Update README.md

Sync readmes
2026-10-02 22:45:07 +02:00 · 2024-07-11 15:42:32 -07:00 · 2024-07-11 15:42:16 -07:00 · 2024-07-11 15:16:58 -07:00 · 2024-07-11 15:14:12 -07:00
231 changed files with 15849 additions and 31748 deletions
@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
+description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
 labels: ["02 Bug Report"]
 body:
  - type: markdown
@@ -15,11 +15,11 @@ body:
        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
        if there's another way to solve your problem:
        
-        [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [GitHub search](https://github.com/langchain-ai/langgraph),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangChain Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [API Reference](https://api.python.langchain.com/en/stable/),
+        [GitHub search](https://github.com/langchain-ai/langchain),
+        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
        [LangChain ChatBot](https://chat.langchain.com/)
  - type: checkboxes
    id: checks
@@ -54,13 +54,13 @@ body:
        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.

      placeholder: |
-        from langgraph.graph import StateGraph
+        from langchain_core.runnables import RunnableLambda

        def bad_code(inputs) -> int:
          raise NotImplementedError('For demo purpose')
          
-        chain = StateGraph(list)
-        chain.invoke('Hello!')
+          chain = RunnableLambda(bad_code)
+          chain.invoke('Hello!')
      render: python
  - type: textarea
    id: error
@@ -82,7 +82,7 @@ body:

        Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
      placeholder: |
-        * I'm trying to use the `langgraph` library to do X.
+        * I'm trying to use the `langchain` library to do X.
        * I expect to see Y.
        * Instead, it does Z.
    validations:
@@ -102,7 +102,7 @@ body:
        
        python -m langchain_core.sys_info
      placeholder: |
-        "pip freeze | grep langgraph"
+        "pip freeze | grep langchain"
        platform
        python version
        
@@ -3,13 +3,13 @@ version: 2.1
 contact_links:
  - name: 🤔 Question or Problem
    about: Ask a question or ask about a problem in GitHub Discussions.
-    url: https://github.com/langchain-ai/langgraph/discussions/categories/q-a
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/q-a
+  - name: Discord
+    url: https://discord.gg/6adMQxSpJS
+    about: General community discussions
  - name: Feature Request
-    url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/ideas
    about: Suggest a feature or an idea
  - name: Show and tell
    about: Show what you built with LangChain
-    url: https://github.com/langchain-ai/langgraph/discussions/categories/show-and-tell
-  - name: Slack
-    url:  https://www.langchain.com/join-community
-    about: General community discussions
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/show-and-tell
@@ -1,5 +1,5 @@
 name: Documentation
-description: Report an issue related to the LangGraph documentation.
+description: Report an issue related to the LangChain documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
 labels: [03 - Documentation]

@@ -36,10 +36,7 @@
            working-directory: [
                "libs/langgraph",
                "libs/sdk-py",
-                "libs/cli",
-                "libs/checkpoint",
-                "libs/checkpoint-sqlite",
-                "libs/checkpoint-postgres"
+                "libs/cli"
            ]
        uses: ./.github/workflows/_lint.yml
        with:
@@ -53,10 +50,7 @@
          matrix:
            working-directory: [
                "libs/langgraph",
-                "libs/cli",
-                "libs/checkpoint",
-                "libs/checkpoint-sqlite",
-                "libs/checkpoint-postgres"
+                "libs/cli"
            ]
        uses: ./.github/workflows/_test.yml
        with:
@@ -6,7 +6,7 @@ on:
      working-directory:
        required: true
        type: string
-        default: "libs/langgraph"
+        default: 'libs/langgraph'

 env:
  PYTHON_VERSION: "3.11"
@@ -104,7 +104,7 @@ jobs:
            REGEX="^$SHORT_PKG_NAME==\\d+\\.\\d+\\.\\d+((a|b|rc)\\d+)?\$"
          fi
          echo $REGEX
-          PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1 || echo "")
+          PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1)
          echo $PREV_TAG
          if [ "$TAG" == "$PREV_TAG" ]; then
            echo "No new version to release"
@@ -137,7 +137,8 @@ jobs:
      - build
      - release-notes
    permissions: write-all
-    uses: ./.github/workflows/_test_release.yml
+    uses:
+      ./.github/workflows/_test_release.yml
    with:
      working-directory: ${{ inputs.working-directory }}
    secrets: inherit
@@ -197,15 +198,9 @@ jobs:
              "$PKG_NAME==$VERSION" \
          )

-          if [[ "$PKG_NAME" == *checkpoint* ]]; then
-            # since checkpoint packages are namespace packages, import them with . convention
-            # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
-            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
-          else
-            # Replace all dashes in the package name with underscores,
-            # since that's how Python imports packages with dashes in the name.
-            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
-          fi
+          # Replace all dashes in the package name with underscores,
+          # since that's how Python imports packages with dashes in the name.
+          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"

          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"

@@ -1,28 +0,0 @@
-name: Check File Size
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-  workflow_dispatch:
-
-jobs:
-  file-size-check:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@v44
-      - name: Filter by size
-        run: |
-          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M)
-          if [ -n "$large_added_files" ]; then
-            echo "Large files added: $large_added_files"
-            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
-            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
-            exit 1
-          fi
@@ -1,14 +1,6 @@
-.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
+.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell

-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
+build-docs:
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict

@@ -16,7 +8,7 @@ serve-clean-docs: clean-docs
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph

-serve-docs: build-typedoc
+serve-docs:
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty

@@ -3,6 +3,7 @@
 ![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
+[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)

 ⚡ Building language agents as graphs ⚡
@@ -10,6 +11,9 @@
 > [!NOTE]
 > Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).

+> [!TIP]
+> Looking to deploy your LangGraph application? [Join the waitlist](https://www.langchain.com/langgraph-cloud-beta) for [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/), our managed service for deploying and hosting LangGraph applications.
+
 ## 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.
@@ -53,12 +57,10 @@ export LANGSMITH_API_KEY=lsv2_sk_...
 ```

 ```python
-from typing import Annotated, Literal, TypedDict
-
 from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint import MemorySaver
 from langgraph.graph import END, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode

@@ -69,8 +71,8 @@ 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."
+        return ["It's 60 degrees and foggy."]
+    return ["It's 90 degrees and sunny."]


 tools = [search]
@@ -80,7 +82,7 @@ tool_node = ToolNode(tools)
 model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)

 # Define the function that determines whether to continue or not
-def should_continue(state: MessagesState) -> Literal["tools", END]:
+def should_continue(state: MessagesState) -> Union[Literal["tools"], type(END)]:
    messages = state['messages']
    last_message = messages[-1]
    # If the LLM makes a tool call, then we route to the "tools" node
@@ -1,4 +1,2 @@
 *.ipynb
 site/
-docs/tutorials/**/*.png
-docs/cloud/reference/sdk/js_ts_sdk_ref.md
@@ -27,8 +27,6 @@ _MANUAL = {
        "streaming-events-from-within-tools-without-langchain.ipynb",
        "streaming-from-final-node.ipynb",
        "persistence.ipynb",
-        "input_output_schema.ipynb",
-        "pass_private_state.ipynb",
        "memory/manage-conversation-history.ipynb",
        "memory/delete-messages.ipynb",
        "memory/add-summary-conversation-history.ipynb",
@@ -40,10 +38,6 @@ _MANUAL = {
        "subgraph.ipynb",
        "force-calling-a-tool-first.ipynb",
        "pass-run-time-values-to-tools.ipynb",
-        "tool-calling.ipynb",
-        "tool-calling-errors.ipynb",
-        "pass-config-to-tools.ipynb",
-        "many-tools.ipynb",
        "dynamic-returning-direct.ipynb",
        "managing-agent-steps.ipynb",
        "respond-in-format.ipynb",
@@ -59,7 +53,6 @@ _MANUAL = {
        "human_in_the_loop/time-travel.ipynb",
        "human_in_the_loop/edit-graph-state.ipynb",
        "human_in_the_loop/wait-user-input.ipynb",
-        "node-retries.ipynb",
    ],
    "tutorials": [
        "introduction.ipynb",
@@ -95,14 +88,13 @@ _HIDE = set(
        "dynamically-returning-directly.ipynb",
        "force-calling-a-tool-first.ipynb",
        "managing-agent-steps.ipynb",
+        "pass-run-time-values-to-tools.ipynb",
        "respond-in-format.ipynb",
        "quickstart.ipynb",
        "human-in-the-loop.ipynb",
        "learning.ipynb",
        "docs/quickstart.ipynb",
        "tutorials/rag-agent-testing.ipynb",
-        "tutorials/rag-agent-testing-local.ipynb",
-        "tutorials/tool-calling-agent-local.ipynb",
        "time-travel.ipynb",
        "code_assistant/langgraph_code_assistant_mistral.ipynb",
    ]
@@ -12,10 +12,6 @@ An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It

 The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the <a href="../reference/api/api_ref.html#tag/assistantscreate" target="_blank">API reference</a> for more details.

-#### Configuring Assistants
-
-You can save custom assistants from the same graph to set different default prompts, models, and other configurations without changing a line of code in your graph. This allows you the ability to quickly test out different configurations without having to rewrite your graph every time, and also give users the flexibility to select different configurations when using your LangGraph application. See <a href="https://langchain-ai.github.io/langgraph/cloud/how-tos/cloud_examples/configuration_cloud/">this</a> how-to for information on how to configure a deployed graph. 
-
 ### Threads

 A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
@@ -2,10 +2,9 @@

 LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith" target="_blank">LangSmith</a>. To deploy a LangGraph Cloud API, navigate to the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>.

-## Prerequisites
+## Setup GitHub Repository

-1. LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
-1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not build and run successfully (i.e. `langgraph up`), deploying to LangGraph Cloud will fail as well.
+LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.

 ## Create New Deployment

@@ -1,146 +0,0 @@
-# Rebuild Graph at Runtime
-
-You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this.
-
-!!! note "Note"
-    In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it
-
-## Prerequisites
-
-Make sure to check out [this how-to guide](./setup.md) on setting up your app for deployment first.
-
-## Define graphs
-
-Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:
-
-```
-my-app/
-|-- requirements.txt
-|-- .env
-|-- openai_agent.py     # code for your graph
-```
-
-where the graph is defined in `openai_agent.py`. 
-
-### No rebuild
-
-In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of `openai_agent.py`, which looks like the following:
-
-```python
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, MessageGraph
-
-model = ChatOpenAI(temperature=0)
-
-graph_workflow = MessageGraph()
-
-graph_workflow.add_node("agent", model)
-graph_workflow.add_edge("agent", END)
-graph_workflow.set_entry_point("agent")
-
-agent = graph_workflow.compile()
-```
-
-To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.:
-
-```
-{
-    "dependencies": ["."],
-    "graphs": {
-        "openai_agent": "./openai_agent.py:agent",
-    },
-    "env": "./.env"
-}
-```
-
-### Rebuild
-
-To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows:
-
-```python
-from typing import Annotated, TypedDict
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, MessageGraph
-from langgraph.graph.state import StateGraph
-from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode
-from langchain_core.tools import tool
-from langchain_core.messages import BaseMessage
-from langchain_core.runnables import RunnableConfig
-
-
-class State(TypedDict):
-    messages: Annotated[list[BaseMessage], add_messages]
-
-
-model = ChatOpenAI(temperature=0)
-
-def make_default_graph():
-    """Make a simple LLM agent"""
-    graph_workflow = StateGraph(State)
-    def call_model(state):
-        return {"messages": [model.invoke(state["messages"])]}
-
-    graph_workflow.add_node("agent", call_model)
-    graph_workflow.add_edge("agent", END)
-    graph_workflow.set_entry_point("agent")
-
-    agent = graph_workflow.compile()
-    return agent
-
-
-def make_alternative_graph():
-    """Make a tool-calling agent"""
-
-    @tool
-    def add(a: float, b: float):
-        """Adds two numbers."""
-        return a + b
-
-    tool_node = ToolNode([add])
-    model_with_tools = model.bind_tools([add])
-    def call_model(state):
-        return {"messages": [model_with_tools.invoke(state["messages"])]}
-
-    def should_continue(state: State):
-        if state["messages"][-1].tool_calls:
-            return "tools"
-        else:
-            return END
-
-    graph_workflow = StateGraph(State)
-
-    graph_workflow.add_node("agent", call_model)
-    graph_workflow.add_node("tools", tool_node)
-    graph_workflow.add_edge("tools", "agent")
-    graph_workflow.set_entry_point("agent")
-    graph_workflow.add_conditional_edges("agent", should_continue)
-
-    agent = graph_workflow.compile()
-    return agent
-
-
-# this is the graph making function that will decide which graph to
-# build based on the provided config
-def make_graph(config: RunnableConfig):
-    user_id = config.get("configurable", {}).get("user_id")
-    # route to different graph state / structure based on the user ID
-    if user_id == "1":
-        return make_default_graph()
-    else:
-        return make_alternative_graph()
-```
-
-Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`:
-
-```
-{
-    "dependencies": ["."],
-    "graphs": {
-        "openai_agent": "./openai_agent.py:make_graph",
-    },
-    "env": "./.env"
-}
-```
-
-See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
@@ -1,9 +1,6 @@
 # How to Set Up a LangGraph Application for Deployment

-A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies. 
-
-!!! tip "Setup with pyproject.toml"
-    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.
+A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies. If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.

 The final repo structure will look something like this:

@@ -22,22 +19,6 @@ After each step, an example file directory is provided to demonstrate how code c

 Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).

-The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
-```
-langgraph>=0.1.19,<0.2.0
-langchain-core>=0.2.8,<0.3.0
-langsmith>=0.1.63
-orjson>=3.10.1
-httpx>=0.27.0
-tenacity>=8.3.0
-uvicorn>=0.29.0
-sse-starlette>=2.1.0
-uvloop>=0.19.0
-httptools>=0.6.1
-jsonschema-rs>=0.18.0
-croniter>=1.0.1
-```
-
 Example `requirements.txt` file:
 ```
 langgraph
@@ -89,7 +70,7 @@ agent = graph_workflow.compile()
 ```

 !!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
+    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.

 Example file directory:
 ```
@@ -134,6 +115,10 @@ my-app/
 |-- langgraph.json      # configuration file for LangGraph
 ```

+## Upload to GitHub
+
+To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.
+
 ## Next

-After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
+After you setup your repo, it's time to [deploy your app](./cloud.md).
@@ -20,22 +20,6 @@ After each step, an example file directory is provided to demonstrate how code c

 Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).

-The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
-```
-langgraph>=0.1.19,<0.2.0
-langchain-core>=0.2.8,<0.3.0
-langsmith>=0.1.63
-orjson>=3.10.1
-httpx>=0.27.0
-tenacity>=8.3.0
-uvicorn>=0.29.0
-sse-starlette>=2.1.0
-uvloop>=0.19.0
-httptools>=0.6.1
-jsonschema-rs>=0.18.0
-croniter>=1.0.1
-```
-
 Example `pyproject.toml` file:

 ```toml
@@ -49,7 +33,7 @@ readme = "README.md"

 [tool.poetry.dependencies]
 python = ">=3.9.0,<3.13"
-langgraph = "^0.1.7"
+langgraph = "^0.1.0"
 langchain-fireworks = "^0.1.3"


@@ -118,7 +102,7 @@ agent = graph_workflow.compile()
 ```

 !!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
+The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.

 Example file directory:

@@ -149,9 +133,6 @@ Example `langgraph.json` file:

 Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).

-!!! warning "Configuration Location"
-    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
-
 Example file directory:

 ```bash
@@ -164,6 +145,10 @@ my-app/
 └── pyproject.toml
 ```

+## Upload to GitHub
+
+To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.
+
 ## Next

-After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
+After you setup your repo, it's time to [deploy your app](./cloud.md).
@@ -2,8 +2,6 @@

 This guide assumes you have a LangGraph app correctly set up with a proper configuration file and a corresponding compiled graph, and that you have a proper LangChain API key.

-Testing locally ensures that there are no errors or conflicts with Python dependencies and confirms that the configuration file is specified correctly.
-
 ## Setup

 Install the proper packages:
@@ -12,12 +10,6 @@ Install the proper packages:
 pip install langgraph-cli
 ```

-Ensure you have an API key, which you can create from the LangSmith UI (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
-
-```python
-LANGCHAIN_API_KEY = *********
-```
-
 ## Start the API server

 Once you have downloaded the CLI, you can run the following command to start the API server for local testing:
@@ -11,13 +11,10 @@ Welcome to the LangGraph Cloud how-to guides! These guides provide practical, st

 LangGraph Cloud gives you best in class observability, testing, and hosting services. Read more about them in these how to guides:

- [How to set up app for deployment (requirements.txt)](../deployment/setup.md)
- [How to set up app for deployment (pyproject.toml)](../deployment/setup_pyproject.md)
- [How to test locally](../deployment/test_locally.md)
+- [How to set up app for deployment](../deployment/setup.md)
 - [How to deploy to LangGraph cloud](../deployment/cloud.md)
 - [How to self-host](../deployment/self_hosted.md)

-
 ## Streaming

 Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:
@@ -52,7 +49,6 @@ When creating complex graphs, leaving every decision up to the LLM can be danger
 LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.

 - [How to enter LangGraph Studio](./test_deployment.md)
- [How to enter LangGraph Studio for local deployment](./test_local_deployment.md)
 - [How to test your graph in LangGraph Studio](./invoke_studio.md)
 - [Interact with threads in LangGraph Studio](./threads_studio.md)

@@ -1,15 +1,13 @@
 # Invoke Assistant

-The LangGraph Studio lets you test different configurations and inputs to your graph. It also provides a nice visualization of your graph during execution so it is easy to see which nodes are being run and what the outputs of each individual node are.
+The LangGraph Studio lets you test different configurations and inputs to your graph. The UI allows you to see exactly how your 

 1. The LangGraph Studio UI displays a visualization of the selected assistant.
-    1. In the top-left dropdown menu of the left-hand pane, select an assistant.
+    1. In the top-right dropdown menu of the left-hand pane, select an assistant.
    1. In the bottom of the left-hand pane, edit the `Input` and `Configure` the assistant.
    1. Select `Submit` to invoke the selected assistant.
 1. View output of the invocation in the right-hand pane.

-The following video shows these exact steps being carried out:
+The following GIF shows these exact steps being carried out:

-<video controls allowfullscreen="true" poster="../img/studio_input_poster.png">
-    <source src="../img/studio_input.mp4" type="video/mp4">
-</video>
+![Using LangGraph Studio](./img/studio_input.gif)
@@ -9,8 +9,6 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
 1. In the top-right corner, select `Open LangGraph Studio`.
 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).

-The following video shows these exact steps being carried out:
+The following GIF shows these exact steps being carried out:

-<video controls allowfullscreen="true" poster="../img/studio_usage_poster.png">
-    <source src="../img/studio_usage.mp4" type="video/mp4">
-</video>
+![Using LangGraph Studio](./img/studio_usage.gif)
@@ -1,28 +0,0 @@
-# LangGraph Studio With Local Deployment
-
-!!! warning "Browser Compatibility"
-    Viewing the studio page of a local LangGraph deployment does not work in Safari. Use Chrome instead.
-
-## Setup
-
-Make sure you have setup your app correctly, by creating a compiled graph, a `.env` file with any environment variables, and a `langgraph.json` config file that points to your environment file and compiled graph. See [here](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/) for more detailed instructions.
-
-After you have your app setup, head into the directory with your `langgraph.json` file and call `langgraph up -c langgraph.json --watch` to start the API server in watch mode which means it will restart on code changes, which is ideal for local testing. If the API server start correctly you should see logs that look something like this:
-
-    Ready!
-    - API: http://localhost:8123
-    2024-06-26 19:20:41,056:INFO:uvicorn.access 127.0.0.1:44138 - "GET /ok HTTP/1.1" 200
-
-Read this [reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#up) to learn about all the options for starting the API server.
-
-## Access Studio
-
-Once you have successfully started the API server, you can access the studio by going to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123` (see warning above if using Safari).
-
-If everything is working correctly you should see the studio show up looking something like this (with your graph diagram on the left hand side):
-
-![LangGraph Studio](./img/studio_screenshot.png)
-
-## Use the Studio for Testing
-
-To learn about how to use the studio for testing, read the [LangGraph Studio how-tos](https://langchain-ai.github.io/langgraph/cloud/how-tos/#langgraph-studio).
@@ -6,18 +6,14 @@
 1. View the state of the thread (i.e. the output) in the right-hand pane.
 1. To create a new thread, select `+ New Thread`.

-The following video shows these exact steps being carried out:
+The following GIF shows these exact steps being carried out:

-<video controls="true" allowfullscreen="true" poster="../img/studio_threads_poster.png">
-    <source src="../img/studio_threads.mp4" type="video/mp4">
-</video>
+![Using LangGraph Studio](./img/studio_threads.gif)

 ## Edit Thread State

 The LangGraph Studio UI contains features for editing thread state. Explore these features in the right-hand pane. Select the `Edit` icon, modify the desired state, and then select `Fork` to invoke the assistant with the updated state.

-The following video shows how to edit a thread in the studio:
+The following GIF shows how to edit a thread in the studio:

-<video controls allowfullscreen="true" poster="../img/studio_forks_poster.png">
-    <source src="../img/studio_forks.mp4" type="video/mp4">
-</video>
+![Using LangGraph Studio](./img/studio_forks.gif)
@@ -1,19 +1,12 @@
 # LangGraph Cloud (beta)

-!!! tip
-    - LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
-    - LangGraph Cloud is an optional managed hosting service for LangGraph, which provides additional features geared towards production deployments.
-    - We are actively contributing improvements back to LangGraph informed by our work on LangGraph Cloud.
-    - You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project.
+!!! danger "Important"
+    LangGraph Cloud is a closed source, paid product in an invite-only stage. We are currently focused on providing high bandwidth support to make our select early customers successful. If you are interested in applying for access, please fill out [this form](https://www.langchain.com/langgraph-cloud-beta).

 !!! warning "Under Construction"
    LangGraph Cloud documentation is under construction. Contents may change until general availability.

-
-<video controls preload="auto" allowfullscreen="true" poster="how-tos/img/studio_forks_poster.png">
-    <source src="how-tos/img/studio_forks.mp4" type="video/mp4">
-</video>
-
+![GIF](./how-tos/img/studio_input.gif)

 ## Overview

@@ -13,7 +13,7 @@ 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> |
+| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.graph.CompiledGraph`. |
 | `env` | Path to `.env` file or a mapping from environment variable to its value. |
 | `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
 | `pip_config_file`| Path to `pip` config file. |
@@ -49,7 +49,7 @@ Example:
        "."
    ],
    "graphs": {
-        "my_graph_id": "./your_package/your_file.py:make_graph"
+        "my_graph_id": "./your_package/your_file.py:variable"
    },
    "env": {
        "OPENAI_API_KEY": "secret-key"
@@ -20,7 +20,7 @@ Low Level Concepts
 - [State](low_level.md#state)
    - [Schema](low_level.md#schema)
    - [Reducers](low_level.md#reducers)
-    - [MessageState](low_level.md#working-with-messages-in-graph-state)
+    - [MessageState](low_level.md#messagestate)
 - [Nodes](low_level.md#nodes)
    - [`START` node](low_level.md#start-node)
    - [`END` node](low_level.md#end-node)
@@ -46,17 +46,9 @@ The first thing you do when you define a graph is define the `State` of the grap

 The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/state-model.ipynb) as your graph state to add **default values** and additional data validation.

-By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [notebook here](../how-tos/input_output_schema.ipynb) for how to use.
-
-By default, all nodes in the graph will share the same state. This means that they will read and write to the same state channels. It is possible to have nodes write to private state channels inside the graph for internal node communication - see [this notebook](../how-tos/pass_private_state.ipynb) for how to do that.
-
 ### Reducers

-Reducers are key to understanding how updates from nodes are applied to the `State`. Each key in the `State` has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. There are a few different types of reducers, starting with the default type of reducer:
-
-#### Default Reducer
-
-These two examples show how to use the default reducer:
+Reducers are key to understanding how updates from nodes are applied to the `State`. Each key in the `State` has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. Let's take a look at a few examples to understand them better.

 **Example A:**

@@ -83,48 +75,22 @@ class State(TypedDict):

 In this example, we've used the `Annotated` type to specify a reducer function (`operator.add`) for the second key (`bar`). Note that the first key remains unchanged. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["hi", "bye"]}`. Notice here that the `bar` key is updated by adding the two lists together.

-#### Context Reducer
+### MessageState

-You can use `Context` channels to define shared resources (such as database connections) that are managed outside of your graph's nodes and excluded from checkpointing. The context manager provided to the Context channel is entered before the first step of the graph execution and exited after the last step, allowing you to set up and clean up resources for the duration of the graph invocation. Read this [how to](https://langchain-ai.github.io/langgraph/how-tos/state-context-key) to see an example of using the `Context` channel in your graph.
-
-### Working with Messages in Graph State
-
-#### Why use messages?
-
-Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's [`ChatModel`](https://python.langchain.com/v0.2/docs/concepts/#chat-models) in particular accepts a list of `Message` objects as inputs. These messages come in a variety of forms such as `HumanMessage` (user input) or `AIMessage` (LLM response). To read more about what message objects are, please refer to [this](https://python.langchain.com/v0.2/docs/concepts/#messages) conceptual guide.
-
-#### Using Messages in your Graph
-
-In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of `Message` objects and annotate it with a reducer function (see `messages` key in the example below). The reducer function is vital to telling the graph how to update the list of `Message` objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use `operator.add` as a reducer.
-
-However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use `operator.add`, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt `add_messages` function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.
-
-#### Serialization
-
-In addition to keeping track of message IDs, the `add_messages` function will also try to deserialize messages into LangChain `Message` objects whenever a state update is received on the `messages` channel. See more information on LangChain serialization/deserialization [here](https://python.langchain.com/v0.2/docs/how_to/serialization/). This allows sending graph inputs / state updates in the following format:
-
-```python
-# this is supported
-{"messages": [HumanMessage(content="message")]}
-
-# and this is also supported
-{"messages": [{"type": "human", "content": "message"}]}
-```
-
-Since the state updates are always deserialized into LangChain `Messages` when using `add_messages`, you should use dot notation to access message attributes, like `state["messages"][-1].content`. Below is an example of a graph that uses `add_messages` as it's reducer function.
+`MessageState` is one of the few opinionated components in LangGraph. `MessageState` is a special state designed to make it easy to use a list of messages as a key in your state. Specifically, `MessageState` is defined as:

 ```python
 from langchain_core.messages import AnyMessage
 from langgraph.graph.message import add_messages
 from typing import Annotated, TypedDict

-class GraphState(TypedDict):
+class MessagesState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]
 ```

-#### MessagesState
+What this is doing is creating a `TypedDict` with a single key: `messages`. This is a list of `Message` objects, with `add_messages` as a reducer. `add_messages` basically adds messages to the existing list (it also does some nice extra things, like convert from OpenAI message format to the standard LangChain message format, handle updates based on message IDs, etc).

-Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesState` which makes it easy to use messages. `MessagesState` is defined with a single `messages` key which is a list of `AnyMessage` objects and uses the `add_messages` reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like:
+We often see a list of messages being a key component of state, so this prebuilt state is intended to make it easy to use messages. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like:

 ```python
 from langgraph.graph import MessagesState
@@ -214,7 +180,7 @@ graph.add_edge("node_a", "node_b")
 If you want to **optionally** route to 1 or more edges (or optionally terminate), you can use the [add_conditional_edges][langgraph.graph.StateGraph.add_conditional_edges] method. This method accepts the name of a node and a "routing function" to call after that node is executed:

 ```python
-graph.add_conditional_edges("node_a", routing_function)
+graph.add_edge("node_a", routing_function)
 ```

 Similar to nodes, the `routing_function` accept the current `state` of the graph and return a value.
@@ -224,7 +190,7 @@ By default, the return value `routing_function` is used as the name of the node
 You can optionally provide a dictionary that maps the `routing_function`'s output to the name of the next node.

 ```python
-graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
+graph.add_edge("node_a", routing_function, {True: "node_b", False: "node_c"})
 ```

 ### Entry Point
@@ -361,16 +327,6 @@ The final thing you specify when calling `update_state` is `as_node`. This updat

 The reason this matters is that the next steps in the graph to execute depend on the last node to have given an update, so this can be used to control which node executes next.

-## Graph Migrations
-
-LangGraph can easily handle migrations of graph definitions (nodes, edges, and state) even when using a checkpointer to track state.
-
- For threads at the end of the graph (i.e. not interrupted) you can change the entire topology of the graph (i.e. all nodes and edges, remove, add, rename, etc)
- For threads currently interrupted, we support all topology changes other than renaming / removing nodes (as that thread could now be about to enter a node that no longer exists) -- if this is a blocker please reach out and we can prioritize a solution.
- For modifying state, we have full backwards and forwards compatibility for adding and removing keys
- State keys that are renamed lose their saved state in existing threads
- State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change -- if this is a blocker please reach out and we can prioritize a solution.
-
 ## Configuration

 When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.
@@ -25,7 +25,7 @@ LangGraph makes it easy to persist state across graph runs. The guide below show
 - [How to manage conversation history](memory/manage-conversation-history.ipynb)
 - [How to delete messages](memory/delete-messages.ipynb)
 - [How to add summary conversation memory](memory/add-summary-conversation-history.ipynb)
- [How to use Postgres checkpointer for persistence](persistence_postgres.ipynb)
+- [How to create a custom checkpointer using Postgres](persistence_postgres.ipynb)
 - [How to create a custom checkpointer using MongoDB](persistence_mongodb.ipynb)
 - [How to create a custom checkpointer using Redis](persistence_redis.ipynb)

@@ -54,29 +54,12 @@ These guides show how to use different streaming modes.
 - [How to stream events from within a tool without LangChain models](streaming-events-from-within-tools-without-langchain.ipynb)
 - [How to stream events from the final node](streaming-from-final-node.ipynb)

-## Tool calling
-
- [How to call tools using ToolNode](tool-calling.ipynb)
- [How to handle tool calling errors](tool-calling-errors.ipynb)
- [How to pass graph state to tools](pass-run-time-values-to-tools.ipynb)
- [How to pass config to tools](pass-config-to-tools.ipynb)
- [How to handle large numbers of tools](many-tools.ipynb)
-
-## State Management
-
- [Use Pydantic model as state](state-model.ipynb)
- [Use a context object in state](state-context-key.ipynb)
- [Have a separate input and output schema](input_output_schema.ipynb)
- [Pass private state between nodes inside the graph](pass_private_state.ipynb)
-
 ## Other
-
 - [How to run graph asynchronously](async.ipynb)
 - [How to visualize your graph](visualization.ipynb)
 - [How to add runtime configuration to your graph](configuration.ipynb)
 - [How to use a Pydantic model as your state](state-model.ipynb)
 - [How to use a context object in state](state-context-key.ipynb)
- [How to add node retries](node-retries.ipynb)

 ## Prebuilt ReAct Agent

@@ -7,23 +7,19 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w
 - Resilience for long-running, error-prone agents
 - Time travel retry and branch from a previous checkpoint

-Key checkpointer interfaces and primitives are defined in [`langgraph_checkpoint`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint) library.
-
 ### Checkpoint

-::: langgraph.checkpoint.base.Checkpoint
-
-### CheckpointMetadata
-
-::: langgraph.checkpoint.base.CheckpointMetadata
+::: langgraph.checkpoint.Checkpoint

 ### BaseCheckpointSaver

 ::: langgraph.checkpoint.base.BaseCheckpointSaver
+handler: python

 ### SerializerProtocol

-::: langgraph.checkpoint.base.SerializerProtocol
+::: langgraph.checkpoint.SerializerProtocol
+handler: python

 ## Implementations

@@ -32,23 +28,14 @@ LangGraph also natively provides the following checkpoint implementations.
 ### MemorySaver

 ::: langgraph.checkpoint.memory.MemorySaver
+handler: python

 ### AsyncSqliteSaver

-::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver
+::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver
+handler: python

 ### SqliteSaver

 ::: langgraph.checkpoint.sqlite.SqliteSaver
-
-### AsyncPostgresSaver
-
-::: langgraph.checkpoint.postgres.aio.AsyncPostgresSaver
-
-### PostgresSaver
-
-::: langgraph.checkpoint.postgres.PostgresSaver
-handler: python
-
-
 handler: python
@@ -65,8 +65,4 @@ builder.add_conditional_edges("my_node", my_condition)

 ## Send

-::: langgraph.constants.Send
-
-## RetryPolicy
-
-::: langgraph.pregel.types.RetryPolicy
+::: langgraph.constants.Send
@@ -55,13 +55,4 @@ from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ValidationNode
 ```

-::: langgraph.prebuilt.ValidationNode
-
-## InjectedState
-
-```python
-from langgraph.prebuilt import InjectedState
-```
-
-::: langgraph.prebuilt.InjectedState
-    handler: python
+::: langgraph.prebuilt.ValidationNode
@@ -12,6 +12,8 @@ theme:
    repo: fontawesome/brands/git-alt
  features:
    - announce.dismiss
+    - content.action.edit
+    - content.action.view
    - content.code.annotate
    - content.code.copy
    - content.code.select
@@ -134,7 +136,7 @@ nav:
          - Manage conversation history: how-tos/memory/manage-conversation-history.ipynb
          - Delete messages: how-tos/memory/delete-messages.ipynb
          - Add summary of the conversation history: how-tos/memory/add-summary-conversation-history.ipynb
-          - Use Postgres checkpointer for persistence: how-tos/persistence_postgres.ipynb
+          - Create custom checkpointer using Postgres: how-tos/persistence_postgres.ipynb
          - Create custom checkpointer using MongoDB: how-tos/persistence_mongodb.ipynb
          - Create custom checkpointer using Redis: how-tos/persistence_redis.ipynb
      - Human-in-the-loop:
@@ -152,22 +154,12 @@ nav:
          - Stream events from within tools: how-tos/streaming-events-from-within-tools.ipynb
          - Stream events from within tools without LangChain models: how-tos/streaming-events-from-within-tools-without-langchain.ipynb
          - Stream events from the final node: how-tos/streaming-from-final-node.ipynb
-      - Tool calling:
-          - Call tools using ToolNode: how-tos/tool-calling.ipynb
-          - Handle tool calling errors: how-tos/tool-calling-errors.ipynb
-          - Pass graph state to tools: how-tos/pass-run-time-values-to-tools.ipynb
-          - Pass config to tools: how-tos/pass-config-to-tools.ipynb
-          - Handle many tools: how-tos/many-tools.ipynb
-      - State Management:
-          - Use Pydantic model as state: how-tos/state-model.ipynb
-          - Use a context object in state: how-tos/state-context-key.ipynb
-          - Have a separate input and output schema: how-tos/input_output_schema.ipynb
-          - Pass private state between nodes inside the graph: how-tos/pass_private_state.ipynb
      - Other:
          - Run graph asynchronously: how-tos/async.ipynb
          - Visualize your graph: how-tos/visualization.ipynb
          - Add runtime configuration: how-tos/configuration.ipynb
-          - Add node retries: how-tos/node-retries.ipynb
+          - Use Pydantic model as state: how-tos/state-model.ipynb
+          - Use a context object in state: how-tos/state-context-key.ipynb
      - Prebuilt ReAct Agent:
          - Create a ReAct agent: how-tos/create-react-agent.ipynb
          - Add memory to a ReAct agent: how-tos/create-react-agent-memory.ipynb
@@ -190,12 +182,10 @@ nav:
          - Quick Start: "cloud/quick_start.md"
      - How-to Guides: 
          - "cloud/how-tos/index.md"
-          - Setup:
+          - Deployment:
              - Setup App: "cloud/deployment/setup.md"
              - Setup App (pyproject.toml): "cloud/deployment/setup_pyproject.md"
-              - Rebuild Graph at Runtime: "cloud/deployment/graph_rebuild.md"
              - Test App Locally: "cloud/deployment/test_locally.md"
-          - Deployment:
              - Deploy to Cloud: "cloud/deployment/cloud.md"
              - Self-Host: "cloud/deployment/self_hosted.md"
          - Streaming:
@@ -217,7 +207,6 @@ nav:
              - Replay and Branch from Prior States: "cloud/how-tos/human_in_the_loop_time_travel.md"
          - LangGraph Studio:
              - Test Cloud Deployment: "cloud/how-tos/test_deployment.md"
-              - Test Local Deployment: "cloud/how-tos/test_local_deployment.md"
              - Invoke graph in LangGraph Studio: "cloud/how-tos/invoke_studio.md"
              - Interact with threads in LangGraph Studio: "cloud/how-tos/threads_studio.md"
          - Different Types of Runs:
@@ -146,13 +146,6 @@
      display: none !important;
    }

-    .jupyter-wrapper .jp-CodeCell .jp-Cell-inputWrapper .jp-InputPrompt.jp-InputArea-prompt {
-      display: none !important;
-    }
-
-    .jupyter-wrapper .jp-Notebook .jp-Cell .jp-OutputPrompt {
-      display: none !important;
-    }
  </style>
 {% endblock %}

@@ -466,7 +466,7 @@
   "source": [
    "import json\n",
    "\n",
-    "from langgraph.graph import END, START\n",
+    "from langgraph.graph import END\n",
    "\n",
    "\n",
    "def _get_last_ai_message(messages):\n",
@@ -554,11 +554,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "\n",
    "from langgraph.graph import MessageGraph\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = MessageGraph()\n",
    "nodes = {\n",
    "    \"general\": \"general\",\n",
@@ -577,7 +577,7 @@
    "workflow.add_conditional_edges(\"tools\", _route, nodes)\n",
    "workflow.add_conditional_edges(\"music\", _route, nodes)\n",
    "workflow.add_conditional_edges(\"customer\", _route, nodes)\n",
-    "workflow.add_conditional_edges(START, _route, nodes)\n",
+    "workflow.set_conditional_entry_point(_route, nodes)\n",
    "graph = workflow.compile()"
   ]
  },
@@ -588,7 +588,7 @@
   "metadata": {},
   "outputs": [
    {
-     "name": "stdout",
+     "name": "stdin",
     "output_type": "stream",
     "text": [
      "User (q/Q to quit):  what music do you have?\n"
@@ -613,7 +613,7 @@
     ]
    },
    {
-     "name": "stdout",
+     "name": "stdin",
     "output_type": "stream",
     "text": [
      "User (q/Q to quit):  how about shakira?\n"
@@ -664,7 +664,7 @@
     ]
    },
    {
-     "name": "stdout",
+     "name": "stdin",
     "output_type": "stream",
     "text": [
      "User (q/Q to quit):  hm cool\n"
@@ -701,7 +701,7 @@
     ]
    },
    {
-     "name": "stdout",
+     "name": "stdin",
     "output_type": "stream",
     "text": [
      "User (q/Q to quit):  q\n"
@@ -176,10 +176,10 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import START, MessageGraph\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "workflow = MessageGraph()\n",
    "workflow.add_node(\"info\", chain)\n",
    "workflow.add_node(\"prompt\", prompt_gen_chain)\n",
@@ -154,7 +154,7 @@
   "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
   "metadata": {},
   "outputs": [],
-   "source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = MemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
+   "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = builder.compile(checkpointer=memory)"]
  },
  {
   "cell_type": "code",
@@ -1,247 +1,247 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-      "metadata": {},
-      "source": [
-        "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
-        "\n",
-        "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
-        "\n",
-        "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%capture --no-stderr\n",
-        "%pip install -U langgraph langchain-openai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "OPENAI_API_KEY:  ········\n"
-          ]
-        }
-      ],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "\n",
-        "def _set_env(var: str):\n",
-        "    if not os.environ.get(var):\n",
-        "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-        "\n",
-        "\n",
-        "_set_env(\"OPENAI_API_KEY\")\n",
-        "\n",
-        "# Recommended\n",
-        "_set_env(\"LANGCHAIN_API_KEY\")\n",
-        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-      "metadata": {},
-      "source": [
-        "## Code"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# First we initialize the model we want to use.\n",
-        "from langchain_openai import ChatOpenAI\n",
-        "\n",
-        "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-        "\n",
-        "\n",
-        "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-        "\n",
-        "from typing import Literal\n",
-        "\n",
-        "from langchain_core.tools import tool\n",
-        "\n",
-        "\n",
-        "@tool\n",
-        "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
-        "    \"\"\"Use this to get weather information.\"\"\"\n",
-        "    if city == \"nyc\":\n",
-        "        return \"It might be cloudy in nyc\"\n",
-        "    elif city == \"sf\":\n",
-        "        return \"It's always sunny in sf\"\n",
-        "    else:\n",
-        "        raise AssertionError(\"Unknown city\")\n",
-        "\n",
-        "\n",
-        "tools = [get_weather]\n",
-        "\n",
-        "# We need a checkpointer to enable human-in-the-loop patterns\n",
-        "from langgraph.checkpoint.memory import MemorySaver\n",
-        "\n",
-        "memory = MemorySaver()\n",
-        "\n",
-        "# Define the graph\n",
-        "\n",
-        "from langgraph.prebuilt import create_react_agent\n",
-        "\n",
-        "graph = create_react_agent(\n",
-        "    model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "00407425-506d-4ffd-9c86-987921d8c844",
-      "metadata": {},
-      "source": [
-        "## Usage\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def print_stream(stream):\n",
-        "    for s in stream:\n",
-        "        message = s[\"messages\"][-1]\n",
-        "        if isinstance(message, tuple):\n",
-        "            print(message)\n",
-        "        else:\n",
-        "            message.pretty_print()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "================================\u001b[1m Human Message \u001b[0m=================================\n",
-            "\n",
-            "What's the weather in SF?\n",
-            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-            "Tool Calls:\n",
-            "  get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n",
-            " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n",
-            "  Args:\n",
-            "    city: sf\n"
-          ]
-        }
-      ],
-      "source": [
-        "config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
-        "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n",
-        "\n",
-        "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Next step:  ('tools',)\n"
-          ]
-        }
-      ],
-      "source": [
-        "snapshot = graph.get_state(config)\n",
-        "print(\"Next step: \", snapshot.next)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-            "Name: get_weather\n",
-            "\n",
-            "It's always sunny in sf\n",
-            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-            "\n",
-            "The weather in San Francisco is currently sunny.\n"
-          ]
-        }
-      ],
-      "source": [
-        "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de",
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.1"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+   "metadata": {},
+   "source": [
+    "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
+    "\n",
+    "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
+    "\n",
+    "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
+   ]
  },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OPENAI_API_KEY:  ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
+    "\n",
+    "# Recommended\n",
+    "_set_env(\"LANGCHAIN_API_KEY\")\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+   "metadata": {},
+   "source": [
+    "## Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we initialize the model we want to use.\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "\n",
+    "\n",
+    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+    "\n",
+    "from typing import Literal\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "# We need a checkpointer to enable human-in-the-loop patterns\n",
+    "from langgraph.checkpoint import MemorySaver\n",
+    "\n",
+    "memory = MemorySaver()\n",
+    "\n",
+    "# Define the graph\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "graph = create_react_agent(\n",
+    "    model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00407425-506d-4ffd-9c86-987921d8c844",
+   "metadata": {},
+   "source": [
+    "## Usage\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_stream(stream):\n",
+    "    for s in stream:\n",
+    "        message = s[\"messages\"][-1]\n",
+    "        if isinstance(message, tuple):\n",
+    "            print(message)\n",
+    "        else:\n",
+    "            message.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "What's the weather in SF?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n",
+      " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n",
+      "  Args:\n",
+      "    city: sf\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n",
+    "\n",
+    "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Next step:  ('tools',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "snapshot = graph.get_state(config)\n",
+    "print(\"Next step: \", snapshot.next)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_weather\n",
+      "\n",
+      "It's always sunny in sf\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in San Francisco is currently sunny.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
@@ -1,255 +1,255 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-      "metadata": {},
-      "source": [
-        "# How to add memory to the prebuilt ReAct agent\n",
-        "\n",
-        "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
-        "\n",
-        "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%capture --no-stderr\n",
-        "%pip install -U langgraph langchain-openai"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "OPENAI_API_KEY:  ········\n"
-          ]
-        }
-      ],
-      "source": [
-        "import getpass\n",
-        "import os\n",
-        "\n",
-        "\n",
-        "def _set_env(var: str):\n",
-        "    if not os.environ.get(var):\n",
-        "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-        "\n",
-        "\n",
-        "_set_env(\"OPENAI_API_KEY\")\n",
-        "\n",
-        "# Recommended\n",
-        "_set_env(\"LANGCHAIN_API_KEY\")\n",
-        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-      "metadata": {},
-      "source": [
-        "## Code"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# First we initialize the model we want to use.\n",
-        "from langchain_openai import ChatOpenAI\n",
-        "\n",
-        "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-        "\n",
-        "\n",
-        "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-        "\n",
-        "from typing import Literal\n",
-        "\n",
-        "from langchain_core.tools import tool\n",
-        "\n",
-        "\n",
-        "@tool\n",
-        "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
-        "    \"\"\"Use this to get weather information.\"\"\"\n",
-        "    if city == \"nyc\":\n",
-        "        return \"It might be cloudy in nyc\"\n",
-        "    elif city == \"sf\":\n",
-        "        return \"It's always sunny in sf\"\n",
-        "    else:\n",
-        "        raise AssertionError(\"Unknown city\")\n",
-        "\n",
-        "\n",
-        "tools = [get_weather]\n",
-        "\n",
-        "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
-        "# to retain the chat context between interactions\n",
-        "from langgraph.checkpoint.memory import MemorySaver\n",
-        "\n",
-        "memory = MemorySaver()\n",
-        "\n",
-        "# Define the graph\n",
-        "\n",
-        "from langgraph.prebuilt import create_react_agent\n",
-        "\n",
-        "graph = create_react_agent(model, tools=tools, checkpointer=memory)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "00407425-506d-4ffd-9c86-987921d8c844",
-      "metadata": {},
-      "source": [
-        "## Usage\n",
-        "\n",
-        "Let's interact with it multiple times to show that it can remember"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def print_stream(stream):\n",
-        "    for s in stream:\n",
-        "        message = s[\"messages\"][-1]\n",
-        "        if isinstance(message, tuple):\n",
-        "            print(message)\n",
-        "        else:\n",
-        "            message.pretty_print()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "================================\u001b[1m Human Message \u001b[0m=================================\n",
-            "\n",
-            "What's the weather in NYC?\n",
-            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-            "Tool Calls:\n",
-            "  get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
-            " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
-            "  Args:\n",
-            "    city: nyc\n",
-            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-            "Name: get_weather\n",
-            "\n",
-            "It might be cloudy in nyc\n",
-            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-            "\n",
-            "The weather in NYC might be cloudy.\n"
-          ]
-        }
-      ],
-      "source": [
-        "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-        "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-        "\n",
-        "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
-      "metadata": {},
-      "source": [
-        "Notice that when we pass the same the same thread ID, the chat history is preserved"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "187479f9-32fa-4611-9487-cf816ba2e147",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "================================\u001b[1m Human Message \u001b[0m=================================\n",
-            "\n",
-            "What's it known for?\n",
-            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-            "\n",
-            "New York City (NYC) is known for many things, including:\n",
-            "\n",
-            "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
-            "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
-            "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
-            "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
-            "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
-            "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
-            "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
-            "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
-            "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
-            "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
-          ]
-        }
-      ],
-      "source": [
-        "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
-        "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.1"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+   "metadata": {},
+   "source": [
+    "# How to add memory to the prebuilt ReAct agent\n",
+    "\n",
+    "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
+    "\n",
+    "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
+   ]
  },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OPENAI_API_KEY:  ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
+    "\n",
+    "# Recommended\n",
+    "_set_env(\"LANGCHAIN_API_KEY\")\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+   "metadata": {},
+   "source": [
+    "## Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we initialize the model we want to use.\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "\n",
+    "\n",
+    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+    "\n",
+    "from typing import Literal\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
+    "# to retain the chat context between interactions\n",
+    "from langgraph.checkpoint import MemorySaver\n",
+    "\n",
+    "memory = MemorySaver()\n",
+    "\n",
+    "# Define the graph\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "graph = create_react_agent(model, tools=tools, checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00407425-506d-4ffd-9c86-987921d8c844",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "Let's interact with it multiple times to show that it can remember"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_stream(stream):\n",
+    "    for s in stream:\n",
+    "        message = s[\"messages\"][-1]\n",
+    "        if isinstance(message, tuple):\n",
+    "            print(message)\n",
+    "        else:\n",
+    "            message.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "What's the weather in NYC?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
+      " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
+      "  Args:\n",
+      "    city: nyc\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_weather\n",
+      "\n",
+      "It might be cloudy in nyc\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in NYC might be cloudy.\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+    "\n",
+    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
+   "metadata": {},
+   "source": [
+    "Notice that when we pass the same the same thread ID, the chat history is preserved"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "187479f9-32fa-4611-9487-cf816ba2e147",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "What's it known for?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "New York City (NYC) is known for many things, including:\n",
+      "\n",
+      "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
+      "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
+      "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
+      "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
+      "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
+      "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
+      "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
+      "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
+      "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
+      "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
+    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
@@ -9,7 +9,7 @@
    "\n",
    "This tutorial will show how to add a custom system prompt to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
    "\n",
-    "You can add a custom system prompt by passing a string to the `state_modifier` param."
+    "You can add a custom system prompt by passing a string to the `messages_modifier` param."
   ]
  },
  {
@@ -112,7 +112,7 @@
    "\n",
    "from langgraph.prebuilt import create_react_agent\n",
    "\n",
-    "graph = create_react_agent(model, tools=tools, state_modifier=prompt)"
+    "graph = create_react_agent(model, tools=tools, messages_modifier=prompt)"
   ]
  },
  {
@@ -39,10 +39,7 @@
   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
-   ]
+   "source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_openai"]
  },
  {
   "cell_type": "markdown",
@@ -58,18 +55,7 @@
   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
+   "source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n    if not os.environ.get(var):\n        os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"OPENAI_API_KEY\")"]
  },
  {
   "cell_type": "markdown",
@@ -85,10 +71,7 @@
   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "_set_env(\"LANGCHAIN_API_KEY\")"
-   ]
+   "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"]
  },
  {
   "cell_type": "markdown",
@@ -106,22 +89,7 @@
   "id": "6098e5cb",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from typing import Annotated\n",
-    "\n",
-    "from typing_extensions import TypedDict\n",
-    "\n",
-    "from langgraph.graph.message import add_messages\n",
-    "\n",
-    "# `add_messages`` essentially does this\n",
-    "# (with more robust handling)\n",
-    "# def add_messages(left: list, right: list):\n",
-    "#     return left + right\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list, add_messages]"
-   ]
+   "source": ["from typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import add_messages\n\n# `add_messages`` essentially does this\n# (with more robust handling)\n# def add_messages(left: list, right: list):\n#     return left + right\n\n\nclass State(TypedDict):\n    messages: Annotated[list, add_messages]"]
  },
  {
   "cell_type": "markdown",
@@ -141,22 +109,7 @@
   "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def search(query: str):\n",
-    "    \"\"\"Call to surf the web.\"\"\"\n",
-    "    # This is a placeholder for the actual implementation\n",
-    "    # Don't let the LLM know this though 😊\n",
-    "    return [\n",
-    "        \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
-    "    ]\n",
-    "\n",
-    "\n",
-    "tools = [search]"
-   ]
+   "source": ["from langchain_core.tools import tool\n\n\n@tool\ndef search(query: str):\n    \"\"\"Call to surf the web.\"\"\"\n    # This is a placeholder for the actual implementation\n    # Don't let the LLM know this though 😊\n    return [\n        \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n    ]\n\n\ntools = [search]"]
  },
  {
   "cell_type": "markdown",
@@ -174,11 +127,7 @@
   "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import ToolExecutor\n",
-    "\n",
-    "tool_executor = ToolExecutor(tools)"
-   ]
+   "source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"]
  },
  {
   "cell_type": "markdown",
@@ -199,11 +148,7 @@
   "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(temperature=0)"
-   ]
+   "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"]
  },
  {
   "cell_type": "markdown",
@@ -221,9 +166,7 @@
   "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "model = model.bind_tools(tools)"
-   ]
+   "source": ["model = model.bind_tools(tools)"]
  },
  {
   "cell_type": "markdown",
@@ -258,53 +201,7 @@
   "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langchain_core.messages import ToolMessage\n",
-    "\n",
-    "from langgraph.prebuilt import ToolInvocation\n",
-    "\n",
-    "\n",
-    "# Define the function that determines whether to continue or not\n",
-    "def should_continue(state):\n",
-    "    messages = state[\"messages\"]\n",
-    "    last_message = messages[-1]\n",
-    "    # If there is no function call, then we finish\n",
-    "    if not last_message.tool_calls:\n",
-    "        return \"end\"\n",
-    "    # Otherwise if there is, we continue\n",
-    "    else:\n",
-    "        return \"continue\"\n",
-    "\n",
-    "\n",
-    "# Define the function that calls the model\n",
-    "def call_model(state):\n",
-    "    messages = state[\"messages\"]\n",
-    "    response = model.invoke(messages)\n",
-    "    # We return a list, because this will get added to the existing list\n",
-    "    return {\"messages\": [response]}\n",
-    "\n",
-    "\n",
-    "# Define the function to execute tools\n",
-    "def call_tool(state):\n",
-    "    messages = state[\"messages\"]\n",
-    "    # Based on the continue condition\n",
-    "    # we know the last message involves a function call\n",
-    "    last_message = messages[-1]\n",
-    "    # We construct an ToolInvocation from the function_call\n",
-    "    tool_call = last_message.tool_calls[0]\n",
-    "    action = ToolInvocation(\n",
-    "        tool=tool_call[\"name\"],\n",
-    "        tool_input=tool_call[\"args\"],\n",
-    "    )\n",
-    "    # We call the tool_executor and get back a response\n",
-    "    response = tool_executor.invoke(action)\n",
-    "    # We use the response to create a ToolMessage\n",
-    "    tool_message = ToolMessage(\n",
-    "        content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n",
-    "    )\n",
-    "    # We return a list, because this will get added to the existing list\n",
-    "    return {\"messages\": [tool_message]}"
-   ]
+   "source": ["from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n    messages = state[\"messages\"]\n    last_message = messages[-1]\n    # If there is no function call, then we finish\n    if not last_message.tool_calls:\n        return \"end\"\n    # Otherwise if there is, we continue\n    else:\n        return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n    messages = state[\"messages\"]\n    response = model.invoke(messages)\n    # We return a list, because this will get added to the existing list\n    return {\"messages\": [response]}\n\n\n# Define the function to execute tools\ndef call_tool(state):\n    messages = state[\"messages\"]\n    # Based on the continue condition\n    # we know the last message involves a function call\n    last_message = messages[-1]\n    # We construct an ToolInvocation from the function_call\n    tool_call = last_message.tool_calls[0]\n    action = ToolInvocation(\n        tool=tool_call[\"name\"],\n        tool_input=tool_call[\"args\"],\n    )\n    # We call the tool_executor and get back a response\n    response = tool_executor.invoke(action)\n    # We use the response to create a ToolMessage\n    tool_message = ToolMessage(\n        content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n    )\n    # We return a list, because this will get added to the existing list\n    return {\"messages\": [tool_message]}"]
  },
  {
   "cell_type": "markdown",
@@ -322,45 +219,7 @@
   "id": "812b4e70-4956-4415-8880-db48b3dcbad2",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langgraph.graph import END, StateGraph, START\n",
-    "\n",
-    "# Define a new graph\n",
-    "workflow = StateGraph(State)\n",
-    "\n",
-    "# Define the two nodes we will cycle between\n",
-    "workflow.add_node(\"agent\", call_model)\n",
-    "workflow.add_node(\"action\", call_tool)\n",
-    "\n",
-    "# Set the entrypoint as `agent`\n",
-    "# This means that this node is the first one called\n",
-    "workflow.add_edge(START, \"agent\")\n",
-    "\n",
-    "# We now add a conditional edge\n",
-    "workflow.add_conditional_edges(\n",
-    "    # First, we define the start node. We use `agent`.\n",
-    "    # This means these are the edges taken after the `agent` node is called.\n",
-    "    \"agent\",\n",
-    "    # Next, we pass in the function that will determine which node is called next.\n",
-    "    should_continue,\n",
-    "    # Finally we pass in a mapping.\n",
-    "    # The keys are strings, and the values are other nodes.\n",
-    "    # END is a special node marking that the graph should finish.\n",
-    "    # What will happen is we will call `should_continue`, and then the output of that\n",
-    "    # will be matched against the keys in this mapping.\n",
-    "    # Based on which one it matches, that node will then be called.\n",
-    "    {\n",
-    "        # If `tools`, then we call the tool node.\n",
-    "        \"continue\": \"action\",\n",
-    "        # Otherwise we finish.\n",
-    "        \"end\": END,\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "# We now add a normal edge from `tools` to `agent`.\n",
-    "# This means that after `tools` is called, `agent` node is called next.\n",
-    "workflow.add_edge(\"action\", \"agent\")"
-   ]
+   "source": ["from langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(State)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n    # First, we define the start node. We use `agent`.\n    # This means these are the edges taken after the `agent` node is called.\n    \"agent\",\n    # Next, we pass in the function that will determine which node is called next.\n    should_continue,\n    # Finally we pass in a mapping.\n    # The keys are strings, and the values are other nodes.\n    # END is a special node marking that the graph should finish.\n    # What will happen is we will call `should_continue`, and then the output of that\n    # will be matched against the keys in this mapping.\n    # Based on which one it matches, that node will then be called.\n    {\n        # If `tools`, then we call the tool node.\n        \"continue\": \"action\",\n        # Otherwise we finish.\n        \"end\": END,\n    },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"]
  },
  {
   "cell_type": "markdown",
@@ -378,11 +237,7 @@
   "id": "6845ed6a-d155-4105-9160-28849877248b",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "\n",
-    "memory = MemorySaver()"
-   ]
+   "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
  },
  {
   "cell_type": "markdown",
@@ -400,12 +255,7 @@
   "id": "79d29875-8aa8-434c-9f20-1c58346a6249",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "# Finally, we compile it!\n",
-    "# This compiles it into a LangChain Runnable,\n",
-    "# meaning you can use it as you would any other runnable\n",
-    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"
-   ]
+   "source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"]
  },
  {
   "cell_type": "markdown",
@@ -432,11 +282,7 @@
     "output_type": "display_data"
    }
   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "display(Image(app.get_graph().draw_mermaid_png()))"
-   ]
+   "source": ["from IPython.display import Image, display\n\ndisplay(Image(app.get_graph().draw_mermaid_png()))"]
  },
  {
   "cell_type": "markdown",
@@ -467,14 +313,7 @@
     ]
    }
   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
-    "thread = {\"configurable\": {\"thread_id\": \"2\"}}\n",
-    "inputs = [HumanMessage(content=\"hi! I'm bob\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["from langchain_core.messages import HumanMessage\n\nthread = {\"configurable\": {\"thread_id\": \"2\"}}\ninputs = [HumanMessage(content=\"hi! I'm bob\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "code",
@@ -495,11 +334,7 @@
     ]
    }
   ],
-   "source": [
-    "inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "code",
@@ -523,11 +358,7 @@
     ]
    }
   ],
-   "source": [
-    "inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "markdown",
@@ -561,10 +392,7 @@
     ]
    }
   ],
-   "source": [
-    "for event in app.stream(None, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["for event in app.stream(None, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "markdown",
@@ -599,43 +427,7 @@
   "id": "5454f436-d56e-4499-9381-06192aca1b56",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "import json\n",
-    "from typing import Optional\n",
-    "\n",
-    "from langchain_core.messages import AIMessage\n",
-    "\n",
-    "\n",
-    "# Helper function to construct message asking for verification\n",
-    "def generate_verification_message(message: AIMessage) -> None:\n",
-    "    \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n",
-    "    serialized_tool_calls = json.dumps(\n",
-    "        message.tool_calls,\n",
-    "        indent=2,\n",
-    "    )\n",
-    "    return AIMessage(\n",
-    "        content=(\n",
-    "            \"I plan to invoke the following tools, do you approve?\\n\\n\"\n",
-    "            \"Type 'y' if you do, anything else to stop.\\n\\n\"\n",
-    "            f\"{serialized_tool_calls}\"\n",
-    "        ),\n",
-    "        id=message.id,\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "# Helper function to stream output from the graph\n",
-    "def stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n",
-    "    \"\"\"Stream app, catching tool calls.\"\"\"\n",
-    "    tool_call_message = None\n",
-    "    for event in app.stream(inputs, thread, stream_mode=\"values\"):\n",
-    "        message = event[\"messages\"][-1]\n",
-    "        if isinstance(message, AIMessage) and message.tool_calls:\n",
-    "            tool_call_message = message\n",
-    "        else:\n",
-    "            message.pretty_print()\n",
-    "\n",
-    "    return tool_call_message"
-   ]
+   "source": ["import json\nfrom typing import Optional\n\nfrom langchain_core.messages import AIMessage\n\n\n# Helper function to construct message asking for verification\ndef generate_verification_message(message: AIMessage) -> None:\n    \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n    serialized_tool_calls = json.dumps(\n        message.tool_calls,\n        indent=2,\n    )\n    return AIMessage(\n        content=(\n            \"I plan to invoke the following tools, do you approve?\\n\\n\"\n            \"Type 'y' if you do, anything else to stop.\\n\\n\"\n            f\"{serialized_tool_calls}\"\n        ),\n        id=message.id,\n    )\n\n\n# Helper function to stream output from the graph\ndef stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n    \"\"\"Stream app, catching tool calls.\"\"\"\n    tool_call_message = None\n    for event in app.stream(inputs, thread, stream_mode=\"values\"):\n        message = event[\"messages\"][-1]\n        if isinstance(message, AIMessage) and message.tool_calls:\n            tool_call_message = message\n        else:\n            message.pretty_print()\n\n    return tool_call_message"]
  },
  {
   "cell_type": "code",
@@ -722,43 +514,7 @@
     ]
    }
   ],
-   "source": [
-    "import uuid\n",
-    "\n",
-    "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n",
-    "\n",
-    "tool_call_message = stream_app_catch_tool_calls(\n",
-    "    {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n",
-    "    thread,\n",
-    ")\n",
-    "\n",
-    "while tool_call_message:\n",
-    "    verification_message = generate_verification_message(tool_call_message)\n",
-    "    verification_message.pretty_print()\n",
-    "    input_message = HumanMessage(input())\n",
-    "    if input_message.content == \"exit\":\n",
-    "        break\n",
-    "    input_message.pretty_print()\n",
-    "\n",
-    "    # First we update the state with the verification message and the input message.\n",
-    "    # note that `generate_verification_message` sets the message ID to be the same\n",
-    "    # as the ID from the original tool call message. Updating the state with this\n",
-    "    # message will overwrite the previous tool call.\n",
-    "    snapshot = app.get_state(thread)\n",
-    "    snapshot.values[\"messages\"] += [verification_message, input_message]\n",
-    "\n",
-    "    if input_message.content == \"y\":\n",
-    "        tool_call_message.id = str(uuid.uuid4())\n",
-    "        # If verified, we append the tool call message to the state\n",
-    "        # and resume execution.\n",
-    "        snapshot.values[\"messages\"] += [tool_call_message]\n",
-    "        app.update_state(thread, snapshot.values, as_node=\"agent\")\n",
-    "    else:\n",
-    "        # Otherwise, resume execution from the input message.\n",
-    "        app.update_state(thread, snapshot.values, as_node=\"__start__\")\n",
-    "\n",
-    "    tool_call_message = stream_app_catch_tool_calls(None, thread)"
-   ]
+   "source": ["import uuid\n\nthread = {\"configurable\": {\"thread_id\": \"3\"}}\n\ntool_call_message = stream_app_catch_tool_calls(\n    {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n    thread,\n)\n\nwhile tool_call_message:\n    verification_message = generate_verification_message(tool_call_message)\n    verification_message.pretty_print()\n    input_message = HumanMessage(input())\n    if input_message.content == \"exit\":\n        break\n    input_message.pretty_print()\n\n    # First we update the state with the verification message and the input message.\n    # note that `generate_verification_message` sets the message ID to be the same\n    # as the ID from the original tool call message. Updating the state with this\n    # message will overwrite the previous tool call.\n    snapshot = app.get_state(thread)\n    snapshot.values[\"messages\"] += [verification_message, input_message]\n\n    if input_message.content == \"y\":\n        tool_call_message.id = str(uuid.uuid4())\n        # If verified, we append the tool call message to the state\n        # and resume execution.\n        snapshot.values[\"messages\"] += [tool_call_message]\n        app.update_state(thread, snapshot.values, as_node=\"agent\")\n    else:\n        # Otherwise, resume execution from the input message.\n        app.update_state(thread, snapshot.values, as_node=\"__start__\")\n\n    tool_call_message = stream_app_catch_tool_calls(None, thread)"]
  },
  {
   "cell_type": "markdown",
@@ -779,34 +535,7 @@
   "id": "03232f16-d6fe-46d0-afa0-a6f0d0bf16de",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list, add_messages]\n",
-    "    tool_call_message: Optional[AIMessage]\n",
-    "\n",
-    "\n",
-    "def call_model(state):\n",
-    "    messages = state[\"messages\"]\n",
-    "    if messages[-1].content == \"y\":\n",
-    "        return {\n",
-    "            \"messages\": [state[\"tool_call_message\"]],\n",
-    "            \"tool_call_message\": None,\n",
-    "        }\n",
-    "    else:\n",
-    "        response = model.invoke(messages)\n",
-    "        if response.tool_calls:\n",
-    "            verification_message = generate_verification_message(response)\n",
-    "            response.id = str(uuid.uuid4())\n",
-    "            return {\n",
-    "                \"messages\": [verification_message],\n",
-    "                \"tool_call_message\": response,\n",
-    "            }\n",
-    "        else:\n",
-    "            return {\n",
-    "                \"messages\": [response],\n",
-    "                \"tool_call_message\": None,\n",
-    "            }"
-   ]
+   "source": ["class State(TypedDict):\n    messages: Annotated[list, add_messages]\n    tool_call_message: Optional[AIMessage]\n\n\ndef call_model(state):\n    messages = state[\"messages\"]\n    if messages[-1].content == \"y\":\n        return {\n            \"messages\": [state[\"tool_call_message\"]],\n            \"tool_call_message\": None,\n        }\n    else:\n        response = model.invoke(messages)\n        if response.tool_calls:\n            verification_message = generate_verification_message(response)\n            response.id = str(uuid.uuid4())\n            return {\n                \"messages\": [verification_message],\n                \"tool_call_message\": response,\n            }\n        else:\n            return {\n                \"messages\": [response],\n                \"tool_call_message\": None,\n            }"]
  },
  {
   "cell_type": "markdown",
@@ -822,27 +551,7 @@
   "id": "502dc688-c926-407e-8759-8c9e39eb4257",
   "metadata": {},
   "outputs": [],
-   "source": [
-    "workflow = StateGraph(State)\n",
-    "\n",
-    "workflow.add_node(\"agent\", call_model)\n",
-    "workflow.add_node(\"action\", call_tool)\n",
-    "\n",
-    "workflow.add_edge(START, \"agent\")\n",
-    "\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"agent\",\n",
-    "    should_continue,\n",
-    "    {\n",
-    "        \"continue\": \"action\",\n",
-    "        \"end\": END,\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "workflow.add_edge(\"action\", \"agent\")\n",
-    "\n",
-    "app = workflow.compile(checkpointer=memory)"
-   ]
+   "source": ["workflow = StateGraph(State)\n\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\nworkflow.add_edge(START, \"agent\")\n\nworkflow.add_conditional_edges(\n    \"agent\",\n    should_continue,\n    {\n        \"continue\": \"action\",\n        \"end\": END,\n    },\n)\n\nworkflow.add_edge(\"action\", \"agent\")\n\napp = workflow.compile(checkpointer=memory)"]
  },
  {
   "cell_type": "code",
@@ -875,13 +584,7 @@
     ]
    }
   ],
-   "source": [
-    "thread = {\"configurable\": {\"thread_id\": \"4\"}}\n",
-    "\n",
-    "inputs = [HumanMessage(content=\"what's the weather in sf?\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["thread = {\"configurable\": {\"thread_id\": \"4\"}}\n\ninputs = [HumanMessage(content=\"what's the weather in sf?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "code",
@@ -914,11 +617,7 @@
     ]
    }
   ],
-   "source": [
-    "inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  },
  {
   "cell_type": "code",
@@ -949,11 +648,7 @@
     ]
    }
   ],
-   "source": [
-    "inputs = [HumanMessage(content=\"y\")]\n",
-    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
+   "source": ["inputs = [HumanMessage(content=\"y\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
  }
 ],
 "metadata": {
@@ -1,92 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f262985e-e973-4a27-9c9e-dbb3a06a35b7",
-   "metadata": {},
-   "source": [
-    "# How to define input/output schema for your graph\n",
-    "\n",
-    "By default, `StateGraph` takes in a single schema and all nodes are expected to communicate with that schema. However, it is also possible to define explicit input and output schemas for a graph. This is helpful if you want to draw a distinction between input and output keys.\n",
-    "\n",
-    "In this notebook we'll walk through an example of this. At a high level, in order to do this you simply have to pass in `input=..., output=...` when defining the graph. Let's see an example below!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6ec0eb77-874e-443e-8c73-93125b515106",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'bye'}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langgraph.graph import StateGraph, START, END\n",
-    "from typing import TypedDict\n",
-    "\n",
-    "class InputState(TypedDict):\n",
-    "    question: str\n",
-    "\n",
-    "class OutputState(TypedDict):\n",
-    "    answer: str\n",
-    "\n",
-    "def answer_node(state: InputState):\n",
-    "    return {\"answer\": \"bye\"}\n",
-    "\n",
-    "graph = StateGraph(input=InputState, output=OutputState)\n",
-    "graph.add_node(answer_node)\n",
-    "graph.add_edge(START, \"answer_node\")\n",
-    "graph.add_edge(\"answer_node\", END)\n",
-    "graph = graph.compile()\n",
-    "\n",
-    "graph.invoke({\"question\": \"hi\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a68836f-98e1-4684-a8a6-c1473c73460c",
-   "metadata": {},
-   "source": [
-    "Notice that the output of invoke only includes the output schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b952a554-f2a4-4be3-81ab-2e08f0f441c2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -84,9 +84,8 @@
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
-   "id": "9c374e41-f9b7-439e-a520-6d8c853c5220",
+   "id": "ef7bcad1-1274-4b7c-a2e9-365180ef3a31",
   "metadata": {},
   "source": [
    "## Part 1: Build a Basic Chatbot\n",
@@ -121,24 +120,13 @@
    "graph_builder = StateGraph(State)"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "31c755cd-8994-4867-bdff-96a55d7beae7",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Note</p>\n",
-    "    <p>\n",
-    "    The first thing you do when you define a graph is define the <code>State</code> of the graph. The <code>State</code> consists of the schema of the graph as well as reducer functions which specify how to apply updates to the state. In our example <code>State</code> is a <code>TypedDict</code> with a single key: <code>messages</code>. The <code>messages</code> key is annotated with the <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\"><code>add_messages</code></a> reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\">this conceptual guide</a> to learn more about state, reducers and other low-level concepts.\n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "4137feed-746e-4c72-a34a-f7a699ad5dcf",
   "metadata": {},
   "source": [
+    "**Notice** that we've defined our `State` as a TypedDict with a single key: `messages`. The `messages` key is annotated with the [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function, which tells LangGraph to append new messages to the existing list, rather than overwriting it.\n",
+    "\n",
    "So now our graph knows two things:\n",
    "\n",
    "1. Every `node` we define will receive the current `State` as input and return a value that updates that state.\n",
@@ -848,7 +836,7 @@
    "\n",
    "We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But before we get too ahead of ourselves, let's add checkpointing to enable multi-turn conversations.\n",
    "\n",
-    "To get started, create a `MemorySaver` checkpointer."
+    "To get started, create a `SqliteSaver` checkpointer."
   ]
  },
  {
@@ -858,9 +846,9 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "\n",
-    "memory = MemorySaver()"
+    "memory = SqliteSaver.from_conn_string(\":memory:\")"
   ]
  },
  {
@@ -868,7 +856,7 @@
   "id": "08d3d11a-1b42-4cbb-8e11-2a4294263d90",
   "metadata": {},
   "source": [
-    "**Notice** we're using an in-memory checkpointer. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect to your own DB.\n",
+    "**Notice** that we've specified `:memory` as the Sqlite DB path. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to connect to your own DB and/or use one of the other checkpointer classes.\n",
    "\n",
    "Next define the graph. Now that you've already built your own `BasicToolNode`, we'll replace it with LangGraph's prebuilt `ToolNode` and `tools_condition`, since these do some nice things like parallel API execution. Apart from that, the following is all copied from Part 2."
   ]
@@ -1199,7 +1187,7 @@
    "from langchain_core.messages import BaseMessage\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode\n",
@@ -1236,8 +1224,7 @@
    "graph = graph_builder.compile(checkpointer=memory)\n",
    "```\n",
    "</pre>\n",
-    "</pre>\n",
-    "</details>"
+    "</pre>details"
   ]
  },
  {
@@ -1277,12 +1264,12 @@
    "from langchain_core.messages import BaseMessage\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph, START\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode, tools_condition\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "\n",
    "\n",
    "class State(TypedDict):\n",
@@ -1508,7 +1495,7 @@
    "from langchain_core.messages import BaseMessage\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode\n",
@@ -1543,7 +1530,7 @@
    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
    "graph_builder.set_entry_point(\"chatbot\")\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = graph_builder.compile(\n",
    "    checkpointer=memory,\n",
    "    # This is new!\n",
@@ -1593,7 +1580,7 @@
    "from langchain_core.messages import BaseMessage\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph, START\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -1627,7 +1614,7 @@
    ")\n",
    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
    "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = graph_builder.compile(\n",
    "    checkpointer=memory,\n",
    "    # This is new!\n",
@@ -2092,7 +2079,7 @@
    "from langchain_core.messages import BaseMessage\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph, START\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2289,7 +2276,7 @@
    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
    "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = graph_builder.compile(\n",
    "    checkpointer=memory,\n",
    "    # We interrupt before 'human' here instead.\n",
@@ -2539,7 +2526,7 @@
    "from langchain_core.pydantic_v1 import BaseModel\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2626,7 +2613,7 @@
    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
    "graph_builder.set_entry_point(\"chatbot\")\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = graph_builder.compile(\n",
    "    checkpointer=memory,\n",
    "    interrupt_before=[\"human\"],\n",
@@ -2665,11 +2652,11 @@
    "\n",
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_core.messages import AIMessage, ToolMessage\n",
+    "from langchain_core.messages import AIMessage, BaseMessage, ToolMessage\n",
    "from langchain_core.pydantic_v1 import BaseModel\n",
    "from typing_extensions import TypedDict\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import StateGraph, START\n",
    "from langgraph.graph.message import add_messages\n",
    "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2756,7 +2743,7 @@
    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
    "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "graph = graph_builder.compile(\n",
    "    checkpointer=memory,\n",
    "    interrupt_before=[\"human\"],\n",
@@ -3068,9 +3055,9 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "langgraph",
+   "display_name": "Python 3 (ipykernel)",
   "language": "python",
-   "name": "langgraph"
+   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
@@ -3082,7 +3069,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.1"
  }
 },
 "nbformat": 4,
@@ -105,31 +105,28 @@
    "\n",
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.messages import SystemMessage, RemoveMessage\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import MessagesState, StateGraph, START, END\n",
    "\n",
-    "memory = MemorySaver()\n",
-    "\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "\n",
    "# We will add a `summary` attribute (in addition to `messages` key,\n",
    "# which MessagesState already has)\n",
    "class State(MessagesState):\n",
    "    summary: str\n",
    "\n",
-    "\n",
    "# We will use this model for both the conversation and the summarization\n",
    "model = ChatAnthropic(model_name=\"claude-3-haiku-20240307\")\n",
    "\n",
-    "\n",
    "# Define the logic to call the model\n",
    "def call_model(state: State):\n",
    "    # If a summary exists, we add this in as a system message\n",
-    "    summary = state.get(\"summary\", \"\")\n",
+    "    summary = state.get('summary', '')\n",
    "    if summary:\n",
    "        system_message = f\"Summary of conversation earlier: {summary}\"\n",
-    "        messages = [SystemMessage(content=system_message)] + state[\"messages\"]\n",
+    "        messages = [SystemMessage(content=system_message)] + state['messages']\n",
    "    else:\n",
-    "        messages = state[\"messages\"]\n",
+    "        messages = state['messages']\n",
    "    response = model.invoke(messages)\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": [response]}\n",
@@ -148,7 +145,7 @@
    "\n",
    "def summarize_conversation(state: State):\n",
    "    # First, we summarize the conversation\n",
-    "    summary = state.get(\"summary\", \"\")\n",
+    "    summary = state.get('summary', '')\n",
    "    if summary:\n",
    "        # If a summary already exists, we use a different system prompt\n",
    "        # to summarize it than if one didn't\n",
@@ -158,13 +155,17 @@
    "        )\n",
    "    else:\n",
    "        summary_message = \"Create a summary of the conversation above:\"\n",
-    "\n",
-    "    messages = state[\"messages\"] + [HumanMessage(content=summary_message)]\n",
+    "        \n",
+    "    messages = state['messages'] + [HumanMessage(content=summary_message)]\n",
    "    response = model.invoke(messages)\n",
    "    # We now need to delete messages that we no longer want to show up\n",
    "    # I will delete all but the last two messages, but you can change this\n",
-    "    delete_messages = [RemoveMessage(id=m.id) for m in state[\"messages\"][:-2]]\n",
-    "    return {\"summary\": response.content, \"messages\": delete_messages}\n",
+    "    delete_messages = [RemoveMessage(id=m.id) for m in state['messages'][:-2]]\n",
+    "    return {\n",
+    "        \"summary\": response.content,\n",
+    "        \"messages\": delete_messages\n",
+    "    }\n",
+    "    \n",
    "\n",
    "\n",
    "# Define a new graph\n",
@@ -211,10 +212,10 @@
   "source": [
    "def print_update(update):\n",
    "    for k, v in update.items():\n",
-    "        for m in v[\"messages\"]:\n",
+    "        for m in v['messages']:\n",
    "            m.pretty_print()\n",
-    "        if \"summary\" in v:\n",
-    "            print(v[\"summary\"])"
+    "        if 'summary' in v:\n",
+    "            print(v['summary'])"
   ]
  },
  {
@@ -28,7 +28,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
   "metadata": {},
   "outputs": [],
@@ -50,15 +50,7 @@
   "execution_count": 2,
   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "ANTHROPIC_API_KEY:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
@@ -102,7 +94,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
   "id": "378899a9-3b9a-4748-95b6-eb00e0828677",
   "metadata": {},
   "outputs": [],
@@ -112,11 +104,11 @@
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.tools import tool\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import MessagesState, StateGraph, START\n",
    "from langgraph.prebuilt import ToolNode\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "\n",
    "\n",
    "@tool\n",
@@ -184,7 +176,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
   "id": "57b27553-21be-43e5-ac48-d1d0a3aa0dca",
   "metadata": {},
   "outputs": [
@@ -197,7 +189,7 @@
      "hi! I'm bob\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
-      "It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I assist you today?\n",
+      "Hello Bob! It's nice to meet you. How can I assist you today?\n",
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "what's my name?\n",
@@ -233,26 +225,26 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
   "id": "8a850529-d038-48f7-b5a2-8d4d2923f83a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[HumanMessage(content=\"hi! I'm bob\", id='bc1c6dd2-3bb9-4aa9-b7af-3c6af7e173ea'),\n",
-       " AIMessage(content=\"It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I assist you today?\", response_metadata={'id': 'msg_01XPSAenmSqK8rX2WgPZHfz7', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-1c69af09-adb1-412d-9010-2456e5a555fb-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44}),\n",
-       " HumanMessage(content=\"what's my name?\", id='f3c71afe-8ce2-4ed0-991e-65021f03b0a5'),\n",
-       " AIMessage(content='Your name is Bob, as you introduced yourself at the beginning of our conversation.', response_metadata={'id': 'msg_01BPZdwsjuMAbC1YAkqawXaF', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 19}}, id='run-b2eb9137-2f4e-446f-95f5-3d5f621a2cf8-0', usage_metadata={'input_tokens': 52, 'output_tokens': 19, 'total_tokens': 71})]"
+       "[HumanMessage(content=\"hi! I'm bob\", id='3e1098f8-2657-42d3-b58a-7c2f46930b8c'),\n",
+       " AIMessage(content=\"Hello Bob! It's nice to meet you. How can I assist you today?\", response_metadata={'id': 'msg_01HT8MUEN4p16wbYv9Xm7kfr', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 20}}, id='run-86348912-72c4-42b0-b3e0-a47c4ebd1e52-0'),\n",
+       " HumanMessage(content=\"what's my name?\", id='9c3ef235-ec5c-4e57-a3b2-c17502de496d'),\n",
+       " AIMessage(content='Your name is Bob, as you introduced yourself at the beginning of our conversation.', response_metadata={'id': 'msg_01LVhb56f6RpAAoxASZrLzmK', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 40, 'output_tokens': 19}}, id='run-e3d7447f-046a-4dfa-8813-38134dbcd1ef-0')]"
      ]
     },
-     "execution_count": 6,
+     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "messages = app.get_state(config).values[\"messages\"]\n",
+    "messages = app.get_state(config).values['messages']\n",
    "messages"
   ]
  },
@@ -266,33 +258,24 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
   "id": "df1a0970-7e64-4170-beef-2855d10eef42",
   "metadata": {},
   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/vadymbarda/.virtualenvs/langgraph/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The class `RemoveMessage` is in beta. It is actively being worked on, so the API may change.\n",
-      "  warn_beta(\n"
-     ]
-    },
    {
     "data": {
      "text/plain": [
       "{'configurable': {'thread_id': '2',\n",
-       "  'thread_ts': '1ef42d00-d9ad-6f24-8005-feb089654def'}}"
+       "  'thread_ts': '1ef3d750-5bc4-67c6-8005-9490a1b276f5'}}"
      ]
     },
-     "execution_count": 7,
+     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.messages import RemoveMessage\n",
-    "\n",
    "app.update_state(config, {\"messages\": RemoveMessage(id=messages[0].id)})"
   ]
  },
@@ -306,25 +289,25 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
   "id": "8bfe4ffa-e170-43bc-aec4-6e36ac620931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[AIMessage(content=\"It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I assist you today?\", response_metadata={'id': 'msg_01XPSAenmSqK8rX2WgPZHfz7', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-1c69af09-adb1-412d-9010-2456e5a555fb-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44}),\n",
-       " HumanMessage(content=\"what's my name?\", id='f3c71afe-8ce2-4ed0-991e-65021f03b0a5'),\n",
-       " AIMessage(content='Your name is Bob, as you introduced yourself at the beginning of our conversation.', response_metadata={'id': 'msg_01BPZdwsjuMAbC1YAkqawXaF', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 19}}, id='run-b2eb9137-2f4e-446f-95f5-3d5f621a2cf8-0', usage_metadata={'input_tokens': 52, 'output_tokens': 19, 'total_tokens': 71})]"
+       "[AIMessage(content=\"Hello Bob! It's nice to meet you. How can I assist you today?\", response_metadata={'id': 'msg_01HT8MUEN4p16wbYv9Xm7kfr', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 20}}, id='run-86348912-72c4-42b0-b3e0-a47c4ebd1e52-0'),\n",
+       " HumanMessage(content=\"what's my name?\", id='9c3ef235-ec5c-4e57-a3b2-c17502de496d'),\n",
+       " AIMessage(content='Your name is Bob, as you introduced yourself at the beginning of our conversation.', response_metadata={'id': 'msg_01LVhb56f6RpAAoxASZrLzmK', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 40, 'output_tokens': 19}}, id='run-e3d7447f-046a-4dfa-8813-38134dbcd1ef-0')]"
      ]
     },
-     "execution_count": 8,
+     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "messages = app.get_state(config).values[\"messages\"]\n",
+    "messages = app.get_state(config).values['messages']\n",
    "messages"
   ]
  },
@@ -340,7 +323,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
   "id": "bb22ede0-e153-4fd0-a4c0-f9af2f7663b1",
   "metadata": {},
   "outputs": [],
@@ -350,11 +333,10 @@
    "\n",
    "\n",
    "def delete_messages(state):\n",
-    "    messages = state[\"messages\"]\n",
+    "    messages = state['messages']\n",
    "    if len(messages) > 3:\n",
    "        return {\"messages\": [RemoveMessage(id=m.id) for m in messages[:-3]]}\n",
    "\n",
-    "\n",
    "# We need to modify the logic to call delete_messages rather than end right away\n",
    "def should_continue(state: MessagesState) -> Literal[\"action\", \"delete_messages\"]:\n",
    "    \"\"\"Return the next node to execute.\"\"\"\n",
@@ -376,10 +358,7 @@
    "\n",
    "\n",
    "workflow.add_edge(START, \"agent\")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"agent\",\n",
-    "    should_continue,\n",
-    ")\n",
+    "workflow.add_conditional_edges(\"agent\", should_continue,)\n",
    "workflow.add_edge(\"action\", \"agent\")\n",
    "\n",
    "# This is the new edge we're adding: after we delete messages, we finish\n",
@@ -397,7 +376,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
   "id": "3975f34c-c243-40ea-b9d2-424d50a48dc9",
   "metadata": {},
   "outputs": [
@@ -405,11 +384,21 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[('human', \"hi! I'm bob\")]\n",
-      "[('human', \"hi! I'm bob\"), ('ai', \"Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.\")]\n",
-      "[('human', \"hi! I'm bob\"), ('ai', \"Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.\"), ('human', \"what's my name?\")]\n",
-      "[('human', \"hi! I'm bob\"), ('ai', \"Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.\"), ('human', \"what's my name?\"), ('ai', 'You said your name is Bob, so that is the name I have for you.')]\n",
-      "[('ai', \"Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.\"), ('human', \"what's my name?\"), ('ai', 'You said your name is Bob, so that is the name I have for you.')]\n"
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "hi! I'm bob\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "It's nice to meet you, Bob! How can I assist you today?\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "what's my name?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "You said your name is Bob.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "You said your name is Bob.\n"
     ]
    }
   ],
@@ -419,12 +408,12 @@
    "config = {\"configurable\": {\"thread_id\": \"3\"}}\n",
    "input_message = HumanMessage(content=\"hi! I'm bob\")\n",
    "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
-    "    print([(message.type, message.content) for message in event[\"messages\"]])\n",
+    "    event[\"messages\"][-1].pretty_print()\n",
    "\n",
    "\n",
    "input_message = HumanMessage(content=\"what's my name?\")\n",
    "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
-    "    print([(message.type, message.content) for message in event[\"messages\"]])"
+    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
@@ -437,25 +426,25 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
   "id": "a3e15abb-81d8-4072-9f10-61ae0fd61dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[AIMessage(content=\"Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.\", response_metadata={'id': 'msg_01XPEgPPbcnz5BbGWUDWTmzG', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 48}}, id='run-eded3820-b6a9-4d66-9210-03ca41787ce6-0', usage_metadata={'input_tokens': 12, 'output_tokens': 48, 'total_tokens': 60}),\n",
-       " HumanMessage(content=\"what's my name?\", id='a0ea2097-3280-402b-92e1-67177b807ae8'),\n",
-       " AIMessage(content='You said your name is Bob, so that is the name I have for you.', response_metadata={'id': 'msg_01JGT62pxhrhN4SykZ57CSjW', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 68, 'output_tokens': 20}}, id='run-ace3519c-81f8-45fe-a777-91f42d48b3a3-0', usage_metadata={'input_tokens': 68, 'output_tokens': 20, 'total_tokens': 88})]"
+       "[AIMessage(content=\"It's nice to meet you, Bob! How can I assist you today?\", response_metadata={'id': 'msg_01QMoxepDiCcKQ6XFgge1QQT', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 19}}, id='run-de13ba05-095d-4fd1-907a-6766ef3bf57b-0'),\n",
+       " HumanMessage(content=\"what's my name?\", id='8292e725-8fc4-487e-a9b6-75f8b136bec2'),\n",
+       " AIMessage(content='You said your name is Bob.', response_metadata={'id': 'msg_01DfWfaxavdMCqtoQRmC3mc4', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 39, 'output_tokens': 10}}, id='run-28167c82-e126-47e4-854c-623e50c8af22-0')]"
      ]
     },
-     "execution_count": 11,
+     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "messages = app.get_state(config).values[\"messages\"]\n",
+    "messages = app.get_state(config).values['messages']\n",
    "messages"
   ]
  },
@@ -478,9 +467,9 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "langgraph",
+   "display_name": "Python 3 (ipykernel)",
   "language": "python",
-   "name": "langgraph"
+   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
@@ -492,7 +481,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.1"
  }
 },
 "nbformat": 4,
@@ -103,11 +103,11 @@
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.tools import tool\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import MessagesState, StateGraph, START\n",
    "from langgraph.prebuilt import ToolNode\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "\n",
    "\n",
    "@tool\n",
@@ -138,7 +138,7 @@
    "\n",
    "# Define the function that calls the model\n",
    "def call_model(state: MessagesState):\n",
-    "    response = bound_model.invoke(state[\"messages\"])\n",
+    "    response = model.invoke(state[\"messages\"])\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": response}\n",
    "\n",
@@ -234,11 +234,11 @@
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.tools import tool\n",
    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
    "from langgraph.graph import MessagesState, StateGraph, START\n",
    "from langgraph.prebuilt import ToolNode\n",
    "\n",
-    "memory = MemorySaver()\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
    "\n",
    "\n",
    "@tool\n",
@@ -275,7 +275,7 @@
    "# Define the function that calls the model\n",
    "def call_model(state: MessagesState):\n",
    "    messages = filter_messages(state[\"messages\"])\n",
-    "    response = bound_model.invoke(messages)\n",
+    "    response = model.invoke(messages)\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": response}\n",
    "\n",
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to add node retry policies\n",
-    "\n",
-    "There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. \n",
-    "\n",
-    "In order to configure the retry policy, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RetryPolicy(initial_interval=0.5, backoff_factor=2.0, max_interval=128.0, max_attempts=3, jitter=True, retry_on=<function default_retry_on at 0x1157419e0>)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langgraph.pregel import RetryPolicy\n",
-    "\n",
-    "RetryPolicy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want more information on what each of the parameters does, be sure to read the [reference](https://langchain-ai.github.io/langgraph/reference/graphs/#retrypolicy).\n",
-    "\n",
-    "## Passing a retry policy to a node\n",
-    "\n",
-    "Lastly, we can pass `RetryPolicy` objects when we call the `add_node` function. In the example below we pass two different retry policies to each of our nodes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import operator\n",
-    "import sqlite3\n",
-    "from typing import Annotated, Sequence, TypedDict\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
-    "from langgraph.graph import END, StateGraph, START\n",
-    "from langchain_community.utilities import SQLDatabase\n",
-    "from langchain_core.messages import AIMessage\n",
-    "\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///:memory:\")\n",
-    "\n",
-    "model = ChatAnthropic(model_name=\"claude-2.1\")\n",
-    "\n",
-    "\n",
-    "class AgentState(TypedDict):\n",
-    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
-    "\n",
-    "\n",
-    "def query_database(state):\n",
-    "    query_result = db.run(\"SELECT * FROM Artist LIMIT 10;\")\n",
-    "    return {\"messages\": [AIMessage(content=query_result)]}\n",
-    "\n",
-    "\n",
-    "def call_model(state):\n",
-    "    response = model.invoke(state[\"messages\"])\n",
-    "    return {\"messages\": [response]}\n",
-    "\n",
-    "\n",
-    "# Define a new graph\n",
-    "workflow = StateGraph(AgentState)\n",
-    "workflow.add_node(\n",
-    "    \"query_database\",\n",
-    "    query_database,\n",
-    "    retry=RetryPolicy(retry_on=sqlite3.OperationalError),\n",
-    ")\n",
-    "workflow.add_node(\"model\", call_model, retry=RetryPolicy(max_attempts=5))\n",
-    "workflow.add_edge(START, \"model\")\n",
-    "workflow.add_edge(\"model\", \"query_database\")\n",
-    "workflow.add_edge(\"query_database\", END)\n",
-    "\n",
-    "app = workflow.compile()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -1,126 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "47ed5db3-bda5-49e1-bf75-23e08c9a3af0",
-   "metadata": {},
-   "source": [
-    "# How to pass private state\n",
-    "\n",
-    "Oftentimes, you may want nodes to be able to pass state to each other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n",
-    "\n",
-    "Let's take a look at an example below. In this example, we will create a RAG pipeline that:\n",
-    "1. Takes in a user question\n",
-    "2. Uses an LLM to generate a search query\n",
-    "3. Retrieves documents for that generated query\n",
-    "4. Generates a final answer based on those documents\n",
-    "\n",
-    "We will have a separate node for each step. We will only have the `question` and `answer` on the overall state. However, we will need separate states for the `search_query` and the `documents` - we will pass these as private state keys.\n",
-    "\n",
-    "Let's look at an example!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3114c3ad-0ade-47ba-9488-53d6f7671578",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'foo', 'answer': 'fo\\n\\nfo\\n\\nfoo'}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langgraph.graph import StateGraph, START, END\n",
-    "from typing import TypedDict\n",
-    "\n",
-    "\n",
-    "# The overall state of the graph\n",
-    "class OverallState(TypedDict):\n",
-    "    question: str\n",
-    "    answer: str\n",
-    "\n",
-    "\n",
-    "# This is what the node that generates the query will return\n",
-    "class QueryOutputState(TypedDict):\n",
-    "    query: str\n",
-    "\n",
-    "\n",
-    "# This is what the node that retrieves the documents will return\n",
-    "class DocumentOutputState(TypedDict):\n",
-    "    docs: list[str]\n",
-    "\n",
-    "\n",
-    "# This is what the node that generates the final answer will take in\n",
-    "class GenerateInputState(OverallState, DocumentOutputState):\n",
-    "    pass\n",
-    "\n",
-    "\n",
-    "# Node to generate query\n",
-    "def generate_query(state: OverallState) -> QueryOutputState:\n",
-    "    # Replace this with real logic\n",
-    "    return {\"query\": state[\"question\"][:2]}\n",
-    "\n",
-    "\n",
-    "# Node to retrieve documents\n",
-    "def retrieve_documents(state: QueryOutputState) -> DocumentOutputState:\n",
-    "    # Replace this with real logic\n",
-    "    return {\"docs\": [state['query']] * 2}\n",
-    "\n",
-    "\n",
-    "# Node to generate answer\n",
-    "def generate(state: GenerateInputState) -> OverallState:\n",
-    "    return {\"answer\": \"\\n\\n\".join(state['docs'] + [state['question']])}\n",
-    "\n",
-    "\n",
-    "graph = StateGraph(OverallState)\n",
-    "graph.add_node(generate_query)\n",
-    "graph.add_node(retrieve_documents)\n",
-    "graph.add_node(generate)\n",
-    "graph.add_edge(START, \"generate_query\")\n",
-    "graph.add_edge(\"generate_query\", \"retrieve_documents\")\n",
-    "graph.add_edge(\"retrieve_documents\", \"generate\")\n",
-    "graph.add_edge(\"generate\", END)\n",
-    "graph = graph.compile()\n",
-    "\n",
-    "graph.invoke({\"question\": \"foo\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ffc2d8c-717f-42c9-b0aa-15b178a5cc8b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -252,7 +252,7 @@
   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
   "metadata": {},
   "outputs": [],
-   "source": ["### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.get_relevant_documents(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score[\"score\"]\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}\n\n\n### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score[\"score\"]\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score[\"score\"]\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""]
+   "source": ["### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.get_relevant_documents(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score[\"score\"]\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}\n\n\n### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score[\"score\"]\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score[\"score\"]\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""]
  },
  {
   "cell_type": "markdown",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Jacob LeeandGitHub	b6c46d1a45	Update README.md	2024-07-11 15:42:32 -07:00
Jacob LeeandGitHub	9c7dafb75a	Update README.md	2024-07-11 15:42:16 -07:00
jacoblee93	b67c3d1edd	Sync readmes	2024-07-11 15:16:58 -07:00
Jacob LeeandGitHub	634fd082c5	Fix example CC @vbarda	2024-07-11 15:14:12 -07:00