sdk0.1.27

sdk: Use ASGI transport when called inside langgraph-api

.github/workflows/ci.yml

@@ -36,7 +36,9 @@
             working-directory: [
                 "libs/langgraph",
                 "libs/sdk-py",
-                "libs/cli"
+                "libs/cli",
+                "libs/checkpoint",
+                "libs/checkpoint-sqlite"
             ]
         uses: ./.github/workflows/_lint.yml
         with:
@@ -50,7 +52,9 @@
           matrix:
             working-directory: [
                 "libs/langgraph",
-                "libs/cli"
+                "libs/cli",
+                "libs/checkpoint",
+                "libs/checkpoint-sqlite"
             ]
         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 ⚡
@@ -62,7 +61,7 @@ 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.checkpoint.memory import MemorySaver
 from langgraph.graph import END, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode
 

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",
@@ -98,6 +100,8 @@ _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

@@ -12,6 +12,10 @@ An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It
 
 The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the <a href="../reference/api/api_ref.html#tag/assistantscreate" target="_blank">API reference</a> for more details.
 
+#### Configuring Assistants
+
+You can save custom assistants from the same graph to set different default prompts, models, and other configurations without changing a line of code in your graph. This allows you the ability to quickly test out different configurations without having to rewrite your graph every time, and also give users the flexibility to select different configurations when using your LangGraph application. See <a href="https://langchain-ai.github.io/langgraph/cloud/how-tos/cloud_examples/configuration_cloud/">this</a> how-to for information on how to configure a deployed graph. 
+
 ### Threads
 
 A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.

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, MessageGraph
+
+model = ChatOpenAI(temperature=0)
+
+graph_workflow = MessageGraph()
+
+graph_workflow.add_node("agent", model)
+graph_workflow.add_edge("agent", END)
+graph_workflow.set_entry_point("agent")
+
+agent = graph_workflow.compile()
+```
+
+To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.:
+
+```
+{
+    "dependencies": ["."],
+    "graphs": {
+        "openai_agent": "./openai_agent.py:agent",
+    },
+    "env": "./.env"
+}
+```
+
+### Rebuild
+
+To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows:
+
+```python
+from typing import Annotated, TypedDict
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, MessageGraph
+from langgraph.graph.state import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode
+from langchain_core.tools import tool
+from langchain_core.messages import BaseMessage
+from langchain_core.runnables import RunnableConfig
+
+
+class State(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+model = ChatOpenAI(temperature=0)
+
+def make_default_graph():
+    """Make a simple LLM agent"""
+    graph_workflow = StateGraph(State)
+    def call_model(state):
+        return {"messages": [model.invoke(state["messages"])]}
+
+    graph_workflow.add_node("agent", call_model)
+    graph_workflow.add_edge("agent", END)
+    graph_workflow.set_entry_point("agent")
+
+    agent = graph_workflow.compile()
+    return agent
+
+
+def make_alternative_graph():
+    """Make a tool-calling agent"""
+
+    @tool
+    def add(a: float, b: float):
+        """Adds two numbers."""
+        return a + b
+
+    tool_node = ToolNode([add])
+    model_with_tools = model.bind_tools([add])
+    def call_model(state):
+        return {"messages": [model_with_tools.invoke(state["messages"])]}
+
+    def should_continue(state: State):
+        if state["messages"][-1].tool_calls:
+            return "tools"
+        else:
+            return END
+
+    graph_workflow = StateGraph(State)
+
+    graph_workflow.add_node("agent", call_model)
+    graph_workflow.add_node("tools", tool_node)
+    graph_workflow.add_edge("tools", "agent")
+    graph_workflow.set_entry_point("agent")
+    graph_workflow.add_conditional_edges("agent", should_continue)
+
+    agent = graph_workflow.compile()
+    return agent
+
+
+# this is the graph making function that will decide which graph to
+# build based on the provided config
+def make_graph(config: RunnableConfig):
+    user_id = config.get("configurable", {}).get("user_id")
+    # route to different graph state / structure based on the user ID
+    if user_id == "1":
+        return make_default_graph()
+    else:
+        return make_alternative_graph()
+```
+
+Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`:
+
+```
+{
+    "dependencies": ["."],
+    "graphs": {
+        "openai_agent": "./openai_agent.py:make_graph",
+    },
+    "env": "./.env"
+}
+```
+
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)

docs/docs/cloud/deployment/setup.md

@@ -1,6 +1,9 @@
 # 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. 
+
+!!! 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.
 
 The final repo structure will look something like this:
 
@@ -19,6 +22,22 @@ After each step, an example file directory is provided to demonstrate how code c
 
 Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).
 
+The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
+```
+langgraph>=0.1.19,<0.2.0
+langchain-core>=0.2.8,<0.3.0
+langsmith>=0.1.63
+orjson>=3.10.1
+httpx>=0.27.0
+tenacity>=8.3.0
+uvicorn>=0.29.0
+sse-starlette>=2.1.0
+uvloop>=0.19.0
+httptools>=0.6.1
+jsonschema-rs>=0.18.0
+croniter>=1.0.1
+```
+
 Example `requirements.txt` file:
 ```
 langgraph
@@ -70,7 +89,7 @@ agent = graph_workflow.compile()
 ```
 
 !!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
+    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
 
 Example file directory:
 ```
@@ -115,10 +134,6 @@ my-app/
 |-- langgraph.json      # configuration file for LangGraph
 ```
 
-## Upload to GitHub
-
-To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.
-
 ## Next
 
-After you setup your 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

@@ -20,6 +20,22 @@ After each step, an example file directory is provided to demonstrate how code c
 
 Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).
 
+The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
+```
+langgraph>=0.1.19,<0.2.0
+langchain-core>=0.2.8,<0.3.0
+langsmith>=0.1.63
+orjson>=3.10.1
+httpx>=0.27.0
+tenacity>=8.3.0
+uvicorn>=0.29.0
+sse-starlette>=2.1.0
+uvloop>=0.19.0
+httptools>=0.6.1
+jsonschema-rs>=0.18.0
+croniter>=1.0.1
+```
+
 Example `pyproject.toml` file:
 
 ```toml
@@ -33,7 +49,7 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.9.0,<3.13"
-langgraph = "^0.1.0"
+langgraph = "^0.1.7"
 langchain-fireworks = "^0.1.3"
 
 
@@ -148,10 +164,6 @@ my-app/
 └── pyproject.toml
 ```
 
-## Upload to GitHub
-
-To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.
-
 ## Next
 
-After you setup your 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/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/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/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

@@ -12,7 +12,11 @@
 !!! 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
 

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/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

@@ -46,6 +46,10 @@ 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.
@@ -75,22 +79,44 @@ 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
+### Working with Messages in Graph State
 
-`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:
+#### 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 +353,16 @@ 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.

docs/docs/how-tos/index.md

@@ -61,6 +61,13 @@ These guides show how to use different streaming modes.
 - [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)
 
+## State Management
+
+- [Use Pydantic model as state](state-model.ipynb)
+- [Use a context object in state](state-context-key.ipynb)
+- [Have a separate input and output schema](input_output_schema.ipynb)
+- [Pass private state between nodes inside the graph](pass_private_state.ipynb)
+
 ## Other
 
 - [How to run graph asynchronously](async.ipynb)

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
 
@@ -33,7 +35,7 @@ LangGraph also natively provides the following checkpoint implementations.
 
 ### AsyncSqliteSaver
 
-::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver
+::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver
 
 ### SqliteSaver
 

docs/mkdocs.yml

@@ -157,12 +157,15 @@ 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
+      - 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 +189,12 @@ 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:

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": "3.11.1"
     }
-   ],
-   "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/input_output_schema.ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f262985e-e973-4a27-9c9e-dbb3a06a35b7",
+   "metadata": {},
+   "source": [
+    "# How to define input/output schema for your graph\n",
+    "\n",
+    "By default, `StateGraph` takes in a single schema and all nodes are expected to communicate with that schema. However, it is also possible to define explicit input and output schemas for a graph. This is helpful if you want to draw a distinction between input and output keys.\n",
+    "\n",
+    "In this notebook we'll walk through an example of this. At a high level, in order to do this you simply have to pass in `input=..., output=...` when defining the graph. Let's see an example below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6ec0eb77-874e-443e-8c73-93125b515106",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': 'bye'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from typing import TypedDict\n",
+    "\n",
+    "class InputState(TypedDict):\n",
+    "    question: str\n",
+    "\n",
+    "class OutputState(TypedDict):\n",
+    "    answer: str\n",
+    "\n",
+    "def answer_node(state: InputState):\n",
+    "    return {\"answer\": \"bye\"}\n",
+    "\n",
+    "check = SqliteSaver.from_conn_string(\":memory:\")\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",
@@ -3056,9 +3068,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langgraph",
    "language": "python",
-   "name": "python3"
+   "name": "langgraph"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3070,7 +3082,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

examples/node-retries.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. \n",
     "\n",
-    "In order to configure the retry policty, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters:"
+    "In order to configure the retry policy, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters:"
    ]
   },
   {

examples/pass_private_state.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "47ed5db3-bda5-49e1-bf75-23e08c9a3af0",
+   "metadata": {},
+   "source": [
+    "# How to pass private state\n",
+    "\n",
+    "Oftentimes, you may want nodes to be able to pass state to eachv other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n",
+    "\n",
+    "Let's take a look at an example below. In this example, we will create a RAG pipeline that:\n",
+    "1. Takes in a user question\n",
+    "2. Uses an LLM to generate a search query\n",
+    "3. Retrieves documents for that generated query\n",
+    "4. Generates a final answer based on those documents\n",
+    "\n",
+    "We will have a separate node for each step. We will only have the `question` and `answer` on the overall state. However, we will need separate states for the `search_query` and the `documents` - we will pass these as private state keys.\n",
+    "\n",
+    "Let's look at an example!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3114c3ad-0ade-47ba-9488-53d6f7671578",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'foo', 'answer': 'fo\\n\\nfo\\n\\nfoo'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from typing import TypedDict\n",
+    "\n",
+    "\n",
+    "# The overall state of the graph\n",
+    "class OverallState(TypedDict):\n",
+    "    question: str\n",
+    "    answer: str\n",
+    "\n",
+    "\n",
+    "# This is what the node that generates the query will return\n",
+    "class QueryOutputState(TypedDict):\n",
+    "    query: str\n",
+    "\n",
+    "\n",
+    "# This is what the node that retrieves the documents will return\n",
+    "class DocumentOutputState(TypedDict):\n",
+    "    docs: list[str]\n",
+    "\n",
+    "\n",
+    "# This is what the node that generates the final answer will take in\n",
+    "class GenerateInputState(OverallState, DocumentOutputState):\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "# Node to generate query\n",
+    "def generate_query(state: OverallState) -> QueryOutputState:\n",
+    "    # Replace this with real logic\n",
+    "    return {\"query\": state[\"question\"][:2]}\n",
+    "\n",
+    "\n",
+    "# Node to retrieve documents\n",
+    "def retrieve_documents(state: QueryOutputState) -> DocumentOutputState:\n",
+    "    # Replace this with real logic\n",
+    "    return {\"docs\": [state['query']] * 2}\n",
+    "\n",
+    "\n",
+    "# Node to generate answer\n",
+    "def generate(state: GenerateInputState) -> OverallState:\n",
+    "    return {\"answer\": \"\\n\\n\".join(state['docs'] + [state['question']])}\n",
+    "\n",
+    "\n",
+    "graph = StateGraph(OverallState)\n",
+    "graph.add_node(generate_query)\n",
+    "graph.add_node(retrieve_documents)\n",
+    "graph.add_node(generate)\n",
+    "graph.add_edge(START, \"generate_query\")\n",
+    "graph.add_edge(\"generate_query\", \"retrieve_documents\")\n",
+    "graph.add_edge(\"retrieve_documents\", \"generate\")\n",
+    "graph.add_edge(\"generate\", END)\n",
+    "graph = graph.compile()\n",
+    "\n",
+    "graph.invoke({\"question\": \"foo\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ffc2d8c-717f-42c9-b0aa-15b178a5cc8b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/visualization.ipynb

@@ -268,7 +268,7 @@
    ],
    "source": [
     "from IPython.display import Image, display\n",
-    "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeColors\n",
+    "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n",
     "\n",
     "display(\n",
     "    Image(\n",
@@ -340,7 +340,7 @@
     "    Image(\n",
     "        app.get_graph().draw_mermaid_png(\n",
     "            curve_style=CurveStyle.LINEAR,\n",
-    "            node_colors=NodeColors(start=\"#ffdfba\", end=\"#baffc9\", other=\"#fad7de\"),\n",
+    "            node_colors=NodeStyles(first=\"#ffdfba\", last=\"#baffc9\", default=\"#fad7de\"),\n",
     "            wrap_label_n_words=9,\n",
     "            output_file_path=None,\n",
     "            draw_method=MermaidDrawMethod.PYPPETEER,\n",

libs/checkpoint-sqlite/Makefile

@@ -0,0 +1,34 @@
+.PHONY: test test_watch lint format
+
+######################
+# TESTING AND COVERAGE
+######################
+
+test:
+	poetry run pytest tests
+
+test_watch:
+	poetry run ptw .
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff .
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff --select I --fix $(PYTHON_FILES)

libs/checkpoint-sqlite/README.md

@@ -0,0 +1,91 @@
+# LangGraph SQLite Checkpoint
+
+Implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via `aiosqlite`)
+
+## Usage
+
+```python
+
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+checkpointer = SqliteSaver.from_conn_string(":memory:")
+checkpoint = {
+  "v": 1,
+  "ts": "2024-07-31T20:14:19.804150+00:00",
+  "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+  "channel_values": {
+    "my_key": "meow",
+    "node": "node"
+  },
+  "channel_versions": {
+    "__start__": 2,
+    "my_key": 3,
+    "start:node": 3,
+    "node": 3
+  },
+  "versions_seen": {
+    "__input__": {},
+    "__start__": {
+      "__start__": 1
+    },
+    "node": {
+      "start:node": 2
+    }
+  },
+  "pending_sends": [],
+  "current_tasks": {}
+}
+
+# store checkpoint
+checkpointer.put(thread_config, checkpoint, {})
+
+# load checkpoint
+checkpointer.get(thread_config)
+
+# list checkpoints
+list(checkpointer.list(thread_config))
+```
+
+### Async
+
+```python
+
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+
+checkpointer = AsyncSqliteSaver.from_conn_string(":memory:")
+checkpoint = {
+  "v": 1,
+  "ts": "2024-07-31T20:14:19.804150+00:00",
+  "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+  "channel_values": {
+    "my_key": "meow",
+    "node": "node"
+  },
+  "channel_versions": {
+    "__start__": 2,
+    "my_key": 3,
+    "start:node": 3,
+    "node": 3
+  },
+  "versions_seen": {
+    "__input__": {},
+    "__start__": {
+      "__start__": 1
+    },
+    "node": {
+      "start:node": 2
+    }
+  },
+  "pending_sends": [],
+  "current_tasks": {}
+}
+
+# store checkpoint
+await checkpointer.aput(thread_config, checkpoint, {})
+
+# load checkpoint
+await checkpointer.aget(thread_config)
+
+# list checkpoints
+[c async for c in checkpointer.alist(thread_config)]
+```

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py

@@ -1,5 +1,3 @@
-import json
-import pickle
 import sqlite3
 import threading
 from contextlib import AbstractContextManager, contextmanager
@@ -10,50 +8,23 @@ from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple
 from langchain_core.runnables import RunnableConfig
 from typing_extensions import Self
 
-from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    EmptyChannelError,
     SerializerProtocol,
+    get_checkpoint_id,
 )
-from langgraph.errors import EmptyChannelError
-from langgraph.serde.jsonplus import JsonPlusSerializer
-
-
-class JsonPlusSerializerCompat(JsonPlusSerializer):
-    """A serializer that supports loading pickled checkpoints for backwards compatibility.
-
-    This serializer extends the JsonPlusSerializer and adds support for loading pickled
-    checkpoints. If the input data starts with b"\x80" and ends with b".", it is treated
-    as a pickled checkpoint and loaded using pickle.loads(). Otherwise, the default
-    JsonPlusSerializer behavior is used.
-
-    Examples:
-        >>> import pickle
-        >>> from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat
-        >>>
-        >>> serializer = JsonPlusSerializerCompat()
-        >>> pickled_data = pickle.dumps({"key": "value"})
-        >>> loaded_data = serializer.loads(pickled_data)
-        >>> print(loaded_data)  # Output: {"key": "value"}
-        >>>
-        >>> json_data = '{"key": "value"}'.encode("utf-8")
-        >>> loaded_data = serializer.loads(json_data)
-        >>> print(loaded_data)  # Output: {"key": "value"}
-    """
-
-    def loads(self, data: bytes) -> Any:
-        if data.startswith(b"\x80") and data.endswith(b"."):
-            return pickle.loads(data)
-        return super().loads(data)
-
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import ChannelProtocol
+from langgraph.checkpoint.sqlite.utils import search_where
 
 _AIO_ERROR_MSG = (
     "The SqliteSaver does not support async methods. "
     "Consider using AsyncSqliteSaver instead.\n"
-    "from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver\n"
+    "from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n"
     "Note: AsyncSqliteSaver requires the aiosqlite package to use.\n"
     "Install with:\n`pip install aiosqlite`\n"
     "See https://langchain-ai.github.io/langgraph/reference/checkpoints/asyncsqlitesaver"
@@ -92,11 +63,9 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         >>> graph.get_state(config)
         >>> result = graph.invoke(3, config)
         >>> graph.get_state(config)
-        StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '1', 'thread_ts': '2024-05-04T06:32:42.235444+00:00'}}, parent_config=None)
+        StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_id': '0c62ca34-ac19-445d-bbb0-5b4984975b2a'}}, parent_config=None)
     """  # noqa
 
-    serde = JsonPlusSerializerCompat()
-
     conn: sqlite3.Connection
     is_setup: bool
 
@@ -107,6 +76,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         serde: Optional[SerializerProtocol] = None,
     ) -> None:
         super().__init__(serde=serde)
+        self.jsonplus_serde = JsonPlusSerializer()
         self.conn = conn
         self.is_setup = False
         self.lock = threading.Lock()
@@ -165,20 +135,24 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             PRAGMA journal_mode=WAL;
             CREATE TABLE IF NOT EXISTS checkpoints (
                 thread_id TEXT NOT NULL,
-                thread_ts TEXT NOT NULL,
-                parent_ts TEXT,
+                checkpoint_ns TEXT NOT NULL DEFAULT '',
+                checkpoint_id TEXT NOT NULL,
+                parent_checkpoint_id TEXT,
+                type TEXT,
                 checkpoint BLOB,
                 metadata BLOB,
-                PRIMARY KEY (thread_id, thread_ts)
+                PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
             );
             CREATE TABLE IF NOT EXISTS writes (
                 thread_id TEXT NOT NULL,
-                thread_ts TEXT NOT NULL,
+                checkpoint_ns TEXT NOT NULL DEFAULT '',
+                checkpoint_id TEXT NOT NULL,
                 task_id TEXT NOT NULL,
                 idx INTEGER NOT NULL,
                 channel TEXT NOT NULL,
+                type TEXT,
                 value BLOB,
-                PRIMARY KEY (thread_id, thread_ts, task_id, idx)
+                PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
             );
             """
         )
@@ -211,7 +185,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         """Get a checkpoint tuple from the database.
 
         This method retrieves a checkpoint tuple from the SQLite database based on the
-        provided config. If the config contains a "thread_ts" key, the checkpoint with
+        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
         the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
         for the given thread ID is retrieved.
 
@@ -234,63 +208,76 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             >>> config = {
             ...    "configurable": {
             ...        "thread_id": "1",
-            ...        "thread_ts": "2024-05-04T06:32:42.235444+00:00",
+            ...        "checkpoint_id": "2024-05-04T06:32:42.235444+00:00",
             ...    }
             ... }
             >>> checkpoint_tuple = memory.get_tuple(config)
             >>> print(checkpoint_tuple)
             CheckpointTuple(...)
         """  # noqa
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
         with self.cursor(transaction=False) as cur:
             # find the latest checkpoint for the thread_id
-            if config["configurable"].get("thread_ts"):
+            if checkpoint_id := get_checkpoint_id(config):
                 cur.execute(
-                    "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND thread_ts = ?",
+                    "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?",
                     (
                         str(config["configurable"]["thread_id"]),
-                        str(config["configurable"]["thread_ts"]),
+                        checkpoint_ns,
+                        checkpoint_id,
                     ),
                 )
             else:
                 cur.execute(
-                    "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? ORDER BY thread_ts DESC LIMIT 1",
-                    (str(config["configurable"]["thread_id"]),),
+                    "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1",
+                    (str(config["configurable"]["thread_id"]), checkpoint_ns),
                 )
             # if a checkpoint is found, return it
             if value := cur.fetchone():
-                if not config["configurable"].get("thread_ts"):
+                (
+                    thread_id,
+                    checkpoint_id,
+                    parent_checkpoint_id,
+                    type,
+                    checkpoint,
+                    metadata,
+                ) = value
+                if not get_checkpoint_id(config):
                     config = {
                         "configurable": {
-                            "thread_id": value[0],
-                            "thread_ts": value[1],
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
                         }
                     }
                 # find any pending writes
                 cur.execute(
-                    "SELECT task_id, channel, value FROM writes WHERE thread_id = ? AND thread_ts = ?",
+                    "SELECT task_id, channel, type, value FROM writes WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?",
                     (
                         str(config["configurable"]["thread_id"]),
-                        str(config["configurable"]["thread_ts"]),
+                        checkpoint_ns,
+                        str(config["configurable"]["checkpoint_id"]),
                     ),
                 )
                 # deserialize the checkpoint and metadata
                 return CheckpointTuple(
                     config,
-                    self.serde.loads(value[3]),
-                    self.serde.loads(value[4]) if value[4] is not None else {},
+                    self.serde.loads_typed((type, checkpoint)),
+                    self.jsonplus_serde.loads(metadata) if metadata is not None else {},
                     (
                         {
                             "configurable": {
-                                "thread_id": value[0],
-                                "thread_ts": value[2],
+                                "thread_id": thread_id,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": parent_checkpoint_id,
                             }
                         }
-                        if value[2]
+                        if parent_checkpoint_id
                         else None
                     ),
                     [
-                        (task_id, channel, self.serde.loads(value))
-                        for task_id, channel, value in cur
+                        (task_id, channel, self.serde.loads_typed((type, value)))
+                        for task_id, channel, type, value in cur
                     ],
                 )
 
@@ -326,33 +313,48 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             [CheckpointTuple(...), CheckpointTuple(...)]
 
             >>> config = {"configurable": {"thread_id": "1"}}
-            >>> before = {"configurable": {"thread_ts": "2024-05-04T06:32:42.235444+00:00"}}
+            >>> before = {"configurable": {"checkpoint_id": "2024-05-04T06:32:42.235444+00:00"}}
             >>> checkpoints = list(memory.list(config, before=before))
             >>> print(checkpoints)
             [CheckpointTuple(...), ...]
         """
         where, param_values = search_where(config, filter, before)
-        query = f"""SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata
+        query = f"""SELECT thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata
         FROM checkpoints
         {where}
-        ORDER BY thread_ts DESC"""
+        ORDER BY checkpoint_id DESC"""
         if limit:
             query += f" LIMIT {limit}"
         with self.cursor(transaction=False) as cur:
             cur.execute(query, param_values)
-            for thread_id, thread_ts, parent_ts, value, metadata in cur:
+            for (
+                thread_id,
+                checkpoint_ns,
+                checkpoint_id,
+                parent_checkpoint_id,
+                type,
+                checkpoint,
+                metadata,
+            ) in cur:
                 yield CheckpointTuple(
-                    {"configurable": {"thread_id": thread_id, "thread_ts": thread_ts}},
-                    self.serde.loads(value),
-                    self.serde.loads(metadata) if metadata is not None else {},
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
+                        }
+                    },
+                    self.serde.loads_typed((type, checkpoint)),
+                    self.jsonplus_serde.loads(metadata) if metadata is not None else {},
                     (
                         {
                             "configurable": {
                                 "thread_id": thread_id,
-                                "thread_ts": parent_ts,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": parent_checkpoint_id,
                             }
                         }
-                        if parent_ts
+                        if parent_checkpoint_id
                         else None
                     ),
                 )
@@ -385,23 +387,30 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             >>> checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "data": {"key": "value"}}
             >>> saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}})
             >>> print(saved_config)
-            {"configurable": {"thread_id": "1", "thread_ts": 2024-05-04T06:32:42.235444+00:00"}}
+            {"configurable": {"thread_id": "1", "checkpoint_id": 2024-05-04T06:32:42.235444+00:00"}}
         """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
+        serialized_metadata = self.jsonplus_serde.dumps(metadata)
         with self.lock, self.cursor() as cur:
             cur.execute(
-                "INSERT OR REPLACE INTO checkpoints (thread_id, thread_ts, parent_ts, checkpoint, metadata) VALUES (?, ?, ?, ?, ?)",
+                "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)",
                 (
                     str(config["configurable"]["thread_id"]),
+                    checkpoint_ns,
                     checkpoint["id"],
-                    config["configurable"].get("thread_ts"),
-                    self.serde.dumps(checkpoint),
-                    self.serde.dumps(metadata),
+                    config["configurable"].get("checkpoint_id"),
+                    type_,
+                    serialized_checkpoint,
+                    serialized_metadata,
                 ),
             )
         return {
             "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "thread_ts": checkpoint["id"],
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
             }
         }
 
@@ -422,15 +431,16 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         """
         with self.lock, self.cursor() as cur:
             cur.executemany(
-                "INSERT OR REPLACE INTO writes (thread_id, thread_ts, task_id, idx, channel, value) VALUES (?, ?, ?, ?, ?, ?)",
+                "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
                 [
                     (
                         str(config["configurable"]["thread_id"]),
-                        str(config["configurable"]["thread_ts"]),
+                        str(config["configurable"]["checkpoint_ns"]),
+                        str(config["configurable"]["checkpoint_id"]),
                         task_id,
                         idx,
                         channel,
-                        self.serde.dumps(value),
+                        *self.serde.dumps_typed(value),
                     )
                     for idx, (channel, value) in enumerate(writes)
                 ],
@@ -476,7 +486,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         """
         raise NotImplementedError(_AIO_ERROR_MSG)
 
-    def get_next_version(self, current: Optional[str], channel: BaseChannel) -> str:
+    def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str:
         """Generate the next version ID for a channel.
 
         This method creates a new version identifier for a channel based on its current version.
@@ -494,86 +504,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             current_v = int(current.split(".")[0])
         next_v = current_v + 1
         try:
-            next_h = md5(self.serde.dumps(channel.checkpoint())).hexdigest()
+            next_h = md5(self.serde.dumps_typed(channel.checkpoint())[1]).hexdigest()
         except EmptyChannelError:
             next_h = ""
         return f"{next_v:032}.{next_h}"
-
-
-def _metadata_predicate(
-    metadata_filter: Dict[str, Any],
-) -> Tuple[Sequence[str], Sequence[Any]]:
-    """Return WHERE clause predicates for (a)search() given metadata filter.
-
-    This method returns a tuple of a string and a tuple of values. The string
-    is the parametered WHERE clause predicate (excluding the WHERE keyword):
-    "column1 = ? AND column2 IS ?". The tuple of values contains the values
-    for each of the corresponding parameters.
-    """
-
-    def _where_value(query_value: Any) -> Tuple[str, Any]:
-        """Return tuple of operator and value for WHERE clause predicate."""
-        if query_value is None:
-            return ("IS ?", None)
-        elif (
-            isinstance(query_value, str)
-            or isinstance(query_value, int)
-            or isinstance(query_value, float)
-        ):
-            return ("= ?", query_value)
-        elif isinstance(query_value, bool):
-            return ("= ?", 1 if query_value else 0)
-        elif isinstance(query_value, dict) or isinstance(query_value, list):
-            # query value for JSON object cannot have trailing space after separators (, :)
-            # SQLite json_extract() returns JSON string without whitespace
-            return ("= ?", json.dumps(query_value, separators=(",", ":")))
-        else:
-            return ("= ?", str(query_value))
-
-    predicates = []
-    param_values = []
-
-    # process metadata query
-    for query_key, query_value in metadata_filter.items():
-        operator, param_value = _where_value(query_value)
-        predicates.append(
-            f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}"
-        )
-        param_values.append(param_value)
-
-    return (predicates, param_values)
-
-
-def search_where(
-    config: Optional[RunnableConfig],
-    filter: Optional[Dict[str, Any]],
-    before: Optional[RunnableConfig] = None,
-) -> Tuple[str, Sequence[Any]]:
-    """Return WHERE clause predicates for (a)search() given metadata filter
-    and `before` config.
-
-    This method returns a tuple of a string and a tuple of values. The string
-    is the parametered WHERE clause predicate (including the WHERE keyword):
-    "WHERE column1 = ? AND column2 IS ?". The tuple of values contains the
-    values for each of the corresponding parameters.
-    """
-    wheres = []
-    param_values = []
-
-    # construct predicate for config filter
-    if config is not None:
-        wheres.append("thread_id = ?")
-        param_values.append(config["configurable"]["thread_id"])
-
-    # construct predicate for metadata filter
-    if filter:
-        metadata_predicates, metadata_values = _metadata_predicate(filter)
-        wheres.extend(metadata_predicates)
-        param_values.extend(metadata_values)
-
-    # construct predicate for `before`
-    if before is not None:
-        wheres.append("thread_ts < ?")
-        param_values.append(before["configurable"]["thread_ts"])
-
-    return ("WHERE " + " AND ".join(wheres) if wheres else "", param_values)

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py

@@ -23,8 +23,10 @@ from langgraph.checkpoint.base import (
     CheckpointMetadata,
     CheckpointTuple,
     SerializerProtocol,
+    get_checkpoint_id,
 )
-from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat, search_where
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.sqlite.utils import search_where
 
 T = TypeVar("T", bound=callable)
 
@@ -84,9 +86,8 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
 
         ```pycon
         >>> import asyncio
-        >>> import aiosqlite
         >>>
-        >>> from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
+        >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
         >>> from langgraph.graph import StateGraph
         >>>
         >>> builder = StateGraph(int)
@@ -104,7 +105,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         ```pycon
         >>> import asyncio
         >>> import aiosqlite
-        >>> from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
+        >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
         >>>
         >>> async def main():
         >>>     async with aiosqlite.connect("checkpoints.db") as conn:
@@ -114,13 +115,10 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         ...         saved_config = await saver.aput(config, checkpoint)
         ...         print(saved_config)
         >>> asyncio.run(main())
-        {"configurable": {"thread_id": "1", "thread_ts": "2023-05-03T10:00:00Z"}}
+        {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
         ```
     """
 
-    serde = JsonPlusSerializerCompat()
-
-    conn: aiosqlite.Connection
     lock: asyncio.Lock
     is_setup: bool
 
@@ -131,6 +129,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         serde: Optional[SerializerProtocol] = None,
     ):
         super().__init__(serde=serde)
+        self.jsonplus_serde = JsonPlusSerializer()
         self.conn = conn
         self.lock = asyncio.Lock()
         self.is_setup = False
@@ -145,6 +144,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         Returns:
             AsyncSqliteSaver: A new AsyncSqliteSaver instance.
         """
+
         return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string))
 
     async def __aenter__(self) -> Self:
@@ -210,20 +210,24 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
                 PRAGMA journal_mode=WAL;
                 CREATE TABLE IF NOT EXISTS checkpoints (
                     thread_id TEXT NOT NULL,
-                    thread_ts TEXT NOT NULL,
-                    parent_ts TEXT,
+                    checkpoint_ns TEXT NOT NULL DEFAULT '',
+                    checkpoint_id TEXT NOT NULL,
+                    parent_checkpoint_id TEXT,
+                    type TEXT,
                     checkpoint BLOB,
                     metadata BLOB,
-                    PRIMARY KEY (thread_id, thread_ts)
+                    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
                 );
                 CREATE TABLE IF NOT EXISTS writes (
                     thread_id TEXT NOT NULL,
-                    thread_ts TEXT NOT NULL,
+                    checkpoint_ns TEXT NOT NULL DEFAULT '',
+                    checkpoint_id TEXT NOT NULL,
                     task_id TEXT NOT NULL,
                     idx INTEGER NOT NULL,
                     channel TEXT NOT NULL,
+                    type TEXT,
                     value BLOB,
-                    PRIMARY KEY (thread_id, thread_ts, task_id, idx)
+                    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
                 );
                 """
             ):
@@ -235,7 +239,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         """Get a checkpoint tuple from the database asynchronously.
 
         This method retrieves a checkpoint tuple from the SQLite database based on the
-        provided config. If the config contains a "thread_ts" key, the checkpoint with
+        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
         the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
         for the given thread ID is retrieved.
 
@@ -246,56 +250,69 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
             Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
         """
         await self.setup()
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
         async with self.conn.cursor() as cur:
             # find the latest checkpoint for the thread_id
-            if config["configurable"].get("thread_ts"):
+            if checkpoint_id := get_checkpoint_id(config):
                 await cur.execute(
-                    "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND thread_ts = ?",
+                    "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?",
                     (
                         str(config["configurable"]["thread_id"]),
-                        str(config["configurable"]["thread_ts"]),
+                        checkpoint_ns,
+                        checkpoint_id,
                     ),
                 )
             else:
                 await cur.execute(
-                    "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? ORDER BY thread_ts DESC LIMIT 1",
-                    (str(config["configurable"]["thread_id"]),),
+                    "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1",
+                    (str(config["configurable"]["thread_id"]), checkpoint_ns),
                 )
             # if a checkpoint is found, return it
             if value := await cur.fetchone():
-                if not config["configurable"].get("thread_ts"):
+                (
+                    thread_id,
+                    checkpoint_id,
+                    parent_checkpoint_id,
+                    type,
+                    checkpoint,
+                    metadata,
+                ) = value
+                if not get_checkpoint_id(config):
                     config = {
                         "configurable": {
-                            "thread_id": value[0],
-                            "thread_ts": value[1],
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
                         }
                     }
                 # find any pending writes
                 await cur.execute(
-                    "SELECT task_id, channel, value FROM writes WHERE thread_id = ? AND thread_ts = ?",
+                    "SELECT task_id, channel, type, value FROM writes WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?",
                     (
                         str(config["configurable"]["thread_id"]),
-                        str(config["configurable"]["thread_ts"]),
+                        checkpoint_ns,
+                        str(config["configurable"]["checkpoint_id"]),
                     ),
                 )
                 # deserialize the checkpoint and metadata
                 return CheckpointTuple(
                     config,
-                    self.serde.loads(value[3]),
-                    self.serde.loads(value[4]) if value[4] is not None else {},
+                    self.serde.loads_typed((type, checkpoint)),
+                    self.jsonplus_serde.loads(metadata) if metadata is not None else {},
                     (
                         {
                             "configurable": {
-                                "thread_id": value[0],
-                                "thread_ts": value[2],
+                                "thread_id": thread_id,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": parent_checkpoint_id,
                             }
                         }
-                        if value[2]
+                        if parent_checkpoint_id
                         else None
                     ),
                     [
-                        (task_id, channel, self.serde.loads(value))
-                        async for task_id, channel, value in cur
+                        (task_id, channel, self.serde.loads_typed((type, value)))
+                        async for task_id, channel, type, value in cur
                     ],
                 )
 
@@ -323,26 +340,41 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         """
         await self.setup()
         where, param_values = search_where(config, filter, before)
-        query = f"""SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata
+        query = f"""SELECT thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata
         FROM checkpoints
         {where}
-        ORDER BY thread_ts DESC"""
+        ORDER BY checkpoint_id DESC"""
         if limit:
             query += f" LIMIT {limit}"
         async with self.conn.execute(query, param_values) as cursor:
-            async for thread_id, thread_ts, parent_ts, value, metadata in cursor:
+            async for (
+                thread_id,
+                checkpoint_ns,
+                checkpoint_id,
+                parent_checkpoint_id,
+                type,
+                checkpoint,
+                metadata,
+            ) in cursor:
                 yield CheckpointTuple(
-                    {"configurable": {"thread_id": thread_id, "thread_ts": thread_ts}},
-                    self.serde.loads(value),
-                    self.serde.loads(metadata) if metadata is not None else {},
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
+                        }
+                    },
+                    self.serde.loads_typed((type, checkpoint)),
+                    self.jsonplus_serde.loads(metadata) if metadata is not None else {},
                     (
                         {
                             "configurable": {
                                 "thread_id": thread_id,
-                                "thread_ts": parent_ts,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": parent_checkpoint_id,
                             }
                         }
-                        if parent_ts
+                        if parent_checkpoint_id
                         else None
                     ),
                 )
@@ -367,21 +399,28 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
             RunnableConfig: The updated config containing the saved checkpoint's timestamp.
         """
         await self.setup()
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
+        serialized_metadata = self.jsonplus_serde.dumps(metadata)
         async with self.conn.execute(
-            "INSERT OR REPLACE INTO checkpoints (thread_id, thread_ts, parent_ts, checkpoint, metadata) VALUES (?, ?, ?, ?, ?)",
+            "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)",
             (
                 str(config["configurable"]["thread_id"]),
+                checkpoint_ns,
                 checkpoint["id"],
-                config["configurable"].get("thread_ts"),
-                self.serde.dumps(checkpoint),
-                self.serde.dumps(metadata),
+                config["configurable"].get("checkpoint_id"),
+                type_,
+                serialized_checkpoint,
+                serialized_metadata,
             ),
         ):
             await self.conn.commit()
         return {
             "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "thread_ts": checkpoint["id"],
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
             }
         }
 
@@ -402,15 +441,16 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         """
         await self.setup()
         async with self.conn.executemany(
-            "INSERT OR REPLACE INTO writes (thread_id, thread_ts, task_id, idx, channel, value) VALUES (?, ?, ?, ?, ?, ?)",
+            "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
             [
                 (
                     str(config["configurable"]["thread_id"]),
-                    str(config["configurable"]["thread_ts"]),
+                    str(config["configurable"]["checkpoint_ns"]),
+                    str(config["configurable"]["checkpoint_id"]),
                     task_id,
                     idx,
                     channel,
-                    self.serde.dumps(value),
+                    *self.serde.dumps_typed(value),
                 )
                 for idx, (channel, value) in enumerate(writes)
             ],

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py

@@ -0,0 +1,88 @@
+import json
+from typing import Any, Dict, Optional, Sequence, Tuple
+
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import get_checkpoint_id
+
+
+def _metadata_predicate(
+    metadata_filter: Dict[str, Any],
+) -> Tuple[Sequence[str], Sequence[Any]]:
+    """Return WHERE clause predicates for (a)search() given metadata filter.
+
+    This method returns a tuple of a string and a tuple of values. The string
+    is the parametered WHERE clause predicate (excluding the WHERE keyword):
+    "column1 = ? AND column2 IS ?". The tuple of values contains the values
+    for each of the corresponding parameters.
+    """
+
+    def _where_value(query_value: Any) -> Tuple[str, Any]:
+        """Return tuple of operator and value for WHERE clause predicate."""
+        if query_value is None:
+            return ("IS ?", None)
+        elif (
+            isinstance(query_value, str)
+            or isinstance(query_value, int)
+            or isinstance(query_value, float)
+        ):
+            return ("= ?", query_value)
+        elif isinstance(query_value, bool):
+            return ("= ?", 1 if query_value else 0)
+        elif isinstance(query_value, dict) or isinstance(query_value, list):
+            # query value for JSON object cannot have trailing space after separators (, :)
+            # SQLite json_extract() returns JSON string without whitespace
+            return ("= ?", json.dumps(query_value, separators=(",", ":")))
+        else:
+            return ("= ?", str(query_value))
+
+    predicates = []
+    param_values = []
+
+    # process metadata query
+    for query_key, query_value in metadata_filter.items():
+        operator, param_value = _where_value(query_value)
+        predicates.append(
+            f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}"
+        )
+        param_values.append(param_value)
+
+    return (predicates, param_values)
+
+
+def search_where(
+    config: Optional[RunnableConfig],
+    filter: Optional[Dict[str, Any]],
+    before: Optional[RunnableConfig] = None,
+) -> Tuple[str, Sequence[Any]]:
+    """Return WHERE clause predicates for (a)search() given metadata filter
+    and `before` config.
+
+    This method returns a tuple of a string and a tuple of values. The string
+    is the parametered WHERE clause predicate (including the WHERE keyword):
+    "WHERE column1 = ? AND column2 IS ?". The tuple of values contains the
+    values for each of the corresponding parameters.
+    """
+    wheres = []
+    param_values = []
+
+    # construct predicate for config filter
+    if config is not None:
+        wheres.append("thread_id = ?")
+        param_values.append(config["configurable"]["thread_id"])
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        wheres.append("checkpoint_ns = ?")
+        param_values.append(checkpoint_ns)
+
+    # construct predicate for metadata filter
+    if filter:
+        metadata_predicates, metadata_values = _metadata_predicate(filter)
+        wheres.extend(metadata_predicates)
+        param_values.extend(metadata_values)
+
+    # construct predicate for `before`
+    if before is not None:
+        wheres.append("checkpoint_id < ?")
+        param_values.append(get_checkpoint_id(before))
+
+    return ("WHERE " + " AND ".join(wheres) if wheres else "", param_values)

libs/checkpoint-sqlite/poetry.lock

@@ -0,0 +1,835 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "aiosqlite"
+version = "0.20.0"
+description = "asyncio bridge to the standard sqlite3 module"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
+    {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
+]
+
+[package.dependencies]
+typing_extensions = ">=4.0"
+
+[package.extras]
+dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "certifi"
+version = "2024.7.4"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
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libs/checkpoint-sqlite/pyproject.toml

@@ -0,0 +1,55 @@
+[tool.poetry]
+name = "langgraph-checkpoint-sqlite"
+version = "1.0.0"
+description = "Library with a SQLite implementation of LangGraph checkpoint saver."
+authors = []
+license = "MIT"
+readme = "README.md"
+repository = "https://www.github.com/langchain-ai/langgraph"
+packages = [{ include = "langgraph" }]
+
+[tool.poetry.dependencies]
+python = "^3.9.0"
+langgraph-checkpoint = "^1.0.0"
+aiosqlite = "^0.20.0"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.1.4"
+codespell = "^2.2.0"
+pytest = "^7.2.1"
+pytest-asyncio = "^0.21.1"
+pytest-mock = "^3.11.1"
+pytest-watcher = "^0.4.1"
+mypy = "^1.10.0"
+langgraph-checkpoint = {path = "../checkpoint", develop = true}
+
+[tool.pytest.ini_options]
+# --strict-markers will raise errors on unknown marks.
+# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
+#
+# https://docs.pytest.org/en/7.1.x/reference/reference.html
+# --strict-config       any warnings encountered while parsing the `pytest`
+#                       section of the configuration file raise errors.
+addopts = "--strict-markers --strict-config --durations=5 -vv"
+asyncio_mode = "auto"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+lint.select = [
+  "E",  # pycodestyle
+  "F",  # Pyflakes
+  "UP", # pyupgrade
+  "B",  # flake8-bugbear
+  "I",  # isort
+]
+lint.ignore = ["E501", "B008", "UP007", "UP006"]
+
+[tool.pytest-watcher]
+now = true
+delay = 0.1
+runner_args = ["--ff", "-v", "--tb", "short"]
+patterns = ["*.py"]

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -0,0 +1,112 @@
+import pytest
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+
+
+class TestAsyncSqliteSaver:
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        # objects for test setup
+        self.config_1: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-1",
+                # for backwards compatibility testing
+                "thread_ts": "1",
+                "checkpoint_ns": "",
+            }
+        }
+        self.config_2: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_id": "2",
+                "checkpoint_ns": "",
+            }
+        }
+        self.config_3: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_id": "2-inner",
+                "checkpoint_ns": "inner",
+            }
+        }
+
+        self.chkpnt_1: Checkpoint = empty_checkpoint()
+        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
+        self.chkpnt_3: Checkpoint = empty_checkpoint()
+
+        self.metadata_1: CheckpointMetadata = {
+            "source": "input",
+            "step": 2,
+            "writes": {},
+            "score": 1,
+        }
+        self.metadata_2: CheckpointMetadata = {
+            "source": "loop",
+            "step": 1,
+            "writes": {"foo": "bar"},
+            "score": None,
+        }
+        self.metadata_3: CheckpointMetadata = {}
+
+    async def test_asearch(self):
+        async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+            await saver.aput(self.config_1, self.chkpnt_1, self.metadata_1)
+            await saver.aput(self.config_2, self.chkpnt_2, self.metadata_2)
+            await saver.aput(self.config_3, self.chkpnt_3, self.metadata_3)
+
+            # call method / assertions
+            query_1: CheckpointMetadata = {"source": "input"}  # search by 1 key
+            query_2: CheckpointMetadata = {
+                "step": 1,
+                "writes": {"foo": "bar"},
+            }  # search by multiple keys
+            query_3: CheckpointMetadata = {}  # search by no keys, return all checkpoints
+            query_4: CheckpointMetadata = {"source": "update", "step": 1}  # no match
+
+            search_results_1 = [c async for c in saver.alist(None, filter=query_1)]
+            assert len(search_results_1) == 1
+            assert search_results_1[0].metadata == self.metadata_1
+
+            search_results_2 = [c async for c in saver.alist(None, filter=query_2)]
+            assert len(search_results_2) == 1
+            assert search_results_2[0].metadata == self.metadata_2
+
+            search_results_3 = [c async for c in saver.alist(None, filter=query_3)]
+            assert len(search_results_3) == 3
+
+            search_results_4 = [c async for c in saver.alist(None, filter=query_4)]
+            assert len(search_results_4) == 0
+
+            # search by config (defaults to root graph checkpoints)
+            search_results_5 = [
+                c
+                async for c in saver.alist({"configurable": {"thread_id": "thread-2"}})
+            ]
+            assert len(search_results_5) == 1
+            assert search_results_5[0].config["configurable"]["checkpoint_ns"] == ""
+
+            # search by config and checkpoint_ns
+            search_results_6 = [
+                c
+                async for c in saver.alist(
+                    {
+                        "configurable": {
+                            "thread_id": "thread-2",
+                            "checkpoint_ns": "inner",
+                        }
+                    }
+                )
+            ]
+            assert len(search_results_6) == 1
+            assert (
+                search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner"
+            )
+
+            # TODO: test before and limit params

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -0,0 +1,166 @@
+import pytest
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.checkpoint.sqlite.utils import _metadata_predicate, search_where
+
+
+class TestSqliteSaver:
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        # objects for test setup
+        self.config_1: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-1",
+                # for backwards compatibility testing
+                "thread_ts": "1",
+                "checkpoint_ns": "",
+            }
+        }
+        self.config_2: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_id": "2",
+                "checkpoint_ns": "",
+            }
+        }
+        self.config_3: RunnableConfig = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_id": "2-inner",
+                "checkpoint_ns": "inner",
+            }
+        }
+
+        self.chkpnt_1: Checkpoint = empty_checkpoint()
+        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
+        self.chkpnt_3: Checkpoint = empty_checkpoint()
+
+        self.metadata_1: CheckpointMetadata = {
+            "source": "input",
+            "step": 2,
+            "writes": {},
+            "score": 1,
+        }
+        self.metadata_2: CheckpointMetadata = {
+            "source": "loop",
+            "step": 1,
+            "writes": {"foo": "bar"},
+            "score": None,
+        }
+        self.metadata_3: CheckpointMetadata = {}
+
+    def test_search(self):
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # set up test
+            # save checkpoints
+            saver.put(self.config_1, self.chkpnt_1, self.metadata_1)
+            saver.put(self.config_2, self.chkpnt_2, self.metadata_2)
+            saver.put(self.config_3, self.chkpnt_3, self.metadata_3)
+
+            # call method / assertions
+            query_1: CheckpointMetadata = {"source": "input"}  # search by 1 key
+            query_2: CheckpointMetadata = {
+                "step": 1,
+                "writes": {"foo": "bar"},
+            }  # search by multiple keys
+            query_3: CheckpointMetadata = {}  # search by no keys, return all checkpoints
+            query_4: CheckpointMetadata = {"source": "update", "step": 1}  # no match
+
+            search_results_1 = list(saver.list(None, filter=query_1))
+            assert len(search_results_1) == 1
+            assert search_results_1[0].metadata == self.metadata_1
+
+            search_results_2 = list(saver.list(None, filter=query_2))
+            assert len(search_results_2) == 1
+            assert search_results_2[0].metadata == self.metadata_2
+
+            search_results_3 = list(saver.list(None, filter=query_3))
+            assert len(search_results_3) == 3
+
+            search_results_4 = list(saver.list(None, filter=query_4))
+            assert len(search_results_4) == 0
+
+            # search by config (defaults to root graph checkpoints)
+            search_results_5 = list(
+                saver.list({"configurable": {"thread_id": "thread-2"}})
+            )
+            assert len(search_results_5) == 1
+            assert search_results_5[0].config["configurable"]["checkpoint_ns"] == ""
+
+            # search by config and checkpoint_ns
+            search_results_6 = list(
+                saver.list(
+                    {
+                        "configurable": {
+                            "thread_id": "thread-2",
+                            "checkpoint_ns": "inner",
+                        }
+                    }
+                )
+            )
+            assert len(search_results_6) == 1
+            assert (
+                search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner"
+            )
+
+            # TODO: test before and limit params
+
+    def test_search_where(self):
+        # call method / assertions
+        expected_predicate_1 = "WHERE json_extract(CAST(metadata AS TEXT), '$.source') = ? AND json_extract(CAST(metadata AS TEXT), '$.step') = ? AND json_extract(CAST(metadata AS TEXT), '$.writes') = ? AND json_extract(CAST(metadata AS TEXT), '$.score') = ? AND checkpoint_id < ?"
+        expected_param_values_1 = ["input", 2, "{}", 1, "1"]
+        assert search_where(None, self.metadata_1, self.config_1) == (
+            expected_predicate_1,
+            expected_param_values_1,
+        )
+
+    def test_metadata_predicate(self):
+        # call method / assertions
+        expected_predicate_1 = [
+            "json_extract(CAST(metadata AS TEXT), '$.source') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.step') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.writes') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.score') = ?",
+        ]
+        expected_predicate_2 = [
+            "json_extract(CAST(metadata AS TEXT), '$.source') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.step') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.writes') = ?",
+            "json_extract(CAST(metadata AS TEXT), '$.score') IS ?",
+        ]
+        expected_predicate_3 = []
+
+        expected_param_values_1 = ["input", 2, "{}", 1]
+        expected_param_values_2 = ["loop", 1, '{"foo":"bar"}', None]
+        expected_param_values_3 = []
+
+        assert _metadata_predicate(self.metadata_1) == (
+            expected_predicate_1,
+            expected_param_values_1,
+        )
+        assert _metadata_predicate(self.metadata_2) == (
+            expected_predicate_2,
+            expected_param_values_2,
+        )
+        assert _metadata_predicate(self.metadata_3) == (
+            expected_predicate_3,
+            expected_param_values_3,
+        )
+
+    async def test_informative_async_errors(self):
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # call method / assertions
+            with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
+                await saver.aget(self.config_1)
+            with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
+                await saver.aget_tuple(self.config_1)
+            with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
+                async for _ in saver.alist(self.config_1):
+                    pass

libs/checkpoint/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/checkpoint/Makefile

@@ -0,0 +1,34 @@
+.PHONY: test test_watch lint format
+
+######################
+# TESTING AND COVERAGE
+######################
+
+test:
+	poetry run pytest tests
+
+test_watch:
+	poetry run ptw .
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff .
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff --select I --fix $(PYTHON_FILES)