langgraph checkpoint: new library for checkpoint interfaces (#1163)

--------- Co-authored-by: Nuno Campos <nuno@langchain.dev>
2026-08-28 10:49:56 +02:00 · 2024-07-31 16:45:52 -04:00
parent 913a2d975b
commit ae74825ea7
54 changed files with 10660 additions and 9415 deletions
@@ -61,7 +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

@@ -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

@@ -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
 }
@@ -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
 }
@@ -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.
@@ -0,0 +1,31 @@
+.PHONY: test lint format
+
+######################
+# TESTING AND COVERAGE
+######################
+
+test:
+	poetry run pytest tests
+
+######################
+# 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_checkpoint
+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)
@@ -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` or `thread_ts` when running the graph.
+
+- `thread_id` is simply the ID of a thread. This is always required
+- `thread_ts` 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", "thread_ts": "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.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))
+```
@@ -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
+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."""

@@ -268,9 +301,7 @@ 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 +391,9 @@ 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 +407,10 @@ 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
@@ -9,8 +9,8 @@ from uuid import UUID
 from langchain_core.load.load import Reviver
 from langchain_core.load.serializable import Serializable

-from langgraph.constants import Send
-from langgraph.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import SendProtocol

 LC_REVIVER = Reviver()

@@ -64,7 +64,7 @@ class JsonPlusSerializer(SerializerProtocol):
            )
        elif isinstance(obj, Enum):
            return self._encode_constructor_args(obj.__class__, args=[obj.value])
-        elif isinstance(obj, Send):
+        elif isinstance(obj, SendProtocol):
            return self._encode_constructor_args(
                obj.__class__, kwargs={"node": obj.node, "arg": obj.arg}
            )
@@ -0,0 +1,66 @@
+from typing import (
+    Any,
+    AsyncGenerator,
+    Generator,
+    Optional,
+    Protocol,
+    Sequence,
+    TypeVar,
+    runtime_checkable,
+)
+
+from langchain_core.runnables import RunnableConfig
+from typing_extensions import Self
+
+Value = TypeVar("Value")
+Update = TypeVar("Update")
+C = TypeVar("C")
+
+
+class ChannelProtocol(Protocol[Value, Update, C]):
+    # Mirrors langgraph.channels.base.BaseChannel
+    @property
+    def ValueType(self) -> Any:
+        ...
+
+    @property
+    def UpdateType(self) -> Any:
+        ...
+
+    def checkpoint(self) -> Optional[C]:
+        ...
+
+    def from_checkpoint(
+        self, checkpoint: Optional[C], config: RunnableConfig
+    ) -> Generator[Self, None, None]:
+        ...
+
+    async def afrom_checkpoint(
+        self, checkpoint: Optional[C], config: RunnableConfig
+    ) -> AsyncGenerator[Self, None]:
+        ...
+
+    def update(self, values: Sequence[Update]) -> bool:
+        ...
+
+    def get(self) -> Value:
+        ...
+
+    def consume(self) -> bool:
+        ...
+
+
+@runtime_checkable
+class SendProtocol(Protocol):
+    # Mirrors langgraph.constants.Send
+    node: str
+    arg: Any
+
+    def __hash__(self) -> int:
+        ...
+
+    def __repr__(self) -> str:
+        ...
+
+    def __eq__(self, value: object) -> bool:
+        ...
@@ -1,5 +1,3 @@
-import json
-import pickle
 import sqlite3
 import threading
 from contextlib import AbstractContextManager, contextmanager
@@ -10,50 +8,21 @@ 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,
 )
-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.types import ChannelProtocol
+from langgraph.checkpoint.sqlite.utils import JsonPlusSerializerCompat, 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"
@@ -476,7 +445,7 @@ 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.
@@ -498,82 +467,3 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
        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)
@@ -24,7 +24,7 @@ from langgraph.checkpoint.base import (
    CheckpointTuple,
    SerializerProtocol,
 )
-from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat, search_where
+from langgraph.checkpoint.sqlite.utils import JsonPlusSerializerCompat, search_where

 T = TypeVar("T", bound=callable)

@@ -84,9 +84,8 @@ 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 +103,7 @@ 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:
@@ -120,7 +119,6 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):

    serde = JsonPlusSerializerCompat()

-    conn: aiosqlite.Connection
    lock: asyncio.Lock
    is_setup: bool

@@ -145,6 +143,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
        Returns:
            AsyncSqliteSaver: A new AsyncSqliteSaver instance.
        """
+
        return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string))

    async def __aenter__(self) -> Self:
@@ -0,0 +1,114 @@
+import json
+import pickle
+from typing import Any, Dict, Optional, Sequence, Tuple
+
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.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)
+
+
+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)
@@ -0,0 +1,879 @@
+# This file is automatically @generated by Poetry 1.8.2 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"
+files = [
+    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
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+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.extras]
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+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+optional = false
+python-versions = ">=3.7"
+files = [
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+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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+version = "2.2.2"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
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+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "watchdog"
+version = "4.0.1"
+description = "Filesystem events monitoring"
+optional = false
+python-versions = ">=3.8"
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+    {file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
+    {file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
+    {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,<4.0"
+content-hash = "16d70d6ae432212abef4f8a0ba9411e593ff63b9106695d8714589fa0cabf97f"
@@ -0,0 +1,49 @@
+[tool.poetry]
+name = "langgraph-checkpoint"
+version = "0.1.0"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+authors = []
+license = "MIT"
+readme = "README.md"
+repository = "https://www.github.com/langchain-ai/langgraph"
+packages = [{ include = "langgraph" }]
+
+[tool.poetry.dependencies]
+python = "^3.9.0,<4.0"
+langchain-core = ">=0.2.22,<0.3"
+
+[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-watch = "^4.2.0"
+mypy = "^1.10.0"
+dataclasses-json = "^0.6.7"
+aiosqlite = "^0.20.0"
+
+[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"]
@@ -1,9 +1,13 @@
 import pytest
 from langchain_core.runnables import RunnableConfig

-from langgraph.channels.manager import create_checkpoint
-from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
-from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver


 class TestAsyncSqliteSaver:
@@ -9,7 +9,7 @@ from langchain_core.pydantic_v1 import BaseModel as LcBaseModel
 from langchain_core.runnables import RunnableMap
 from pydantic import BaseModel

-from langgraph.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer


 class MyPydantic(BaseModel):
@@ -1,8 +1,12 @@
 import pytest
 from langchain_core.runnables import RunnableConfig

-from langgraph.channels.manager import create_checkpoint
-from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
 from langgraph.checkpoint.memory import MemorySaver


@@ -1,14 +1,14 @@
 import pytest
 from langchain_core.runnables import RunnableConfig

-from langgraph.channels.manager import create_checkpoint
-from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint
-from langgraph.checkpoint.sqlite import (
-    _AIO_ERROR_MSG,
-    SqliteSaver,
-    _metadata_predicate,
-    search_where,
+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:
@@ -116,10 +116,10 @@ class TestSqliteSaver:

    async def test_informative_async_errors(self):
        # call method / assertions
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
+        with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
            await self.sqlite_saver.aget(self.config_1)
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
+        with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
            await self.sqlite_saver.aget_tuple(self.config_1)
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
+        with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
            async for _ in self.sqlite_saver.alist(self.config_1):
                pass
@@ -61,7 +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

@@ -1,3 +0,0 @@
-from langgraph.version import __version__
-
-__all__ = ["__version__"]
@@ -1,13 +1,10 @@
 from contextlib import AsyncExitStack, ExitStack, asynccontextmanager, contextmanager
-from datetime import datetime, timezone
-from typing import Any, AsyncGenerator, Generator, Mapping, Optional
+from typing import AsyncGenerator, Generator, Mapping

 from langchain_core.runnables import RunnableConfig

 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import Checkpoint
-from langgraph.checkpoint.id import uuid6
-from langgraph.errors import EmptyChannelError


@contextmanager
@@ -40,34 +37,3 @@ async def AsyncChannelsManager(
            )
            for k, v in channels.items()
        }
-
-
-def create_checkpoint(
-    checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, BaseChannel]],
-    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", []),
-        # checkpoints are saved only at the end of a step, ie. when current tasks should be cleared
-        current_tasks={},
-    )
@@ -1,13 +0,0 @@
-from langgraph.checkpoint.base import (
-    BaseCheckpointSaver,
-    Checkpoint,
-    SerializerProtocol,
-)
-from langgraph.checkpoint.memory import MemorySaver
-
-__all__ = [
-    "BaseCheckpointSaver",
-    "Checkpoint",
-    "MemorySaver",
-    "SerializerProtocol",
-]
@@ -1,3 +1,6 @@
+from langgraph.checkpoint.base import EmptyChannelError
+
+
 class GraphRecursionError(RecursionError):
    """Raised when the graph has exhausted the maximum number of steps.

@@ -17,13 +20,6 @@ class GraphRecursionError(RecursionError):
    pass


-class EmptyChannelError(Exception):
-    """Raised when attempting to get the value of a channel that hasn't been updated
-    for the first time yet."""
-
-    pass
-
-
 class InvalidUpdateError(Exception):
    """Raised when attempting to update a channel with an invalid sequence of updates."""

@@ -40,3 +36,12 @@ class EmptyInputError(Exception):
    """Raised when graph receives an empty input."""

    pass
+
+
+__all__ = [
+    "GraphRecursionError",
+    "InvalidUpdateError",
+    "GraphInterrupt",
+    "EmptyInputError",
+    "EmptyChannelError",
+]
@@ -24,7 +24,7 @@ from langchain_core.runnables.graph import Graph as DrawableGraph
 from langchain_core.runnables.graph import Node as DrawableNode

 from langgraph.channels.ephemeral_value import EphemeralValue
-from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, START, TAG_HIDDEN, Send
 from langgraph.errors import InvalidUpdateError
 from langgraph.pregel import Channel, Pregel
@@ -29,7 +29,7 @@ from langgraph.channels.dynamic_barrier_value import DynamicBarrierValue, WaitFo
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.named_barrier_value import NamedBarrierValue
-from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import InvalidUpdateError
 from langgraph.graph.graph import (
@@ -85,7 +85,7 @@ class StateGraph(Graph):
    Examples:
        >>> from langchain_core.runnables import RunnableConfig
        >>> from typing_extensions import Annotated, TypedDict
-        >>> from langgraph.checkpoint import MemorySaver
+        >>> from langgraph.checkpoint.memory import MemorySaver
        >>> from langgraph.graph import StateGraph
        >>>
        >>> def reducer(a: list, b: int | None) -> int:
@@ -23,7 +23,7 @@ from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_function

 from langgraph._api.deprecation import deprecated, deprecated_parameter
-from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.graph import END, StateGraph
 from langgraph.graph.graph import CompiledGraph
 from langgraph.graph.message import add_messages
@@ -472,7 +472,7 @@ def create_react_agent(
        Add "chat memory" to the graph:

        ```pycon
-        >>> from langgraph.checkpoint import MemorySaver
+        >>> from langgraph.checkpoint.memory import MemorySaver
        >>> graph = create_react_agent(model, tools, checkpointer=MemorySaver())
        >>> config = {"configurable": {"thread_id": "thread-1"}}
        >>> def print_stream(graph, inputs, config):
@@ -55,11 +55,11 @@ from langgraph.channels.context import Context
 from langgraph.channels.manager import (
    AsyncChannelsManager,
    ChannelsManager,
-    create_checkpoint,
 )
 from langgraph.checkpoint.base import (
    BaseCheckpointSaver,
    copy_checkpoint,
+    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.constants import (
@@ -25,8 +25,13 @@ from langchain_core.runnables.config import (

 from langgraph.channels.base import BaseChannel
 from langgraph.channels.context import Context
-from langgraph.channels.manager import ChannelsManager, create_checkpoint
-from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint, copy_checkpoint
+from langgraph.channels.manager import ChannelsManager
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    Checkpoint,
+    copy_checkpoint,
+    create_checkpoint,
+)
 from langgraph.constants import (
    CONFIG_KEY_CHECKPOINTER,
    CONFIG_KEY_READ,
@@ -28,7 +28,6 @@ from langgraph.channels.base import BaseChannel
 from langgraph.channels.manager import (
    AsyncChannelsManager,
    ChannelsManager,
-    create_checkpoint,
 )
 from langgraph.checkpoint.base import (
    BaseCheckpointSaver,
@@ -37,6 +36,7 @@ from langgraph.checkpoint.base import (
    CheckpointTuple,
    PendingWrite,
    copy_checkpoint,
+    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.constants import CONFIG_KEY_READ, CONFIG_KEY_RESUMING, INPUT, INTERRUPT
@@ -112,18 +112,21 @@ frozenlist = ">=1.1.0"

 [[package]]
 name = "aiosqlite"
-version = "0.19.0"
+version = "0.20.0"
 description = "asyncio bridge to the standard sqlite3 module"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "aiosqlite-0.19.0-py3-none-any.whl", hash = "sha256:edba222e03453e094a3ce605db1b970c4b3376264e56f32e2a4959f948d66a96"},
-    {file = "aiosqlite-0.19.0.tar.gz", hash = "sha256:95ee77b91c8d2808bd08a59fbebf66270e9090c3d92ffbf260dc0db0b979577d"},
+    {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 = ["aiounittest (==1.4.1)", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"]
-docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"]
+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"
@@ -654,21 +657,6 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]

-[[package]]
-name = "dataclasses-json"
-version = "0.6.7"
-description = "Easily serialize dataclasses to and from JSON."
-optional = false
-python-versions = "<4.0,>=3.7"
-files = [
-    {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
-    {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
-]
-
-[package.dependencies]
-marshmallow = ">=3.18.0,<4.0.0"
-typing-inspect = ">=0.4.0,<1"
-
 [[package]]
 name = "debugpy"
 version = "1.8.1"
@@ -1829,6 +1817,22 @@ packaging = ">=23.2,<25"
 requests = ">=2,<3"
 types-requests = ">=2.31.0.2,<3.0.0.0"

+[[package]]
+name = "langgraph-checkpoint"
+version = "0.1.0"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "^3.9.0,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.22,<0.3"
+
+[package.source]
+type = "directory"
+url = "../checkpoint"
+
 [[package]]
 name = "langsmith"
 version = "0.1.79"
@@ -1914,25 +1918,6 @@ files = [
    {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
 ]

-[[package]]
-name = "marshmallow"
-version = "3.21.3"
-description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
-    {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
-]
-
-[package.dependencies]
-packaging = ">=17.0"
-
-[package.extras]
-dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
-tests = ["pytest", "pytz", "simplejson"]
-
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -3904,21 +3889,6 @@ files = [
    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]

-[[package]]
-name = "typing-inspect"
-version = "0.9.0"
-description = "Runtime inspection utilities for typing module."
-optional = false
-python-versions = "*"
-files = [
-    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
-    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
-]
-
-[package.dependencies]
-mypy-extensions = ">=0.3.0"
-typing-extensions = ">=3.7.4"
-
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -4179,4 +4149,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "18b26895b05f2f7cdcd08d59ba164ac788e0e8005e3ec5d7089469b4f3a96aea"
+content-hash = "67b7cf32bfc11e115fa71c28af7880d63faff72a499d54fc2735ebff2bc13c90"
@@ -22,7 +22,6 @@ syrupy = "^4.0.2"
 httpx = "^0.26.0"
 pytest-watcher = "^0.4.1"
 langchain = ">=0.1.0"
-aiosqlite = "^0.19.0"
 grandalf = "^0.8"
 mypy = "^1.6.0"
 ruff = "^0.1.4"
@@ -30,9 +29,10 @@ jupyter = "^1.0.0"
 langchainhub = "^0.1.14"
 langchain-openai = ">=0.1.2"
 langchain-anthropic = ">=0.1.8"
-dataclasses-json = "^0.6.7"
 pytest-xdist = {extras = ["psutil"], version = "^3.6.1"}
 pytest-repeat = "^0.9.3"
+langgraph-checkpoint = {path = "../checkpoint", develop = true}
+aiosqlite = "^0.20.0"

 [tool.poetry.group.dev]
 optional = true
@@ -45,6 +45,8 @@ from langgraph.checkpoint.base import (
    CheckpointTuple,
 )
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.constants import Send
 from langgraph.errors import InvalidUpdateError
@@ -60,8 +62,6 @@ from langgraph.prebuilt.chat_agent_executor import (
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import Channel, GraphRecursionError, Pregel, StateSnapshot
 from langgraph.pregel.retry import RetryPolicy
-from langgraph.serde.base import SerializerProtocol
-from langgraph.serde.jsonplus import JsonPlusSerializer
 from tests.any_str import AnyStr
 from tests.memory_assert import (
    MemorySaverAssertCheckpointMetadata,
@@ -39,10 +39,14 @@ from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.context import Context
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
-from langgraph.checkpoint import BaseCheckpointSaver
-from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
-from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, CheckpointTuple
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+)
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
 from langgraph.constants import Send
 from langgraph.errors import InvalidUpdateError
 from langgraph.graph import END, Graph, StateGraph
@@ -927,26 +927,30 @@ test-ui = ["calysto-bash"]

 [[package]]
 name = "langchain-core"
-version = "0.2.8"
+version = "0.2.26"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.8-py3-none-any.whl", hash = "sha256:172c81c858dc1f3123cc72b7e44e10f44c92f8a761cae18c364081f6c208e9f6"},
-    {file = "langchain_core-0.2.8.tar.gz", hash = "sha256:2db866a4514672c4875b69d5590aa2ed50aa0d144874268bef68d74b5e7f33f9"},
+    {file = "langchain_core-0.2.26-py3-none-any.whl", hash = "sha256:ab7bc58d8037349d06ad8c3eee2ea776e26af7f57cce330eecbed5b98e1c4d56"},
+    {file = "langchain_core-0.2.26.tar.gz", hash = "sha256:20c7792eb6c256dc50892d41f07f7fd8e12e5868dbb059fa316a278977bdf4f6"},
 ]

 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
 langsmith = ">=0.1.75,<0.2.0"
 packaging = ">=23.2,<25"
-pydantic = ">=1,<3"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<9.0.0"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+typing-extensions = ">=4.7"

 [[package]]
 name = "langgraph"
-version = "0.1.0"
+version = "0.1.17"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
@@ -954,15 +958,31 @@ files = []
 develop = true

 [package.dependencies]
-langchain-core = ">=0.2,<0.3"
+langchain-core = ">=0.2.22,<0.3"

 [package.source]
 type = "directory"
 url = "libs/langgraph"

+[[package]]
+name = "langgraph-checkpoint"
+version = "0.1.0"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "^3.9.0,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.22,<0.3"
+
+[package.source]
+type = "directory"
+url = "libs/checkpoint"
+
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.23"
+version = "0.1.26"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -2669,4 +2689,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "70e41571a1230938107dc420734b8dfdae4e71cdc3dca8281366c45441e8d73d"
+content-hash = "350503bbf9ee5685fdbcd93b1fb4e9adc967e71205d2a26bf25c93ce108c2cf7"
@@ -11,6 +11,7 @@ python = "^3.10"

 [tool.poetry.group.docs.dependencies]
 langgraph = { path = "libs/langgraph/", develop = true }
+langgraph-checkpoint = { path = "libs/checkpoint/", develop = true }
 langgraph-sdk = {path = "libs/sdk-py", develop = true}
 mkdocs = "^1.6.0"
 mkdocstrings = "^0.25.1"