiyqsVqgdUSsBSa3zWY15WMcVZDnQO1kvBCtaUndUu8M8Y1f/8Qg45OFvvfvd1Z6hYtTv9t1d37bjdf2TPKKW56cvA8f1bQ/cqlw+BHHSbD805+p0UXFTfei9MmXd5fvyV5x6MPbtde6X+zpdpuGDl33k8E7Lr61bftVsOlv69/b2rHujZzxK85dyhm76jj19/bY3a/+NUwpfbrjzsYctmsx2dx2FtvQTt3Y/FhO1d3PK14c/d7Wz/cN2Xb9x2XHutfe6lW29zblf/KdtVcXX/ls0+un5InP1u3ccnDEhIa2N6K7kjfufL8l8Ye+6pF1PmvDp7e+N+HPG3LwqV3ZPy0fu+fi6u5PVp+YtSh7ztHVSzbMenb7keELp9z88PiI9aW7roxb5S7b9HHPxqfeiakfnPl35MCCw3NfWOzt6pxbyDT2zU48c7rD93T9iCGvtr7vzB156fzRSWsfH9MwSK1qLF9nWbWg53rj1GOvvb2t+7ojuebOlZFbtspbTFLTjoYL/ww99cM/TrzcedqxO/v8W7f/cnvXCWL973aN/uhMftfJU884/3Pzq/ubJ7y9+d7U0ZJyf97U0TPKflvd8ELr+f3Nrw7v6f7o5rXdZ34z+ixVHZv8uvt59+YnEufbrE+u6q3a/C18xa/zEqcnH7gaueCoHvX4K12LH7u54ciE4TfGqSX5P7iU6N3wUtbGtb4lxJr33702fra7p/e5MVmj5h78x7vrK3ZMFBb+imwelt0yrOjAB6t6brhunVjR2PHLXucgEMn64qz7r/Wfzb+yUF7AN09Z9KeXFz737XFXm5fsHLqm/d7YMf7qc3UzVwe+XD255Be/7616vnX64P3ejwd/sdM8w/PYhR99OUyvmqxB9a9EDjjQ838B05+eOQ==

docs/cassettes/wait-user-input-functional_29.msgpack.zlib

@@ -0,0 +1 @@
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

docs/codespell_notebooks.sh

@@ -1,5 +1,5 @@
 ERROR_FOUND=0
-for file in $(find $1 -name "*.ipynb"); do
+for file in $(find $1 -name "*.ipynb" | grep -v ".ipynb_checkpoints"); do
     OUTPUT=$(cat "$file" | jupytext --from ipynb --to py:percent | codespell -)
     if [ -n "$OUTPUT" ]; then
         echo "Errors found in $file"
@@ -10,4 +10,4 @@ done
 
 if [ "$ERROR_FOUND" -ne 0 ]; then
     exit 1
-fi
+fi

docs/docs/cloud/quick_start.md

@@ -90,7 +90,7 @@ For this guide, we'll use the pre-built Python [**ReAct Agent**](https://github.
     </figure>
 
 
-## Lagraph Studio Web UI
+## LangGraph Studio Web UI
 
 Once your application is deployed, you can test it in **LangGraph Studio**. 
 

docs/docs/cloud/reference/api/openapi_control_plane.json

@@ -561,6 +561,16 @@
           },
           "resource": {
             "$ref": "#/components/schemas/ResourceService"
+          },
+          "status": {
+            "type": "string",
+            "enum": [
+              "AWAITING_DATABASE",
+              "READY",
+              "AWAITING_DELETE",
+              "UNKNOWN"
+            ],
+            "description": "Deployment status of the project.\n\nNon-terminal statuses: `AWAITING_DATABASE`, `AWAITING_DELETE`. All other statuses are terminal."
           }
         }
       },

docs/docs/cloud/reference/cli.md

@@ -4,20 +4,26 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 ## Installation
 
-1. Ensure that Docker is installed (e.g. `docker --version`).
-2. Install the `langgraph-cli` package:
- 
-    === "pip"
-        ```bash    
-        pip install langgraph-cli
-        ```
+1.  Ensure that Docker is installed (e.g. `docker --version`).
+2.  Install the CLI package:
 
-    === "Homebrew (MacOS only)"
+    === "Python"
         ```bash
+        pip install langgraph-cli
+
+        # Install via Homebrew
         brew install langgraph-cli
         ```
- 
-3. Run the command `langgraph --help` to confirm that the CLI is installed.
+
+    === "JS"
+        ```bash
+        npx @langchain/langgraph-cli
+
+        # Install globally, will be available as `langgraphjs`
+        npm install -g @langchain/langgraph-cli
+        ```
+
+3.  Run the command `langgraph --help` or `npx @langchain/langgraph-cli --help` to confirm that the CLI is working correctly.
 
 [](){#langgraph.json}
 
@@ -25,17 +31,6 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 The LangGraph CLI requires a JSON configuration file with the following keys:
 
-| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `dependencies`     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                  |
-| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
-| `auth`             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                      |
-| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| `store`            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul>                                                                  |
-| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
     <p>
@@ -43,253 +38,418 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     </p>
 </div>
 
+=== "Python"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                                                     |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`python_version`</span>   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
+=== "JS"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
 ### Examples
 
-#### Basic Configuration
+=== "Python"
+    
+    #### Basic Configuration
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  }
-}
-```
-
-#### Adding semantic search to the store
-
-All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
-
-The `fields` configuration determines which parts of your documents to embed:
-
-- If omitted or set to `["$"]`, the entire document will be embedded
-- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
-- Documents missing specified fields will still be stored but won't have embeddings for those fields
-- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "openai:text-embedding-3-small",
-      "dims": 1536,
-      "fields": ["$"]
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      }
     }
-  }
-}
-```
+    ```
 
-!!! note "Common model dimensions"
-        - openai:text-embedding-3-large: 3072
-        - openai:text-embedding-3-small: 1536
-        - openai:text-embedding-ada-002: 1536
-        - cohere:embed-english-v3.0: 1024
-        - cohere:embed-english-light-v3.0: 384
-        - cohere:embed-multilingual-v3.0: 1024
-        - cohere:embed-multilingual-light-v3.0: 384
+    #### Adding semantic search to the store
 
-#### Semantic search with a custom embedding function
+    All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
 
-If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+    The `fields` configuration determines which parts of your documents to embed:
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "./embeddings.py:embed_texts",
-      "dims": 768,
-      "fields": ["text", "summary"]
-    }
-  }
-}
-```
+    - If omitted or set to `["$"]`, the entire document will be embedded
+    - To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
+    - Documents missing specified fields will still be stored but won't have embeddings for those fields
+    - You can still override which fields to embed on a specific item at `put` time using the `index` parameter
 
-The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
-
-```python
-# embeddings.py
-def embed_texts(texts: list[str]) -> list[list[float]]:
-    """Custom embedding function for semantic search."""
-    # Implementation using your preferred embedding model
-    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
-```
-
-#### Adding custom authentication
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  },
-  "auth": {
-    "path": "./auth.py:auth",
-    "openapi": {
-      "securitySchemes": {
-        "apiKeyAuth": {
-          "type": "apiKey",
-          "in": "header",
-          "name": "X-API-Key"
-        }
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
       },
-      "security": [
-        {"apiKeyAuth": []}
-      ]
-    },
-    "disable_studio_auth": false
-  }
-}
-```
+      "store": {
+        "index": {
+          "embed": "openai:text-embedding-3-small",
+          "dims": 1536,
+          "fields": ["$"]
+        }
+      }
+    }
+    ```
+
+    !!! note "Common model dimensions" 
+        - `openai:text-embedding-3-large`: 3072 
+        - `openai:text-embedding-3-small`: 1536 
+        - `openai:text-embedding-ada-002`: 1536 
+        - `cohere:embed-english-v3.0`: 1024 
+        - `cohere:embed-english-light-v3.0`: 384 
+        - `cohere:embed-multilingual-v3.0`: 1024 
+        - `cohere:embed-multilingual-light-v3.0`: 384
+
+    #### Semantic search with a custom embedding function
+
+    If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
+      },
+      "store": {
+        "index": {
+          "embed": "./embeddings.py:embed_texts",
+          "dims": 768,
+          "fields": ["text", "summary"]
+        }
+      }
+    }
+    ```
+
+    The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
+
+    ```python
+    # embeddings.py
+    def embed_texts(texts: list[str]) -> list[list[float]]:
+        """Custom embedding function for semantic search."""
+        # Implementation using your preferred embedding model
+        return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
+    ```
+
+    #### Adding custom authentication
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "auth": {
+        "path": "./auth.py:auth",
+        "openapi": {
+          "securitySchemes": {
+            "apiKeyAuth": {
+              "type": "apiKey",
+              "in": "header",
+              "name": "X-API-Key"
+            }
+          },
+          "security": [{ "apiKeyAuth": [] }]
+        },
+        "disable_studio_auth": false
+      }
+    }
+    ```
+
+    See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
+
+
+=== "JS"
+    
+    #### Basic Configuration
+
+    ```json
+    {
+      "graphs": {
+        "chat": "./src/graph.ts:graph"
+      }
+    }
+    ```
 
-See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
 
 ## Commands
 
-The base command for the LangGraph CLI is `langgraph`.
-
 **Usage**
 
-```
-langgraph [OPTIONS] COMMAND [ARGS]
-```
+=== "Python"
+
+    The base command for the LangGraph CLI is `langgraph`.
+
+    ```
+    langgraph [OPTIONS] COMMAND [ARGS]
+    ```
+=== "JS"
+
+    The base command for the LangGraph.js CLI is `langgraphjs`. 
+
+    ```
+    npx @langchain/langgraph-cli [OPTIONS] COMMAND [ARGS]
+    ```
+
+    We recommend using `npx` to always use the latest version of the CLI.
 
 ### `dev`
 
-Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+=== "Python"
 
-!!! note "Python only"
+    Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
 
-    Currently, the CLI only supports Python >= 3.11.
-    JS support is coming soon.
+    !!! note
 
-**Installation**
+        Currently, the CLI only supports Python >= 3.11.
 
-This command requires the "inmem" extra to be installed:
+    **Installation**
 
-```bash
-pip install -U "langgraph-cli[inmem]"
-```
+    This command requires the "inmem" extra to be installed:
 
-**Usage**
+    ```bash
+    pip install -U "langgraph-cli[inmem]"
+    ```
 
-```
-langgraph dev [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dev [OPTIONS]
+    ```
 
-| Option                        | Default          | Description                                                                         |
-| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
-| `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
-| `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
-| `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
-| `--no-reload`                 |                  | Disable auto-reload                                                                 |
-| `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
-| `--no-browser`                |                  | Disable automatic browser opening                                                   |
-| `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
-| `--help`                      |                  | Display command documentation                                                       |
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
+
+
+=== "JS"
+
+    Run LangGraph API server in development mode with hot reloading capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dev [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
 
 ### `build`
 
-Build LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Build LangGraph Cloud API server Docker image.
 
-```
-langgraph build [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
+
+=== "JS"
+
+    Build LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--no-pull`          |                  | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                      |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
 
-| Option               | Default          | Description                                                                                                                  |
-| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
-| `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
-| `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
-| `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
-| `--help`             |                  | Display command documentation.                                                                                               |
 
 ### `up`
 
-Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+=== "Python"
 
-**Usage**
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
-```
-langgraph up [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph up [OPTIONS]
+    ```
 
-| Option                       | Default                   | Description                                                                                                             |
-| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
-| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
-| `--watch`                    |                           | Restart on file changes                                                                                                 |
-| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
-| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
-| `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
-| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
-| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
-| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
-| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
-| `--help`                     |                           | Display command documentation.                                                                                          |
+    **Options**
+
+    | Option                       | Default                   | Description                                                                                                             |
+    | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | `--watch`                    |                           | Restart on file changes                                                                                                 |
+    | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
+    | `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
+    | `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
+    | `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+    | `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
+    | `--help`                     |                           | Display command documentation.                                                                                          |
+
+=== "JS"
+
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli up [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                                                                 | Default                   | Description                                                                                                             |
+    | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
+    | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | <span style="white-space: nowrap;">`-d, --docker-compose FILE`</span>  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | <span style="white-space: nowrap;">`-p, --port INTEGER`</span>         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | <span style="white-space: nowrap;">`--no-pull`</span>                  |                           | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                 |
+    | <span style="white-space: nowrap;">`--recreate`</span>                 |                           | Recreate containers even if their configuration and image haven't changed                                               |
+    | <span style="white-space: nowrap;">`--help`</span>                     |                           | Display command documentation.                                                                                          |
 
 ### `dockerfile`
 
-Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
 
-```
-langgraph dockerfile [OPTIONS] SAVE_PATH
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dockerfile [OPTIONS] SAVE_PATH
+    ```
 
-| Option              | Default          | Description                                                                                                     |
-| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
-| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
-| `--help`            |                  | Show this message and exit.                                                                                     |
+    **Options**
 
-Example:
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
 
-```bash
-langgraph dockerfile -c langgraph.json Dockerfile
-```
+    Example:
 
-This generates a Dockerfile that looks similar to:
+    ```bash
+    langgraph dockerfile -c langgraph.json Dockerfile
+    ```
 
-```dockerfile
-FROM langchain/langgraph-api:3.11
+    This generates a Dockerfile that looks similar to:
 
-ADD ./pipconf.txt /pipconfig.txt
+    ```dockerfile
+    FROM langchain/langgraph-api:3.11
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
+    ADD ./pipconf.txt /pipconfig.txt
 
-ADD ./graphs /deps/__outer_graphs/src
-RUN set -ex && \
-    for line in '[project]' \
-                'name = "graphs"' \
-                'version = "0.1"' \
-                '[tool.setuptools.package-data]' \
-                '"*" = ["**/*"]'; do \
-        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
-    done
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+    ADD ./graphs /deps/__outer_graphs/src
+    RUN set -ex && \
+        for line in '[project]' \
+                    'name = "graphs"' \
+                    'version = "0.1"' \
+                    '[tool.setuptools.package-data]' \
+                    '"*" = ["**/*"]'; do \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
+        done
 
-ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
-???+ note "Updating your langgraph.json file"
-    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+    ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+
+=== "JS"
+
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dockerfile [OPTIONS] SAVE_PATH
+    ```
+
+    **Options**
+
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
+
+    Example:
+
+    ```bash
+    npx @langchain/langgraph-cli dockerfile -c langgraph.json Dockerfile
+    ```
+
+    This generates a Dockerfile that looks similar to:
+
+    ```dockerfile
+    FROM langchain/langgraphjs-api:20
+    
+    ADD . /deps/agent
+    
+    RUN cd /deps/agent && yarn install
+    
+    ENV LANGSERVE_GRAPHS='{"agent":"./src/react_agent/graph.ts:graph"}'
+    
+    WORKDIR /deps/agent
+    
+    RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `npx @langchain/langgraph-cli dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.

docs/docs/concepts/deployment_options.md

@@ -51,12 +51,13 @@ For more information, please see:
 
     The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
 
-The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
+The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
 
 With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
 
 You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
 
+[Cron jobs](../cloud/how-tos/cron_jobs.md) are not available for Self-Hosted Lite deployments.
 
 For more information, please see:
 

docs/docs/concepts/functional_api.md

@@ -0,0 +1,938 @@
+# Functional API
+
+!!! warning "Beta"
+    The Functional API is currently in **beta** and is subject to change. Please [report any issues](https://github.com/langchain-ai/langgraph/issues) or feedback to the LangGraph team.
+
+## Overview
+
+The **Functional API** allows you to add LangGraph's key features -- [persistence](./persistence.md), [memory](./memory.md), [human-in-the-loop](./human_in_the_loop.md), and [streaming](./streaming.md) — to your applications with minimal changes to your existing code.
+
+It is designed to integrate these features into existing code that may use standard language primitives for branching and control flow, such as `if` statements, `for` loops, and function calls. Unlike many data orchestration frameworks that require restructuring code into an explicit pipeline or DAG, the Functional API allows you to incorporate these capabilities without enforcing a rigid execution model.  
+
+The Functional API uses two key building blocks:  
+
+- **`@entrypoint`** – Marks a function as the starting point of a workflow, encapsulating logic and managing execution flow, including handling long-running tasks and interrupts.  
+- **`@task`** – Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously within an entrypoint. Tasks return a future-like object that can be awaited or resolved synchronously.  
+
+This provides a minimal abstraction for building workflows with state management and streaming.
+
+!!! tip 
+
+    For users who prefer a more declarative approach, LangGraph's [Graph API](./low_level.md) allows you to define workflows using a Graph paradigm. Both APIs share the same underlying runtime, so you can use them together in the same application.
+    Please see the [Functional API vs. Graph API](#functional-api-vs-graph-api) section for a comparison of the two paradigms.
+
+## Example
+
+Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review.
+
+```python
+from langgraph.func import entrypoint, task
+from langgraph.types import interrupt
+
+@task
+def write_essay(topic: str) -> str:
+    """Write an essay about the given topic."""
+    time.sleep(1) # A placeholder for a long-running task.
+    return f"An essay about topic: {topic}"
+
+@entrypoint(checkpointer=MemorySaver())
+def workflow(topic: str) -> dict:
+    """A simple workflow that writes an essay and asks for a review."""
+    essay = write_essay("cat").result()
+    is_approved = interrupt({
+        # Any json-serializable payload provided to interrupt as argument.
+        # It will be surfaced on the client side as an Interrupt when streaming data
+        # from the workflow.
+        "essay": essay, # The essay we want reviewed.
+        # We can add any additional information that we need.
+        # For example, introduce a key called "action" with some instructions.
+        "action": "Please approve/reject the essay",
+    })
+
+    return {
+        "essay": essay, # The essay that was generated
+        "is_approved": is_approved, # Response from HIL
+    }
+```
+
+??? example "Detailed Explanation"
+
+    This workflow will write an essay about the topic "cat" and then pause to get a review from a human. The workflow can be interrupted for an indefinite amount of time until a review is provided.
+
+    When the workflow is resumed, it executes from the very start, but because the result of the `write_essay` task was already saved, the task result will be loaded from the checkpoint instead of being recomputed.
+
+    ```python
+    import time
+    import uuid
+
+    from langgraph.func import entrypoint, task
+    from langgraph.types import interrupt
+    from langgraph.checkpoint.memory import MemorySaver
+
+    @task
+    def write_essay(topic: str) -> str:
+        """Write an essay about the given topic."""
+        time.sleep(1) # This is a placeholder for a long-running task.
+        return f"An essay about topic: {topic}"
+
+    @entrypoint(checkpointer=MemorySaver())
+    def workflow(topic: str) -> dict:
+        """A simple workflow that writes an essay and asks for a review."""
+        essay = write_essay("cat").result()
+        is_approved = interrupt({
+            # Any json-serializable payload provided to interrupt as argument.
+            # It will be surfaced on the client side as an Interrupt when streaming data
+            # from the workflow.
+            "essay": essay, # The essay we want reviewed.
+            # We can add any additional information that we need.
+            # For example, introduce a key called "action" with some instructions.
+            "action": "Please approve/reject the essay",
+        })
+
+        return {
+            "essay": essay, # The essay that was generated
+            "is_approved": is_approved, # Response from HIL
+        }
+
+    thread_id = str(uuid.uuid4())
+
+    config = {
+        "configurable": {
+            "thread_id": thread_id
+        }
+    }
+
+    for item in workflow.stream("cat", config):
+        print(item)
+    ```
+
+    ```pycon
+    {'write_essay': 'An essay about topic: cat'}
+    {'__interrupt__': (Interrupt(value={'essay': 'An essay about topic: cat', 'action': 'Please approve/reject the essay'}, resumable=True, ns=['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'], when='during'),)}
+    ```
+
+    An essay has been written and is ready for review. Once the review is provided, we can resume the workflow:
+
+    ```python
+    from langgraph.types import Command
+
+    # Get review from a user (e.g., via a UI)
+    # In this case, we're using a bool, but this can be any json-serializable value.
+    human_review = True
+
+    for item in workflow.stream(Command(resume=human_review), config):
+        print(item)
+    ```
+
+    ```pycon
+    {'workflow': {'essay': 'An essay about topic: cat', 'is_approved': False}}
+    ```
+
+    The workflow has been completed and the review has been added to the essay.
+
+## Entrypoint
+
+The [`@entrypoint`][langgraph.func.entrypoint] decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](./low_level.md#interrupt).
+
+### Definition
+
+An **entrypoint** is defined by decorating a function with the `@entrypoint` decorator. 
+
+The function **must accept a single positional argument**, which serves as the workflow input. If you need to pass multiple pieces of data, use a dictionary as the input type for the first argument.
+
+Decorating a function with an `entrypoint` produces a [`Pregel`][langgraph.pregel.Pregel.stream] instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
+
+You will usually want to pass a **checkpointer** to the `@entrypoint` decorator to enable persistence and use features like **human-in-the-loop**.
+
+=== "Sync"
+
+    ```python
+    from langgraph.func import entrypoint
+
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(some_input: dict) -> int:
+        # some logic that may involve long-running tasks like API calls,
+        # and may be interrupted for human-in-the-loop.
+        ...
+        return result
+    ```
+
+=== "Async"
+
+    ```python
+    from langgraph.func import entrypoint
+
+    @entrypoint(checkpointer=checkpointer)
+    async def my_workflow(some_input: dict) -> int:
+        # some logic that may involve long-running tasks like API calls,
+        # and may be interrupted for human-in-the-loop
+        ...
+        return result 
+    ```
+
+!!! important "Serialization"
+
+    The **inputs** and **outputs** of entrypoints must be JSON-serializable to support checkpointing. Please see the [serialization](#serialization) section for more details.
+
+
+### Injectable Parameters
+
+When declaring an `entrypoint`, you can request access to additional parameters that will be injected automatically at run time. These parameters include:
+
+
+| Parameter    | Description                                                                                                                                       |
+|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
+| **previous** | Access the the state associated with the previous `checkpoint` for the given thread. See [state management](#state-management).                   |
+| **store**    | An instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for [long-term memory](#long-term-memory).                                     |
+| **writer**   | For streaming custom data, to write custom data to the `custom` stream. Useful for [streaming custom data](#streaming-custom-data).               |
+| **config**   | For accessing run time configuration. See [RunnableConfig](https://python.langchain.com/docs/concepts/runnables/#runnableconfig) for information. |
+
+!!! important
+
+    Declare the parameters with the appropriate name and type annotation.
+
+??? example "Requesting Injectable Parameters"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.func import entrypoint
+    from langgraph.store.base import BaseStore
+    from langgraph.store.memory import InMemoryStore
+
+    in_memory_store = InMemoryStore(...)  # An instance of InMemoryStore for long-term memory
+
+    @entrypoint(
+        checkpointer=checkpointer,  # Specify the checkpointer
+        store=in_memory_store  # Specify the store
+    )  
+    def my_workflow(
+        some_input: dict,  # The input (e.g., passed via `invoke`)
+        *,
+        previous: Any = None, # For short-term memory
+        store: BaseStore,  # For long-term memory
+        writer: StreamWriter,  # For streaming custom data
+        config: RunnableConfig  # For accessing the configuration passed to the entrypoint
+    ) -> ...:
+    ```
+
+### Executing
+
+Using the [`@entrypoint`](#entrypoint) yields a [`Pregel`][langgraph.pregel.Pregel.stream] object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods.
+
+=== "Invoke"
+
+    ```python
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    my_workflow.invoke(some_input, config)  # Wait for the result synchronously
+    ```
+
+=== "Async Invoke"
+
+    ```python
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    await my_workflow.ainvoke(some_input, config)  # Await result asynchronously
+    ```
+
+=== "Stream"
+    
+    ```python
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+
+    for chunk in my_workflow.stream(some_input, config):
+        print(chunk)
+    ```
+
+=== "Async Stream"
+
+    ```python
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+
+    async for chunk in my_workflow.astream(some_input, config):
+        print(chunk)
+    ```
+
+### Resuming
+
+Resuming an execution after an [interrupt][langgraph.types.interrupt] can be done by passing a **resume** value to the [Command][langgraph.types.Command] primitive.
+
+=== "Invoke"
+
+    ```python
+    from langgraph.types import Command
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    my_workflow.invoke(Command(resume=some_resume_value), config)
+    ```
+
+=== "Async Invoke"
+
+    ```python
+    from langgraph.types import Command
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    await my_workflow.ainvoke(Command(resume=some_resume_value), config)
+    ```
+
+=== "Stream"
+
+    ```python
+    from langgraph.types import Command
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    for chunk in my_workflow.stream(Command(resume=some_resume_value), config):
+        print(chunk)
+    ```
+
+=== "Async Stream"
+
+    ```python
+    from langgraph.types import Command
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+
+    async for chunk in my_workflow.astream(Command(resume=some_resume_value), config):
+        print(chunk)
+    ```
+
+**Resuming after an error**
+
+
+To resume after an error, run the `entrypoint` with a `None` and the same **thread id** (config).
+
+This assumes that the underlying **error** has been resolved and execution can proceed successfully.
+
+=== "Invoke"
+
+    ```python
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    my_workflow.invoke(None, config)
+    ```
+
+=== "Async Invoke"
+
+    ```python
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    await my_workflow.ainvoke(None, config)
+    ```
+
+=== "Stream"
+
+    ```python
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+    
+    for chunk in my_workflow.stream(None, config):
+        print(chunk)
+    ```
+
+=== "Async Stream"
+
+    ```python
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread_id"
+        }
+    }
+
+    async for chunk in my_workflow.astream(None, config):
+        print(chunk)
+    ```
+
+### State Management
+
+When an `entrypoint` is defined with a `checkpointer`, it stores information between successive invocations on the same **thread id** in [checkpoints](persistence.md#checkpoints). 
+
+This allows accessing the state from the previous invocation using the `previous` parameter.
+
+By default, the `previous` parameter is the return value of the previous invocation.
+
+```python
+@entrypoint(checkpointer=checkpointer)
+def my_workflow(number: int, *, previous: Any = None) -> int:
+    previous = previous or 0
+    return number + previous
+
+config = {
+    "configurable": {
+        "thread_id": "some_thread_id"
+    }
+}
+
+my_workflow.invoke(1, config)  # 1 (previous was None)
+my_workflow.invoke(2, config)  # 3 (previous was 1 from the previous invocation)
+```
+
+#### `entrypoint.final`
+
+[entrypoint.final][langgraph.func.entrypoint.final] is a special primitive that can be returned from an entrypoint and allows **decoupling** the value that is **saved in the checkpoint** from the **return value of the entrypoint**.
+
+The first value is the return value of the entrypoint, and the second value is the value that will be saved in the checkpoint. The type annotation is `entrypoint.final[return_type, save_type]`.
+
+```python
+@entrypoint(checkpointer=checkpointer)
+def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
+    previous = previous or 0
+    # This will return the previous value to the caller, saving
+    # 2 * number to the checkpoint, which will be used in the next invocation 
+    # for the `previous` parameter.
+    return entrypoint.final(value=previous, save=2 * number)
+
+config = {
+    "configurable": {
+        "thread_id": "1"
+    }
+}
+
+my_workflow.invoke(3, config)  # 0 (previous was None)
+my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)
+```
+
+## Task
+
+A **task** represents a discrete unit of work, such as an API call or data processing step. It has two key characteristics:
+
+* **Asynchronous Execution**: Tasks are designed to be executed asynchronously, allowing multiple operations to run concurrently without blocking.
+* **Checkpointing**: Task results are saved to a checkpoint, enabling resumption of the workflow from the last saved state. (See [persistence](persistence.md) for more details).
+
+### Definition
+
+Tasks are defined using the `@task` decorator, which wraps a regular Python function.
+
+```python
+from langgraph.func import task
+
+@task()
+def slow_computation(input_value):
+    # Simulate a long-running operation
+    ...
+    return result
+```
+
+!!! important "Serialization"
+
+    The **outputs** of tasks must be JSON-serializable to support checkpointing.
+
+### Execution
+
+**Tasks** can only be called from within an **entrypoint**, another **task**, or a [state graph node](./low_level.md#nodes). 
+
+Tasks *cannot* be called directly from the main application code. 
+
+When you call a **task**, it returns *immediately* with a future object. A future is a placeholder for a result that will be available later.
+
+To obtain the result of a **task**, you can either wait for it synchronously (using `result()`) or await it asynchronously (using `await`).
+
+
+=== "Synchronous Invocation"
+
+    ```python
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(some_input: int) -> int:
+        future = slow_computation(some_input)
+        return future.result()  # Wait for the result synchronously
+    ```
+
+=== "Asynchronous Invocation"
+
+    ```python
+    @entrypoint(checkpointer=checkpointer)
+    async def my_workflow(some_input: int) -> int:
+        return await slow_computation(some_input)  # Await result asynchronously
+    ```
+
+## When to use a task
+
+**Tasks** are useful in the following scenarios:
+
+- **Checkpointing**: When you need to save the result of a long-running operation to a checkpoint, so you don't need to recompute it when resuming the workflow.
+- **Human-in-the-loop**: If you're building a workflow that requires human intervention, you MUST use **tasks** to encapsulate any randomness (e.g., API calls) to ensure that the workflow can be resumed correctly. See the [determinism](#determinism) section for more details.
+- **Parallel Execution**: For I/O-bound tasks, **tasks** enable parallel execution, allowing multiple operations to run concurrently without blocking (e.g., calling multiple APIs).
+- **Observability**: Wrapping operations in **tasks** provides a way to track the progress of the workflow and monitor the execution of individual operations using [LangSmith](https://docs.smith.langchain.com/).
+- **Retryable Work**: When work needs to be retried to handle failures or inconsistencies, **tasks** provide a way to encapsulate and manage the retry logic.
+ 
+## Serialization
+
+There are two key aspects to serialization in LangGraph:
+
+1. `@entrypoint` inputs and outputs must be JSON-serializable.
+2. `@task` outputs must be JSON-serializable.
+
+These requirements are necessary for enabling checkpointing and workflow resumption. Use python primitives
+like dictionaries, lists, strings, numbers, and booleans to ensure that your inputs and outputs are serializable.
+
+Serialization ensures that workflow state, such as task results and intermediate values, can be reliably saved and restored. This is critical for enabling human-in-the-loop interactions, fault tolerance, and parallel execution.
+
+Providing non-serializable inputs or outputs will result in a runtime error when a workflow is configured with a checkpointer.
+
+## Determinism
+
+To utilize features like **human-in-the-loop**, any randomness should be encapsulated inside of **tasks**. This guarantees that when execution is halted (e.g., for human in the loop) and then resumed, it will follow the same *sequence of steps*, even if **task** results are non-deterministic.
+
+LangGraph achieves this behavior by persisting **task** and [**subgraph**](./low_level.md#subgraphs) results as they execute. A well-designed workflow ensures that resuming execution follows the *same sequence of steps*, allowing previously computed results to be retrieved correctly without having to re-execute them. This is particularly useful for long-running **tasks** or **tasks** with non-deterministic results, as it avoids repeating previously done work and allows resuming from essentially the same 
+
+While different runs of a workflow can produce different results, resuming a **specific** run should always follow the same sequence of recorded steps. This allows LangGraph to efficiently look up **task** and **subgraph** results that were executed prior to the graph being interrupted and avoid recomputing them.
+
+## Idempotency
+
+Idempotency ensures that running the same operation multiple times produces the same result. This helps prevent duplicate API calls and redundant processing if a step is rerun due to a failure. Always place API calls inside **tasks** functions for checkpointing, and design them to be idempotent in case of re-execution. Re-execution can occur if a **task** starts, but does not complete successfully. Then, if the workflow is resumed, the **task** will run again. Use idempotency keys or verify existing results to avoid duplication.
+
+## Functional API vs. Graph API
+
+The **Functional API** and the [Graph APIs (StateGraph)](./low_level.md#stategraph) provide two different paradigms to create applications with LangGraph. Here are some key differences:
+
+- **Control flow**: The Functional API does not require thinking about graph structure. You can use standard Python constructs to define workflows. This will usually trim the amount of code you need to write.
+- **State management**: The **GraphAPI** requires declaring a [**State**](./low_level.md#state) and may require defining [**reducers**](./low_level.md#reducers) to manage updates to the graph state. `@entrypoint` and `@tasks` do not require explicit state management as their state is scoped to the function and is not shared across functions.
+- **Checkpointing**: Both APIs generate and use checkpoints. In the **Graph API** a new checkpoint is generated after every [superstep](./low_level.md). In the **Functional API**, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint.
+- **Visualization**: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime.
+
+## Common Pitfalls
+
+### Handling side effects
+
+Encapsulate side effects (e.g., writing to a file, sending an email) in tasks to ensure they are not executed multiple times when resuming a workflow.
+
+=== "Incorrect"
+
+    In this example, a side effect (writing to a file) is directly included in the workflow, so it will be executed a second time when resuming the workflow.
+
+    ```python
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(inputs: dict) -> int:
+        # This code will be executed a second time when resuming the workflow.
+        # Which is likely not what you want.
+        # highlight-next-line
+        with open("output.txt", "w") as f:
+            # highlight-next-line
+            f.write("Side effect executed")
+        value = interrupt("question")
+        return value
+    ```
+
+=== "Correct"
+
+    In this example, the side effect is encapsulated in a task, ensuring consistent execution upon resumption.
+
+    ```python
+    from langgraph.func import task
+
+    # highlight-next-line
+    @task
+    # highlight-next-line
+    def write_to_file():
+        with open("output.txt", "w") as f:
+            f.write("Side effect executed")
+
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(inputs: dict) -> int:
+        # The side effect is now encapsulated in a task.
+        write_to_file().result()
+        value = interrupt("question")
+        return value
+    ```
+
+### Non-deterministic control flow
+
+Operations that might give different results each time (like getting current time or random numbers) should be encapsulated in tasks to ensure that on resume, the same result is returned.
+
+* In a task: Get random number (5) → interrupt → resume → (returns 5 again) → ...
+* Not in a task: Get random number (5) → interrupt → resume → get new random number (7) → ...
+
+This is especially important when using **human-in-the-loop** workflows with multiple interrupts calls. LangGraph keeps a list
+of resume values for each task/entrypoint. When an interrupt is encountered, it's matched with the corresponding resume value.
+This matching is strictly **index-based**, so the order of the resume values should match the order of the interrupts.
+
+If order of execution is not maintained when resuming, one `interrupt` call may be matched with the wrong `resume` value, leading to incorrect results.
+
+Please read the section on [determinism](#determinism) for more details.
+
+=== "Incorrect"
+
+    In this example, the workflow uses the current time to determine which task to execute. This is non-deterministic because the result of the workflow depends on the time at which it is executed.
+
+    ```python
+    from langgraph.func import entrypoint
+
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(inputs: dict) -> int:
+        t0 = inputs["t0"]
+        # highlight-next-line
+        t1 = time.time()
+        
+        delta_t = t1 - t0
+        
+        if delta_t > 1:
+            result = slow_task(1).result()
+            value = interrupt("question")
+        else:
+            result = slow_task(2).result()
+            value = interrupt("question")
+            
+        return {
+            "result": result,
+            "value": value
+        }
+    ```
+
+=== "Correct"
+
+    In this example, the workflow uses the input `t0` to determine which task to execute. This is deterministic because the result of the workflow depends only on the input.
+
+    ```python
+    import time
+
+    from langgraph.func import task
+
+    # highlight-next-line
+    @task
+    # highlight-next-line
+    def get_time() -> float:
+        return time.time()
+
+    @entrypoint(checkpointer=checkpointer)
+    def my_workflow(inputs: dict) -> int:
+        t0 = inputs["t0"]
+        # highlight-next-line
+        t1 = get_time().result()
+        
+        delta_t = t1 - t0
+        
+        if delta_t > 1:
+            result = slow_task(1).result()
+            value = interrupt("question")
+        else:
+            result = slow_task(2).result()
+            value = interrupt("question")
+            
+        return {
+            "result": result,
+            "value": value
+        }
+    ```
+
+## Patterns
+
+Below are a few simple patterns that show examples of **how to** use the **Functional API**.
+
+When defining an `entrypoint`, input is restricted to the first argument of the function. To pass multiple inputs, you can use a dictionary.
+
+```python
+@entrypoint(checkpointer=checkpointer)
+def my_workflow(inputs: dict) -> int:
+    value = inputs["value"]
+    another_value = inputs["another_value"]
+    ...
+
+my_workflow.invoke({"value": 1, "another_value": 2})  
+```
+
+### Parallel execution
+
+Tasks can be executed in parallel by invoking them concurrently and waiting for the results. This is useful for improving performance in IO bound tasks (e.g., calling APIs for LLMs).
+
+```python
+@task
+def add_one(number: int) -> int:
+    return number + 1
+
+@entrypoint(checkpointer=checkpointer)
+def graph(numbers: list[int]) -> list[str]:
+    futures = [add_one(i) for i in numbers]
+    return [f.result() for f in futures]
+```
+
+### Calling subgraphs
+
+The **Functional API** and the [**Graph API**](./low_level.md) can be used together in the same application as they share the same underlying runtime.
+
+```python
+from langgraph.func import entrypoint
+from langgraph.graph import StateGraph
+
+builder = StateGraph()
+...
+some_graph = builder.compile()
+
+@entrypoint()
+def some_workflow(some_input: dict) -> int:
+    # Call a graph defined using the graph API
+    result_1 = some_graph.invoke(...)
+    # Call another graph defined using the graph API
+    result_2 = another_graph.invoke(...)
+    return {
+        "result_1": result_1,
+        "result_2": result_2
+    }
+```
+
+### Calling other entrypoints
+
+You can call other **entrypoints** from within an **entrypoint** or a **task**.
+
+```python
+@entrypoint() # Will automatically use the checkpointer from the parent entrypoint
+def some_other_workflow(inputs: dict) -> int:
+    return inputs["value"]
+
+@entrypoint(checkpointer=checkpointer)
+def my_workflow(inputs: dict) -> int:
+    value = some_other_workflow.invoke({"value": 1})
+    return value
+```
+
+### Streaming custom data
+
+You can stream custom data from an **entrypoint** by using the `StreamWriter` type. This allows you to write custom data to the `custom` stream.
+
+```python
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.func import entrypoint, task
+from langgraph.types import StreamWriter
+
+@task
+def add_one(x):
+    return x + 1
+
+@task
+def add_two(x):
+    return x + 2
+
+checkpointer = MemorySaver()
+
+@entrypoint(checkpointer=checkpointer)
+def main(inputs, writer: StreamWriter) -> int:
+    """A simple workflow that adds one and two to a number."""
+    writer("hello") # Write some data to the `custom` stream
+    add_one(inputs['number']).result() # Will write data to the `updates` stream
+    writer("world") # Write some more data to the `custom` stream
+    add_two(inputs['number']).result() # Will write data to the `updates` stream
+    return 5 
+
+config = {
+    "configurable": {
+        "thread_id": "1"
+    }
+}
+
+for chunk in main.stream({"number": 1}, stream_mode=["custom", "updates"], config=config):
+    print(chunk)
+```
+
+```pycon
+('updates', {'add_one': 2})
+('updates', {'add_two': 3})
+('custom', 'hello')
+('custom', 'world')
+('updates', {'main': 5})
+```
+
+!!! important
+
+    The `writer` parameter is automatically injected at run time. It will only be injected if the 
+    parameter name appears in the function signature with that *exact* name.
+
+
+### Retry policy
+
+```python
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.func import entrypoint, task
+from langgraph.types import RetryPolicy
+
+attempts = 0
+
+# Let's configure the RetryPolicy to retry on ValueError.
+# The default RetryPolicy is optimized for retrying specific network errors.
+retry_policy = RetryPolicy(retry_on=ValueError)
+
+@task(retry=retry_policy) 
+def get_info():
+    global attempts
+    attempts += 1
+
+    if attempts < 2:
+        raise ValueError('Failure')
+    return "OK"
+
+checkpointer = MemorySaver()
+
+@entrypoint(checkpointer=checkpointer)
+def main(inputs, writer):
+    return get_info().result()
+
+config = {
+    "configurable": {
+        "thread_id": "1"
+    }
+}
+
+main.invoke({'any_input': 'foobar'}, config=config)
+```
+
+```pycon
+'OK'
+```
+
+### Resuming after an error
+
+```python
+import time
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.func import entrypoint, task
+from langgraph.types import StreamWriter
+
+# Global variable to track the number of attempts
+attempts = 0
+
+@task()
+def get_info():
+    """
+    Simulates a task that fails once before succeeding.
+    Raises an exception on the first attempt, then returns "OK" on subsequent tries.
+    """
+    global attempts
+    attempts += 1
+
+    if attempts < 2:
+        raise ValueError("Failure")  # Simulate a failure on the first attempt
+    return "OK"
+
+# Initialize an in-memory checkpointer for persistence
+checkpointer = MemorySaver()
+
+@task
+def slow_task():
+    """
+    Simulates a slow-running task by introducing a 1-second delay.
+    """
+    time.sleep(1)
+    return "Ran slow task."
+
+@entrypoint(checkpointer=checkpointer)
+def main(inputs, writer: StreamWriter):
+    """
+    Main workflow function that runs the slow_task and get_info tasks sequentially.
+
+    Parameters:
+    - inputs: Dictionary containing workflow input values.
+    - writer: StreamWriter for streaming custom data.
+
+    The workflow first executes `slow_task` and then attempts to execute `get_info`,
+    which will fail on the first invocation.
+    """
+    slow_task_result = slow_task().result()  # Blocking call to slow_task
+    get_info().result()  # Exception will be raised here on the first attempt
+    return slow_task_result
+
+# Workflow execution configuration with a unique thread identifier
+config = {
+    "configurable": {
+        "thread_id": "1"  # Unique identifier to track workflow execution
+    }
+}
+
+# This invocation will take ~1 second due to the slow_task execution
+try:
+    # First invocation will raise an exception due to the `get_info` task failing
+    main.invoke({'any_input': 'foobar'}, config=config)
+except ValueError:
+    pass  # Handle the failure gracefully
+```
+
+When we resume execution, we won't need to re-run the `slow_task` as its result is already saved in the checkpoint.
+
+```python
+main.invoke(None, config=config)
+```
+
+```pycon
+'Ran slow task.'
+```
+
+### Human-in-the-loop
+
+The functional API supports [human-in-the-loop](human_in_the_loop.md) workflows using the `interrupt` function and the `Command` primitive.
+
+Please see the following examples for more details:
+
+* [How to wait for user input (Functional API)](../how-tos/wait-user-input-functional.ipynb): Shows how to implement a simple human-in-the-loop workflow using the functional API.
+* [How to review tool calls (Functional API)](../how-tos/review-tool-calls-functional.ipynb): Guide demonstrates how to implement human-in-the-loop workflows in a ReAct agent using the LangGraph Functional API.
+
+### Short-term memory
+
+[State management](#state-management) using the **previous** parameter and optionally using the `entrypoint.final` primitive can be used to implement [short term memory](memory.md).
+
+Please see the following how-to guides for more details:
+
+*  [How to add thread-level persistence (functional API)](../how-tos/persistence-functional.ipynb): Shows how to add thread-level persistence to a functional API workflow and implements a simple chatbot.
+
+### Long-term memory
+
+[long-term memory](memory.md#long-term-memory) allows storing information across different **thread ids**. This could be useful for learning information
+about a given user in one conversation and using it in another.
+
+Please see the following how-to guides for more details:
+
+* [How to add cross-thread persistence (functional API)](../how-tos/cross-thread-persistence-functional.ipynb): Shows how to add cross-thread persistence to a functional API workflow and implements a simple chatbot.
+
+### Workflows
+
+* [Workflows and agent](../tutorials/workflows/index.md) guide for more examples of how to build workflows using the Functional API.
+
+### Agents
+
+* [How to create a React agent from scratch (Functional API)](../how-tos/react-agent-from-scratch-functional.ipynb): Shows how to create a simple React agent from scratch using the functional API.
+* [How to build a multi-agent network](../how-tos/multi-agent-network-functional.ipynb): Shows how to build a multi-agent network using the functional API.
+* [How to add multi-turn conversation in a multi-agent application (functional API)](../how-tos/multi-agent-multi-turn-convo-functional.ipynb): allow an end-user to engage in a multi-turn conversation with one or more agents.  
+

docs/docs/concepts/high_level.md

@@ -1,58 +1,26 @@
 # Why LangGraph?
 
-LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.
+## LLM applications
 
-However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
+LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. [Workflows](https://www.anthropic.com/research/building-effective-agents) have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "[agentic system](https://www.anthropic.com/research/building-effective-agents)". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
 
-- Using an LLM to route between two potential paths
-- Using an LLM to decide which of many tools to call
-- Using an LLM to decide whether the generated answer is sufficient or more work is need
+![Agent Workflow](img/agent_workflow.png)
 
-There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
+## What LangGraph provides
 
-![Agent Types](img/agent_types.png)
+LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:
 
-Several concepts are utilized in many agent architectures:
+### Persistence
 
-- [Tool calling](agentic_concepts.md#tool-calling): this is often how LLMs make decisions
-- Action taking: often times, the LLMs' outputs are used as the input to an action
-- [Memory](agentic_concepts.md#memory): reliable systems need to have knowledge of things that occurred
-- [Planning](agentic_concepts.md#planning): planning steps (either explicit or implicit) are useful for ensuring that the LLM, when making decisions, makes them in the highest fidelity way.
+LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:
 
-## Challenges
+- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.
 
-In practice, there is often a trade-off between control and reliability. As we give LLMs more control, the application often become less reliable. This can be due to factors such as LLM non-determinism and / or errors in selecting tools (or steps) that the agent uses (takes).
+### Streaming
 
-![Agent Challenge](img/challenge.png)
+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.ipynb#updates)) and [tokens from LLM calls](../how-tos/streaming-tokens.ipynb) embedded in an application.
 
-## Core Principles
+### Debugging and Deployment
 
-The motivation of LangGraph is to help bend the curve, preserving higher reliability as we give the agent more control over the application. We'll outline a few specific pillars of LangGraph that make it well suited for building reliable agents. 
-
-![Langgraph](img/langgraph.png)
-
-**Controllability**
-
-LangGraph gives the developer a high degree of [control](../how-tos/index.md#controllability) by expressing the flow of the application as a set of nodes and edges. All nodes can access and modify a common state (memory). The control flow of the application can set using edges that connect nodes, either deterministically or via conditional logic. 
-
-**Persistence**
-
-LangGraph gives the developer many options for [persisting](../how-tos/index.md#persistence) graph state using short-term or long-term (e.g., via a database) memory. 
-
-**Human-in-the-Loop**
-
-The persistence layer enables several different [human-in-the-loop](../how-tos/index.md#human-in-the-loop) interaction patterns with agents; for example, it's possible to pause an agent, review its state, edit it state, and approve a follow-up step. 
-
-**Streaming**
-
-LangGraph comes with first class support for [streaming](../how-tos/index.md#streaming), which can expose state to the user (or developer) over the course of agent execution. LangGraph supports streaming of both events ([like a tool call being taken](../how-tos/stream-updates.ipynb)) as well as of [tokens that an LLM may emit](../how-tos/streaming-tokens.ipynb).
-
-## Debugging
-
-Once you've built a graph, you often want to test and debug it. [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file) is a specialized IDE for visualization and debugging of LangGraph applications.
-
-![Langgraph Studio](img/lg_studio.png)
-
-## Deployment
-
-Once you have confidence in your LangGraph application, many developers want an easy path to deployment. [LangGraph Platform](../concepts/index.md#langgraph-platform) offers a range of options for deploying LangGraph graphs.
+LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 

docs/docs/concepts/index.md

@@ -28,6 +28,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
 - [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.  
 - [Streaming](streaming.md): 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. 
+- [Functional API (beta)](functional_api.md): An alternative to [Graph API (StateGraph)](low_level.md#stategraph) for development in LangGraph.
 - [FAQ](faq.md): Frequently asked questions about LangGraph.
 
 ## LangGraph Platform
@@ -71,7 +72,7 @@ The LangGraph Platform comprises several components that work together to suppor
 ### Deployment Options
 
 
-- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
+- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
 - [Cloud SaaS](./langgraph_cloud.md): Hosted as part of LangSmith.
 - [Bring Your Own Cloud](./bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
 - [Self-Hosted Enterprise](./self_hosted.md): Completely managed by you.

docs/docs/concepts/langgraph_cloud.md

@@ -21,6 +21,12 @@ Resource Allocation:
 
 See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
 
+## Revision
+
+A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
+
+See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
+
 ## Persistence
 
 A dedicated database is automatically created for each deployment. The database serves as the [persistence layer](../concepts/persistence.md) for the deployment.
@@ -41,12 +47,6 @@ Scale down actions are delayed for 30 minutes before any action is taken. In oth
 
 In the future, the autoscaling implementation may evolve to accommodate other metrics such as background run queue size.
 
-## Revision
-
-A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
-
-See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
-
 ## Asynchronous Deployment
 
 Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
@@ -55,12 +55,26 @@ Infrastructure for [deployments](#deployment) and [revisions](#revision) are pro
 - When a subsequent revision is created for a deployment, there is no database creation step. The deployment time for a subsequent revision is significantly faster compared to the deployment time of the initial revision.
 - The deployment process for each revision contains a build step, which can take up to a few minutes.
 
-!!! info "Database creation for `Development` type deployments takes longer than database creation for `Production` type deployments."
+## LangSmith Integration
+
+A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployemnt. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING_V2` and `LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set internally, automatically. Traces are created for each run and are emitted to the tracing project automatically.
+
+When a deployment is deleted, the traces and the tracing project are not deleted.
+
+## Automatic Deletion
+
+Deployments are automatically deleted after 28 consecutive days of non-use (it is in an unused state). A deployment is in an unused state if there are no traces emitted to LangSmith from the deployment after 28 consecutive days. On any given day, if a deployment emits a trace to LangSmith, the counter for consecutive days of non-use is reset.
+
+- An email notification is sent after 7 consecutive days of non-use.
+- A deployment is deleted after 28 consecutive days of non-use.
+
+!!! danger "Data Cannot Be Recovered"
+    After a deployment is deleted, the data (i.e. [persistence](#persistence)) from the deployment cannot be recovered.
 
 ## Architecture
 
 !!! warning "Subject to Change"
-The Cloud SaaS deployment architecture may change in the future.
+    The Cloud SaaS deployment architecture may change in the future.
 
 A high-level diagram of a Cloud SaaS deployment.
 

docs/docs/concepts/low_level.md

@@ -22,10 +22,6 @@ A super-step can be considered a single iteration over the graph nodes. Nodes th
 
 The `StateGraph` class is the main graph class to use. This is parameterized by a user defined `State` object.
 
-### MessageGraph
-
-The `MessageGraph` class is a special type of graph. The `State` of a `MessageGraph` is ONLY a list of messages. This class is rarely used except for chatbots, as most applications require the `State` to be more complex than a list of messages.
-
 ### Compiling your graph
 
 To build your graph, you first define the [state](#state), you then add [nodes](#nodes) and [edges](#edges), and then you compile it. What exactly is compiling your graph and why is it needed?
@@ -359,6 +355,29 @@ Use `Command` when you need to **both** update the graph state **and** route to
 
 Use [conditional edges](#conditional-edges) to route between nodes conditionally without updating the state.
 
+### Navigating to a node in a parent graph
+
+If you are using [subgraphs](#subgraphs), you might want to navigate from a node a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
+
+```python
+def my_node(state: State) -> Command[Literal["my_other_node"]]:
+    return Command(
+        update={"foo": "bar"},
+        goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph
+        graph=Command.PARENT
+    )
+```
+
+!!! note
+
+    Setting `graph` to `Command.PARENT` will navigate to the closest parent graph.
+
+!!! important "State updates with `Command.PARENT`"
+
+    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/command.ipynb#navigating-to-a-node-in-a-parent-graph).
+
+This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs).
+
 ### Using inside tools
 
 A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. To update the graph state from the tool, you can return `Command(update={"my_custom_key": "foo", "messages": [...]})` from the tool: