checkpoint-postgres: release 1.0.4 (#1493)

* checkpoint-postgres: allow passing pool

* make psycopg_pool a non-dev dependency

* code review

* lockfile

* move methods

* relax requirements, remove binary

* add binary to dev dependencies

* update readme

.github/workflows/_test.yml

@@ -24,13 +24,7 @@ jobs:
     name: "test #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
-      - name: Get changed files
-        id: changed-files
-        uses: Ana06/get-changed-files@v2.2.0
-        with:
-          filter: "${{ inputs.working-directory }}/**"
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        if: steps.changed-files.outputs.all
         uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
@@ -39,20 +33,17 @@ jobs:
           cache-key: core
 
       - name: Install dependencies
-        if: steps.changed-files.outputs.all
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: poetry install --with dev
 
       - name: Run core tests
-        if: steps.changed-files.outputs.all
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
           make test
 
       - name: Ensure the tests did not create any additional files
-        if: steps.changed-files.outputs.all
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |

.github/workflows/ci.yml

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

.github/workflows/release.yml

@@ -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 $REGEX | head -1)
+          PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1 || echo "")
           echo $PREV_TAG
           if [ "$TAG" == "$PREV_TAG" ]; then
             echo "No new version to release"
@@ -137,8 +137,7 @@ jobs:
       - build
       - release-notes
     permissions: write-all
-    uses:
-      ./.github/workflows/_test_release.yml
+    uses: ./.github/workflows/_test_release.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
@@ -198,9 +197,15 @@ jobs:
               "$PKG_NAME==$VERSION" \
           )
 
-          # 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)"
+          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
 
           poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 

.github/workflows/size.yml

@@ -0,0 +1,28 @@
+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

Makefile

@@ -1,6 +1,14 @@
-.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell
+.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
 
-build-docs:
+build-typedoc:
+	cd libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
+
+
+
+build-docs: build-typedoc
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
 
@@ -8,7 +16,7 @@ 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:
+serve-docs: build-typedoc
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
 

README.md

@@ -3,7 +3,6 @@
 ![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 ⚡
@@ -11,9 +10,6 @@
 > [!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.
@@ -62,8 +58,8 @@ 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 import MemorySaver
-from langgraph.graph import END, StateGraph, MessagesState
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode
 
 
@@ -73,8 +69,8 @@ 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]
@@ -111,7 +107,7 @@ workflow.add_node("tools", tool_node)
 
 # Set the entrypoint as `agent`
 # This means that this node is the first one called
-workflow.set_entry_point("agent")
+workflow.add_edge(START, "agent")
 
 # We now add a conditional edge
 workflow.add_conditional_edges(

docs/.gitignore

@@ -1,3 +1,4 @@
 *.ipynb
 site/
 docs/tutorials/**/*.png
+docs/cloud/reference/sdk/js_ts_sdk_ref.md

docs/_scripts/copy_notebooks.py

@@ -27,6 +27,8 @@ _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",
@@ -41,6 +43,7 @@ _MANUAL = {
         "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",
@@ -53,9 +56,11 @@ _MANUAL = {
         "create-react-agent-memory.ipynb",
         "create-react-agent-hitl.ipynb",
         "human_in_the_loop/breakpoints.ipynb",
+        "human_in_the_loop/dynamic_breakpoints.ipynb",
         "human_in_the_loop/time-travel.ipynb",
         "human_in_the_loop/edit-graph-state.ipynb",
         "human_in_the_loop/wait-user-input.ipynb",
+        "human_in_the_loop/review-tool-calls.ipynb",
         "node-retries.ipynb",
     ],
     "tutorials": [
@@ -98,6 +103,8 @@ _HIDE = set(
         "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",
     ]

docs/docs/cloud/concepts/api.md

@@ -10,7 +10,11 @@ The LangGraph Cloud API consists of a few core data models: [Assistants](#assist
 
 An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It abstracts the cognitive architecture of the graph and contains instance specific configuration and metadata. Multiple assistants can reference the same graph but can contain different configuration and metadata, which may differentiate the behavior of the assistants. An assistant (i.e. the graph) is invoked as part of a run.
 
-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.
+The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../reference/api/api_ref.html#tag/assistantscreate) 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
 
@@ -20,13 +24,13 @@ The state of a thread at a particular point in time is called a checkpoint.
 
 For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/low_level.md#checkpointer).
 
-The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the <a href="../reference/api/api_ref.html#tag/threadscreate" target="_blank">API reference</a> for more details.
+The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../reference/api/api_ref.html#tag/threadscreate) for more details.
 
 ### Runs
 
 A run is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a thread.
 
-The LangGraph Cloud API provides several endpoints for creating and managing runs. See the <a href="../reference/api/api_ref.html#tag/runscreate" target="_blank">API reference</a> for more details.
+The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../reference/api/api_ref.html#tag/runscreate) for more details.
 
 ### Cron Jobs
 
@@ -37,7 +41,7 @@ It's often useful to run graphs on some schedule. LangGraph Cloud supports cron
 
 Note that this sends the same input to the thread every time. See the [how-to guide](../how-tos/cloud_examples/cron_jobs.ipynb) for creating cron jobs.
 
-The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the <a href="../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons" target="_blank">API reference</a> for more details.
+The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
 
 ## Features
 
@@ -55,7 +59,7 @@ Streaming is critical for making LLM applications feel responsive to end users.
 
 You can also specify multiple streaming modes at the same time. See the [how-to guide](../how-tos/stream_multiple.md) for configuring multiple streaming modes at the same time.
 
-See the <a href="../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/stream" target="_blank">API reference</a> for how to create streaming runs.
+See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/stream) for how to create streaming runs.
 
 ### Human-in-the-Loop
 

docs/docs/cloud/deployment/graph_rebuild.md

@@ -0,0 +1,146 @@
+# 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, START, MessageGraph
+
+model = ChatOpenAI(temperature=0)
+
+graph_workflow = MessageGraph()
+
+graph_workflow.add_node("agent", model)
+graph_workflow.add_edge("agent", END)
+graph_workflow.add_edge(START, "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, START, 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.add_edge(START, "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.add_edge(START, "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)

docs/docs/cloud/deployment/setup.md

@@ -1,16 +1,30 @@
 # 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. 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.
+
+This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
+
+!!! 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.
+
+!!! tip "Setup with a Monorepo"
+If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
 
 The final repo structure will look something like this:
 
 ```bash
 my-app/
-|-- requirements.txt    # package dependencies
-|-- .env                # environment variables
-|-- openai_agent.py     # code for an agent
-|-- anthropic_agent.py  # code for another agent
-|-- langgraph.json      # configuration file for LangGraph
+├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
+│   ├── requirements.txt # package dependencies
+│   ├── __init__.py
+│   └── agent.py # code for constructing your graph
+├── .env # environment variables
+└── langgraph.json # configuration file for LangGraph
 ```
 
 After each step, an example file directory is provided to demonstrate how code can be organized.
@@ -19,16 +33,43 @@ 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>=0.2.7,<0.3.0
+langgraph-checkpoint>=1.0.4
+langchain-core>=0.2.27,<0.3.0
+langsmith>=0.1.63
+orjson>=3.9.7
+httpx>=0.25.0
+tenacity>=8.0.0
+uvicorn>=0.26.0
+sse-starlette>=2.1.0
+uvloop>=0.18.0
+httptools>=0.5.0
+jsonschema-rs>=0.16.3
+croniter>=1.0.1
+structlog>=23.1.0
+redis>=5.0.0,<6.0.0
+```
+
 Example `requirements.txt` file:
+
 ```
 langgraph
+langchain_anthropic
+tavily-python
+langchain_community
 langchain_openai
+
 ```
 
 Example file directory:
-```
+
+```bash
 my-app/
-|-- requirements.txt    # Python packages required for your graph
+├── my_agent # all project code lies within here
+│   └── requirements.txt # package dependencies
 ```
 
 ## Specify Environment Variables
@@ -36,6 +77,7 @@ my-app/
 Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for a deployment.
 
 Example `.env` file:
+
 ```
 MY_ENV_VAR_1=foo
 MY_ENV_VAR_2=bar
@@ -43,42 +85,66 @@ OPENAI_API_KEY=key
 ```
 
 Example file directory:
-```
+
+```bash
 my-app/
-|-- requirements.txt
-|-- .env                # file with environment variables
+├── my_agent # all project code lies within here
+│   └── requirements.txt # package dependencies
+└── .env # environment variables
 ```
 
 ## Define Graphs
 
 Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][compiledgraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
 
-Example `openai_agent.py` file:
+Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example) to see their implementation):
+
 ```python
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, MessageGraph
+# my_agent/agent.py
+from typing import TypedDict, Literal
 
-model = ChatOpenAI(temperature=0)
+from langgraph.graph import StateGraph, END, START
+from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
+from my_agent.utils.state import AgentState # import state
 
-graph_workflow = MessageGraph()
+# Define the config
+class GraphConfig(TypedDict):
+    model_name: Literal["anthropic", "openai"]
 
-graph_workflow.add_node("agent", model)
-graph_workflow.add_edge("agent", END)
-graph_workflow.set_entry_point("agent")
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+workflow.add_edge(START, "agent")
+workflow.add_conditional_edges(
+    "agent",
+    should_continue,
+    {
+        "continue": "action",
+        "end": END,
+    },
+)
+workflow.add_edge("action", "agent")
 
-agent = graph_workflow.compile()
+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 (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
 
 Example file directory:
-```
+
+```bash
 my-app/
-|-- requirements.txt
-|-- .env
-|-- openai_agent.py     # code for your graph
-|-- anthropic_agent.py  # code for your graph
+├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
+│   ├── requirements.txt # package dependencies
+│   ├── __init__.py
+│   └── agent.py # code for constructing your graph
+└── .env # environment variables
 ```
 
 ## Create LangGraph API Config
@@ -86,39 +152,39 @@ my-app/
 Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
 
 Example `langgraph.json` file:
+
 ```json
 {
-    "dependencies": [
-        "."
-    ],
-    "graphs": {
-        "openai_agent": "./openai_agent.py:agent",
-        "anthropic_agent": "./anthropic_agent.py:agent"
-    },
-    "env": "./.env"
+  "dependencies": ["./my_agent"],
+  "graphs": {
+    "agent": "./my_agent/agent.py:graph"
+  },
+  "env": ".env"
 }
 ```
 
 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.
+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
 my-app/
-|-- requirements.txt
-|-- .env
-|-- openai_agent.py
-|-- anthropic_agent.py
-|-- langgraph.json      # configuration file for LangGraph
+├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
+│   ├── requirements.txt # package dependencies
+│   ├── __init__.py
+│   └── agent.py # code for constructing your graph
+├── .env # environment variables
+└── 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 repo, it's time to [deploy your app](./cloud.md).
+After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).

docs/docs/cloud/deployment/setup_pyproject.md

@@ -1,16 +1,29 @@
 # 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 `pyproject.toml` to define your package's dependencies. If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md).
+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 `pyproject.toml` to define your package's dependencies. 
+
+This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
+
+!!! tip "Setup with requirements.txt"
+  If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md).
+
+!!! tip "Setup with a Monorepo"
+    If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
 
 The final repo structure will look something like this:
 
 ```bash
 my-app/
 ├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
 │   ├── __init__.py
-│   └── agent.py # code for your graph
-│-- .env # environment variables
-│-- langgraph.json  # configuration file for LangGraph
+│   └── agent.py # code for constructing your graph
+├── .env # environment variables
+├── langgraph.json  # configuration file for LangGraph
 └── pyproject.toml # dependencies for your project
 ```
 
@@ -20,6 +33,25 @@ 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>=0.2.7,<0.3.0
+langgraph-checkpoint>=1.0.4
+langchain-core>=0.2.27,<0.3.0
+langsmith>=0.1.63
+orjson>=3.9.7
+httpx>=0.25.0
+tenacity>=8.0.0
+uvicorn>=0.26.0
+sse-starlette>=2.1.0
+uvloop>=0.18.0
+httptools>=0.5.0
+jsonschema-rs>=0.16.3
+croniter>=1.0.1
+structlog>=24.4.0
+redis>=5.0.8,<6.0.0
+```
+
 Example `pyproject.toml` file:
 
 ```toml
@@ -33,7 +65,7 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.9.0,<3.13"
-langgraph = "^0.1.0"
+langgraph = "^0.2.0"
 langchain-fireworks = "^0.1.3"
 
 
@@ -46,9 +78,6 @@ Example file directory:
 
 ```bash
 my-app/
-├── my_agent
-│   ├── __init__.py
-│   └── agent.py
 └── pyproject.toml   # Python packages required for your graph
 ```
 
@@ -68,10 +97,7 @@ Example file directory:
 
 ```bash
 my-app/
-├── my_agent
-│   ├── __init__.py
-│   └── agent.py
-|-- .env             # file with environment variables
+├── .env             # file with environment variables
 └── pyproject.toml
 ```
 
@@ -79,26 +105,35 @@ my-app/
 
 Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][compiledgraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
 
-Example `agent.py` file:
+Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):
 
 ```python
 # my_agent/agent.py
-from langchain_fireworks import ChatFireworks
-from langgraph.graph import END, StateGraph, add_messages
-from typing_extensions import TypedDict, Annotated
+from typing import TypedDict, Literal
 
-model = ChatFireworks(model="accounts/fireworks/models/firefunction-v2", temperature=0)
+from langgraph.graph import StateGraph, END, START
+from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
+from my_agent.utils.state import AgentState # import state
 
-class State(TypedDict):
-    messages: Annotated[list, add_messages]
+# Define the config
+class GraphConfig(TypedDict):
+    model_name: Literal["anthropic", "openai"]
 
-graph_workflow = StateGraph(State)
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+workflow.add_edge(START, "agent")
+workflow.add_conditional_edges(
+    "agent",
+    should_continue,
+    {
+        "continue": "action",
+        "end": END,
+    },
+)
+workflow.add_edge("action", "agent")
 
-graph_workflow.add_node("agent", model)
-graph_workflow.add_edge("agent", END)
-graph_workflow.set_entry_point("agent")
-
-agent = graph_workflow.compile()
+graph = workflow.compile()
 ```
 
 !!! warning "Assign `CompiledGraph` to Variable"
@@ -108,10 +143,15 @@ Example file directory:
 
 ```bash
 my-app/
-├── my_agent
+├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
 │   ├── __init__.py
-│   └── agent.py # code for your graph
-|-- .env
+│   └── agent.py # code for constructing your graph
+├── .env
 └── pyproject.toml
 ```
 
@@ -125,9 +165,9 @@ Example `langgraph.json` file:
 {
   "dependencies": ["."],
   "graphs": {
-    "my_fantastic_agent": "./my_agent/agent.py:agent"
+    "agent": "./my_agent/agent.py:graph"
   },
-  "env": "./.env"
+  "env": ".env"
 }
 ```
 
@@ -140,18 +180,19 @@ Example file directory:
 
 ```bash
 my-app/
-├── my_agent
+├── my_agent # all project code lies within here
+│   ├── utils # utilities for your graph
+│   │   ├── __init__.py
+│   │   ├── tools.py # tools for your graph
+│   │   ├── nodes.py # node functions for you graph
+│   │   └── state.py # state definition of your graph
 │   ├── __init__.py
-│   └── agent.py # code for your graph
-│-- .env
-│-- langgraph.json      # configuration file for LangGraph
-└── pyproject.toml
+│   └── agent.py # code for constructing your graph
+├── .env # environment variables
+├── langgraph.json  # configuration file for LangGraph
+└── pyproject.toml # dependencies for your project
 ```
 
-## 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 repo, it's time to [deploy your app](./cloud.md).
+After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).

docs/docs/cloud/deployment/test_locally.md

@@ -38,6 +38,46 @@ Ready!
 
 We can now interact with the API server using the LangGraph SDK. First, we need to start our client, select our assistant (in this case a graph we called "agent", make sure to select the proper assistant you wish to test).
 
+You can either initialize by passing authentication or by setting an environment variable.
+
+#### Initialize with authentication
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+
+    # only pass the url argument to get_client() if you changed the default port when calling langgraph up
+    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGCHAIN_API_KEY>)
+    assistant_id = "agent"
+    thread = await client.threads.create()
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    // only set the apiUrl if you changed the default port when calling langgraph up
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGCHAIN_API_KEY> });
+    const assistantId = "agent"
+    const thread = await client.threads.create();
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+      --header 'x-api-key: <LANGCHAIN_API_KEY>'
+    ```
+  
+
+#### Initialize with environment variables
+
+If you have a `LANGCHAIN_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
+
 === "Python"
 
     ```python
@@ -60,6 +100,14 @@ We can now interact with the API server using the LangGraph SDK. First, we need
     const thread = await client.threads.create();
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 Now we can invoke our graph to ensure it is working. Make sure to change the input to match the proper schema for your graph. 
 
 === "Python"
@@ -96,4 +144,39 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
     }
     ```
 
+  === "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
+       \"stream_mode\": [
+         \"events\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     ' 
+    ```
+
 If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.

docs/docs/cloud/faq/studio.md

@@ -0,0 +1,65 @@
+# Studio FAQs
+
+## Why is my project failing to start?
+
+There are a few reasons that your project might fail to start, here are some of the most common ones.
+
+### Docker issues
+
+LangGraph Studio requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
+
+### Configuration or environment issues
+
+Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables. 
+
+## How does interrupt work?
+
+When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.
+
+## How do I reload the app?
+
+If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
+
+## How does automatic rebuilding work?
+
+One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
+
+### Rebuilds from source code changes
+
+If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
+
+
+### Rebuilds from configuration or dependency changes
+
+If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
+
+## Why is my graph taking so long to startup?
+
+The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
+
+## Why are extra edges showing up in my graph?
+
+If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:
+
+### Solution 1: Include a path map
+
+The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:
+
+```python
+graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
+```
+
+In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.
+
+### Solution 2: Update the typing of the router
+
+Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:
+
+```python
+def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
+    if state['some_condition'] == True:
+        return "node_a"
+    else:
+        return "node_b"
+```
+

docs/docs/cloud/how-tos/check_thread_status.md

@@ -0,0 +1,200 @@
+# Check the Status of your Threads
+
+## Setup
+
+To start, we can setup our client with whatever URL you are hosting your graph from:
+
+### SDK initialization
+
+First, we need to setup our client so that we can communicate with our hosted graph:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>)
+    assistant_id = "agent"
+    thread = await client.threads.create()
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+    const assistantId = agent;
+    const thread = await client.threads.create();
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
+## Find idle threads
+
+We can use the following commands to find threads that are idle, which means that all runs executed on the thread have finished running:
+
+=== "Python"
+
+    ```python
+    print(await client.threads.search(status="idle",limit=1))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log(await client.threads.search({status: "idle",limit:1}));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \  
+    --url <DEPLOYMENT_URL>/threads/search \
+    --header 'Content-Type: application/json' \
+    --data '{"status": "idle", "limit": 1}'
+    ```
+
+Output:
+
+    [{'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
+    'created_at': '2024-08-14T17:36:38.921660+00:00',
+    'updated_at': '2024-08-14T17:36:38.921660+00:00',
+    'metadata': {'graph_id': 'agent'},
+    'status': 'idle',
+    'config': {'configurable': {}}}]
+
+
+## Find interrupted threads
+
+We can use the following commands to find threads that have been interrupted in the middle of a run, which could either mean an error occurred before the run finished or a human-in-the-loop breakpoint was reached and the run is waiting to continue: 
+
+=== "Python"
+
+    ```python
+    print(await client.threads.search(status="interrupted",limit=1))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log(await client.threads.search({status: "interrupted",limit:1}));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \  
+    --url <DEPLOYMENT_URL>/threads/search \
+    --header 'Content-Type: application/json' \
+    --data '{"status": "interrupted", "limit": 1}'
+    ```
+
+Output:
+
+    [{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
+    'created_at': '2024-08-14T17:41:50.235455+00:00',
+    'updated_at': '2024-08-14T17:41:50.235455+00:00',
+    'metadata': {'graph_id': 'agent'},
+    'status': 'interrupted',
+    'config': {'configurable': {}}}]
+    
+## Find busy threads
+
+We can use the following commands to find threads that are busy, meaning they are currently handling the execution of a run:
+
+=== "Python"
+
+    ```python
+    print(await client.threads.search(status="busy",limit=1))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log(await client.threads.search({status: "busy",limit: 1}));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \  
+    --url <DEPLOYMENT_URL>/threads/search \
+    --header 'Content-Type: application/json' \
+    --data '{"status": "busy", "limit": 1}'
+    ```
+
+Output:
+
+    [{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
+    'created_at': '2024-08-14T17:41:50.235455+00:00',
+    'updated_at': '2024-08-14T17:41:50.235455+00:00',
+    'metadata': {'graph_id': 'agent'},
+    'status': 'busy',
+    'config': {'configurable': {}}}]
+
+## Find specific threads
+
+You may also want to check the status of specific threads, which you can do in a few ways:
+
+### Find by ID
+
+You can use the `get` function to find the status of a specific thread, as long as you have the ID saved
+
+=== "Python"
+
+    ```python
+    print((await client.threads.get(<THREAD_ID>))['status'])
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log((await client.threads.get(<THREAD_ID>)).status);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request GET \ 
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID> \
+    --header 'Content-Type: application/json' | jq -r '.status'
+    ```
+
+Output:
+
+    'idle'
+
+### Find by metadata
+
+The search endpoint for threads also allows you to filter on metadata, which can be helpful if you use metadata to tag threads in order to keep them organized:
+
+=== "Python"
+
+    ```python
+    print((await client.threads.search(metadata={"foo":"bar"},limit=1))[0]['status'])
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log((await client.threads.search({metadata: {"foo":"bar"},limit: 1}))[0].status);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \  
+    --url <DEPLOYMENT_URL>/threads/search \
+    --header 'Content-Type: application/json' \
+    --data '{"metadata": {"foo":"bar"}, "limit": 1}' | jq -r '.[0].status'
+    ```
+
+Output:
+
+    'idle'

docs/docs/cloud/how-tos/copy_threads.md

@@ -0,0 +1,132 @@
+# Copying Threads
+
+You may wish to copy (i.e. "fork") an existing thread in order to keep the existing thread's history and create independent runs that do not affect the original thread. This guide shows how you can do that.
+
+## Setup
+
+This code assumes you already have a thread to copy. You can read about what a thread is [here](https://langchain-ai.github.io/langgraph/cloud/concepts/api/#threads) and learn how to stream a run on a thread in [these how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming).
+
+### SDK initialization
+
+First, we need to setup our client so that we can communicate with our hosted graph:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url="<DEPLOYMENT_URL>")
+    assistant_id = "agent"
+    thread = await client.threads.create()
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl:"<DEPLOYMENT_URL>" });
+    const assistantId = agent;
+    const thread = await client.threads.create();
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json' \
+      --data '{
+        "metadata": {}
+      }'
+    ```
+
+## Copying a thread
+
+The code below assumes that a thread you'd like to copy already exists.
+
+Copying a thread will create a new thread with the same history as the existing thread, and then allow you to continue executing runs.
+
+### Create copy
+
+=== "Python"
+
+    ```python
+    copied_thread = await client.threads.copy(<THREAD_ID>)
+    ```
+
+=== "Javascript"
+
+    ```js
+    let copiedThread = await client.threads.copy(<THREAD_ID>);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/copy \
+    --header 'Content-Type: application/json'
+    ```
+
+### Verify copy
+
+We can verify that the history from the prior thread did indeed copy over correctly:
+
+=== "Python"
+
+    ```python
+    def remove_thread_id(d):
+      if 'metadata' in d and 'thread_id' in d['metadata']:
+          del d['metadata']['thread_id']
+      return d
+
+    original_thread_history = list(map(remove_thread_id,await client.threads.get_history(<THREAD_ID>)))
+    copied_thread_history = list(map(remove_thread_id,await client.threads.get_history(copied_thread['thread_id'])))
+
+    # Compare the two histories
+    assert original_thread_history == copied_thread_history
+    # if we made it here the assertion passed!
+    print("The histories are the same.")
+    ```
+
+=== "Javascript"
+
+    ```js
+    function removeThreadId(d) {
+        if (d.metadata && d.metadata.thread_id) {
+            delete d.metadata.thread_id;
+        }
+        return d;
+    }
+
+    // Assuming `client.threads.getHistory(threadId)` is an async function that returns a list of dicts
+    async function compareThreadHistories(threadId, copiedThreadId) {
+        const originalThreadHistory = (await client.threads.getHistory(threadId)).map(removeThreadId);
+        const copiedThreadHistory = (await client.threads.getHistory(copiedThreadId)).map(removeThreadId);
+
+        // Compare the two histories
+        console.assert(JSON.stringify(originalThreadHistory) === JSON.stringify(copiedThreadHistory))
+        // if we made it here the assertion passed!
+        console.log("The histories are the same.");
+    }
+
+    // Example usage
+    compareThreadHistories(<THREAD_ID>, copiedThread.thread_id);
+    ```
+
+=== "CURL"
+
+    ```bash
+    if diff <(
+        curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
+    ) <(
+        curl --request GET --url <DEPLOYMENT_URL>/threads/<COPIED_THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
+    ) >/dev/null; then
+        echo "The histories are the same."
+    else
+        echo "The histories are different."
+    fi
+    ```
+
+Output:
+
+    The histories are the same.

docs/docs/cloud/how-tos/enqueue_concurrent.md

@@ -31,7 +31,7 @@ Then, let's import our required packages and instantiate our client, assistant,
     from langchain_core.messages import convert_to_messages
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -42,7 +42,7 @@ Then, let's import our required packages and instantiate our client, assistant,
     import { Client } from "@langchain/langgraph-sdk";
     
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```

docs/docs/cloud/how-tos/human_in_the_loop_breakpoint.md

@@ -21,7 +21,7 @@ In this how-to we use a simple ReAct style hosted graph (you can see the full co
 
     ```python
     from langgraph_sdk import get_client
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -31,11 +31,19 @@ In this how-to we use a simple ReAct style hosted graph (you can see the full co
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent"
     const thread = await client.threads.create();
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 ## Adding a breakpoint
 
 We now want to add a breakpoint in our graph run, which we will do before a tool is called.
@@ -82,6 +90,42 @@ And, now let's compile it with a breakpoint before the tool node:
       console.log("\n\n");
     }
     ```
+    
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
+       \"interrupt_before\": [\"action\"],
+       \"stream_mode\": [
+         \"messages\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
 
 Output:
 

docs/docs/cloud/how-tos/human_in_the_loop_edit_state.md

@@ -17,7 +17,7 @@ First, we need to setup our client so that we can communicate with our hosted gr
 
     ```python
     from langgraph_sdk import get_client
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -27,11 +27,19 @@ First, we need to setup our client so that we can communicate with our hosted gr
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 ## Editing state
 
 ### Initial invocation
@@ -75,6 +83,42 @@ Now let's invoke our graph, making sure to interrupt before the `action` node.
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"search for weather in SF\"}]},
+       \"interrupt_before\": [\"action\"],
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     {'agent': {'messages': [{'content': [{'text': "Certainly! I'll search for the current weather in San Francisco for you using the search function. Here's how I'll do that:", 'type': 'text'}, {'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-6dbb0167-f8f6-4e2a-ab68-229b2d1fbb64', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
@@ -129,10 +173,22 @@ Now, let's assume we actually meant to search for the weather in Sidi Frej (anot
     await client.threads.updateState(thread['thread_id'], {values:{"messages": lastMessage}});
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \                                                                                      
+    jq '.values.messages[-1] | (.tool_calls[0].args = {"query": "current weather in Sidi Frej"})' | \
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+      --header 'Content-Type: application/json' \
+      --data @-
+    ```
+
 Output:
 
-    {'configurable': {'thread_id': '88d58d3f-4151-47a9-a8e0-e42fdd3527b8',
-      'thread_ts': '1ef3274b-a809-6913-8002-91536ce6554d'}}
+    {'configurable': {'thread_id': '9c8f1a43-9dd8-4017-9271-2c53e57cf66a',
+      'checkpoint_ns': '',
+      'checkpoint_id': '1ef58e7e-3641-649f-8002-8b4305a64858'}}
 
 
 
@@ -171,6 +227,40 @@ Now we can resume our graph run but with the updated state:
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \                                                                             
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"| \ 
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     {'action': {'messages': [{'content': '["I looked up: current weather in Sidi Frej. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '1161b8d1-bee4-4188-9be8-698aecb69f10', 'tool_call_id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}]}}

docs/docs/cloud/how-tos/human_in_the_loop_review_tool_calls.md

@@ -0,0 +1,575 @@
+# Review Tool Calls
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:
+
+- A tool call to execute SQL, which will then be run by the tool
+- A tool call to generate a summary, which will then be saved to the State of the graph
+
+Note that using tool calls is common **whether actually calling tools or not**.
+
+There are typically a few different interactions you may want to do here:
+
+1. Approve the tool call and continue
+2. Modify the tool call manually and then continue
+3. Give natural language feedback, and then pass that back to the agent instead of continuing
+
+We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above
+
+## Setup
+
+We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 
+
+### SDK initialization
+
+First, we need to setup our client so that we can communicate with our hosted graph:
+
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>)
+    assistant_id = "agent"
+    thread = await client.threads.create()
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+    const assistantId = "agent";
+    const thread = await client.threads.create();
+    ```
+
+## Example with no review
+
+Let's look at an example when no review is required (because no tools are called)
+
+=== "Python"
+
+    ```python
+    input = { 'messages':[{ "role":"user", "content":"hi!" }] }
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        input=input,
+        stream_mode="updates",
+        interrupt_before=["action"],
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = {"messages": [{ "role": "human", "content": "hi!"}] }
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "updates",
+        interruptBefore: ["action"],
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}]}
+    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}, {'content': [{'text': "Hello! Welcome. How can I assist you today? Is there anything specific you'd like to know or any information you're looking for?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d65e07fb-43ff-4d98-ab6b-6316191b9c8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 355, 'output_tokens': 31, 'total_tokens': 386}}]}
+
+
+If we check the state, we can see that it is finished
+
+=== "Python"
+
+    ```python
+    state = await client.threads.get_state(thread["thread_id"])
+
+    print(state['next'])
+    ```
+
+=== "Javascript"
+
+    ```js
+    const state = await client.threads.getState(thread["thread_id"]);
+
+    console.log(state.next);
+    ```
+
+Output:
+
+    []
+
+## Example of approving tool
+
+Let's now look at what it looks like to approve a tool call. Note that we don't need to pass an interrupt to our streaming calls because the graph (defined [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage)) was already compiled with an interrupt before the `human_review_node`.
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=input,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "values",
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}]}
+
+
+If we now check, we can see that it is waiting on human review:
+
+=== "Python"
+
+    ```python
+    state = await client.threads.get_state(thread["thread_id"])
+
+    print(state['next'])
+    ```
+
+=== "Javascript"
+
+    ```js
+    const state = await client.threads.getState(thread["thread_id"]);
+
+    console.log(state.next);
+    ```
+
+Output:
+
+    ['human_review_node']
+
+To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs.
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=None,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: undefined,
+        streamMode: "values",
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5d5fd0f1-a939-447e-801a-9aaa812322d3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514}}]}
+
+## Edit Tool Call
+
+Let's now say we want to edit the tool call. E.g. change some of the parameters (or even the tool called!) but then execute that tool.
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=input,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "values",
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6326da9f-6061-4e12-8586-482e32ab4cab', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
+
+
+To do this, we first need to update the state. We can do this by passing a message in with the **same** id of the message we want to overwrite. This will have the effect of **replacing** that old message. Note that this is only possible because of the **reducer** we are using that replaces messages with the same ID - read more about that [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state).
+
+
+=== "Python"
+
+    ```python
+    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
+    state = await client.threads.get_state(thread['thread_id'])
+    print("Current State:")
+    print(state['values'])
+    print("\nCurrent Tool Call ID:")
+    current_content = state['values']['messages'][-1]['content']
+    current_id = state['values']['messages'][-1]['id']
+    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
+    print(tool_call_id)
+
+    # We now need to construct a replacement tool call.
+    # We will change the argument to be `San Francisco, USA`
+    # Note that we could change any number of arguments or tool names - it just has to be a valid one
+    new_message = {
+        "role": "assistant", 
+        "content": current_content,
+        "tool_calls": [
+            {
+                "id": tool_call_id,
+                "name": "weather_search",
+                "args": {"city": "San Francisco, USA"}
+            }
+        ],
+        # This is important - this needs to be the same as the message you replacing!
+        # Otherwise, it will show up as a separate message
+        "id": current_id
+    }
+    await client.threads.update_state(
+        # This is the config which represents this thread
+        thread['thread_id'], 
+        # This is the updated value we want to push
+        {"messages": [new_message]}, 
+        # We push this update acting as our human_review_node
+        as_node="human_review_node"
+    )
+
+    print("\nResuming Execution")
+    # Let's now continue executing from here
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=None,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const state = await client.threads.getState(thread.thread_id);
+    console.log("Current State:");
+    console.log(state.values);
+
+    console.log("\nCurrent Tool Call ID:");
+    const lastMessage = state.values.messages[state.values.messages.length - 1];
+    const currentContent = lastMessage.content;
+    const currentId = lastMessage.id;
+    const toolCallId = lastMessage.tool_calls[0].id;
+    console.log(toolCallId);
+
+    // Construct a replacement tool call
+    const newMessage = {
+        role: "assistant",
+        content: currentContent,
+        tool_calls: [
+            {
+                id: toolCallId,
+                name: "weather_search",
+                args: { city: "San Francisco, USA" }
+            }
+        ],
+        // Ensure the ID is the same as the message you're replacing
+        id: currentId
+    };
+
+    await client.threads.updateState(
+        thread.thread_id,  // Thread ID
+        {
+        values: { "messages": [newMessage] },  // Updated message
+        asNode: "human_review_node"
+        }  // Acting as human_review_node
+    );
+
+    console.log("\nResuming Execution");
+    // Continue executing from here
+    const streamResponseResumed = client.runs.stream(
+    thread["thread_id"],
+    assistantId,
+    {
+        input: undefined,
+        streamMode: "values",
+        interruptBefore: ["action"],
+    }
+    );
+    for await (const chunk of streamResponseResumed) {
+    if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+    }
+    }
+    ```
+
+Output:
+
+    Current State:
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
+
+    Current Tool Call ID:
+    toolu_01VzagzsUGZsNMwW1wHkcw7h
+
+    Resuming Execution
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nBased on the search result, the weather in San Francisco is sunny! It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d90ce97a-39f9-4330-985e-67c5f351a0c5', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 455, 'output_tokens': 52, 'total_tokens': 507}}]}
+
+## Give feedback to a tool call
+
+Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock **RESULT** of the tool call.
+
+There are multiple ways to do this:
+
+You could add a new message to the state (representing the "result" of a tool call)
+You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback
+Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_node` and how it handles different types of messages.
+
+For this example we will just add a single tool call representing the feedback. Let's see this in action!
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=input,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "values",
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-4911ac27-3d7c-4edf-a3ca-c2908e3922eb', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
+
+To do this, we first need to update the state. We can do this by passing a message in with the same **tool call id** of the tool call we want to respond to. Note that this is a **different*** ID from above
+
+=== "Python"
+
+    ```python
+    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
+    state = await client.threads.get_state(thread['thread_id'])
+    print("Current State:")
+    print(state['values'])
+    print("\nCurrent Tool Call ID:")
+    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
+    print(tool_call_id)
+
+    # We now need to construct a replacement tool call.
+    # We will change the argument to be `San Francisco, USA`
+    # Note that we could change any number of arguments or tool names - it just has to be a valid one
+    new_message = {
+        "role": "tool", 
+        # This is our natural language feedback
+        "content": "User requested changes: pass in the country as well",
+        "name": "weather_search",
+        "tool_call_id": tool_call_id
+    }
+    await client.threads.update_state(
+        # This is the config which represents this thread
+        thread['thread_id'], 
+        # This is the updated value we want to push
+        {"messages": [new_message]}, 
+        # We push this update acting as our human_review_node
+        as_node="human_review_node"
+    )
+
+    print("\nResuming execution")
+    # Let's now continue executing from here
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=None,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const state = await client.threads.getState(thread.thread_id);
+    console.log("Current State:");
+    console.log(state.values);
+
+    console.log("\nCurrent Tool Call ID:");
+    const lastMessage = state.values.messages[state.values.messages.length - 1];
+    const toolCallId = lastMessage.tool_calls[0].id;
+    console.log(toolCallId);
+
+    // Construct a replacement tool call
+    const newMessage = {
+        role: "tool",
+        content: "User requested changes: pass in the country as well",
+        name: "weather_search",
+        tool_call_id: toolCallId,
+    };
+
+    await client.threads.updateState(
+        thread.thread_id,  // Thread ID
+        {
+        values: { "messages": [newMessage] },  // Updated message
+        asNode: "human_review_node"
+        }  // Acting as human_review_node
+    );
+
+    console.log("\nResuming Execution");
+    // Continue executing from here
+    const streamResponseEdited = client.runs.stream(
+    thread["thread_id"],
+    assistantId,
+    {
+        input: undefined,
+        streamMode: "values",
+        interruptBefore: ["action"],
+    }
+    );
+    for await (const chunk of streamResponseEdited) {
+    if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+    }
+    }
+    ```
+
+Output:
+
+    Current State:
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
+
+    Current Tool Call ID:
+    toolu_01NNw18j57GEGPZvsa9f1wvX
+
+    Resuming execution
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}]}
+
+We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        "agent",
+        input=None,
+        stream_mode="values",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponseResumed = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: undefined,
+        streamMode: "values",
+      }
+    );
+    for await (const chunk of streamResponseResumed) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+Output:
+
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}]}
+    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6a857bb1-f65b-4b86-93d6-c025e003c777', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 557, 'output_tokens': 38, 'total_tokens': 595}}]}

docs/docs/cloud/how-tos/human_in_the_loop_time_travel.md

@@ -14,7 +14,7 @@ First, we need to setup our client so that we can communicate with our hosted gr
 
     ```python
     from langgraph_sdk import get_client
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -24,11 +24,20 @@ First, we need to setup our client so that we can communicate with our hosted gr
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = agent;
     const thread = await client.threads.create();
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json' \
+      --data {}
+    ```
+
 ## Replay a state
 
 ### Initial invocation
@@ -38,7 +47,7 @@ Before replaying a state - we need to create states to replay from! In order to
 === "Python"
 
     ```python
-    input = { 'messages':[{ "role":"user", "content":"Please search the weather in SF" }] }
+    input = {"messages": [{"role": "user", "content": "Please search the weather in SF"}]}
 
     async for chunk in client.runs.stream(
         thread["thread_id"],
@@ -53,7 +62,7 @@ Before replaying a state - we need to create states to replay from! In order to
 === "Javascript"
 
     ```js
-    const input = {"messages": [{ "role": "human", "content": "Please search the weather in SF"}] }
+    const input = { "messages": [{ "role": "human", "content": "Please search the weather in SF" }] }
 
     const streamResponse = client.runs.stream(
       thread["thread_id"],
@@ -69,6 +78,41 @@ Before replaying a state - we need to create states to replay from! In order to
       }
     }
     ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Please search the weather in SF\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
     
 Output:
 
@@ -100,23 +144,35 @@ Now let's get our list of states, and invoke from the third state (right before
     console.log(stateToReplay['next']);
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -r '.[2].next'
+    ```
+
 Output:
 
     ['action']
 
 
 
-To rerun from a state, we need to pass in the `checkpoint_id` into the config of the run like follows:
+To rerun from a state, we need first issue an empty update to the thread state. Then we need to pass in the resulting `checkpoint_id` as follows:
 
 === "Python"
 
     ```python
+    state_to_replay = states[2]
+    updated_config = await client.threads.update_state(
+        thread["thread_id"],
+        {"messages": []},
+        checkpoint_id=state_to_replay["checkpoint_id"]
+    )
     async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id, # graph_id
         input=None,
         stream_mode="updates",
-        config={"configurable": {"thread_ts": state_to_replay['checkpoint_id']}}
+        checkpoint_id=updated_config["checkpoint_id"]
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -125,13 +181,15 @@ To rerun from a state, we need to pass in the `checkpoint_id` into the config of
 === "Javascript"
 
     ```js
+    const stateToReplay = states[2];
+    const config = await client.threads.updateState(thread["thread_id"], { values: {"messages": [] }, checkpointId: stateToReplay["checkpoint_id"] });
     const streamResponse = client.runs.stream(
       thread["thread_id"],
       assistantId,
       {
         input: null,
         streamMode: "updates",
-        config: {"configurable": {"thread_ts": stateToReplay['checkpoint_id']}},
+        checkpointId: config["checkpoint_id"]
       }
     );
     for await (const chunk of streamResponse) {
@@ -141,6 +199,51 @@ To rerun from a state, we need to pass in the `checkpoint_id` into the config of
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -c '
+        .[2] as $state_to_replay |
+        {
+            values: { messages: .[2].values.messages[-1] },
+            checkpoint_id: $state_to_replay.checkpoint_id
+        }' | \
+    curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+        --header 'Content-Type: application/json' \
+        --data @- | jq .checkpoint_id | \
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"checkpoint_id\": \"$1\",
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'eba650e5-400e-4938-8508-f878dcbcc532', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
@@ -165,7 +268,7 @@ Let's show how to do this to edit the state at a particular point in time. Let's
     # Let's now update the args for that tool call
     last_message['tool_calls'][0]['args'] = {'query': 'current weather in SF'}
 
-    new_state = await client.threads.update_state(thread['thread_id'],{"messages":[last_message]},checkpoint_id=state_to_replay['checkpoint_id'])
+    config = await client.threads.update_state(thread['thread_id'],{"messages":[last_message]},checkpoint_id=state_to_replay['checkpoint_id'])
     ```
 
 === "Javascript"
@@ -176,9 +279,26 @@ Let's show how to do this to edit the state at a particular point in time. Let's
     let lastMessage = stateToReplay['values']['messages'][-1];
 
     // Let's now update the args for that tool call
-    lastMessage['tool_calls'][0]['args'] = {'query': 'current weather in SF'};
+    lastMessage['tool_calls'][0]['args'] = { 'query': 'current weather in SF' };
 
-    const newState = await client.threads.updateState(thread['thread_id'],{values:{"messages":[lastMessage]},checkpointId:stateToReplay['checkpoint_id']});
+    const config = await client.threads.updateState(thread['thread_id'], { values: { "messages": [lastMessage] }, checkpointId: stateToReplay['checkpoint_id'] });
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | \
+    jq -c '
+        .[2] as $state_to_replay |
+        .[2].values.messages[-1].tool_calls[0].args.query = "current weather in SF" |
+        {
+            values: { messages: .[2].values.messages[-1] },
+            checkpoint_id: $state_to_replay.checkpoint_id
+        }' | \
+    curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+        --header 'Content-Type: application/json' \
+        --data @-
     ```
 
 Now we can rerun our graph with this new config, starting from the `new_state`, which is a branch of our `state_to_replay`:
@@ -191,7 +311,7 @@ Now we can rerun our graph with this new config, starting from the `new_state`,
         assistant["assistant_id"], # graph_id
         input=None,
         stream_mode="updates",
-        config={"configurable": {"thread_ts": new_state['configurable']['thread_ts']}}
+        checkpoint_id=config['checkpoint_id']
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -206,7 +326,7 @@ Now we can rerun our graph with this new config, starting from the `new_state`,
       {
         input: null,
         streamMode: "updates",
-        config: {"configurable": {"thread_ts": newState['configurable']['thread_ts']}},
+        checkpointId: config['checkpoint_id'],
       }
     );
     for await (const chunk of streamResponse) {
@@ -216,6 +336,43 @@ Now we can rerun our graph with this new config, starting from the `new_state`,
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
+    jq -c '.checkpoint_id' | \
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"checkpoint_id\": \"$1\",
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
 

docs/docs/cloud/how-tos/human_in_the_loop_user_input.md

@@ -24,7 +24,7 @@ First, we need to setup our client so that we can communicate with our hosted gr
 
     ```python
     from langgraph_sdk import get_client
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -34,11 +34,19 @@ First, we need to setup our client so that we can communicate with our hosted gr
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 ## Waiting for user input
 
 ### Initial invocation
@@ -80,6 +88,42 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
       }
     }
     ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Use the search tool to ask the user where they are, then look up the weather there\"}]},
+       \"interrupt_before\": [\"ask_human\"],
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
     
 Output:
 
@@ -117,11 +161,31 @@ Because we are treating this as a tool call, we will need to update the state as
     await client.threads.updateState(thread['thread_id'], {values: {"messages": toolMessage}, asNode:"ask_human"})
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request GET \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+     | jq -r '.values.messages[-1].tool_calls[0].id' \
+     | sh -c '
+         TOOL_CALL_ID="$1"
+         
+         # Construct the JSON payload
+         JSON_PAYLOAD=$(printf "{\"messages\": [{\"tool_call_id\": \"%s\", \"type\": \"tool\", \"content\": \"san francisco\"}], \"as_node\": \"ask_human\"}" "$TOOL_CALL_ID")
+         
+         # Send the updated state
+         curl --request POST \
+              --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+              --header "Content-Type: application/json" \
+              --data "${JSON_PAYLOAD}"
+     ' _ 
+    ```
+
 Output:
 
-    {'configurable': {'thread_id': '10d0ee61-db47-48fc-a58c-109a1e68cd73',
-      'thread_ts': '1ef32729-3cc3-6647-8002-14dcb621b46e'}}
-
+    {'configurable': {'thread_id': 'a9f322ae-4ed1-41ec-942b-38cb3d342c3a',
+    'checkpoint_ns': '',
+    'checkpoint_id': '1ef58e97-a623-63dd-8002-39a9a9b20be3'}}
 
 
 ### Invoking after receiving human input
@@ -133,7 +197,7 @@ We can now tell the agent to continue. We can just pass in None as the input to
     ```python
     async for chunk in client.runs.stream(
         thread["thread_id"],
-        assistant_id, # graph_id
+        assistant_id,
         input=None,
         stream_mode="updates",
     ):
@@ -158,6 +222,40 @@ We can now tell the agent to continue. We can just pass in None as the input to
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \                                                                             
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"| \ 
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "", $0)
+         event_type = $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "" && event_type != "metadata") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     {'agent': {'messages': [{'content': [{'text': "Thank you for letting me know that you're in San Francisco. Now, I'll use the search function to look up the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-241baed7-db5e-44ce-ac3c-56431705c22b', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

docs/docs/cloud/how-tos/index.md

@@ -46,12 +46,14 @@ When creating complex graphs, leaving every decision up to the LLM can be danger
 - [How to wait for user input](./human_in_the_loop_user_input.md)
 - [How to edit graph state](./human_in_the_loop_edit_state.md)
 - [How to replay and branch from prior states](./human_in_the_loop_time_travel.md)
+- [How to review tool calls](./human_in_the_loop_review_tool_calls.md)
 
 ## LangGraph Studio
 
 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)
 
@@ -71,3 +73,5 @@ Other guides that may prove helpful!
 - [How to configure agents](cloud_examples/configuration_cloud.ipynb)
 - [How to convert LangGraph calls to LangGraph cloud calls](cloud_examples/langgraph_to_langgraph_cloud.ipynb)
 - [How to integrate webhooks](cloud_examples/webhooks.ipynb)
+- [How to copy threads](./copy_threads.md)
+- [How to check status of your threads](./check_thread_status.md)

docs/docs/cloud/how-tos/interrupt_concurrent.md

@@ -29,7 +29,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     from langchain_core.messages import convert_to_messages
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -39,7 +39,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```

docs/docs/cloud/how-tos/invoke_studio.md

@@ -1,13 +1,15 @@
 # Invoke Assistant
 
-The LangGraph Studio lets you test different configurations and inputs to your graph. The UI allows you to see exactly how your 
+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.
 
 1. The LangGraph Studio UI displays a visualization of the selected assistant.
-    1. In the top-right dropdown menu of the left-hand pane, select an assistant.
+    1. In the top-left 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 GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_input.gif)
+<video controls allowfullscreen="true" poster="../img/studio_input_poster.png">
+    <source src="../img/studio_input.mp4" type="video/mp4">
+</video>

docs/docs/cloud/how-tos/reject_concurrent.md

@@ -28,7 +28,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     from langchain_core.messages import convert_to_messages
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -38,7 +38,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```

docs/docs/cloud/how-tos/rollback_concurrent.md

@@ -30,7 +30,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     from langchain_core.messages import convert_to_messages
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     assistant_id = "agent"
     thread = await client.threads.create()
     ```
@@ -40,7 +40,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     const assistantId = "agent";
     const thread = await client.threads.create();
     ```

docs/docs/cloud/how-tos/stream_debug.md

@@ -9,7 +9,7 @@ First let's set up our client and thread:
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -20,7 +20,7 @@ First let's set up our client and thread:
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)

docs/docs/cloud/how-tos/stream_events.md

@@ -6,7 +6,7 @@ This guide covers how to stream events from your graph (`stream_mode="events"`).
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -17,12 +17,19 @@ This guide covers how to stream events from your graph (`stream_mode="events"`).
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
 
 Output:
 
@@ -30,7 +37,9 @@ Output:
     {'thread_id': '3f4c64e0-f792-4a5e-aa07-a4404e06e0bd',
      'created_at': '2024-06-24T22:16:29.301522+00:00',
      'updated_at': '2024-06-24T22:16:29.301522+00:00',
-     'metadata': {}}
+     'metadata': {},
+     'status': 'idle',
+     'config': {}}
 
 
 
@@ -91,6 +100,41 @@ Streaming events produces responses containing an `event` key (in addition to ot
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
+       \"stream_mode\": [
+         \"events\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     ' 
+    ```
+
 Output:
 
     Receiving new event of type: metadata...
@@ -258,9 +302,11 @@ Token-by-token streaming can be implemented with the `events` streaming mode. Th
     ):
         if (
             chunk.event == "events" and
-            chunk.data["event"] == "on_chat_model_stream"
+            chunk.data["event"] == "on_chat_model_stream" and
+            len(chunk.data["data"]["chunk"]["content"]) > 0 and
+            'text' in chunk.data["data"]["chunk"]["content"][0]
         ):
-            llm_response += chunk.data["data"]["chunk"]["content"]
+            llm_response += chunk.data["data"]["chunk"]["content"][0]['text']
             print(llm_response)
     ```
 
@@ -278,21 +324,88 @@ Token-by-token streaming can be implemented with the `events` streaming mode. Th
       }
     );
     for await (const chunk of streamResponse) {
-      if (chunk.event === "events" && chunk.data.event === "on_chat_model_stream") {
-        llmResponse += chunk.data.data.chunk.content;
+      if (chunk.event === "events" && chunk.data.event === "on_chat_model_stream" && chunk.data.chunk.content.length > 0 && 'text' in chunk.data.chunk.content[0]) {
+        llmResponse += chunk.data.data.chunk.content[0].text;
         console.log(llmResponse);
       }
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
+       \"stream_mode\": [
+         \"events\"
+       ]
+     }" | sed 's/\r$//' | awk '
+    /^event:/ { event = $2 }
+    /^data:/ {
+        json_data = substr($0, index($0, $2))
+
+        if (event == "events") {
+        print json_data
+        }
+    }' | jq -r '
+        select(.event == "on_chat_model_stream") |
+        .data.chunk.content[] | .text // empty
+    ' | awk '
+    BEGIN { llm_response="" }
+    $0 != "" && $0 != "null" {
+        llm_response = llm_response $0
+        print llm_response
+    }'
+    ```
+
 Output:
 
-    b
-    be
-    beg
-    begi
-    begin
-    begine
-    beginen
-    beginend
+    The
+    The search
+    The search results provide
+    The search results provide the current weather conditions
+    The search results provide the current weather conditions in San Francisco.
+    The search results provide the current weather conditions in San Francisco. According
+    The search results provide the current weather conditions in San Francisco. According to the data,
+    The search results provide the current weather conditions in San Francisco. According to the data, as
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12,
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024,
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C).
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The win
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is bl
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 k
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph).
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70%
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km).
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San
+    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San Francisco.
+
 

docs/docs/cloud/how-tos/stream_messages.md

@@ -41,7 +41,7 @@ First let's set up our client and thread:
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -52,20 +52,30 @@ First let's set up our client and thread:
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 Output:
 
     {'thread_id': 'e1431c95-e241-4d1d-a252-27eceb1e5c86',
      'created_at': '2024-06-21T15:48:59.808924+00:00',
      'updated_at': '2024-06-21T15:48:59.808924+00:00',
-     'metadata': {}}
+     'metadata': {},
+     'status': 'idle',
+     'config': {}}
 
-Let's also define a helper function for better formatting of the tool calls in messages
+Let's also define a helper function for better formatting of the tool calls in messages (for CURL we will define a helper script called `process_stream.sh`)
 
 === "Python"
 
@@ -95,6 +105,69 @@ Let's also define a helper function for better formatting of the tool calls in m
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    # process_stream.sh
+
+    format_tool_calls() {
+        echo "$1" | jq -r 'map("Tool Call ID: \(.id), Function: \(.name), Arguments: \(.args)") | join("\n")'
+    }
+
+    process_data_item() {
+        local data_item="$1"
+
+        if echo "$data_item" | jq -e '.role == "user"' > /dev/null; then
+            echo "Human: $(echo "$data_item" | jq -r '.content')"
+        else
+            local tool_calls=$(echo "$data_item" | jq -r '.tool_calls // []')
+            local invalid_tool_calls=$(echo "$data_item" | jq -r '.invalid_tool_calls // []')
+            local content=$(echo "$data_item" | jq -r '.content // ""')
+            local response_metadata=$(echo "$data_item" | jq -r '.response_metadata // {}')
+
+            if [ -n "$content" ] && [ "$content" != "null" ]; then
+                echo "AI: $content"
+            fi
+
+            if [ "$tool_calls" != "[]" ]; then
+                echo "Tool Calls:"
+                format_tool_calls "$tool_calls"
+            fi
+
+            if [ "$invalid_tool_calls" != "[]" ]; then
+                echo "Invalid Tool Calls:"
+                format_tool_calls "$invalid_tool_calls"
+            fi
+
+            if [ "$response_metadata" != "{}" ]; then
+                local finish_reason=$(echo "$response_metadata" | jq -r '.finish_reason // "N/A"')
+                echo "Response Metadata: Finish Reason - $finish_reason"
+            fi
+        fi
+    }
+
+    while IFS=': ' read -r key value; do
+        case "$key" in
+            event)
+                event="$value"
+                ;;
+            data)
+                if [ "$event" = "metadata" ]; then
+                    run_id=$(echo "$value" | jq -r '.run_id')
+                    echo "Metadata: Run ID - $run_id"
+                    echo "------------------------------------------------"
+                elif [ "$event" = "messages/partial" ]; then
+                    echo "$value" | jq -c '.[]' | while read -r data_item; do
+                        process_data_item "$data_item"
+                    done
+                    echo "------------------------------------------------"
+                fi
+                ;;
+        esac
+    done
+    ```
+
+
 Now we can stream by messages, which will return complete messages (at the end of node execution) as well as tokens for any messages generated inside a node:
 
 === "Python"
@@ -201,6 +274,23 @@ Now we can stream by messages, which will return complete messages (at the end o
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --data "{
+    \"assistant_id\": \"agent\",
+    \"config\":{\"configurable\":{\"model_name\":\"openai\"}},
+    \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
+    \"stream_mode\": [
+    \"messages\"
+    ]
+    }" | sed 's/\r$//' | ./process_stream.sh
+    ```
+
+
 Output:
 
     Metadata: Run ID - 1ef2fe5c-6a1d-6575-bc09-d7832711c17e

docs/docs/cloud/how-tos/stream_multiple.md

@@ -9,7 +9,7 @@ First let's set up our client and thread:
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -20,19 +20,28 @@ First let's set up our client and thread:
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
 
 Output:
 
     {'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4',
      'created_at': '2024-06-24T21:30:07.980789+00:00',
      'updated_at': '2024-06-24T21:30:07.980789+00:00',
-     'metadata': {}}
+     'metadata': {},
+     'status': 'idle',
+     'config': {}}
 
 When configuring multiple streaming modes for a run, responses for each respective mode will be produced. In the following example, note that a `list` of modes (`messages`, `events`, `debug`) is passed to the `stream_mode` parameter and the response contains `events`, `debug`, `messages/complete`, `messages/metadata`, and `messages/partial` event types.
 
@@ -90,6 +99,43 @@ When configuring multiple streaming modes for a run, responses for each respecti
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in SF?\"}]},
+       \"stream_mode\": [
+         \"messages\",
+         \"events\",
+         \"debug\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     Receiving new event of type: metadata...

docs/docs/cloud/how-tos/stream_updates.md

@@ -16,7 +16,7 @@ First let's set up our client and thread:
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -27,19 +27,28 @@ First let's set up our client and thread:
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl:"whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
 
 Output:
 
     {'thread_id': '979e3c89-a702-4882-87c2-7a59a250ce16',
      'created_at': '2024-06-21T15:22:07.453100+00:00',
      'updated_at': '2024-06-21T15:22:07.453100+00:00',
-     'metadata': {}}
+     'metadata': {},
+     'status': 'idle',
+     'config': {}}
 
 Now we can stream by updates, which outputs updates made to the state by each node after it has executed:
 
@@ -93,6 +102,41 @@ Now we can stream by updates, which outputs updates made to the state by each no
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in la\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     '
+    ```
+
 Output:
 
     Receiving new event of type: metadata...

docs/docs/cloud/how-tos/stream_values.md

@@ -16,7 +16,7 @@ First let's set up our client and thread:
     ```python
     from langgraph_sdk import get_client
 
-    client = get_client(url="whatever-your-deployment-url-is")
+    client = get_client(url=<DEPLOYMENT_URL>)
     # create thread
     thread = await client.threads.create()
     print(thread)
@@ -27,18 +27,28 @@ First let's set up our client and thread:
     ```js
     import { Client } from "@langchain/langgraph-sdk";
 
-    const client = new Client({ apiUrl: "whatever-your-deployment-url-is" });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
     // create thread
     const thread = await client.threads.create();
     console.log(thread)
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json'
+    ```
+
 Output:
 
     {'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4',
      'created_at': '2024-06-24T21:30:07.980789+00:00',
      'updated_at': '2024-06-24T21:30:07.980789+00:00',
-     'metadata': {}}
+     'metadata': {},
+     'status': 'idle',
+     'config': {}}
 
 Now we can stream by values, which streams the full state of the graph after each node has finished executing:
 
@@ -60,7 +70,6 @@ Now we can stream by values, which streams the full state of the graph after eac
     ```
 
 === "Javascript"
-    
 
     ```js
     const input = {"messages": [{"role": "human", "content": "what's the weather in la"}]}
@@ -80,6 +89,41 @@ Now we can stream by values, which streams the full state of the graph after eac
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
+       \"stream_mode\": [
+         \"values\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     ' 
+    ```
+
 
 Output:
 
@@ -149,6 +193,34 @@ If we want to just get the final result, we can use this endpoint and just keep
     }
     ```
 
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
+       \"stream_mode\": [
+         \"values\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^data:/ { 
+         sub(/^data: /, "", $0)   
+         data_content = $0          
+     }    
+     END {                                               
+         if (data_content != "") {
+             print data_content
+         }
+     }         
+     '
+    ```
+
+
 Output:
 
     {'messages': [{'content': 'what's the weather in la',

docs/docs/cloud/how-tos/test_deployment.md

@@ -9,6 +9,8 @@ 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 GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_usage.gif)
+<video controls allowfullscreen="true" poster="../img/studio_usage_poster.png">
+    <source src="../img/studio_usage.mp4" type="video/mp4">
+</video>

docs/docs/cloud/how-tos/test_local_deployment.md

@@ -0,0 +1,28 @@
+# 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).

docs/docs/cloud/how-tos/threads_studio.md

@@ -6,14 +6,18 @@
 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 GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_threads.gif)
+<video controls="true" allowfullscreen="true" poster="../img/studio_threads_poster.png">
+    <source src="../img/studio_threads.mp4" type="video/mp4">
+</video>
 
 ## 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 GIF shows how to edit a thread in the studio:
+The following video shows how to edit a thread in the studio:
 
-![Using LangGraph Studio](./img/studio_forks.gif)
+<video controls allowfullscreen="true" poster="../img/studio_forks_poster.png">
+    <source src="../img/studio_forks.mp4" type="video/mp4">
+</video>

docs/docs/cloud/index.md

@@ -6,13 +6,14 @@
     - 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.
 
-![GIF](./how-tos/img/studio_input.gif)
+
+<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>
+
 
 ## Overview
 
@@ -22,6 +23,8 @@ The LangGraph Cloud API exposes functionality of your LangGraph application thro
 
 LangGraph Cloud is seamlessly integrated with [LangSmith](https://www.langchain.com/langsmith) and is accessible from within the LangSmith UI.
 
+LangGraph Cloud applications can be tested and debugged using the [LangGraph Studio Desktop](https://github.com/langchain-ai/langgraph-studio). 
+
 ## Key Features
 
 The LangGraph Cloud API supports key LangGraph features in addition to new functionality for enabling complex, agentic workflows.

docs/docs/cloud/quick_start.md

@@ -66,6 +66,16 @@ Now that we have set everything up on our local file system, we are ready to hos
 
 ## Test the graph build locally
 
+### Using LangGraph Studio Desktop (recommended)
+
+![LangGraph Studio Desktop](./img/graph_video_poster.png)
+
+Testing your graph locally is easy with LangGraph Studio Desktop. LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications
+
+With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with [LangSmith](https://smith.langchain.com) so you can collaborate with teammates to debug failure modes.
+
+### Using the LangGraph CLI
+
 Before deploying to the cloud, we probably want to test the building of our graph locally. This is useful to make sure we have configured our [CLI configuration file][langgraph.json] correctly and our graph runs.
 
 In order to do this we can first install the LangGraph CLI

docs/docs/cloud/reference/api/api_ref.md

@@ -3,3 +3,20 @@
 The LangGraph Cloud API reference is available with each deployment at the `/docs` URL path (e.g. `http://localhost:8124/docs`).
 
 Click <a href="/langgraph/cloud/reference/api/api_ref.html" target="_blank">here</a> to view the API reference.
+
+## Authentication
+
+For deployments to LangGraph Cloud, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Cloud API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.
+
+Example `curl` command:
+```shell
+curl --request POST \
+  --url http://localhost:8124/assistants/search \
+  --header 'Content-Type: application/json' \
+  --header 'X-Api-Key: LANGSMITH_API_KEY' \
+  --data '{
+  "metadata": {},
+  "limit": 10,
+  "offset": 0
+}'  
+```

docs/docs/cloud/reference/cli.md

@@ -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 is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.graph.CompiledGraph`. |
+| `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> |
 | `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:variable"
+        "my_graph_id": "./your_package/your_file.py:make_graph"
     },
     "env": {
         "OPENAI_API_KEY": "secret-key"

docs/docs/concepts/agentic_concepts.md

@@ -56,6 +56,25 @@ This is a pretty advanced interaction pattern. In this interaction pattern, the
 
 See [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for how to do this in LangGraph.
 
+## Review Tool Calls
+
+This is a specific type of human-in-the-loop interaction but it's worth calling out because it is so common. A lot of agent decisions are made via tool calling, so having a clear UX for reviewing tool calls is handy.
+
+A tool call consists of:
+- The name of the tool to call
+- Arguments to pass to the tool
+
+Note that these tool calls can obviously be used for actually calling functions, but they can also be used for other purposes, like to route the agent in a specific direction.
+You will want to review the tool call for both of these use cases.
+
+When reviewing tool calls, there are few actions you may want to take.
+
+1. Approve the tool call (and let the agent continue on its way)
+2. Manually change the tool call, either the tool name or the tool arguments (and let the agent continue on its way after that)
+3. Leave feedback on the tool call. This differs from (2) in that you are not changing the tool call directly, but rather leaving natural language feedback suggesting the LLM call it differently (or call a different tool). You could do this by either adding a `ToolMessage` and having the feedback be the result of the tool call, or by adding a `ToolMessage` (that simulates an error) and then a `HumanMessage` (with the feedback).
+
+See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for how to do this in LangGraph.
+
 ## Map-Reduce
 
 A common pattern in agents is to generate a list of objects, do some work on each of those objects, and then combine the results. This is very similar to the common [map-reduce](https://en.wikipedia.org/wiki/MapReduce) operation. This can be tricky for a few reasons. First, it can be tough to define a structured graph ahead of time because the length of the list of objects may be unknown. Second, in order to do this map-reduce you need multiple versions of the state to exist... but the graph shares a common shared state, so how can this be?

docs/docs/concepts/index.md

@@ -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#messagestate)
+    - [MessageState](low_level.md#working-with-messages-in-graph-state)
 - [Nodes](low_level.md#nodes)
     - [`START` node](low_level.md#start-node)
     - [`END` node](low_level.md#end-node)

docs/docs/concepts/low_level.md

@@ -46,9 +46,17 @@ 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. Let's take a look at a few examples to understand them better.
+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:
 
 **Example A:**
 
@@ -75,22 +83,48 @@ 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.
 
-### MessageState
+#### Context Reducer
 
-`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:
+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.
 
 ```python
 from langchain_core.messages import AnyMessage
 from langgraph.graph.message import add_messages
 from typing import Annotated, TypedDict
 
-class MessagesState(TypedDict):
+class GraphState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 ```
 
-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).
+#### MessagesState
 
-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:
+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:
 
 ```python
 from langgraph.graph import MessagesState
@@ -327,6 +361,16 @@ 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.
@@ -389,4 +433,17 @@ LangGraph is built with first class support for streaming. There are several dif
 - [`"updates`](../how-tos/stream-updates.ipynb): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
 - `"debug"`: This streams as much information as possible throughout the execution of the graph.
 
-In addition, you can use the [`astream_events`](../how-tos/streaming-events-from-within-tools.ipynb) method to stream back events that happen _inside_ nodes. This is useful for [streaming tokens of LLM calls](../how-tos/streaming-tokens.ipynb).
+In addition, you can use the [`astream_events`](../how-tos/streaming-events-from-within-tools.ipynb) method to stream back events that happen _inside_ nodes. This is useful for [streaming tokens of LLM calls](../how-tos/streaming-tokens.ipynb) among other things.
+
+Under the hood, the compiled graph and each of its nodes are turned into [runnables](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface). This means that as the graph is executed, certain events are emitted along the way and can be seen if you run the graph using `.astream_events`:
+
+* each node (runnable) emits `on_chain_start` when it starts execution, `on_chain_stream` during the node execution and `on_chain_end` when the node finishes. Node events will have the node name in the event's `name` field
+* the graph will emit `on_chain_start` in the beginning of the graph execution, `on_chain_stream` during each node execution and `on_chain_end` when the graph finishes. Graph events will have the `LangGraph` in the event's `name` field
+* LangGraph writes to state channels emit `on_chain_start` and `on_chain_end` events
+
+Additionally, any events that are created inside your nodes (LLM events, tool events, manually emitted events, etc.) will also be visible in the output of `.astream_events`.
+ 
+You can find a detailed table of all callback events and triggers [here](https://python.langchain.com/v0.2/docs/concepts/#callback-events).
+
+!!! warning "ASYNC IN PYTHON<=3.10"
+    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).

docs/docs/how-tos/index.md

@@ -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 create a custom checkpointer using Postgres](persistence_postgres.ipynb)
+- [How to use Postgres checkpointer for persistence](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)
 
@@ -35,9 +35,11 @@ One of LangGraph's main benefits is that it makes human-in-the-loop workflows ea
 These guides cover common examples of that.
 
 - [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
+- [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
 - [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
 - [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
 - [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
+- [Review tool calls](human_in_the_loop/review-tool-calls.ipynb)
 
 ## Streaming
 
@@ -60,6 +62,14 @@ These guides show how to use different streaming modes.
 - [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
 

docs/docs/reference/checkpoints.md

@@ -7,6 +7,8 @@ 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
@@ -21,7 +23,7 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w
 
 ### SerializerProtocol
 
-::: langgraph.checkpoint.SerializerProtocol
+::: langgraph.checkpoint.base.SerializerProtocol
 
 ## Implementations
 
@@ -33,9 +35,20 @@ LangGraph also natively provides the following checkpoint implementations.
 
 ### AsyncSqliteSaver
 
-::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver
+::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver
 
 ### SqliteSaver
 
 ::: langgraph.checkpoint.sqlite.SqliteSaver
+
+### AsyncPostgresSaver
+
+::: langgraph.checkpoint.postgres.aio.AsyncPostgresSaver
+
+### PostgresSaver
+
+::: langgraph.checkpoint.postgres.PostgresSaver
+handler: python
+
+
 handler: python

docs/docs/tutorials/index.md

@@ -65,4 +65,3 @@ Learn from example implementations of graphs designed for specific scenarios and
 - [Web Navigation](web-navigation/web_voyager.ipynb): Build an agent that can navigate and interact with websites
 - [Competitive Programming](usaco/usaco.ipynb): Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the ["Can Language Models Solve Olympiad Programming?"](https://arxiv.org/abs/2404.10952v1) paper by Shi, Tang, Narasimhan, and Yao.
 - [Complex data extraction](extraction/retries.ipynb): Build an agent that can use function calling to do complex extraction tasks
-- 

docs/mkdocs.yml

@@ -134,14 +134,16 @@ 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
-          - Create custom checkpointer using Postgres: how-tos/persistence_postgres.ipynb
+          - Use Postgres checkpointer for persistence: 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:
           - Add breakpoints: how-tos/human_in_the_loop/breakpoints.ipynb
+          - Add dynamic breakpoints: how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
           - Wait for user input: how-tos/human_in_the_loop/wait-user-input.ipynb
           - View and update past graph state: how-tos/human_in_the_loop/time-travel.ipynb
           - Edit graph state: how-tos/human_in_the_loop/edit-graph-state.ipynb
+          - Review tool calls: how-tos/human_in_the_loop/review-tool-calls.ipynb
       - Streaming:
           - Stream full state: how-tos/stream-values.ipynb
           - Stream state updates: how-tos/stream-updates.ipynb
@@ -157,12 +159,16 @@ nav:
           - 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
-          - Use Pydantic model as state: how-tos/state-model.ipynb
-          - Use a context object in state: how-tos/state-context-key.ipynb
           - Add node retries: how-tos/node-retries.ipynb
       - Prebuilt ReAct Agent:
           - Create a ReAct agent: how-tos/create-react-agent.ipynb
@@ -186,10 +192,12 @@ nav:
           - Quick Start: "cloud/quick_start.md"
       - How-to Guides: 
           - "cloud/how-tos/index.md"
-          - Deployment:
+          - Setup:
               - 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:
@@ -209,8 +217,10 @@ nav:
               - Wait for User Input: "cloud/how-tos/human_in_the_loop_user_input.md"
               - Edit Graph State: "cloud/how-tos/human_in_the_loop_edit_state.md"
               - Replay and Branch from Prior States: "cloud/how-tos/human_in_the_loop_time_travel.md"
+              - Review Tool Calls: "cloud/how-tos/human_in_the_loop_review_tool_calls.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:
@@ -222,6 +232,8 @@ nav:
               - Configure Agents: "cloud/how-tos/cloud_examples/configuration_cloud.ipynb"
               - Convert LangGraph calls to LangGraph Cloud calls: "cloud/how-tos/cloud_examples/langgraph_to_langgraph_cloud.ipynb"
               - Integrate Webhooks: 'cloud/how-tos/cloud_examples/webhooks.ipynb'
+              - Copy Threads: 'cloud/how-tos/copy_threads.md'
+              - Check Status of Threads: "cloud/how-tos/check_thread_status.md"
       - Conceptual Guides:
           - API Concepts: "cloud/concepts/api.md"
           - Cloud Concepts: "cloud/concepts/cloud.md"
@@ -232,6 +244,8 @@ nav:
               - JS/TS: "cloud/reference/sdk/js_ts_sdk_ref.md"
           - CLI: "cloud/reference/cli.md"
           - Environment Variables: "cloud/reference/env_var.md"
+      - FAQ:
+          - Studio: "cloud/faq/studio.md"
 
 markdown_extensions:
   - abbr

examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb

@@ -26,7 +26,10 @@
    "id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
    "metadata": {},
    "outputs": [],
-   "source": ["# %%capture --no-stderr\n# %pip install -U langgraph langchain langchain_openai"]
+   "source": [
+    "# %%capture --no-stderr\n",
+    "# %pip install -U langgraph langchain langchain_openai"
+   ]
   },
   {
    "cell_type": "code",
@@ -34,7 +37,24 @@
    "id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
    "metadata": {},
    "outputs": [],
-   "source": ["import getpass\nimport os\n\n\ndef _set_if_undefined(var: str):\n    if not os.environ.get(var):\n        os.environ[var] = getpass.getpass(f\"Please provide your {var}\")\n\n\n_set_if_undefined(\"OPENAI_API_KEY\")\n_set_if_undefined(\"LANGCHAIN_API_KEY\")\n\n# Optional, add tracing in LangSmith.\n# This will help you visualize and debug the control flow\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Agent Simulation Evaluation\""]
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_if_undefined(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"Please provide your {var}\")\n",
+    "\n",
+    "\n",
+    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
+    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
+    "\n",
+    "# Optional, add tracing in LangSmith.\n",
+    "# This will help you visualize and debug the control flow\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Agent Simulation Evaluation\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -55,7 +75,24 @@
    "id": "828479af-cf9c-4888-a365-599643a96b55",
    "metadata": {},
    "outputs": [],
-   "source": ["from typing import List\n\nimport openai\n\n\n# This is flexible, but you can define your agent here, or call your agent API here.\ndef my_chat_bot(messages: List[dict]) -> dict:\n    system_message = {\n        \"role\": \"system\",\n        \"content\": \"You are a customer support agent for an airline.\",\n    }\n    messages = [system_message] + messages\n    completion = openai.chat.completions.create(\n        messages=messages, model=\"gpt-3.5-turbo\"\n    )\n    return completion.choices[0].message.model_dump()"]
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "import openai\n",
+    "\n",
+    "\n",
+    "# This is flexible, but you can define your agent here, or call your agent API here.\n",
+    "def my_chat_bot(messages: List[dict]) -> dict:\n",
+    "    system_message = {\n",
+    "        \"role\": \"system\",\n",
+    "        \"content\": \"You are a customer support agent for an airline.\",\n",
+    "    }\n",
+    "    messages = [system_message] + messages\n",
+    "    completion = openai.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\"\n",
+    "    )\n",
+    "    return completion.choices[0].message.model_dump()"
+   ]
   },
   {
    "cell_type": "code",
@@ -77,7 +114,9 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["my_chat_bot([{\"role\": \"user\", \"content\": \"hi!\"}])"]
+   "source": [
+    "my_chat_bot([{\"role\": \"user\", \"content\": \"hi!\"}])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -96,7 +135,33 @@
    "id": "32c147df-7f90-4b0d-9a6b-671677020353",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nfrom langchain_openai import ChatOpenAI\n\nsystem_prompt_template = \"\"\"You are a customer of an airline company. \\\nYou are interacting with a user who is a customer support person. \\\n\n{instructions}\n\nWhen you are finished with the conversation, respond with a single word 'FINISHED'\"\"\"\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\"system\", system_prompt_template),\n        MessagesPlaceholder(variable_name=\"messages\"),\n    ]\n)\ninstructions = \"\"\"Your name is Harrison. You are trying to get a refund for the trip you took to Alaska. \\\nYou want them to give you ALL the money back. \\\nThis trip happened 5 years ago.\"\"\"\n\nprompt = prompt.partial(name=\"Harrison\", instructions=instructions)\n\nmodel = ChatOpenAI()\n\nsimulated_user = prompt | model"]
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "system_prompt_template = \"\"\"You are a customer of an airline company. \\\n",
+    "You are interacting with a user who is a customer support person. \\\n",
+    "\n",
+    "{instructions}\n",
+    "\n",
+    "When you are finished with the conversation, respond with a single word 'FINISHED'\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system_prompt_template),\n",
+    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "    ]\n",
+    ")\n",
+    "instructions = \"\"\"Your name is Harrison. You are trying to get a refund for the trip you took to Alaska. \\\n",
+    "You want them to give you ALL the money back. \\\n",
+    "This trip happened 5 years ago.\"\"\"\n",
+    "\n",
+    "prompt = prompt.partial(name=\"Harrison\", instructions=instructions)\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "simulated_user = prompt | model"
+   ]
   },
   {
    "cell_type": "code",
@@ -115,7 +180,12 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["from langchain_core.messages import HumanMessage\n\nmessages = [HumanMessage(content=\"Hi! How can I help you?\")]\nsimulated_user.invoke({\"messages\": messages})"]
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "messages = [HumanMessage(content=\"Hi! How can I help you?\")]\n",
+    "simulated_user.invoke({\"messages\": messages})"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -153,7 +223,20 @@
    "id": "69e2a3a3-40f3-4223-9136-113738440be9",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_community.adapters.openai import convert_message_to_dict\nfrom langchain_core.messages import AIMessage\n\n\ndef chat_bot_node(messages):\n    # Convert from LangChain format to the OpenAI format, which our chatbot function expects.\n    messages = [convert_message_to_dict(m) for m in messages]\n    # Call the chat bot\n    chat_bot_response = my_chat_bot(messages)\n    # Respond with an AI Message\n    return AIMessage(content=chat_bot_response[\"content\"])"]
+   "source": [
+    "from langchain_community.adapters.openai import convert_message_to_dict\n",
+    "from langchain_core.messages import AIMessage\n",
+    "\n",
+    "\n",
+    "def chat_bot_node(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "    # Convert from LangChain format to the OpenAI format, which our chatbot function expects.\n",
+    "    messages = [convert_message_to_dict(m) for m in messages]\n",
+    "    # Call the chat bot\n",
+    "    chat_bot_response = my_chat_bot(messages)\n",
+    "    # Respond with an AI Message\n",
+    "    return {\"messages\": [AIMessage(content=chat_bot_response[\"content\"])]}"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -169,7 +252,26 @@
    "id": "7cad7527-ffa5-4c30-8585-b54a7a18bd98",
    "metadata": {},
    "outputs": [],
-   "source": ["def _swap_roles(messages):\n    new_messages = []\n    for m in messages:\n        if isinstance(m, AIMessage):\n            new_messages.append(HumanMessage(content=m.content))\n        else:\n            new_messages.append(AIMessage(content=m.content))\n    return new_messages\n\n\ndef simulated_user_node(messages):\n    # Swap roles of messages\n    new_messages = _swap_roles(messages)\n    # Call the simulated user\n    response = simulated_user.invoke({\"messages\": new_messages})\n    # This response is an AI message - we need to flip this to be a human message\n    return HumanMessage(content=response.content)"]
+   "source": [
+    "def _swap_roles(messages):\n",
+    "    new_messages = []\n",
+    "    for m in messages:\n",
+    "        if isinstance(m, AIMessage):\n",
+    "            new_messages.append(HumanMessage(content=m.content))\n",
+    "        else:\n",
+    "            new_messages.append(AIMessage(content=m.content))\n",
+    "    return new_messages\n",
+    "\n",
+    "\n",
+    "def simulated_user_node(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "    # Swap roles of messages\n",
+    "    new_messages = _swap_roles(messages)\n",
+    "    # Call the simulated user\n",
+    "    response = simulated_user.invoke({\"messages\": new_messages})\n",
+    "    # This response is an AI message - we need to flip this to be a human message\n",
+    "    return {\"messages\": [HumanMessage(content=response.content)]}"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -192,7 +294,16 @@
    "id": "28004fbf-a2f3-46b7-bde7-46c7adaf97fb",
    "metadata": {},
    "outputs": [],
-   "source": ["def should_continue(messages):\n    if len(messages) > 6:\n        return \"end\"\n    elif messages[-1].content == \"FINISHED\":\n        return \"end\"\n    else:\n        return \"continue\""]
+   "source": [
+    "def should_continue(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "    if len(messages) > 6:\n",
+    "        return \"end\"\n",
+    "    elif messages[-1].content == \"FINISHED\":\n",
+    "        return \"end\"\n",
+    "    else:\n",
+    "        return \"continue\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -210,7 +321,37 @@
    "id": "0b597e4b-4cbb-4bbc-82e5-f7e31275964c",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.graph import END, MessageGraph, START\n\ngraph_builder = MessageGraph()\ngraph_builder.add_node(\"user\", simulated_user_node)\ngraph_builder.add_node(\"chat_bot\", chat_bot_node)\n# Every response from  your chat bot will automatically go to the\n# simulated user\ngraph_builder.add_edge(\"chat_bot\", \"user\")\ngraph_builder.add_conditional_edges(\n    \"user\",\n    should_continue,\n    # If the finish criteria are met, we will stop the simulation,\n    # otherwise, the virtual user's message will be sent to your chat bot\n    {\n        \"end\": END,\n        \"continue\": \"chat_bot\",\n    },\n)\n# The input will first go to your chat bot\ngraph_builder.add_edge(START, \"chat_bot\")\nsimulation = graph_builder.compile()"]
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "from langgraph.graph.message import add_messages\n",
+    "from typing import Annotated\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, add_messages]\n",
+    "\n",
+    "\n",
+    "graph_builder = StateGraph(State)\n",
+    "graph_builder.add_node(\"user\", simulated_user_node)\n",
+    "graph_builder.add_node(\"chat_bot\", chat_bot_node)\n",
+    "# Every response from  your chat bot will automatically go to the\n",
+    "# simulated user\n",
+    "graph_builder.add_edge(\"chat_bot\", \"user\")\n",
+    "graph_builder.add_conditional_edges(\n",
+    "    \"user\",\n",
+    "    should_continue,\n",
+    "    # If the finish criteria are met, we will stop the simulation,\n",
+    "    # otherwise, the virtual user's message will be sent to your chat bot\n",
+    "    {\n",
+    "        \"end\": END,\n",
+    "        \"continue\": \"chat_bot\",\n",
+    "    },\n",
+    ")\n",
+    "# The input will first go to your chat bot\n",
+    "graph_builder.add_edge(START, \"chat_bot\")\n",
+    "simulation = graph_builder.compile()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -251,7 +392,13 @@
      ]
     }
    ],
-   "source": ["for chunk in simulation.stream([]):\n    # Print out all events aside from the final end chunk\n    if END not in chunk:\n        print(chunk)\n        print(\"----\")"]
+   "source": [
+    "for chunk in simulation.stream({}):\n",
+    "    # Print out all events aside from the final end chunk\n",
+    "    if END not in chunk:\n",
+    "        print(chunk)\n",
+    "        print(\"----\")"
+   ]
   },
   {
    "cell_type": "code",
@@ -259,7 +406,7 @@
    "id": "dde4f2b5-cfe8-4ff0-99ea-fe2c5fed70c0",
    "metadata": {},
    "outputs": [],
-   "source": [""]
+   "source": []
   }
  ],
  "metadata": {

examples/chatbots/customer-support.ipynb

@@ -32,7 +32,10 @@
     "scrolled": true
    },
    "outputs": [],
-   "source": ["%%capture --no-stderr\n%pip install -U langgraph langchain-community langchain-openai scikit-learn"]
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-community langchain-openai scikit-learn"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -48,7 +51,15 @@
    "id": "3d1ef253-6b0c-4481-868c-e1fe84f2c8ff",
    "metadata": {},
    "outputs": [],
-   "source": ["import requests\n\nurl = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\nresponse = requests.get(url)\n\nwith open(\"Chinook.db\", \"wb\") as file:\n    file.write(response.content)"]
+   "source": [
+    "import requests\n",
+    "\n",
+    "url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n",
+    "response = requests.get(url)\n",
+    "\n",
+    "with open(\"Chinook.db\", \"wb\") as file:\n",
+    "    file.write(response.content)"
+   ]
   },
   {
    "cell_type": "code",
@@ -77,7 +88,12 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["from langchain_community.utilities import SQLDatabase\n\ndb = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\ndb.get_usable_table_names()"]
+   "source": [
+    "from langchain_community.utilities import SQLDatabase\n",
+    "\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
+    "db.get_usable_table_names()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -96,7 +112,11 @@
    "id": "d9ea4e80-30e6-4d46-b480-35f0be2fb055",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")"]
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -118,7 +138,9 @@
    "id": "ea958e9f-ab1f-49b5-bd85-16332055297c",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_core.messages import HumanMessage, SystemMessage"]
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -137,7 +159,12 @@
    "id": "975b039a",
    "metadata": {},
    "outputs": [],
-   "source": ["# This tool is given to the agent to look up information about a customer\ndef get_customer_info(customer_id: int):\n    \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n    return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")"]
+   "source": [
+    "# This tool is given to the agent to look up information about a customer\n",
+    "def get_customer_info(customer_id: int):\n",
+    "    \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n",
+    "    return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")"
+   ]
   },
   {
    "cell_type": "code",
@@ -145,7 +172,20 @@
    "id": "1d5fa446",
    "metadata": {},
    "outputs": [],
-   "source": ["customer_prompt = \"\"\"Your job is to help a user update their profile.\n\nYou only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n\nIf you are unable to help the user, you can \"\"\"\n\n\ndef get_customer_messages(messages):\n    return [SystemMessage(content=customer_prompt)] + messages\n\n\ncustomer_chain = get_customer_messages | model.bind_tools([get_customer_info])"]
+   "source": [
+    "customer_prompt = \"\"\"Your job is to help a user update their profile.\n",
+    "\n",
+    "You only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n",
+    "\n",
+    "If you are unable to help the user, you can \"\"\"\n",
+    "\n",
+    "\n",
+    "def get_customer_messages(messages):\n",
+    "    return [SystemMessage(content=customer_prompt)] + messages\n",
+    "\n",
+    "\n",
+    "customer_chain = get_customer_messages | model.bind_tools([get_customer_info])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -166,7 +206,19 @@
    "id": "a8604a3b-b484-4b2b-a914-4236cb98c524",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_community.vectorstores import SKLearnVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\nartists = db._execute(\"select * from Artist\")\nsongs = db._execute(\"select * from Track\")\nartist_retriever = SKLearnVectorStore.from_texts(\n    [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n).as_retriever()\nsong_retriever = SKLearnVectorStore.from_texts(\n    [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n).as_retriever()"]
+   "source": [
+    "from langchain_community.vectorstores import SKLearnVectorStore\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "artists = db._execute(\"select * from Artist\")\n",
+    "songs = db._execute(\"select * from Track\")\n",
+    "artist_retriever = SKLearnVectorStore.from_texts(\n",
+    "    [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n",
+    ").as_retriever()\n",
+    "song_retriever = SKLearnVectorStore.from_texts(\n",
+    "    [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n",
+    ").as_retriever()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -182,7 +234,16 @@
    "id": "0a2a2b74",
    "metadata": {},
    "outputs": [],
-   "source": ["def get_albums_by_artist(artist):\n    \"\"\"Get albums by an artist (or similar artists).\"\"\"\n    docs = artist_retriever.get_relevant_documents(artist)\n    artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n    return db.run(\n        f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n        include_columns=True,\n    )"]
+   "source": [
+    "def get_albums_by_artist(artist):\n",
+    "    \"\"\"Get albums by an artist (or similar artists).\"\"\"\n",
+    "    docs = artist_retriever.get_relevant_documents(artist)\n",
+    "    artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n",
+    "    return db.run(\n",
+    "        f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n",
+    "        include_columns=True,\n",
+    "    )"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -198,7 +259,16 @@
    "id": "da533f50",
    "metadata": {},
    "outputs": [],
-   "source": ["def get_tracks_by_artist(artist):\n    \"\"\"Get songs by an artist (or similar artists).\"\"\"\n    docs = artist_retriever.invoke(artist)\n    artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n    return db.run(\n        f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n        include_columns=True,\n    )"]
+   "source": [
+    "def get_tracks_by_artist(artist):\n",
+    "    \"\"\"Get songs by an artist (or similar artists).\"\"\"\n",
+    "    docs = artist_retriever.invoke(artist)\n",
+    "    artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n",
+    "    return db.run(\n",
+    "        f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n",
+    "        include_columns=True,\n",
+    "    )"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -214,7 +284,11 @@
    "id": "b3c07010",
    "metadata": {},
    "outputs": [],
-   "source": ["def check_for_songs(song_title):\n    \"\"\"Check if a song exists by its name.\"\"\"\n    return song_retriever.invoke(song_title)"]
+   "source": [
+    "def check_for_songs(song_title):\n",
+    "    \"\"\"Check if a song exists by its name.\"\"\"\n",
+    "    return song_retriever.invoke(song_title)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -230,7 +304,23 @@
    "id": "72a14d5c",
    "metadata": {},
    "outputs": [],
-   "source": ["song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n\nYou only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n\nWhen looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\non similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n\n\ndef get_song_messages(messages):\n    return [SystemMessage(content=song_system_message)] + messages\n\n\nsong_recc_chain = get_song_messages | model.bind_tools(\n    [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n)"]
+   "source": [
+    "song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n",
+    "\n",
+    "You only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n",
+    "\n",
+    "When looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\n",
+    "on similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n",
+    "\n",
+    "\n",
+    "def get_song_messages(messages):\n",
+    "    return [SystemMessage(content=song_system_message)] + messages\n",
+    "\n",
+    "\n",
+    "song_recc_chain = get_song_messages | model.bind_tools(\n",
+    "    [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n",
+    ")"
+   ]
   },
   {
    "cell_type": "code",
@@ -249,7 +339,10 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\nsong_recc_chain.invoke(msgs)"]
+   "source": [
+    "msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\n",
+    "song_recc_chain.invoke(msgs)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -267,7 +360,32 @@
    "id": "73e74268",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n\nclass Router(BaseModel):\n    \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n\n    choice: str = Field(description=\"should be one of: music, customer\")\n\n\nsystem_message = \"\"\"Your job is to help as a customer service representative for a music store.\n\nYou should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n\n- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n\nIf the user is asking or wants to ask about updating or accessing their information, send them to that route.\nIf the user is asking or wants to ask about music, send them to that route.\nOtherwise, respond.\"\"\"\n\n\ndef get_messages(messages):\n    return [SystemMessage(content=system_message)] + messages"]
+   "source": [
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class Router(BaseModel):\n",
+    "    \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n",
+    "\n",
+    "    choice: str = Field(description=\"should be one of: music, customer\")\n",
+    "\n",
+    "\n",
+    "system_message = \"\"\"Your job is to help as a customer service representative for a music store.\n",
+    "\n",
+    "You should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n",
+    "\n",
+    "- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n",
+    "- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n",
+    "\n",
+    "If the user is asking or wants to ask about updating or accessing their information, send them to that route.\n",
+    "If the user is asking or wants to ask about music, send them to that route.\n",
+    "Otherwise, respond.\"\"\"\n",
+    "\n",
+    "\n",
+    "def get_messages(messages):\n",
+    "    return [SystemMessage(content=system_message)] + messages"
+   ]
   },
   {
    "cell_type": "code",
@@ -275,7 +393,9 @@
    "id": "ddf27314",
    "metadata": {},
    "outputs": [],
-   "source": ["chain = get_messages | model.bind_tools([Router])"]
+   "source": [
+    "chain = get_messages | model.bind_tools([Router])"
+   ]
   },
   {
    "cell_type": "code",
@@ -294,7 +414,10 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\nchain.invoke(msgs)"]
+   "source": [
+    "msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\n",
+    "chain.invoke(msgs)"
+   ]
   },
   {
    "cell_type": "code",
@@ -313,7 +436,10 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\nchain.invoke(msgs)"]
+   "source": [
+    "msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\n",
+    "chain.invoke(msgs)"
+   ]
   },
   {
    "cell_type": "code",
@@ -321,7 +447,15 @@
    "id": "bd6ddd8b-7500-46a7-811d-3bcb937bda51",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_core.messages import AIMessage\n\n\ndef add_name(message, name):\n    _dict = message.dict()\n    _dict[\"name\"] = name\n    return AIMessage(**_dict)"]
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "\n",
+    "\n",
+    "def add_name(message, name):\n",
+    "    _dict = message.dict()\n",
+    "    _dict[\"name\"] = name\n",
+    "    return AIMessage(**_dict)"
+   ]
   },
   {
    "cell_type": "code",
@@ -329,7 +463,45 @@
    "id": "27494de5-8345-4c23-bc0e-81e0dd5d47d8",
    "metadata": {},
    "outputs": [],
-   "source": ["import json\n\nfrom langgraph.graph import END, START\n\n\ndef _get_last_ai_message(messages):\n    for m in messages[::-1]:\n        if isinstance(m, AIMessage):\n            return m\n    return None\n\n\ndef _is_tool_call(msg):\n    return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n\n\ndef _route(messages):\n    last_message = messages[-1]\n    if isinstance(last_message, AIMessage):\n        if not last_message.tool_calls:\n            return END\n        else:\n            if last_message.name == \"general\":\n                if len(last_message.tool_calls) > 1:\n                    raise ValueError(\"Too many tools\")\n                return last_message.tool_calls[0][\"args\"][\"choice\"]\n            else:\n                return \"tools\"\n    last_m = _get_last_ai_message(messages)\n    if last_m is None:\n        return \"general\"\n    if last_m.name == \"music\":\n        return \"music\"\n    elif last_m.name == \"customer\":\n        return \"customer\"\n    else:\n        return \"general\""]
+   "source": [
+    "import json\n",
+    "\n",
+    "from langgraph.graph import END, START\n",
+    "\n",
+    "\n",
+    "def _get_last_ai_message(messages):\n",
+    "    for m in messages[::-1]:\n",
+    "        if isinstance(m, AIMessage):\n",
+    "            return m\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def _is_tool_call(msg):\n",
+    "    return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n",
+    "\n",
+    "\n",
+    "def _route(messages):\n",
+    "    last_message = messages[-1]\n",
+    "    if isinstance(last_message, AIMessage):\n",
+    "        if not last_message.tool_calls:\n",
+    "            return END\n",
+    "        else:\n",
+    "            if last_message.name == \"general\":\n",
+    "                if len(last_message.tool_calls) > 1:\n",
+    "                    raise ValueError(\"Too many tools\")\n",
+    "                return last_message.tool_calls[0][\"args\"][\"choice\"]\n",
+    "            else:\n",
+    "                return \"tools\"\n",
+    "    last_m = _get_last_ai_message(messages)\n",
+    "    if last_m is None:\n",
+    "        return \"general\"\n",
+    "    if last_m.name == \"music\":\n",
+    "        return \"music\"\n",
+    "    elif last_m.name == \"customer\":\n",
+    "        return \"customer\"\n",
+    "    else:\n",
+    "        return \"general\""
+   ]
   },
   {
    "cell_type": "code",
@@ -337,7 +509,12 @@
    "id": "8aec704a-46fe-4fb3-bdee-11c3bbffc370",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.prebuilt import ToolNode\n\ntools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\ntool_node = ToolNode(tools)"]
+   "source": [
+    "from langgraph.prebuilt import ToolNode\n",
+    "\n",
+    "tools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\n",
+    "tool_node = ToolNode(tools)"
+   ]
   },
   {
    "cell_type": "code",
@@ -345,7 +522,16 @@
    "id": "4d5b75c6-73e0-4922-a765-a15be63f869e",
    "metadata": {},
    "outputs": [],
-   "source": ["def _filter_out_routes(messages):\n    ms = []\n    for m in messages:\n        if _is_tool_call(m):\n            if m.name == \"general\":\n                continue\n        ms.append(m)\n    return ms"]
+   "source": [
+    "def _filter_out_routes(messages):\n",
+    "    ms = []\n",
+    "    for m in messages:\n",
+    "        if _is_tool_call(m):\n",
+    "            if m.name == \"general\":\n",
+    "                continue\n",
+    "        ms.append(m)\n",
+    "    return ms"
+   ]
   },
   {
    "cell_type": "code",
@@ -353,7 +539,13 @@
    "id": "fd4dbf98-dbb3-411a-bad6-2bb334072aaf",
    "metadata": {},
    "outputs": [],
-   "source": ["from functools import partial\n\ngeneral_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\nmusic_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\ncustomer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")"]
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "general_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\n",
+    "music_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\n",
+    "customer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")"
+   ]
   },
   {
    "cell_type": "code",
@@ -361,7 +553,33 @@
    "id": "dcade924",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nfrom langgraph.graph import MessageGraph\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = MessageGraph()\nnodes = {\n    \"general\": \"general\",\n    \"music\": \"music\",\n    END: END,\n    \"tools\": \"tools\",\n    \"customer\": \"customer\",\n}\n# Define a new graph\nworkflow = MessageGraph()\nworkflow.add_node(\"general\", general_node)\nworkflow.add_node(\"music\", music_node)\nworkflow.add_node(\"customer\", customer_node)\nworkflow.add_node(\"tools\", tool_node)\nworkflow.add_conditional_edges(\"general\", _route, nodes)\nworkflow.add_conditional_edges(\"tools\", _route, nodes)\nworkflow.add_conditional_edges(\"music\", _route, nodes)\nworkflow.add_conditional_edges(\"customer\", _route, nodes)\nworkflow.add_conditional_edges(START, _route, nodes)\ngraph = workflow.compile()"]
+   "source": [
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "\n",
+    "from langgraph.graph import MessageGraph\n",
+    "\n",
+    "memory = MemorySaver()\n",
+    "graph = MessageGraph()\n",
+    "nodes = {\n",
+    "    \"general\": \"general\",\n",
+    "    \"music\": \"music\",\n",
+    "    END: END,\n",
+    "    \"tools\": \"tools\",\n",
+    "    \"customer\": \"customer\",\n",
+    "}\n",
+    "# Define a new graph\n",
+    "workflow = MessageGraph()\n",
+    "workflow.add_node(\"general\", general_node)\n",
+    "workflow.add_node(\"music\", music_node)\n",
+    "workflow.add_node(\"customer\", customer_node)\n",
+    "workflow.add_node(\"tools\", tool_node)\n",
+    "workflow.add_conditional_edges(\"general\", _route, nodes)\n",
+    "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",
+    "graph = workflow.compile()"
+   ]
   },
   {
    "cell_type": "code",
@@ -370,7 +588,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "User (q/Q to quit):  what music do you have?\n"
@@ -395,7 +613,7 @@
      ]
     },
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "User (q/Q to quit):  how about shakira?\n"
@@ -446,7 +664,7 @@
      ]
     },
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "User (q/Q to quit):  hm cool\n"
@@ -483,7 +701,7 @@
      ]
     },
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "User (q/Q to quit):  q\n"
@@ -497,7 +715,27 @@
      ]
     }
    ],
-   "source": ["import uuid\n\nfrom langchain_core.messages import HumanMessage\n\nfrom langgraph.graph.graph import START\n\nhistory = []\nwhile True:\n    user = input(\"User (q/Q to quit): \")\n    if user in {\"q\", \"Q\"}:\n        print(\"AI: Byebye\")\n        break\n    history.append(HumanMessage(content=user))\n    async for output in graph.astream(history):\n        for key, value in output.items():\n            print(f\"Output from node '{key}':\")\n            print(\"---\")\n            print(value)\n        print(\"\\n---\\n\")"]
+   "source": [
+    "import uuid\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "from langgraph.graph.graph import START\n",
+    "\n",
+    "history = []\n",
+    "while True:\n",
+    "    user = input(\"User (q/Q to quit): \")\n",
+    "    if user in {\"q\", \"Q\"}:\n",
+    "        print(\"AI: Byebye\")\n",
+    "        break\n",
+    "    history.append(HumanMessage(content=user))\n",
+    "    async for output in graph.astream(history):\n",
+    "        for key, value in output.items():\n",
+    "            print(f\"Output from node '{key}':\")\n",
+    "            print(\"---\")\n",
+    "            print(value)\n",
+    "        print(\"\\n---\\n\")"
+   ]
   }
  ],
  "metadata": {

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

@@ -154,7 +154,7 @@
    "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
    "metadata": {},
    "outputs": [],
-   "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)"]
+   "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)"]
   },
   {
    "cell_type": "code",

examples/create-react-agent-hitl.ipynb

@@ -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": [
+  "cells": [
     {
-     "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": [
+      "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."
+      ]
+    },
     {
-     "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": [
+      "cell_type": "markdown",
+      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
     {
-     "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": [
+      "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"
+      ]
+    },
     {
-     "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"
-     ]
+      "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": "3.12.2"
     }
-   ],
-   "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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

examples/create-react-agent-memory.ipynb

@@ -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": [
+  "cells": [
     {
-     "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": [
+      "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`"
+      ]
+    },
     {
-     "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": [
+      "cell_type": "markdown",
+      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
     {
-     "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"
-     ]
+      "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"
     }
-   ],
-   "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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

examples/human-in-the-loop.ipynb

@@ -39,7 +39,10 @@
    "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",
@@ -55,7 +58,18 @@
    "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
    "metadata": {},
    "outputs": [],
-   "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\")"]
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -71,7 +85,10 @@
    "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",
@@ -89,7 +106,22 @@
    "id": "6098e5cb",
    "metadata": {},
    "outputs": [],
-   "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]"]
+   "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]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -109,7 +141,22 @@
    "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
    "metadata": {},
    "outputs": [],
-   "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]"]
+   "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]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -127,7 +174,11 @@
    "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"]
+   "source": [
+    "from langgraph.prebuilt import ToolExecutor\n",
+    "\n",
+    "tool_executor = ToolExecutor(tools)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -148,7 +199,11 @@
    "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"]
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(temperature=0)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -166,7 +221,9 @@
    "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
    "metadata": {},
    "outputs": [],
-   "source": ["model = model.bind_tools(tools)"]
+   "source": [
+    "model = model.bind_tools(tools)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -201,7 +258,53 @@
    "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
    "metadata": {},
    "outputs": [],
-   "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]}"]
+   "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]}"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -219,7 +322,45 @@
    "id": "812b4e70-4956-4415-8880-db48b3dcbad2",
    "metadata": {},
    "outputs": [],
-   "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\")"]
+   "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\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -237,7 +378,11 @@
    "id": "6845ed6a-d155-4105-9160-28849877248b",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
+   "source": [
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "\n",
+    "memory = MemorySaver()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -255,7 +400,12 @@
    "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\napp = 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\n",
+    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -282,7 +432,11 @@
      "output_type": "display_data"
     }
    ],
-   "source": ["from IPython.display import Image, display\n\ndisplay(Image(app.get_graph().draw_mermaid_png()))"]
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "display(Image(app.get_graph().draw_mermaid_png()))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -313,7 +467,14 @@
      ]
     }
    ],
-   "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()"]
+   "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()"
+   ]
   },
   {
    "cell_type": "code",
@@ -334,7 +495,11 @@
      ]
     }
    ],
-   "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()"]
+   "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()"
+   ]
   },
   {
    "cell_type": "code",
@@ -358,7 +523,11 @@
      ]
     }
    ],
-   "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()"]
+   "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()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -392,7 +561,10 @@
      ]
     }
    ],
-   "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",
@@ -427,7 +599,43 @@
    "id": "5454f436-d56e-4499-9381-06192aca1b56",
    "metadata": {},
    "outputs": [],
-   "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"]
+   "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"
+   ]
   },
   {
    "cell_type": "code",
@@ -514,7 +722,43 @@
      ]
     }
    ],
-   "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)"]
+   "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)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -535,7 +779,34 @@
    "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\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            }"]
+   "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",
+    "            }"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -551,7 +822,27 @@
    "id": "502dc688-c926-407e-8759-8c9e39eb4257",
    "metadata": {},
    "outputs": [],
-   "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)"]
+   "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)"
+   ]
   },
   {
    "cell_type": "code",
@@ -584,7 +875,13 @@
      ]
     }
    ],
-   "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()"]
+   "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()"
+   ]
   },
   {
    "cell_type": "code",
@@ -617,7 +914,11 @@
      ]
     }
    ],
-   "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()"]
+   "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()"
+   ]
   },
   {
    "cell_type": "code",
@@ -648,7 +949,11 @@
      ]
     }
    ],
-   "source": ["inputs = [HumanMessage(content=\"y\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n    event[\"messages\"][-1].pretty_print()"]
+   "source": [
+    "inputs = [HumanMessage(content=\"y\")]\n",
+    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
   }
  ],
  "metadata": {

examples/input_output_schema.ipynb

@@ -0,0 +1,96 @@
+{
+ "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",
+    "\n",
+    "class InputState(TypedDict):\n",
+    "    question: str\n",
+    "\n",
+    "\n",
+    "class OutputState(TypedDict):\n",
+    "    answer: str\n",
+    "\n",
+    "\n",
+    "def answer_node(state: InputState):\n",
+    "    return {\"answer\": \"bye\"}\n",
+    "\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
+}

examples/introduction.ipynb

@@ -84,8 +84,9 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "ef7bcad1-1274-4b7c-a2e9-365180ef3a31",
+   "id": "9c374e41-f9b7-439e-a520-6d8c853c5220",
    "metadata": {},
    "source": [
     "## Part 1: Build a Basic Chatbot\n",
@@ -120,13 +121,24 @@
     "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",
@@ -162,7 +174,7 @@
    "id": "b6c1dcd9-fb86-4649-81b4-ff6ce20a2e46",
    "metadata": {},
    "source": [
-    "**Notice** how the `chatbot` node function takes the current `State` as input and returns an updated `messages` list. This is the basic pattern for all LangGraph node functions.\n",
+    "**Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key \"messages\". This is the basic pattern for all LangGraph node functions.\n",
     "\n",
     "The `add_messages` function in our `State` will append the llm's response messages to whatever messages are already in the state.\n",
     "\n",
@@ -836,7 +848,7 @@
     "\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 `SqliteSaver` checkpointer."
+    "To get started, create a `MemorySaver` checkpointer."
    ]
   },
   {
@@ -846,9 +858,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")"
+    "memory = MemorySaver()"
    ]
   },
   {
@@ -856,7 +868,7 @@
    "id": "08d3d11a-1b42-4cbb-8e11-2a4294263d90",
    "metadata": {},
    "source": [
-    "**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",
+    "**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",
     "\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."
    ]
@@ -1187,7 +1199,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode\n",
@@ -1244,19 +1256,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "5a81608a-373a-4339-b1c6-65b73a92b983",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/wfh/code/lc/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The method `ChatAnthropic.bind_tools` is in beta. It is actively being worked on, so the API may change.\n",
-      "  warn_beta(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from typing import Annotated\n",
     "\n",
@@ -1265,12 +1268,12 @@
     "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\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 = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "\n",
     "\n",
     "class State(TypedDict):\n",
@@ -1308,12 +1311,12 @@
    "id": "813505b2-18c1-46e9-b891-20a34232808b",
    "metadata": {},
    "source": [
-    "Now, compile the graph, specifying to `interrupt_before` the `action` node."
+    "Now, compile the graph, specifying to `interrupt_before` the `tools` node."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "b0883e32-1a39-4ce9-ae32-bbd66708fd84",
    "metadata": {},
    "outputs": [],
@@ -1322,14 +1325,14 @@
     "    checkpointer=memory,\n",
     "    # This is new!\n",
     "    interrupt_before=[\"tools\"],\n",
-    "    # Note: can also interrupt __after__ actions, if desired.\n",
+    "    # Note: can also interrupt __after__ tools, if desired.\n",
     "    # interrupt_after=[\"tools\"]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "9f318020-ab7e-415b-a5e2-eddec6d9f3a6",
    "metadata": {},
    "outputs": [
@@ -1342,10 +1345,10 @@
       "I'm learning LangGraph. Could you do some research on it for me?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Okay, let's do some research on LangGraph:\", 'type': 'text'}, {'id': 'toolu_01Be7aRgMEv9cg6ezaFjiCry', 'input': {'query': 'LangGraph'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"Okay, let's look up some information on LangGraph:\", 'type': 'text'}, {'id': 'toolu_01XoHVKTRbipJokQorfifzvh', 'input': {'query': 'LangGraph'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_01Be7aRgMEv9cg6ezaFjiCry)\n",
-      " Call ID: toolu_01Be7aRgMEv9cg6ezaFjiCry\n",
+      "  tavily_search_results_json (toolu_01XoHVKTRbipJokQorfifzvh)\n",
+      " Call ID: toolu_01XoHVKTRbipJokQorfifzvh\n",
       "  Args:\n",
       "    query: LangGraph\n"
      ]
@@ -1373,17 +1376,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "9bb7af46-9b4f-4bb1-b8b9-e9ddf7dbc82c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('action',)"
+       "('tools',)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1398,12 +1401,12 @@
    "id": "89326046-2b11-4812-8b6d-8780306ec275",
    "metadata": {},
    "source": [
-    "**Notice** that unlike last time, the \"next\" node is set to **'action'**. We've interrupted here! Let's check the tool invocation."
+    "**Notice** that unlike last time, the \"next\" node is set to **'tools'**. We've interrupted here! Let's check the tool invocation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "3facda0a-e6ad-4b28-b627-753ad8c90c15",
    "metadata": {},
    "outputs": [
@@ -1412,10 +1415,11 @@
       "text/plain": [
        "[{'name': 'tavily_search_results_json',\n",
        "  'args': {'query': 'LangGraph'},\n",
-       "  'id': 'toolu_01Be7aRgMEv9cg6ezaFjiCry'}]"
+       "  'id': 'toolu_01XoHVKTRbipJokQorfifzvh',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "effb95d9-b7d5-40c5-9253-253d193b23b2",
    "metadata": {},
    "outputs": [
@@ -1448,18 +1452,19 @@
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: tavily_search_results_json\n",
       "\n",
-      "[{\"url\": \"https://github.com/langchain-ai/langgraph\", \"content\": \"LangGraph is a Python package that extends LangChain Expression Language with the ability to coordinate multiple chains across multiple steps of computation in a cyclic manner. It is inspired by Pregel and Apache Beam and can be used for agent-like behaviors, such as chatbots, with LLMs.\"}, {\"url\": \"https://langchain-ai.github.io/langgraph//\", \"content\": \"LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain . It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. It is inspired by Pregel and Apache Beam .\"}]\n",
+      "[{\"url\": \"https://langchain-ai.github.io/langgraph/tutorials/\", \"content\": \"LangGraph is a framework for building language agents as graphs. Learn how to use LangGraph to create chatbots, code assistants, planning agents, reflection agents, and more with these notebooks.\"}, {\"url\": \"https://github.com/langchain-ai/langgraph\", \"content\": \"LangGraph is a library for creating stateful, multi-actor applications with LLMs, using cycles, controllability, and persistence. Learn how to use LangGraph with examples, integration with LangChain, and streaming support.\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Based on the search results, LangGraph seems to be a Python library that extends the LangChain library to enable more complex, multi-step interactions with large language models (LLMs). Some key points:\n",
+      "Based on the search results, LangGraph seems to be a framework for building language-based AI agents and applications using language models. It provides a modular, graph-based approach for creating chatbots, code assistants, planning agents, and other language-centric applications.\n",
       "\n",
-      "- LangGraph allows coordinating multiple \"chains\" (or actors) over multiple steps of computation, in a cyclic manner. This enables more advanced agent-like behaviors like chatbots.\n",
-      "- It is inspired by distributed graph processing frameworks like Pregel and Apache Beam.\n",
-      "- LangGraph is built on top of the LangChain library, which provides a framework for building applications with LLMs.\n",
+      "Some key things I learned about LangGraph:\n",
       "\n",
-      "So in summary, LangGraph appears to be a powerful tool for building more sophisticated applications and agents using large language models, by allowing you to coordinate multiple steps and actors in a flexible, graph-like manner. It extends the capabilities of the base LangChain library.\n",
+      "- It is designed to make it easier to build stateful, multi-actor applications using large language models (LLMs).\n",
+      "- It provides features like cycles, controllability, and persistence to help manage the complexity of these types of applications.\n",
+      "- LangGraph can be integrated with the LangChain library, which provides additional tools for building LLM-powered applications.\n",
+      "- The framework includes examples and tutorials to help get started with using LangGraph.\n",
       "\n",
-      "Let me know if you need any clarification or have additional questions!\n"
+      "Overall, LangGraph seems like a promising approach for building more advanced, graph-based language applications on top of large language models. Let me know if you need any other details on LangGraph and how it works!\n"
      ]
     }
    ],
@@ -1496,7 +1501,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode\n",
@@ -1531,7 +1536,7 @@
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.set_entry_point(\"chatbot\")\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # This is new!\n",
@@ -1551,7 +1556,7 @@
    "source": [
     "## Part 5: Manually Updating the State\n",
     "\n",
-    "In the previous section, we showed how to interrupt a graph so that a human could inspect its actions. This lets the human `read` the state, but if they want to change they agent's course, they'll need to have `write` access.\n",
+    "In the previous section, we showed how to interrupt a graph so that a human could inspect its actions. This lets the human `read` the state, but if they want to change their agent's course, they'll need to have `write` access.\n",
     "\n",
     "Thankfully, LangGraph lets you **manually update state**! Updating the state lets you control the agent's trajectory by modifying its actions (even modifying the past!). This capability is particularly useful when you want to correct the agent's mistakes, explore alternative paths, or guide the agent towards a specific goal.\n",
     "\n",
@@ -1581,7 +1586,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph, START\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -1615,7 +1620,7 @@
     ")\n",
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # This is new!\n",
@@ -2080,7 +2085,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph, START\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2277,7 +2282,7 @@
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.add_edge(\"human\", \"chatbot\")\n",
     "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # We interrupt before 'human' here instead.\n",
@@ -2527,7 +2532,7 @@
     "from langchain_core.pydantic_v1 import BaseModel\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2614,7 +2619,7 @@
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.add_edge(\"human\", \"chatbot\")\n",
     "graph_builder.set_entry_point(\"chatbot\")\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(\n",
     "    checkpointer=memory,\n",
     "    interrupt_before=[\"human\"],\n",
@@ -2653,11 +2658,11 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_core.messages import AIMessage, BaseMessage, ToolMessage\n",
+    "from langchain_core.messages import AIMessage, ToolMessage\n",
     "from langchain_core.pydantic_v1 import BaseModel\n",
     "from typing_extensions import TypedDict\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import StateGraph, START\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
@@ -2744,7 +2749,7 @@
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.add_edge(\"human\", \"chatbot\")\n",
     "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(\n",
     "    checkpointer=memory,\n",
     "    interrupt_before=[\"human\"],\n",
@@ -3056,7 +3061,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -3070,7 +3075,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

examples/memory/add-summary-conversation-history.ipynb

@@ -105,10 +105,10 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_core.messages import SystemMessage, RemoveMessage\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import MessagesState, StateGraph, START, END\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "\n",
     "\n",
     "# We will add a `summary` attribute (in addition to `messages` key,\n",

examples/memory/delete-messages.ipynb

@@ -112,11 +112,11 @@
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_core.tools import tool\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import MessagesState, StateGraph, START\n",
     "from langgraph.prebuilt import ToolNode\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "\n",
     "\n",
     "@tool\n",

examples/memory/manage-conversation-history.ipynb

@@ -103,11 +103,11 @@
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_core.tools import tool\n",
     "\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import MessagesState, StateGraph, START\n",
     "from langgraph.prebuilt import ToolNode\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\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 = model.invoke(state[\"messages\"])\n",
+    "    response = bound_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.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import MessagesState, StateGraph, START\n",
     "from langgraph.prebuilt import ToolNode\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "\n",
     "\n",
     "@tool\n",
@@ -268,14 +268,14 @@
     "\n",
     "\n",
     "def filter_messages(messages: list):\n",
-    "    # This is very simple helper function which only ever uses the last two messages\n",
+    "    # This is very simple helper function which only ever uses the last message\n",
     "    return messages[-1:]\n",
     "\n",
     "\n",
     "# Define the function that calls the model\n",
     "def call_model(state: MessagesState):\n",
     "    messages = filter_messages(state[\"messages\"])\n",
-    "    response = model.invoke(messages)\n",
+    "    response = bound_model.invoke(messages)\n",
     "    # We return a list, because this will get added to the existing list\n",
     "    return {\"messages\": response}\n",
     "\n",
@@ -372,9 +372,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langgraph-example-dev",
    "language": "python",
-   "name": "python3"
+   "name": "langgraph-example-dev"
   },
   "language_info": {
    "codemirror_mode": {
@@ -386,7 +386,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,