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

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/_scripts/copy_notebooks.py

@@ -101,6 +101,7 @@ _HIDE = set(
         "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:
 
@@ -21,13 +24,11 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 
 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>=0.1.7
-langchain-core>=0.2.7
-orjson>=3.10.1
-langsmith>=0.1.50
-httpx>=0.27.0
-langchain-core>=0.2.8
+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
@@ -88,7 +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:
 ```
@@ -133,10 +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

@@ -22,13 +22,11 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 
 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>=0.1.7
-langchain-core>=0.2.7
-orjson>=3.10.1
-langsmith>=0.1.50
-httpx>=0.27.0
-langchain-core>=0.2.8
+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
@@ -166,10 +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

@@ -49,6 +49,7 @@ The main documented way to specify the schema of a graph is by using `TypedDict`
 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.
@@ -78,22 +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

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

@@ -189,10 +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/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/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
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
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+    {file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.9.0"
+content-hash = "820b33a1587d31b4b79454417b4d956367867accd253129f562579e1afc88f62"

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)

libs/checkpoint/README.md

@@ -0,0 +1,85 @@
+# LangGraph Checkpoint
+
+This library defines the base interface for LangGraph checkpointers. Checkpointers provide persistence layer for LangGraph. They allow you to interact with and manage the graph's state. When you use a graph with a checkpointer, the checkpointer saves a _checkpoint_ of the graph state at every superstep, enabling several powerful capabilities like human-in-the-loop, "memory" between interactions and more.
+
+## Key concepts
+
+### Checkpoint
+
+Checkpoint is a snapshot of the graph state at a given point in time. Checkpoint tuple refers to an object containing checkpoint and the associated config, metadata and pending writes.
+
+### Thread
+
+Threads enable the checkpointing of multiple different runs, making them essential for multi-tenant chat applications and other scenarios where maintaining separate states is necessary. A thread is a unique ID assigned to a series of checkpoints saved by a checkpointer. When using a checkpointer, you must specify a `thread_id` and optionally `checkpoint_id` when running the graph.
+
+- `thread_id` is simply the ID of a thread. This is always required
+- `checkpoint_id` can optionally be passed. This identifier refers to a specific checkpoint within a thread. This can be used to kick of a run of a graph from some point halfway through a thread.
+
+You must pass these when invoking the graph as part of the configurable part of the config, e.g.
+
+```python
+{"configurable": {"thread_id": "1"}}  # valid config
+{"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
+```
+
+### Serde
+
+`langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
+
+### Pending writes
+
+When a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.
+
+## Interface
+
+Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSaver` interface and must implement the following methods:
+
+- `.put` - Store a checkpoint with its configuration and metadata.
+- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
+- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `thread_ts`).
+- `.list` - List checkpoints that match a given configuration and filter criteria.
+
+If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
+
+## Usage
+
+```python
+from langgraph.checkpoint.memory import MemorySaver
+
+checkpointer = MemorySaver()
+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))
+```

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -7,6 +7,7 @@ from typing import (
     Iterator,
     List,
     Literal,
+    Mapping,
     NamedTuple,
     Optional,
     Tuple,
@@ -17,11 +18,13 @@ from typing import (
 
 from langchain_core.runnables import ConfigurableFieldSpec, RunnableConfig
 
-from langgraph.channels.base import BaseChannel
-from langgraph.checkpoint.id import uuid6
-from langgraph.constants import Send
-from langgraph.serde.base import SerializerProtocol
-from langgraph.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.base.id import uuid6
+from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import (
+    ChannelProtocol,
+    SendProtocol,
+)
 
 V = TypeVar("V", int, float, str)
 PendingWrite = Tuple[str, str, Any]
@@ -50,7 +53,7 @@ class CheckpointMetadata(TypedDict, total=False):
     """
     score: Optional[int]
     """The score of the checkpoint.
-    
+
     The score can be used to mark a checkpoint as "good".
     """
 
@@ -65,29 +68,29 @@ class Checkpoint(TypedDict):
     v: int
     """The version of the checkpoint format. Currently 1."""
     id: str
-    """The ID of the checkpoint. This is both unique and monotonically 
+    """The ID of the checkpoint. This is both unique and monotonically
     increasing, so can be used for sorting checkpoints from first to last."""
     ts: str
     """The timestamp of the checkpoint in ISO 8601 format."""
     channel_values: dict[str, Any]
     """The values of the channels at the time of the checkpoint.
-    
+
     Mapping from channel name to channel snapshot value.
     """
     channel_versions: dict[str, Union[str, int, float]]
     """The versions of the channels at the time of the checkpoint.
-    
+
     The keys are channel names and the values are the logical time step
     at which the channel was last updated.
     """
     versions_seen: dict[str, dict[str, Union[str, int, float]]]
     """Map from node ID to map from channel name to version seen.
-    
+
     This keeps track of the versions of the channels that each node has seen.
-    
+
     Used to determine which nodes to execute next.
     """
-    pending_sends: List[Send]
+    pending_sends: List[SendProtocol]
     """List of packets sent to nodes but not yet processed.
     Cleared by the next checkpoint."""
     current_tasks: Dict[str, TaskInfo]
@@ -120,6 +123,36 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
     )
 
 
+def create_checkpoint(
+    checkpoint: Checkpoint,
+    channels: Optional[Mapping[str, ChannelProtocol]],
+    step: int,
+    *,
+    id: Optional[str] = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values: dict[str, Any] = {}
+        for k, v in channels.items():
+            try:
+                values[k] = v.checkpoint()
+            except EmptyChannelError:
+                pass
+    return Checkpoint(
+        v=1,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+        pending_sends=checkpoint.get("pending_sends", []),
+        current_tasks={},
+    )
+
+
 class CheckpointTuple(NamedTuple):
     """A tuple containing a checkpoint and its associated data."""
 
@@ -139,10 +172,19 @@ CheckpointThreadId = ConfigurableFieldSpec(
     is_shared=True,
 )
 
-CheckpointThreadTs = ConfigurableFieldSpec(
-    id="thread_ts",
+CheckpointNS = ConfigurableFieldSpec(
+    id="checkpoint_ns",
+    annotation=str,
+    name="Checkpoint NS",
+    description='Checkpoint namespace. Denotes the path to the subgraph node the checkpoint originates from, separated by `|` character, e.g. `"child|grandchild"`. Defaults to "" (root graph).',
+    default=None,
+    is_shared=True,
+)
+
+CheckpointId = ConfigurableFieldSpec(
+    id="checkpoint_id",
     annotation=Optional[str],
-    name="Thread Timestamp",
+    name="Checkpoint ID",
     description="Pass to fetch a past checkpoint. If None, fetches the latest checkpoint.",
     default=None,
     is_shared=True,
@@ -170,7 +212,7 @@ class BaseCheckpointSaver(ABC):
         *,
         serde: Optional[SerializerProtocol] = None,
     ) -> None:
-        self.serde = serde or self.serde
+        self.serde = maybe_add_typed_methods(serde or self.serde)
 
     @property
     def config_specs(self) -> list[ConfigurableFieldSpec]:
@@ -179,7 +221,7 @@ class BaseCheckpointSaver(ABC):
         Returns:
             list[ConfigurableFieldSpec]: List of configuration field specs.
         """
-        return [CheckpointThreadId, CheckpointThreadTs]
+        return [CheckpointThreadId, CheckpointNS, CheckpointId]
 
     def get(self, config: RunnableConfig) -> Optional[Checkpoint]:
         """Fetch a checkpoint using the given configuration.
@@ -268,9 +310,7 @@ class BaseCheckpointSaver(ABC):
         Raises:
             NotImplementedError: Implement this method in your custom checkpoint saver.
         """
-        raise NotImplementedError(
-            "This method was added in langgraph 0.1.7. Please update your checkpoint saver to implement it."
-        )
+        raise NotImplementedError
 
     async def aget(self, config: RunnableConfig) -> Optional[Checkpoint]:
         """Asynchronously fetch a checkpoint using the given configuration.
@@ -360,11 +400,9 @@ class BaseCheckpointSaver(ABC):
         Raises:
             NotImplementedError: Implement this method in your custom checkpoint saver.
         """
-        raise NotImplementedError(
-            "This method was added in langgraph 0.1.7. Please update your checkpoint saver to implement it."
-        )
+        raise NotImplementedError
 
-    def get_next_version(self, current: Optional[V], channel: BaseChannel) -> V:
+    def get_next_version(self, current: Optional[V], channel: ChannelProtocol) -> V:
         """Generate the next version ID for a channel.
 
         Default is to use integer versions, incrementing by 1. If you override, you can use str/int/float versions,
@@ -378,3 +416,17 @@ class BaseCheckpointSaver(ABC):
             V: The next version identifier, which must be increasing.
         """
         return current + 1 if current is not None else 1
+
+
+class EmptyChannelError(Exception):
+    """Raised when attempting to get the value of a channel that hasn't been updated
+    for the first time yet."""
+
+    pass
+
+
+def get_checkpoint_id(config: RunnableConfig) -> Optional[str]:
+    """Get checkpoint ID in a backwards-compatible manner (fallback on thread_ts)."""
+    return config["configurable"].get(
+        "checkpoint_id", config["configurable"].get("thread_ts")
+    )

libs/checkpoint/langgraph/checkpoint/memory.py

@@ -11,6 +11,7 @@ from langgraph.checkpoint.base import (
     CheckpointMetadata,
     CheckpointTuple,
     SerializerProtocol,
+    get_checkpoint_id,
 )
 
 
@@ -44,7 +45,8 @@ class MemorySaver(BaseCheckpointSaver):
             asyncio.run(coro)  # Output: 2
     """
 
-    storage: defaultdict[str, dict[str, tuple[bytes, bytes, Optional[str]]]]
+    # thread ID ->  checkpoint NS -> checkpoint ID -> checkpoint mapping
+    storage: defaultdict[str, dict[str, dict[str, tuple[bytes, bytes, Optional[str]]]]]
 
     def __init__(
         self,
@@ -52,14 +54,14 @@ class MemorySaver(BaseCheckpointSaver):
         serde: Optional[SerializerProtocol] = None,
     ) -> None:
         super().__init__(serde=serde)
-        self.storage = defaultdict(dict)
+        self.storage = defaultdict(lambda: defaultdict(dict))
         self.writes = defaultdict(list)
 
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Get a checkpoint tuple from the in-memory storage.
 
         This method retrieves a checkpoint tuple from the in-memory storage 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.
 
@@ -70,45 +72,54 @@ class MemorySaver(BaseCheckpointSaver):
             Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
         """
         thread_id = config["configurable"]["thread_id"]
-        if ts := config["configurable"].get("thread_ts"):
-            if saved := self.storage[thread_id].get(ts):
-                checkpoint, metadata, parent_ts = saved
-                writes = self.writes[(thread_id, ts)]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        if checkpoint_id := get_checkpoint_id(config):
+            if saved := self.storage[thread_id][checkpoint_ns].get(checkpoint_id):
+                checkpoint, metadata, parent_checkpoint_id = saved
+                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)]
                 return CheckpointTuple(
                     config=config,
-                    checkpoint=self.serde.loads(checkpoint),
-                    metadata=self.serde.loads(metadata),
+                    checkpoint=self.serde.loads_typed(checkpoint),
+                    metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
-                        (id, c, self.serde.loads(v)) for id, c, v in writes
+                        (id, c, self.serde.loads_typed(v)) for id, c, v in writes
                     ],
                     parent_config={
                         "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,
                 )
         else:
-            if checkpoints := self.storage[thread_id]:
-                ts = max(checkpoints.keys())
-                checkpoint, metadata, parent_ts = checkpoints[ts]
-                writes = self.writes[(thread_id, ts)]
+            if checkpoints := self.storage[thread_id][checkpoint_ns]:
+                checkpoint_id = max(checkpoints.keys())
+                checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
+                writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)]
                 return CheckpointTuple(
-                    config={"configurable": {"thread_id": thread_id, "thread_ts": ts}},
-                    checkpoint=self.serde.loads(checkpoint),
-                    metadata=self.serde.loads(metadata),
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
+                        }
+                    },
+                    checkpoint=self.serde.loads_typed(checkpoint),
+                    metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
-                        (id, c, self.serde.loads(v)) for id, c, v in writes
+                        (id, c, self.serde.loads_typed(v)) for id, c, v in writes
                     ],
                     parent_config={
                         "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,
                 )
 
@@ -135,16 +146,25 @@ class MemorySaver(BaseCheckpointSaver):
             Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
         """
         thread_ids = (config["configurable"]["thread_id"],) if config else self.storage
+        checkpoint_ns = (
+            config["configurable"].get("checkpoint_ns", "") if config else ""
+        )
         for thread_id in thread_ids:
-            for ts, (checkpoint, metadata_b, parent_ts) in sorted(
-                self.storage[thread_id].items(), key=lambda x: x[0], reverse=True
+            for checkpoint_id, (checkpoint, metadata_b, parent_checkpoint_id) in sorted(
+                self.storage[thread_id][checkpoint_ns].items(),
+                key=lambda x: x[0],
+                reverse=True,
             ):
-                # filter by thread_ts
-                if before and ts >= before["configurable"]["thread_ts"]:
+                # filter by checkpoint ID
+                if (
+                    before
+                    and (before_checkpoint_id := get_checkpoint_id(before))
+                    and checkpoint_id >= before_checkpoint_id
+                ):
                     continue
 
                 # filter by metadata
-                metadata = self.serde.loads(metadata_b)
+                metadata = self.serde.loads_typed(metadata_b)
                 if filter and not all(
                     query_value == metadata[query_key]
                     for query_key, query_value in filter.items()
@@ -158,16 +178,23 @@ class MemorySaver(BaseCheckpointSaver):
                     limit -= 1
 
                 yield CheckpointTuple(
-                    config={"configurable": {"thread_id": thread_id, "thread_ts": ts}},
-                    checkpoint=self.serde.loads(checkpoint),
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint_id,
+                        }
+                    },
+                    checkpoint=self.serde.loads_typed(checkpoint),
                     metadata=metadata,
                     parent_config={
                         "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,
                 )
 
@@ -190,19 +217,22 @@ class MemorySaver(BaseCheckpointSaver):
         Returns:
             RunnableConfig: The updated config containing the saved checkpoint's timestamp.
         """
-        self.storage[config["configurable"]["thread_id"]].update(
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        self.storage[thread_id][checkpoint_ns].update(
             {
                 checkpoint["id"]: (
-                    self.serde.dumps(checkpoint),
-                    self.serde.dumps(metadata),
-                    config["configurable"].get("thread_ts"),  # parent
+                    self.serde.dumps_typed(checkpoint),
+                    self.serde.dumps_typed(metadata),
+                    config["configurable"].get("checkpoint_id"),  # parent
                 )
             }
         )
         return {
             "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "thread_ts": checkpoint["id"],
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
             }
         }
 
@@ -226,9 +256,11 @@ class MemorySaver(BaseCheckpointSaver):
             RunnableConfig: The updated config containing the saved writes' timestamp.
         """
         thread_id = config["configurable"]["thread_id"]
-        ts = config["configurable"]["thread_ts"]
-        self.writes[(thread_id, ts)].extend(
-            [(task_id, c, self.serde.dumps(v)) for c, v in writes]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        checkpoint_id = config["configurable"]["checkpoint_id"]
+        key = (thread_id, checkpoint_ns, checkpoint_id)
+        self.writes[key].extend(
+            [(task_id, c, self.serde.dumps_typed(v)) for c, v in writes]
         )
 
     async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:

libs/checkpoint/langgraph/checkpoint/serde/base.py

@@ -0,0 +1,45 @@
+from typing import Any, Protocol
+
+
+class SerializerProtocol(Protocol):
+    """Protocol for serialization and deserialization of objects.
+
+    - `dumps`: Serialize an object to bytes.
+    - `dumps_typed`: Serialize an object to a tuple (type, bytes).
+    - `loads`: Deserialize an object from bytes.
+    - `loads_typed`: Deserialize an object from a tuple (type, bytes).
+
+    Valid implementations include the `pickle`, `json` and `orjson` modules.
+    """
+
+    def dumps(self, obj: Any) -> bytes:
+        ...
+
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        ...
+
+    def loads(self, data: bytes) -> Any:
+        ...
+
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        ...
+
+
+class SerializerCompat(SerializerProtocol):
+    def __init__(self, serde: SerializerProtocol) -> None:
+        self.serde = serde
+
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        return type(obj).__name__, self.serde.dumps(obj)
+
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        return self.serde.loads(data[1])
+
+
+def maybe_add_typed_methods(serde: SerializerProtocol) -> SerializerProtocol:
+    """Wrap serde old serde implementations in a class with loads_typed and dumps_typed for backwards compatibility."""
+
+    if not hasattr(serde, "loads_typed") or not hasattr(serde, "dumps_typed"):
+        return SerializerCompat(serde)
+
+    return serde