checkpoint: move memory into a directory to fix namespace import issues (#1249)

* checkpoint-postgres: new library for postgres checkpointer implementation

---------

Co-authored-by: Nuno Campos <nuno@langchain.dev>

.github/workflows/ci.yml

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

.github/workflows/release.yml

@@ -6,7 +6,7 @@ on:
       working-directory:
         required: true
         type: string
-        default: 'libs/langgraph'
+        default: "libs/langgraph"
 
 env:
   PYTHON_VERSION: "3.11"
@@ -104,7 +104,7 @@ jobs:
             REGEX="^$SHORT_PKG_NAME==\\d+\\.\\d+\\.\\d+((a|b|rc)\\d+)?\$"
           fi
           echo $REGEX
-          PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1)
+          PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1 || echo "")
           echo $PREV_TAG
           if [ "$TAG" == "$PREV_TAG" ]; then
             echo "No new version to release"
@@ -137,8 +137,7 @@ jobs:
       - build
       - release-notes
     permissions: write-all
-    uses:
-      ./.github/workflows/_test_release.yml
+    uses: ./.github/workflows/_test_release.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
@@ -198,9 +197,15 @@ jobs:
               "$PKG_NAME==$VERSION" \
           )
 
-          # Replace all dashes in the package name with underscores,
-          # since that's how Python imports packages with dashes in the name.
-          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
+          if [[ "$PKG_NAME" == *checkpoint* ]]; then
+            # since checkpoint packages are namespace packages, import them with . convention
+            # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
+            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
+          else
+            # Replace all dashes in the package name with underscores,
+            # since that's how Python imports packages with dashes in the name.
+            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
+          fi
 
           poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 

README.md

@@ -10,9 +10,6 @@
 > [!NOTE]
 > Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
 
-> [!TIP]
-> Looking to deploy your LangGraph application? [Join the waitlist](https://www.langchain.com/langgraph-cloud-beta) for [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/), our managed service for deploying and hosting LangGraph applications.
-
 ## Overview
 
 [LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
@@ -61,7 +58,7 @@ 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

@@ -43,6 +43,7 @@ _MANUAL = {
         "tool-calling.ipynb",
         "tool-calling-errors.ipynb",
         "pass-config-to-tools.ipynb",
+        "many-tools.ipynb",
         "dynamic-returning-direct.ipynb",
         "managing-agent-steps.ipynb",
         "respond-in-format.ipynb",

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
@@ -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

@@ -1,9 +1,9 @@
 # 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.

docs/docs/cloud/index.md

@@ -6,9 +6,6 @@
     - We are actively contributing improvements back to LangGraph informed by our work on LangGraph Cloud.
     - You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project.
 
-!!! danger "Important"
-    LangGraph Cloud is a closed source, paid product in an invite-only stage. We are currently focused on providing high bandwidth support to make our select early customers successful. If you are interested in applying for access, please fill out [this form](https://www.langchain.com/langgraph-cloud-beta).
-
 !!! warning "Under Construction"
     LangGraph Cloud documentation is under construction. Contents may change until general availability.
 

docs/docs/how-tos/index.md

@@ -60,6 +60,7 @@ These guides show how to use different streaming modes.
 - [How to handle tool calling errors](tool-calling-errors.ipynb)
 - [How to pass graph state to tools](pass-run-time-values-to-tools.ipynb)
 - [How to pass config to tools](pass-config-to-tools.ipynb)
+- [How to handle large numbers of tools](many-tools.ipynb)
 
 ## State Management
 

docs/docs/reference/checkpoints.md

@@ -7,6 +7,8 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w
 - Resilience for long-running, error-prone agents
 - Time travel retry and branch from a previous checkpoint
 
+Key checkpointer interfaces and primitives are defined in [`langgraph_checkpoint`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint) library.
+
 ### Checkpoint
 
 ::: langgraph.checkpoint.base.Checkpoint
@@ -21,7 +23,7 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w
 
 ### SerializerProtocol
 
-::: langgraph.checkpoint.SerializerProtocol
+::: langgraph.checkpoint.base.SerializerProtocol
 
 ## Implementations
 
@@ -33,7 +35,7 @@ LangGraph also natively provides the following checkpoint implementations.
 
 ### AsyncSqliteSaver
 
-::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver
+::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver
 
 ### SqliteSaver
 

docs/mkdocs.yml

@@ -157,6 +157,7 @@ nav:
           - Handle tool calling errors: how-tos/tool-calling-errors.ipynb
           - Pass graph state to tools: how-tos/pass-run-time-values-to-tools.ipynb
           - Pass config to tools: how-tos/pass-config-to-tools.ipynb
+          - Handle many tools: how-tos/many-tools.ipynb
       - State Management:
           - Use Pydantic model as state: how-tos/state-model.ipynb
           - Use a context object in state: how-tos/state-context-key.ipynb
@@ -189,11 +190,12 @@ 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/node-retries.ipynb

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

examples/pass_private_state.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# How to pass private state\n",
     "\n",
-    "Oftentimes, you may want nodes to be able to pass state to eachv other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n",
+    "Oftentimes, you may want nodes to be able to pass state to each other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n",
     "\n",
     "Let's take a look at an example below. In this example, we will create a RAG pipeline that:\n",
     "1. Takes in a user question\n",

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-postgres/Makefile

@@ -0,0 +1,48 @@
+.PHONY: test test_watch lint format
+
+######################
+# TESTING AND COVERAGE
+######################
+
+start-postgres:
+	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait
+
+stop-postgres:
+	docker compose -f tests/compose-postgres.yml down
+
+test:
+	make start-postgres; \
+	poetry run pytest; \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+test_watch:
+	make start-postgres; \
+	poetry run ptw .; \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+######################
+# 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-postgres/README.md

@@ -0,0 +1,93 @@
+# LangGraph Checkpoint Postgres
+
+Implementation of LangGraph CheckpointSaver that uses Postgres.
+
+## Usage
+
+```python
+from langgraph.checkpoint.postgres import PostgresSaver
+
+write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+read_config = {"configurable": {"thread_id": "1"}}
+
+DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
+    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(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    checkpointer.get(read_config)
+
+    # list checkpoints
+    list(checkpointer.list(read_config))
+```
+
+### Async
+
+```python
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+
+async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+    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(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    await checkpointer.aget(read_config)
+
+    # list checkpoints
+    [c async for c in checkpointer.alist(read_config)]
+```

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

@@ -0,0 +1,255 @@
+import threading
+from contextlib import contextmanager
+from typing import Any, Iterator, List, Optional
+
+from langchain_core.runnables import RunnableConfig
+from psycopg import Connection, Cursor, Pipeline
+from psycopg.rows import dict_row
+from psycopg.types.json import Jsonb
+
+from langgraph.checkpoint.base import (
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    get_checkpoint_id,
+)
+from langgraph.checkpoint.postgres.base import (
+    BasePostgresSaver,
+)
+from langgraph.checkpoint.serde.base import SerializerProtocol
+
+
+class PostgresSaver(BasePostgresSaver):
+    lock: threading.Lock
+
+    is_setup: bool
+
+    def __init__(
+        self,
+        conn: Connection,
+        pipe: Optional[Pipeline] = None,
+        serde: Optional[SerializerProtocol] = None,
+    ) -> None:
+        super().__init__(serde=serde)
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = threading.Lock()
+        self.is_setup = False
+
+    @classmethod
+    @contextmanager
+    def from_conn_string(
+        cls, conn_string: str, *, pipeline: bool = False
+    ) -> Iterator["PostgresSaver"]:
+        """Create a new PostgresSaver instance from a connection string.
+
+        Args:
+            conn_string (str): The Postgres connection info string.
+            pipeline (bool): whether to use Pipeline
+
+        Returns:
+            PostgresSaver: A new PostgresSaver instance.
+        """
+        with Connection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                with conn.pipeline() as pipe:
+                    yield PostgresSaver(conn, pipe)
+            else:
+                yield PostgresSaver(conn)
+
+    def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the SQLite database if they don't
+        already exist. It is called automatically when needed and should not be called
+        directly by the user.
+        """
+        if self.is_setup:
+            return
+        with self.lock:
+            create_table_queries = [
+                self.CREATE_CHECKPOINTS_SQL,
+                self.CREATE_CHECKPOINT_BLOBS_SQL,
+                self.CREATE_CHECKPOINT_WRITES_SQL,
+            ]
+            with self.conn.cursor(binary=True) as cur:
+                for query in create_table_queries:
+                    cur.execute(query)
+
+            if self.pipe:
+                self.pipe.sync()
+
+            self.is_setup = True
+
+    def list(
+        self,
+        config: Optional[RunnableConfig],
+        *,
+        filter: Optional[dict[str, Any]] = None,
+        before: Optional[RunnableConfig] = None,
+        limit: Optional[int] = None,
+    ) -> Iterator[CheckpointTuple]:
+        self.setup()
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
+        if limit:
+            query += f" LIMIT {limit}"
+        # if we change this to use .stream() we need to make sure to close the cursor
+        for value in self.conn.execute(query, args, binary=True):
+            yield CheckpointTuple(
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["checkpoint_id"],
+                    }
+                },
+                {
+                    **self._load_checkpoint(value["checkpoint"]),
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                },
+                value["metadata"],
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None,
+            )
+
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        self.setup()
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_id = get_checkpoint_id(config)
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        if checkpoint_id:
+            args = (thread_id, checkpoint_ns, checkpoint_id)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
+        else:
+            args = (thread_id, checkpoint_ns)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"
+
+        with self._cursor() as cur:
+            cur = self.conn.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            for value in cur:
+                return CheckpointTuple(
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": value["checkpoint_id"],
+                        }
+                    },
+                    {
+                        **self._load_checkpoint(value["checkpoint"]),
+                        "channel_values": self._load_blobs(value["channel_values"]),
+                    },
+                    value["metadata"],
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": value["parent_checkpoint_id"],
+                        }
+                    }
+                    if value["parent_checkpoint_id"]
+                    else None,
+                    self._load_writes(value["pending_writes"]),
+                )
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+        checkpoint_id = configurable.pop(
+            "checkpoint_id", configurable.pop("thread_ts", None)
+        )
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        with self._cursor(pipeline=True) as cur:
+            cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                self._dump_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),
+                    copy["channel_versions"],
+                    new_versions,
+                ),
+            )
+            cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    checkpoint_id,
+                    Jsonb(self._dump_checkpoint(copy)),
+                    Jsonb(metadata),
+                ),
+            )
+        return next_config
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: List[tuple[str, Any]],
+        task_id: str,
+    ) -> None:
+        with self._cursor() as cur:
+            cur.executemany(
+                self.UPSERT_CHECKPOINT_WRITES_SQL,
+                self._dump_writes(
+                    config["configurable"]["thread_id"],
+                    config["configurable"]["checkpoint_ns"],
+                    config["configurable"]["checkpoint_id"],
+                    task_id,
+                    writes,
+                ),
+            )
+
+    @contextmanager
+    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor]:
+        self.setup()
+        if self.pipe:
+            # a connection in pipeline mode can be used concurrently
+            # in multiple threads/coroutines, but only one cursor can be
+            # used at a time
+            try:
+                with self.conn.cursor(binary=True) as cur:
+                    yield cur
+            finally:
+                self.pipe.sync()
+        elif pipeline:
+            # a connection not in pipeline mode can only be used by one
+            # thread/coroutine at a time, so we acquire a lock
+            with self.lock, self.conn.pipeline(), self.conn.cursor(binary=True) as cur:
+                yield cur
+        else:
+            with self.lock, self.conn.cursor(binary=True) as cur:
+                yield cur

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

@@ -0,0 +1,262 @@
+import asyncio
+from contextlib import asynccontextmanager
+from typing import Any, AsyncIterator, Optional
+
+from langchain_core.runnables import RunnableConfig
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline
+from psycopg.rows import dict_row
+from psycopg.types.json import Jsonb
+
+from langgraph.checkpoint.base import (
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    get_checkpoint_id,
+)
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.serde.base import SerializerProtocol
+
+
+class AsyncPostgresSaver(BasePostgresSaver):
+    lock: asyncio.Lock
+
+    is_setup: bool
+
+    def __init__(
+        self,
+        conn: AsyncConnection,
+        pipe: Optional[AsyncPipeline] = None,
+        serde: Optional[SerializerProtocol] = None,
+    ) -> None:
+        super().__init__(serde=serde)
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
+        self.is_setup = False
+
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls, conn_string: str, *, pipeline: bool = False
+    ) -> AsyncIterator["AsyncPostgresSaver"]:
+        """Create a new PostgresSaver instance from a connection string.
+
+        Args:
+            conn_string (str): The Postgres connection info string.
+            pipeline (bool): whether to use AsyncPipeline
+
+        Returns:
+            PostgresSaver: A new PostgresSaver instance.
+        """
+        async with await AsyncConnection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                async with conn.pipeline() as pipe:
+                    yield AsyncPostgresSaver(conn, pipe)
+            else:
+                yield AsyncPostgresSaver(conn)
+
+    async def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the SQLite database if they don't
+        already exist. It is called automatically when needed and should not be called
+        directly by the user.
+        """
+        if self.is_setup:
+            return
+        async with self.lock:
+            create_table_queries = [
+                self.CREATE_CHECKPOINTS_SQL,
+                self.CREATE_CHECKPOINT_BLOBS_SQL,
+                self.CREATE_CHECKPOINT_WRITES_SQL,
+            ]
+            async with self.conn.cursor() as cur:
+                for query in create_table_queries:
+                    await cur.execute(query)
+
+            if self.pipe:
+                await self.pipe.sync()
+
+            self.is_setup = True
+
+    async def alist(
+        self,
+        config: Optional[RunnableConfig],
+        *,
+        filter: Optional[dict[str, Any]] = None,
+        before: Optional[RunnableConfig] = None,
+        limit: Optional[int] = None,
+    ) -> AsyncIterator[CheckpointTuple]:
+        await self.setup()
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
+        if limit:
+            query += f" LIMIT {limit}"
+        # if we change this to use .stream() we need to make sure to close the cursor
+        async for value in await self.conn.execute(query, args, binary=True):
+            yield CheckpointTuple(
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["checkpoint_id"],
+                    }
+                },
+                {
+                    **self._load_checkpoint(value["checkpoint"]),
+                    "channel_values": await asyncio.to_thread(
+                        self._load_blobs, value["channel_values"]
+                    ),
+                },
+                value["metadata"],
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None,
+            )
+
+    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        await self.setup()
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_id = get_checkpoint_id(config)
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        if checkpoint_id:
+            args = (thread_id, checkpoint_ns, checkpoint_id)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
+        else:
+            args = (thread_id, checkpoint_ns)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"
+
+        async with self._cursor() as cur:
+            cur = await self.conn.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            async for value in cur:
+                return CheckpointTuple(
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": value["checkpoint_id"],
+                        }
+                    },
+                    {
+                        **self._load_checkpoint(value["checkpoint"]),
+                        "channel_values": await asyncio.to_thread(
+                            self._load_blobs, value["channel_values"]
+                        ),
+                    },
+                    value["metadata"],
+                    {
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": value["parent_checkpoint_id"],
+                        }
+                    }
+                    if value["parent_checkpoint_id"]
+                    else None,
+                    await asyncio.to_thread(self._load_writes, value["pending_writes"]),
+                )
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        await self.setup()
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+        checkpoint_id = configurable.pop(
+            "checkpoint_id", configurable.pop("thread_ts", None)
+        )
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        async with self._cursor(pipeline=True) as cur:
+            await cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                await asyncio.to_thread(
+                    self._dump_blobs,
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),
+                    copy["channel_versions"],
+                    new_versions,
+                ),
+            )
+            await cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    checkpoint_id,
+                    Jsonb(self._dump_checkpoint(copy)),
+                    Jsonb(metadata),
+                ),
+            )
+        return next_config
+
+    async def aput_writes(
+        self,
+        config: RunnableConfig,
+        writes: list[tuple[str, Any]],
+        task_id: str,
+    ) -> None:
+        async with self._cursor() as cur:
+            await cur.executemany(
+                self.UPSERT_CHECKPOINT_WRITES_SQL,
+                await asyncio.to_thread(
+                    self._dump_writes,
+                    config["configurable"]["thread_id"],
+                    config["configurable"]["checkpoint_ns"],
+                    config["configurable"]["checkpoint_id"],
+                    task_id,
+                    writes,
+                ),
+            )
+
+    @asynccontextmanager
+    async def _cursor(self, *, pipeline: bool = False) -> AsyncIterator[AsyncCursor]:
+        await self.setup()
+        if self.pipe:
+            # a connection in pipeline mode can be used concurrently
+            # in multiple threads/coroutines, but only one cursor can be
+            # used at a time
+            try:
+                async with self.conn.cursor(binary=True) as cur:
+                    yield cur
+            finally:
+                await self.pipe.sync()
+        elif pipeline:
+            # a connection not in pipeline mode can only be used by one
+            # thread/coroutine at a time, so we acquire a lock
+            async with self.lock, self.conn.pipeline(), self.conn.cursor(
+                binary=True
+            ) as cur:
+                yield cur
+        else:
+            async with self.lock, self.conn.cursor(binary=True) as cur:
+                yield cur

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -0,0 +1,256 @@
+from base64 import b64decode, b64encode
+from hashlib import md5
+from typing import Any, List, Optional, Tuple
+
+from langchain_core.runnables import RunnableConfig
+from psycopg.types.json import Jsonb
+
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    Checkpoint,
+    EmptyChannelError,
+    get_checkpoint_id,
+)
+from langgraph.checkpoint.serde.types import ChannelProtocol
+
+MetadataInput = Optional[dict[str, Any]]
+
+SELECT_SQL = """
+select
+    thread_id,
+    checkpoint,
+    checkpoint_ns,
+    checkpoint_id,
+    parent_checkpoint_id,
+    metadata,
+    (
+        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
+        from jsonb_each_text(checkpoint -> 'channel_versions')
+        inner join checkpoint_blobs bl
+            on bl.thread_id = checkpoints.thread_id
+            and bl.checkpoint_ns = checkpoints.checkpoint_ns
+            and bl.channel = jsonb_each_text.key
+            and bl.version = jsonb_each_text.value
+    ) as channel_values,
+    (
+        select
+        array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob])
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.checkpoint_id = checkpoints.checkpoint_id
+    ) as pending_writes
+from checkpoints """
+
+CREATE_CHECKPOINTS_SQL = """
+    CREATE TABLE IF NOT EXISTS checkpoints (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    checkpoint_id TEXT NOT NULL,
+    parent_checkpoint_id TEXT,
+    type TEXT,
+    checkpoint JSONB NOT NULL,
+    metadata JSONB NOT NULL DEFAULT '{}',
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
+);
+"""
+CREATE_CHECKPOINT_BLOBS_SQL = """
+    CREATE TABLE IF NOT EXISTS checkpoint_blobs (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    channel TEXT NOT NULL,
+    version TEXT NOT NULL,
+    type TEXT NOT NULL,
+    blob BYTEA NOT NULL,
+    PRIMARY KEY (thread_id, checkpoint_ns, channel, version)
+);"""
+
+CREATE_CHECKPOINT_WRITES_SQL = """
+    CREATE TABLE IF NOT EXISTS checkpoint_writes (
+    thread_id 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,
+    blob BYTEA NOT NULL,
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
+);
+"""
+
+UPSERT_CHECKPOINT_BLOBS_SQL = """
+    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, version, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, channel, version) DO NOTHING
+"""
+
+UPSERT_CHECKPOINTS_SQL = """
+    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, checkpoint, metadata)
+    VALUES (%s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id)
+    DO UPDATE SET
+        checkpoint = EXCLUDED.checkpoint,
+        metadata = EXCLUDED.metadata;
+"""
+
+UPSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
+"""
+
+
+class BasePostgresSaver(BaseCheckpointSaver):
+    SELECT_SQL = SELECT_SQL
+    CREATE_CHECKPOINTS_SQL = CREATE_CHECKPOINTS_SQL
+    CREATE_CHECKPOINT_BLOBS_SQL = CREATE_CHECKPOINT_BLOBS_SQL
+    CREATE_CHECKPOINT_WRITES_SQL = CREATE_CHECKPOINT_WRITES_SQL
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+
+    def _load_checkpoint(self, checkpoint: dict[str, Any]) -> Checkpoint:
+        if len(checkpoint["pending_sends"]) == 2 and all(
+            isinstance(a, str) for a in checkpoint["pending_sends"]
+        ):
+            type, bs = checkpoint["pending_sends"]
+            return {
+                **checkpoint,
+                "pending_sends": self.serde.loads_typed((type, b64decode(bs))),
+            }
+
+        return checkpoint
+
+    def _dump_checkpoint(self, checkpoint: Checkpoint) -> dict[str, Any]:
+        type, bs = self.serde.dumps_typed(checkpoint["pending_sends"])
+        return {
+            **checkpoint,
+            "pending_sends": (type, b64encode(bs).decode()),
+        }
+
+    def _load_blobs(
+        self, blob_values: list[tuple[bytes, bytes, bytes]]
+    ) -> dict[str, Any]:
+        if not blob_values:
+            return {}
+        return {
+            k.decode(): self.serde.loads_typed((t.decode(), v))
+            for k, t, v in blob_values
+        }
+
+    def _dump_blobs(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        values: dict[str, Any],
+        versions: dict[str, str],
+        new_versions: Optional[dict[str, str]],
+    ) -> list[tuple[str, str, str, str, str, bytes]]:
+        if not versions:
+            return []
+
+        if new_versions:
+            versions = new_versions
+
+        return [
+            (
+                thread_id,
+                checkpoint_ns,
+                k,
+                ver,
+                *self.serde.dumps_typed(values[k]),
+            )
+            for k, ver in versions.items()
+            if k in values
+        ]
+
+    def _load_writes(
+        self, writes: list[tuple[bytes, bytes, bytes, bytes]]
+    ) -> list[tuple[str, str, Any]]:
+        return (
+            [
+                (
+                    tid.decode(),
+                    channel.decode(),
+                    self.serde.loads_typed((t.decode(), v)),
+                )
+                for tid, channel, t, v in writes
+            ]
+            if writes
+            else []
+        )
+
+    def _dump_writes(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        checkpoint_id: str,
+        task_id: str,
+        writes: list[tuple[str, Any]],
+    ) -> list[tuple[str, str, str, int, str, str, bytes]]:
+        return [
+            (
+                thread_id,
+                checkpoint_ns,
+                checkpoint_id,
+                task_id,
+                idx,
+                channel,
+                *self.serde.dumps_typed(value),
+            )
+            for idx, (channel, value) in enumerate(writes)
+        ]
+
+    def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str:
+        if current is None:
+            current_v = 0
+        elif isinstance(current, int):
+            current_v = current
+        else:
+            current_v = int(current.split(".")[0])
+        next_v = current_v + 1
+        try:
+            next_h = md5(self.serde.dumps_typed(channel.checkpoint())[1]).hexdigest()
+        except EmptyChannelError:
+            next_h = ""
+        return f"{next_v:032}.{next_h}"
+
+    def _search_where(
+        self,
+        config: Optional[RunnableConfig],
+        filter: MetadataInput,
+        before: Optional[RunnableConfig] = None,
+    ) -> Tuple[str, List[Any]]:
+        """Return WHERE clause predicates for alist() given config, filter, cursor.
+
+        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 = $1 AND column2 IS $2". The list of values contains the
+        values for each of the corresponding parameters.
+        """
+        wheres = []
+        param_values = []
+
+        # construct predicate for config filter
+        if config:
+            wheres.append("thread_id = %s ")
+            param_values.append(config["configurable"]["thread_id"])
+            checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+            wheres.append("checkpoint_ns = %s")
+            param_values.append(checkpoint_ns)
+
+        # construct predicate for metadata filter
+        if filter:
+            wheres.append("metadata @> %s ")
+            param_values.append(Jsonb(filter))
+
+        # construct predicate for `before`
+        if before is not None:
+            wheres.append("checkpoint_id < %s ")
+            param_values.append(get_checkpoint_id(before))
+
+        return (
+            "WHERE " + " AND ".join(wheres) if wheres else "",
+            param_values,
+        )

libs/checkpoint-postgres/poetry.lock

@@ -0,0 +1,972 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[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"},
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+]
+
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
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+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[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"},
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+]
+
+[[package]]
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+optional = false
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+    {file = "watchdog-4.0.1-py3-none-manylinux2014_i686.whl", hash = "sha256:4107ac5ab936a63952dea2a46a734a23230aa2f6f9db1291bf171dac3ebd53c6"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64.whl", hash = "sha256:6e8c70d2cd745daec2a08734d9f63092b793ad97612470a0ee4cbb8f5f705c57"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f27279d060e2ab24c0aa98363ff906d2386aa6c4dc2f1a374655d4e02a6c5e5e"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_s390x.whl", hash = "sha256:f8affdf3c0f0466e69f5b3917cdd042f89c8c63aebdb9f7c078996f607cdb0f5"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ac7041b385f04c047fcc2951dc001671dee1b7e0615cde772e84b01fbf68ee84"},
+    {file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
+    {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 = "422b6d716b86db072ea3a612287ad20ff5700c18f22d9e9d59cc4e198514519d"

libs/checkpoint-postgres/pyproject.toml

@@ -0,0 +1,52 @@
+[tool.poetry]
+name = "langgraph-checkpoint-postgres"
+version = "1.0.0"
+description = "Library with a Postgres 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,<4.0"
+langgraph-checkpoint = "^1.0.1"
+orjson = ">=3.10.1"
+psycopg = {extras = ["binary"], version = ">=3.1.19"}
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.1.4"
+codespell = "^2.2.0"
+pytest = "^7.2.1"
+anyio = "^4.4.0"
+pytest-asyncio = "^0.21.1"
+pytest-mock = "^3.11.1"
+pytest-watch = "^4.2.0"
+mypy = "^1.10.0"
+psycopg-pool = "^3.2.2"
+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"]

libs/checkpoint-postgres/tests/compose-postgres.yml

@@ -0,0 +1,16 @@
+services:
+  postgres-test:
+    image: postgres:16
+    ports:
+      - "5432:5432"
+    environment:
+      POSTGRES_DB: postgres
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+    healthcheck:
+      test: pg_isready -U postgres
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 60s
+      start_interval: 1s

libs/checkpoint-postgres/tests/conftest.py

@@ -0,0 +1,25 @@
+import pytest
+from psycopg import AsyncConnection
+from psycopg.errors import UndefinedTable
+from psycopg.rows import dict_row
+
+DEFAULT_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+
+
+@pytest.fixture(scope="function")
+async def conn():
+    async with await AsyncConnection.connect(
+        DEFAULT_URI, autocommit=True, prepare_threshold=0, row_factory=dict_row
+    ) as conn:
+        yield conn
+
+
+@pytest.fixture(scope="function", autouse=True)
+async def clear_test_db(conn):
+    """Delete all tables before each test."""
+    try:
+        await conn.execute("DELETE FROM checkpoints")
+        await conn.execute("DELETE FROM checkpoint_blobs")
+        await conn.execute("DELETE FROM checkpoint_writes")
+    except UndefinedTable:
+        pass

libs/checkpoint-postgres/tests/test_async.py

@@ -0,0 +1,113 @@
+import pytest
+from conftest import DEFAULT_URI
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+
+
+class TestAsyncPostgresSaver:
+    @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 AsyncPostgresSaver.from_conn_string(DEFAULT_URI) 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-postgres/tests/test_sync.py

@@ -0,0 +1,112 @@
+import pytest
+from conftest import DEFAULT_URI
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.postgres import PostgresSaver
+
+
+class TestPostgresSaver:
+    @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 PostgresSaver.from_conn_string(DEFAULT_URI) as saver:
+            # 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

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 +1,92 @@
+# 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
+
+write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+read_config = {"configurable": {"thread_id": "1"}}
+
+with SqliteSaver.from_conn_string(":memory:") as checkpointer:
+    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(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    checkpointer.get(read_config)
+
+    # list checkpoints
+    list(checkpointer.list(read_config))
+```
+
+### Async
+
+```python
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+
+async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
+    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(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    await checkpointer.aget(read_config)
+
+    # list checkpoints
+    [c async for c in checkpointer.alist(read_config)]
+```

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

@@ -1,59 +1,29 @@
-import json
-import pickle
 import sqlite3
 import threading
-from contextlib import AbstractContextManager, contextmanager
+from contextlib import contextmanager
 from hashlib import md5
-from types import TracebackType
 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,
+    ChannelVersions,
     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"
@@ -61,7 +31,7 @@ _AIO_ERROR_MSG = (
 )
 
 
-class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
+class SqliteSaver(BaseCheckpointSaver):
     """A checkpoint saver that stores checkpoints in a SQLite database.
 
     Note:
@@ -92,11 +62,9 @@ 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_ns': '', 'checkpoint_id': '0c62ca34-ac19-445d-bbb0-5b4984975b2a'}}, parent_config=None)
     """  # noqa
 
-    serde = JsonPlusSerializerCompat()
-
     conn: sqlite3.Connection
     is_setup: bool
 
@@ -107,48 +75,40 @@ 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()
 
     @classmethod
-    def from_conn_string(cls, conn_string: str) -> "SqliteSaver":
+    @contextmanager
+    def from_conn_string(cls, conn_string: str) -> Iterator["SqliteSaver"]:
         """Create a new SqliteSaver instance from a connection string.
 
         Args:
             conn_string (str): The SQLite connection string.
 
-        Returns:
+        Yields:
             SqliteSaver: A new SqliteSaver instance.
 
         Examples:
 
             In memory:
 
-                memory = SqliteSaver.from_conn_string(":memory:")
+                with SqliteSaver.from_conn_string(":memory:") as memory:
+                    ...
 
             To disk:
 
-                memory = SqliteSaver.from_conn_string("checkpoints.sqlite")
+                with SqliteSaver.from_conn_string("checkpoints.sqlite") as memory:
+                    ...
         """
-        return SqliteSaver(
-            conn=sqlite3.connect(
-                conn_string,
-                # https://ricardoanderegg.com/posts/python-sqlite-thread-safety/
-                check_same_thread=False,
-            )
-        )
-
-    def __enter__(self) -> Self:
-        return self
-
-    def __exit__(
-        self,
-        __exc_type: Optional[type[BaseException]],
-        __exc_value: Optional[BaseException],
-        __traceback: Optional[TracebackType],
-    ) -> Optional[bool]:
-        return self.conn.close()
+        with sqlite3.connect(
+            conn_string,
+            # https://ricardoanderegg.com/posts/python-sqlite-thread-safety/
+            check_same_thread=False,
+        ) as conn:
+            yield SqliteSaver(conn)
 
     def setup(self) -> None:
         """Set up the checkpoint database.
@@ -165,20 +125,24 @@ 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 +175,7 @@ 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 +198,77 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
             >>> config = {
             ...    "configurable": {
             ...        "thread_id": "1",
-            ...        "thread_ts": "2024-05-04T06:32:42.235444+00:00",
+           ...         "checkpoint_ns": "",
+            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
             ...    }
             ... }
             >>> 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
                     ],
                 )
 
@@ -318,41 +296,58 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
 
         Examples:
             >>> from langgraph.checkpoint.sqlite import SqliteSaver
-            >>> memory = SqliteSaver.from_conn_string(":memory:")
+            >>> with SqliteSaver.from_conn_string(":memory:") as memory:
             ... # Run a graph, then list the checkpoints
-            >>> config = {"configurable": {"thread_id": "1"}}
-            >>> checkpoints = list(memory.list(config, limit=2))
+            >>>     config = {"configurable": {"thread_id": "1"}}
+            >>>     checkpoints = list(memory.list(config, limit=2))
             >>> print(checkpoints)
             [CheckpointTuple(...), CheckpointTuple(...)]
 
             >>> config = {"configurable": {"thread_id": "1"}}
-            >>> before = {"configurable": {"thread_ts": "2024-05-04T06:32:42.235444+00:00"}}
-            >>> checkpoints = list(memory.list(config, before=before))
+            >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}}
+            >>> with SqliteSaver.from_conn_string(":memory:") as memory:
+            ... # Run a graph, then list the checkpoints
+            >>>     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
                     ),
                 )
@@ -362,6 +357,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Save a checkpoint to the database.
 
@@ -379,29 +375,35 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager):
         Examples:
 
             >>> from langgraph.checkpoint.sqlite import SqliteSaver
-            >>> memory = SqliteSaver.from_conn_string(":memory:")
-            ... # Run a graph, then list the checkpoints
-            >>> config = {"configurable": {"thread_id": "1"}}
-            >>> 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"}})
+            >>> with SqliteSaver.from_conn_string(":memory:") as memory:
+            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "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_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
         """
+        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 +424,16 @@ 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 +479,7 @@ 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 +497,7 @@ 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

@@ -1,7 +1,6 @@
 import asyncio
 import functools
-from contextlib import AbstractAsyncContextManager
-from types import TracebackType
+from contextlib import asynccontextmanager
 from typing import (
     Any,
     AsyncIterator,
@@ -15,16 +14,18 @@ from typing import (
 
 import aiosqlite
 from langchain_core.runnables import RunnableConfig
-from typing_extensions import Self
 
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
+    ChannelVersions,
     Checkpoint,
     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)
 
@@ -43,7 +44,7 @@ def not_implemented_sync_method(func: T) -> T:
     return wrapper
 
 
-class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
+class AsyncSqliteSaver(BaseCheckpointSaver):
     """An asynchronous checkpoint saver that stores checkpoints in a SQLite database.
 
     This class provides an asynchronous interface for saving and retrieving checkpoints
@@ -84,19 +85,18 @@ 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)
         >>> builder.add_node("add_one", lambda x: x + 1)
         >>> builder.set_entry_point("add_one")
         >>> builder.set_finish_point("add_one")
-        >>> memory = AsyncSqliteSaver.from_conn_string("checkpoints.sqlite")
-        >>> graph = builder.compile(checkpointer=memory)
-        >>> coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
-        >>> asyncio.run(coro)
+        >>> async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory:
+        >>>     graph = builder.compile(checkpointer=memory)
+        >>>     coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
+        >>>     print(asyncio.run(coro))
         Output: 2
         ```
         Raw usage:
@@ -104,23 +104,20 @@ 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:
         ...         saver = AsyncSqliteSaver(conn)
         ...         config = {"configurable": {"thread_id": "1"}}
         ...         checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}}
-        ...         saved_config = await saver.aput(config, checkpoint)
+        ...         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
 
@@ -131,33 +128,26 @@ 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
 
     @classmethod
-    def from_conn_string(cls, conn_string: str) -> "AsyncSqliteSaver":
+    @asynccontextmanager
+    async def from_conn_string(
+        cls, conn_string: str
+    ) -> AsyncIterator["AsyncSqliteSaver"]:
         """Create a new AsyncSqliteSaver instance from a connection string.
 
         Args:
             conn_string (str): The SQLite connection string.
 
-        Returns:
+        Yields:
             AsyncSqliteSaver: A new AsyncSqliteSaver instance.
         """
-        return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string))
-
-    async def __aenter__(self) -> Self:
-        return self
-
-    async def __aexit__(
-        self,
-        __exc_type: Optional[type[BaseException]],
-        __exc_value: Optional[BaseException],
-        __traceback: Optional[TracebackType],
-    ) -> Optional[bool]:
-        if self.is_setup:
-            return await self.conn.close()
+        async with aiosqlite.connect(conn_string) as conn:
+            yield AsyncSqliteSaver(conn)
 
     @not_implemented_sync_method
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
@@ -210,20 +200,24 @@ 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 +229,7 @@ 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 +240,69 @@ 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 +330,41 @@ 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
                     ),
                 )
@@ -352,6 +374,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Save a checkpoint to the database asynchronously.
 
@@ -362,26 +385,34 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager):
             config (RunnableConfig): The config to associate with the checkpoint.
             checkpoint (Checkpoint): The checkpoint to save.
             metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
+            new_versions (dict): New versions as of this write
 
         Returns:
             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 +433,16 @@ 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 = "e073e1a73cdae1fae8ea46499c39e55980cb61c7c8bd6be774c64f80a627eb31"

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 = "^1.0.1"
+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 +1,88 @@
+# 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
+
+write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+read_config = {"configurable": {"thread_id": "1"}}
+
+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(write_config, checkpoint, {}, {})
+
+# load checkpoint
+checkpointer.get(read_config)
+
+# list checkpoints
+list(checkpointer.list(read_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".
     """
 
@@ -59,35 +62,38 @@ class TaskInfo(TypedDict):
     status: Literal["scheduled", "success", "error"]
 
 
+ChannelVersions = dict[str, Union[str, int, float]]
+
+
 class Checkpoint(TypedDict):
     """State snapshot at a given point in time."""
 
     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]]
+    channel_versions: ChannelVersions
     """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]]]
+    versions_seen: dict[str, ChannelVersions]
     """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 +126,36 @@ 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 +175,19 @@ 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 +215,7 @@ 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 +224,7 @@ 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.
@@ -236,6 +281,7 @@ class BaseCheckpointSaver(ABC):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Store a checkpoint with its configuration and metadata.
 
@@ -243,6 +289,7 @@ class BaseCheckpointSaver(ABC):
             config (RunnableConfig): Configuration for the checkpoint.
             checkpoint (Checkpoint): The checkpoint to store.
             metadata (CheckpointMetadata): Additional metadata for the checkpoint.
+            new_versions (dict): New versions as of this write
 
         Returns:
             RunnableConfig: Updated configuration after storing the checkpoint.
@@ -268,9 +315,7 @@ 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.
@@ -328,6 +373,7 @@ class BaseCheckpointSaver(ABC):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Asynchronously store a checkpoint with its configuration and metadata.
 
@@ -335,6 +381,7 @@ class BaseCheckpointSaver(ABC):
             config (RunnableConfig): Configuration for the checkpoint.
             checkpoint (Checkpoint): The checkpoint to store.
             metadata (CheckpointMetadata): Additional metadata for the checkpoint.
+            new_versions (dict): New versions as of this write
 
         Returns:
             RunnableConfig: Updated configuration after storing the checkpoint.
@@ -360,11 +407,9 @@ 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 +423,17 @@ 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/__init__.py

@@ -1,20 +1,26 @@
 import asyncio
 from collections import defaultdict
+from contextlib import AbstractAsyncContextManager, AbstractContextManager
 from functools import partial
+from types import TracebackType
 from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Tuple
 
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
+    ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
     SerializerProtocol,
+    get_checkpoint_id,
 )
 
 
-class MemorySaver(BaseCheckpointSaver):
+class MemorySaver(
+    BaseCheckpointSaver, AbstractContextManager, AbstractAsyncContextManager
+):
     """An in-memory checkpoint saver.
 
     This checkpoint saver stores checkpoints in memory using a defaultdict.
@@ -44,7 +50,8 @@ 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 +59,36 @@ 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 __enter__(self) -> "MemorySaver":
+        return self
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        traceback: Optional[TracebackType],
+    ) -> Optional[bool]:
+        return
+
+    async def __aenter__(self) -> "MemorySaver":
+        return self
+
+    async def __aexit__(
+        self,
+        __exc_type: Optional[type[BaseException]],
+        __exc_value: Optional[BaseException],
+        __traceback: Optional[TracebackType],
+    ) -> Optional[bool]:
+        return
+
     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 +99,54 @@ 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 +173,25 @@ 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 +205,23 @@ 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,
                 )
 
@@ -176,6 +230,7 @@ class MemorySaver(BaseCheckpointSaver):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Save a checkpoint to the in-memory storage.
 
@@ -186,23 +241,27 @@ class MemorySaver(BaseCheckpointSaver):
             config (RunnableConfig): The config to associate with the checkpoint.
             checkpoint (Checkpoint): The checkpoint to save.
             metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
+            new_versions (dict): New versions as of this write
 
         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 +285,11 @@ 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]:
@@ -283,6 +344,7 @@ class MemorySaver(BaseCheckpointSaver):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
     ) -> RunnableConfig:
         """Asynchronous version of put.
 
@@ -290,12 +352,13 @@ class MemorySaver(BaseCheckpointSaver):
             config (RunnableConfig): The config to associate with the checkpoint.
             checkpoint (Checkpoint): The checkpoint to save.
             metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
+            new_versions (dict): New versions as of this write
 
         Returns:
             RunnableConfig: The updated config containing the saved checkpoint's timestamp.
         """
         return await asyncio.get_running_loop().run_in_executor(
-            None, self.put, config, checkpoint, metadata
+            None, self.put, config, checkpoint, metadata, new_versions
         )
 
     async def aput_writes(

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

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -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()
 
@@ -64,10 +64,14 @@ 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}
             )
+        elif isinstance(obj, (bytes, bytearray)):
+            return self._encode_constructor_args(
+                obj.__class__, method="fromhex", args=[obj.hex()]
+            )
         else:
             raise TypeError(
                 f"Object of type {obj.__class__.__name__} is not JSON serializable"
@@ -99,5 +103,24 @@ class JsonPlusSerializer(SerializerProtocol):
             "utf-8", "ignore"
         )
 
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        if isinstance(obj, bytes):
+            return "bytes", obj
+        elif isinstance(obj, bytearray):
+            return "bytearray", obj
+        else:
+            return "json", self.dumps(obj)
+
     def loads(self, data: bytes) -> Any:
         return json.loads(data, object_hook=self._reviver)
+
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        type_, data_ = data
+        if type_ == "bytes":
+            return data_
+        elif type_ == "bytearray":
+            return bytearray(data_)
+        elif type_ == "json":
+            return self.loads(data_)
+        else:
+            raise NotImplementedError(f"Unknown serialization type: {type_}")

libs/checkpoint/langgraph/checkpoint/serde/types.py

@@ -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:
+        ...

libs/checkpoint/poetry.lock

@@ -0,0 +1,850 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
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+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
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+]
+
+[[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-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 = "3bee25f1adc1349de4358a88037693cec47f6a2b88b809490f9198e6313239f6"

libs/checkpoint/pyproject.toml

@@ -0,0 +1,54 @@
+[tool.poetry]
+name = "langgraph-checkpoint"
+version = "1.0.2"
+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-watcher = "^0.4.1"
+mypy = "^1.10.0"
+dataclasses-json = "^0.6.7"
+
+[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/tests/test_jsonplus.py

@@ -0,0 +1,132 @@
+import dataclasses
+import sys
+import uuid
+from datetime import datetime, timezone
+from enum import Enum
+
+import dataclasses_json
+from langchain_core.pydantic_v1 import BaseModel as LcBaseModel
+from langchain_core.runnables import RunnableMap
+from pydantic import BaseModel
+
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+
+class MyPydantic(BaseModel):
+    foo: str
+    bar: int
+
+
+class MyFunnyPydantic(LcBaseModel):
+    foo: str
+    bar: int
+
+
+@dataclasses.dataclass
+class MyDataclass:
+    foo: str
+    bar: int
+
+    def something(self) -> None:
+        pass
+
+
+if sys.version_info < (3, 10):
+
+    class MyDataclassWSlots(MyDataclass):
+        pass
+
+else:
+
+    @dataclasses.dataclass(slots=True)
+    class MyDataclassWSlots:
+        foo: str
+        bar: int
+
+        def something(self) -> None:
+            pass
+
+
+class MyEnum(Enum):
+    FOO = "foo"
+    BAR = "bar"
+
+
+@dataclasses_json.dataclass_json
+@dataclasses.dataclass
+class Person:
+    name: str
+
+
+def test_serde_jsonplus() -> None:
+    uid = uuid.UUID(int=1)
+    current_time = datetime(2024, 4, 19, 23, 4, 57, 51022, timezone.max)
+
+    to_serialize = {
+        "uid": uid,
+        "time": current_time,
+        "my_slotted_class": MyDataclassWSlots("bar", 2),
+        "my_dataclass": MyDataclass("foo", 1),
+        "my_enum": MyEnum.FOO,
+        "my_pydantic": MyPydantic(foo="foo", bar=1),
+        "my_funny_pydantic": MyFunnyPydantic(foo="foo", bar=1),
+        "person": Person(name="foo"),
+        "a_bool": True,
+        "a_none": None,
+        "a_str": "foo",
+        "a_str_nuc": "foo\u0000",
+        "a_str_uc": "foo ⛰️",
+        "a_str_ucuc": "foo \u26f0\ufe0f\u0000",
+        "a_str_ucucuc": "foo \\u26f0\\ufe0f",
+        "text": [
+            "Hello\ud83d\ude00",
+            "Python\ud83d\udc0d",
+            "Surrogate\ud834\udd1e",
+            "Example\ud83c\udf89",
+            "String\ud83c\udfa7",
+            "With\ud83c\udf08",
+            "Surrogates\ud83d\ude0e",
+            "Embedded\ud83d\udcbb",
+            "In\ud83c\udf0e",
+            "The\ud83d\udcd6",
+            "Text\ud83d\udcac",
+            "收花🙄·到",
+        ],
+        "an_int": 1,
+        "a_float": 1.1,
+        "runnable_map": RunnableMap({}),
+    }
+
+    serde = JsonPlusSerializer()
+
+    dumped = serde.dumps_typed(to_serialize)
+
+    assert dumped == (
+        "json",
+        b"""{"uid": {"lc": 2, "type": "constructor", "id": ["uuid", "UUID"], "method": null, "args": ["00000000000000000000000000000001"], "kwargs": {}}, "time": {"lc": 2, "type": "constructor", "id": ["datetime", "datetime"], "method": "fromisoformat", "args": ["2024-04-19T23:04:57.051022+23:59"], "kwargs": {}}, "my_slotted_class": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclassWSlots"], "method": null, "args": [], "kwargs": {"foo": "bar", "bar": 2}}, "my_dataclass": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclass"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_enum": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyEnum"], "method": null, "args": ["foo"], "kwargs": {}}, "my_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_funny_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyFunnyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "person": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "Person"], "method": null, "args": [], "kwargs": {"name": "foo"}}, "a_bool": true, "a_none": null, "a_str": "foo", "a_str_nuc": "foo\\u0000", "a_str_uc": "foo \xe2\x9b\xb0\xef\xb8\x8f", "a_str_ucuc": "foo \xe2\x9b\xb0\xef\xb8\x8f\\u0000", "a_str_ucucuc": "foo \\\\u26f0\\\\ufe0f", "text": ["Hello", "Python", "Surrogate", "Example", "String", "With", "Surrogates", "Embedded", "In", "The", "Text", "\xe6\x94\xb6\xe8\x8a\xb1\xf0\x9f\x99\x84\xc2\xb7\xe5\x88\xb0"], "an_int": 1, "a_float": 1.1, "runnable_map": {"lc": 1, "type": "constructor", "id": ["langchain", "schema", "runnable", "RunnableParallel"], "kwargs": {"steps__": {}}, "name": "RunnableParallel<>", "graph": {"nodes": [{"id": 0, "type": "schema", "data": "Parallel<>Input"}, {"id": 1, "type": "schema", "data": "Parallel<>Output"}], "edges": []}}}""",
+    )
+
+    assert serde.loads_typed(dumped) == {
+        **to_serialize,
+        "text": [v.encode("utf-8", "ignore").decode() for v in to_serialize["text"]],
+    }
+
+
+def test_serde_jsonplus_bytes() -> None:
+    serde = JsonPlusSerializer()
+
+    some_bytes = b"my bytes"
+    dumped = serde.dumps_typed(some_bytes)
+
+    assert dumped == ("bytes", some_bytes)
+    assert serde.loads_typed(dumped) == some_bytes
+
+
+def test_serde_jsonplus_bytearray() -> None:
+    serde = JsonPlusSerializer()
+
+    some_bytearray = bytearray([42])
+    dumped = serde.dumps_typed(some_bytearray)
+
+    assert dumped == ("bytearray", some_bytearray)
+    assert serde.loads_typed(dumped) == some_bytearray

libs/checkpoint/tests/test_memory.py

@@ -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
 
 
@@ -13,14 +17,31 @@ class TestMemorySaver:
 
         # objects for test setup
         self.config_1: RunnableConfig = {
-            "configurable": {"thread_id": "thread-1", "thread_ts": "1"}
+            "configurable": {
+                "thread_id": "thread-1",
+                "checkpoint_ns": "",
+                # for backwards compatibility testing
+                "thread_ts": "1",
+            }
         }
         self.config_2: RunnableConfig = {
-            "configurable": {"thread_id": "thread-2", "thread_ts": "2"}
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_ns": "",
+                "checkpoint_id": "2",
+            }
+        }
+        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",
@@ -34,12 +55,14 @@ class TestMemorySaver:
             "writes": {"foo": "bar"},
             "score": None,
         }
+        self.metadata_3: CheckpointMetadata = {}
 
     async def test_search(self):
         # set up test
         # save checkpoints
-        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1)
-        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2)
+        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
+        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
+        self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
 
         # call method / assertions
         query_1: CheckpointMetadata = {"source": "input"}  # search by 1 key
@@ -64,13 +87,29 @@ class TestMemorySaver:
         search_results_4 = list(self.memory_saver.list(None, filter=query_4))
         assert len(search_results_4) == 0
 
+        # search by config (defaults to root graph checkpoints)
+        search_results_5 = list(
+            self.memory_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(
+            self.memory_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
 
     async def test_asearch(self):
         # set up test
         # save checkpoints
-        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1)
-        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2)
+        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
+        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
 
         # call method / assertions
         query_1: CheckpointMetadata = {"source": "input"}  # search by 1 key

libs/langgraph/Makefile

@@ -14,11 +14,28 @@ coverage:
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
+start-postgres:
+	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait
+
+stop-postgres:
+	docker compose -f tests/compose-postgres.yml down
+
 test:
-	poetry run pytest
+	make start-postgres; \
+	poetry run pytest; \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
 
 test_watch:
-	poetry run ptw .
+	make start-postgres; \
+	poetry run ptw .; \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+test_watch_all:
+	npx concurrently -n langgraph,checkpoint,checkpoint-sqlite,postgres "make test_watch" "make -C ../checkpoint test_watch" "make -C ../checkpoint-sqlite test_watch" "make -C ../checkpoint-postgres test_watch"
 
 ######################
 # LINTING AND FORMATTING

libs/langgraph/README.md

@@ -10,9 +10,6 @@
 > [!NOTE]
 > Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
 
-> [!TIP]
-> Looking to deploy your LangGraph application? [Join the waitlist](https://www.langchain.com/langgraph-cloud-beta) for [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/), our managed service for deploying and hosting LangGraph applications.
-
 ## Overview
 
 [LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
@@ -61,7 +58,7 @@ 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
 

libs/langgraph/langgraph/__init__.py

@@ -1,3 +0,0 @@
-from langgraph.version import __version__
-
-__all__ = ["__version__"]

libs/langgraph/langgraph/_api/deprecation.py

@@ -1,6 +1,6 @@
 import functools
 import warnings
-from typing import Any, Callable, TypeVar, cast
+from typing import Any, Callable, Type, TypeVar, Union, cast
 
 
 class LangGraphDeprecationWarning(DeprecationWarning):
@@ -8,31 +8,57 @@ class LangGraphDeprecationWarning(DeprecationWarning):
 
 
 F = TypeVar("F", bound=Callable[..., Any])
+C = TypeVar("C", bound=Type[Any])
 
 
 def deprecated(
     since: str, alternative: str, *, removal: str = "", example: str = ""
 ) -> Callable[[F], F]:
-    def decorator(func: F) -> F:
-        @functools.wraps(func)
-        def wrapper(*args: Any, **kwargs: Any) -> Any:
-            removal_str = removal if removal else "a future version"
-            message = (
-                f"{func.__name__} is deprecated as of version {since} and will be"
-                f" removed in {removal_str}. Use {alternative} instead.{example}"
-            )
-            warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2)
-            return func(*args, **kwargs)
-
-        docstring = (
-            f"**Deprecated**: This function is deprecated as of version {since}. "
-            f"Use `{alternative}` instead."
+    def decorator(obj: Union[F, C]) -> Union[F, C]:
+        removal_str = removal if removal else "a future version"
+        message = (
+            f"{obj.__name__} is deprecated as of version {since} and will be"
+            f" removed in {removal_str}. Use {alternative} instead.{example}"
         )
-        if func.__doc__:
-            docstring = docstring + f"\n\n{func.__doc__}"
-        wrapper.__doc__ = docstring
+        if isinstance(obj, type):
+            original_init = obj.__init__
 
-        return cast(F, wrapper)
+            @functools.wraps(original_init)
+            def new_init(self, *args: Any, **kwargs: Any) -> None:
+                warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2)
+                original_init(self, *args, **kwargs)
+
+            obj.__init__ = new_init
+
+            docstring = (
+                f"**Deprecated**: This class is deprecated as of version {since}. "
+                f"Use `{alternative}` instead."
+            )
+            if obj.__doc__:
+                docstring = docstring + f"\n\n{obj.__doc__}"
+            obj.__doc__ = docstring
+
+            return cast(C, obj)
+        elif callable(obj):
+
+            @functools.wraps(obj)
+            def wrapper(*args: Any, **kwargs: Any) -> Any:
+                warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2)
+                return obj(*args, **kwargs)
+
+            docstring = (
+                f"**Deprecated**: This function is deprecated as of version {since}. "
+                f"Use `{alternative}` instead."
+            )
+            if obj.__doc__:
+                docstring = docstring + f"\n\n{obj.__doc__}"
+            wrapper.__doc__ = docstring
+
+            return cast(F, wrapper)
+        else:
+            raise TypeError(
+                f"Can only add deprecation decorator to classes or callables, got '{type(obj)}' instead."
+            )
 
     return decorator
 

libs/langgraph/langgraph/channels/__init__.py

@@ -1,11 +1,17 @@
+from langgraph.channels.any_value import AnyValue
 from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.context import Context
+from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
+from langgraph.channels.untracked_value import UntrackedValue
 
 __all__ = [
     "LastValue",
     "Topic",
     "Context",
     "BinaryOperatorAggregate",
+    "UntrackedValue",
+    "EphemeralValue",
+    "AnyValue",
 ]

libs/langgraph/langgraph/channels/manager.py

@@ -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
@@ -40,31 +37,3 @@ async def AsyncChannelsManager(
             )
             for k, v in channels.items()
         }
-
-
-def create_checkpoint(
-    checkpoint: Checkpoint,
-    channels: Mapping[str, BaseChannel],
-    step: int,
-    *,
-    id: Optional[str] = None,
-) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
-    ts = datetime.now(timezone.utc).isoformat()
-    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={},
-    )

libs/langgraph/langgraph/channels/untracked_value.py

@@ -0,0 +1,62 @@
+from contextlib import contextmanager
+from typing import Generator, Generic, Optional, Sequence, Type
+
+from langchain_core.runnables import RunnableConfig
+from typing_extensions import Self
+
+from langgraph.channels.base import BaseChannel, Value
+from langgraph.errors import EmptyChannelError, InvalidUpdateError
+
+
+class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
+    """Stores the last value received, never checkpointed."""
+
+    def __init__(self, typ: Type[Value], guard: bool = True) -> None:
+        self.typ = typ
+        self.guard = guard
+
+    def __eq__(self, value: object) -> bool:
+        return isinstance(value, UntrackedValue) and value.guard == self.guard
+
+    @property
+    def ValueType(self) -> Type[Value]:
+        """The type of the value stored in the channel."""
+        return self.typ
+
+    @property
+    def UpdateType(self) -> Type[Value]:
+        """The type of the update received by the channel."""
+        return self.typ
+
+    def checkpoint(self) -> Value:
+        raise EmptyChannelError()
+
+    @contextmanager
+    def from_checkpoint(
+        self, checkpoint: Optional[Value], config: RunnableConfig
+    ) -> Generator[Self, None, None]:
+        empty = self.__class__(self.typ, self.guard)
+        try:
+            yield empty
+        finally:
+            try:
+                del empty.value
+            except AttributeError:
+                pass
+
+    def update(self, values: Sequence[Value]) -> bool:
+        if len(values) == 0:
+            return False
+        if len(values) != 1 and self.guard:
+            raise InvalidUpdateError(
+                "UntrackedValue can only receive one value per step."
+            )
+
+        self.value = values[-1]
+        return True
+
+    def get(self) -> Value:
+        try:
+            return self.value
+        except AttributeError:
+            raise EmptyChannelError()

libs/langgraph/langgraph/checkpoint/__init__.py

@@ -1,13 +0,0 @@
-from langgraph.checkpoint.base import (
-    BaseCheckpointSaver,
-    Checkpoint,
-    SerializerProtocol,
-)
-from langgraph.checkpoint.memory import MemorySaver
-
-__all__ = [
-    "BaseCheckpointSaver",
-    "Checkpoint",
-    "MemorySaver",
-    "SerializerProtocol",
-]

libs/langgraph/langgraph/constants.py

@@ -21,6 +21,8 @@ TAG_HIDDEN = "langsmith:hidden"
 START = "__start__"
 END = "__end__"
 
+CHECKPOINT_NAMESPACE_SEPARATOR = "|"
+
 
 class Send:
     """A message or packet to send to a specific node in the graph.

libs/langgraph/langgraph/errors.py

@@ -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",
+]

libs/langgraph/langgraph/graph/graph.py

@@ -24,8 +24,14 @@ 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.constants import END, START, TAG_HIDDEN, Send
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.constants import (
+    CHECKPOINT_NAMESPACE_SEPARATOR,
+    END,
+    START,
+    TAG_HIDDEN,
+    Send,
+)
 from langgraph.errors import InvalidUpdateError
 from langgraph.pregel import Channel, Pregel
 from langgraph.pregel.read import PregelNode
@@ -154,6 +160,11 @@ class Graph:
         *,
         metadata: Optional[dict[str, Any]] = None,
     ) -> None:
+        if isinstance(node, str) and CHECKPOINT_NAMESPACE_SEPARATOR in node:
+            raise ValueError(
+                f"'{CHECKPOINT_NAMESPACE_SEPARATOR}' is a reserved character and is not allowed in the node names."
+            )
+
         if self.compiled:
             logger.warning(
                 "Adding a node to a graph that has already been compiled. This will "
@@ -482,6 +493,11 @@ class CompiledGraph(Pregel):
 
         for key, n in self.builder.nodes.items():
             node = n.runnable
+            metadata = n.metadata or {}
+            if key in self.interrupt_before_nodes:
+                metadata["__interrupt"] = "before"
+            elif key in self.interrupt_after_nodes:
+                metadata["__interrupt"] = "after"
             if xray:
                 subgraph = (
                     node.get_graph(
@@ -498,11 +514,11 @@ class CompiledGraph(Pregel):
                         subgraph, prefix=key
                     )
                 else:
-                    n = graph.add_node(node, key)
+                    n = graph.add_node(node, key, metadata=metadata or None)
                     start_nodes[key] = n
                     end_nodes[key] = n
             else:
-                n = graph.add_node(node, key, metadata=n.metadata)
+                n = graph.add_node(node, key, metadata=metadata or None)
                 start_nodes[key] = n
                 end_nodes[key] = n
         for start, end in sorted(self.builder._all_edges):

libs/langgraph/langgraph/graph/state.py

@@ -29,8 +29,8 @@ 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.constants import TAG_HIDDEN
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.constants import CHECKPOINT_NAMESPACE_SEPARATOR, TAG_HIDDEN
 from langgraph.errors import InvalidUpdateError
 from langgraph.graph.graph import (
     END,
@@ -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:
@@ -311,6 +311,12 @@ class StateGraph(Graph):
             raise ValueError(f"Node `{node}` already present.")
         if node == END or node == START:
             raise ValueError(f"Node `{node}` is reserved.")
+
+        if CHECKPOINT_NAMESPACE_SEPARATOR in node:
+            raise ValueError(
+                f"'{CHECKPOINT_NAMESPACE_SEPARATOR}' is a reserved character and is not allowed in the node names."
+            )
+
         try:
             if isfunction(action) and (
                 hints := get_type_hints(action.__call__) or get_type_hints(action)
@@ -412,6 +418,15 @@ class StateGraph(Graph):
                 if not isinstance(val, Context) and not is_managed_value(val)
             ]
         )
+        stream_channels = (
+            "__root__"
+            if len(self.channels) == 1 and "__root__" in self.channels
+            else [
+                key
+                for key, val in self.channels.items()
+                if not isinstance(val, Context) and not is_managed_value(val)
+            ]
+        )
 
         compiled = CompiledStateGraph(
             builder=self,
@@ -421,7 +436,7 @@ class StateGraph(Graph):
             input_channels=START,
             stream_mode="updates",
             output_channels=output_channels,
-            stream_channels=output_channels,
+            stream_channels=stream_channels,
             checkpointer=checkpointer,
             interrupt_before_nodes=interrupt_before,
             interrupt_after_nodes=interrupt_after,
@@ -610,7 +625,7 @@ class CompiledStateGraph(CompiledGraph):
                 if branch.then and branch.then != END:
                     writes.append(
                         ChannelWriteEntry(
-                            f"branch:{start}:{name}:then",
+                            f"branch:{start}:{name}::then",
                             WaitForNames(
                                 {p.node if isinstance(p, Send) else p for p in filtered}
                             ),
@@ -630,12 +645,12 @@ class CompiledStateGraph(CompiledGraph):
         for end in ends:
             if end != END:
                 channel_name = f"branch:{start}:{name}:{end}"
-                self.channels[channel_name] = EphemeralValue(Any)
+                self.channels[channel_name] = EphemeralValue(Any, guard=False)
                 self.nodes[end].triggers.append(channel_name)
 
         # attach then subscriber
         if branch.then and branch.then != END:
-            channel_name = f"branch:{start}:{name}:then"
+            channel_name = f"branch:{start}:{name}::then"
             self.channels[channel_name] = DynamicBarrierValue(str)
             self.nodes[branch.then].triggers.append(channel_name)
             for end in ends:
@@ -699,6 +714,8 @@ def _is_field_channel(typ: Type[Any]) -> Optional[BaseChannel]:
         meta = typ.__metadata__
         if len(meta) >= 1 and isinstance(meta[-1], BaseChannel):
             return meta[-1]
+        elif len(meta) >= 1 and isclass(meta[-1]) and issubclass(meta[-1], BaseChannel):
+            return meta[-1](typ.__origin__ if hasattr(typ, "__origin__") else typ)
     return None
 
 

libs/langgraph/langgraph/managed/base.py

@@ -17,26 +17,23 @@ from typing import (
 from langchain_core.runnables import RunnableConfig
 from typing_extensions import Self, TypeGuard
 
-from langgraph.pregel.types import PregelTaskDescription
-
 if TYPE_CHECKING:
-    from langgraph.pregel import Pregel
+    from langgraph.pregel.types import PregelTaskDescription
 
 V = TypeVar("V")
 
 
 class ManagedValue(ABC, Generic[V]):
-    def __init__(self, config: RunnableConfig, graph: "Pregel") -> None:
+    def __init__(self, config: RunnableConfig) -> None:
         self.config = config
-        self.graph = graph
 
     @classmethod
     @contextmanager
     def enter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
+        cls, config: RunnableConfig, **kwargs: Any
     ) -> Generator[Self, None, None]:
         try:
-            value = cls(config, graph, **kwargs)
+            value = cls(config, **kwargs)
             yield value
         finally:
             # because managed value and Pregel have reference to each other
@@ -49,10 +46,10 @@ class ManagedValue(ABC, Generic[V]):
     @classmethod
     @asynccontextmanager
     async def aenter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
+        cls, config: RunnableConfig, **kwargs: Any
     ) -> AsyncGenerator[Self, None]:
         try:
-            value = cls(config, graph, **kwargs)
+            value = cls(config, **kwargs)
             yield value
         finally:
             # because managed value and Pregel have reference to each other
@@ -63,7 +60,7 @@ class ManagedValue(ABC, Generic[V]):
                 pass
 
     @abstractmethod
-    def __call__(self, step: int, task: PregelTaskDescription) -> V:
+    def __call__(self, step: int, task: "PregelTaskDescription") -> V:
         ...
 
 
@@ -87,15 +84,14 @@ def is_managed_value(value: Any) -> TypeGuard[ManagedValueSpec]:
 def ManagedValuesManager(
     values: dict[str, ManagedValueSpec],
     config: RunnableConfig,
-    graph: "Pregel",
 ) -> Generator[ManagedValueMapping, None, None]:
     if values:
         with ExitStack() as stack:
             yield {
                 key: stack.enter_context(
-                    value.cls.enter(config, graph, **value.kwargs)
+                    value.cls.enter(config, **value.kwargs)
                     if isinstance(value, ConfiguredManagedValue)
-                    else value.enter(config, graph)
+                    else value.enter(config)
                 )
                 for key, value in values.items()
             }
@@ -107,7 +103,6 @@ def ManagedValuesManager(
 async def AsyncManagedValuesManager(
     values: dict[str, ManagedValueSpec],
     config: RunnableConfig,
-    graph: "Pregel",
 ) -> AsyncGenerator[ManagedValueMapping, None]:
     if values:
         async with AsyncExitStack() as stack:
@@ -115,9 +110,9 @@ async def AsyncManagedValuesManager(
             tasks = {
                 asyncio.create_task(
                     stack.enter_async_context(
-                        value.cls.aenter(config, graph, **value.kwargs)
+                        value.cls.aenter(config, **value.kwargs)
                         if isinstance(value, ConfiguredManagedValue)
-                        else value.aenter(config, graph)
+                        else value.aenter(config)
                     )
                 ): key
                 for key, value in values.items()

libs/langgraph/langgraph/managed/few_shot.py

@@ -1,105 +0,0 @@
-from contextlib import asynccontextmanager, contextmanager
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    AsyncGenerator,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Generator,
-    Generic,
-    Iterator,
-    Optional,
-    Sequence,
-    Union,
-)
-
-from langchain_core.runnables import RunnableConfig
-from typing_extensions import Self
-
-from langgraph.channels.manager import AsyncChannelsManager, ChannelsManager
-from langgraph.managed.base import ConfiguredManagedValue, ManagedValue, V
-from langgraph.pregel import Pregel
-from langgraph.pregel.io import read_channels
-from langgraph.pregel.types import PregelTaskDescription
-
-if TYPE_CHECKING:
-    from langgraph.pregel import Pregel
-
-# Metadata filter can be a dict (static) or a function (dynamic) that takes a
-# RunnableConfig and returns a dict. Functions are used for filtering on
-# metadata values that are only available at runtime.
-MetadataFilter = Union[Dict[str, Any], Callable[[RunnableConfig], Dict[str, Any]]]
-
-
-class FewShotExamples(ManagedValue[Sequence[V]], Generic[V]):
-    examples: list[V]
-
-    def __init__(
-        self,
-        config: RunnableConfig,
-        graph: Pregel,
-        k: int = 5,
-        metadata_filter: Optional[MetadataFilter] = None,
-    ) -> None:
-        super().__init__(config, graph)
-        self.k = k
-        self.metadata_filter = metadata_filter or {}
-
-    @classmethod
-    def configure(
-        cls, k: int = 5, metadata_filter: Optional[MetadataFilter] = None
-    ) -> ConfiguredManagedValue:
-        return ConfiguredManagedValue(
-            cls,
-            {
-                "k": k,
-                "metadata_filter": metadata_filter,
-            },
-        )
-
-    @property
-    def metadata_filter_dict(self) -> Dict[str, Any]:
-        if isinstance(self.metadata_filter, Callable):
-            return self.metadata_filter(self.config)
-        else:
-            return self.metadata_filter
-
-    def iter(self, score: int = 1) -> Iterator[V]:
-        for example in self.graph.checkpointer.list(
-            None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k
-        ):
-            with ChannelsManager(
-                self.graph.channels, example.checkpoint, self.config
-            ) as channels:
-                yield read_channels(channels, self.graph.output_channels)
-
-    async def aiter(self, score: int = 1) -> AsyncIterator[V]:
-        async for example in self.graph.checkpointer.alist(
-            None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k
-        ):
-            async with AsyncChannelsManager(
-                self.graph.channels, example.checkpoint, self.config
-            ) as channels:
-                yield read_channels(channels, self.graph.output_channels)
-
-    @classmethod
-    @contextmanager
-    def enter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
-    ) -> Generator[Self, None, None]:
-        with super().enter(config, graph, **kwargs) as value:
-            value.examples = list(value.iter())
-            yield value
-
-    @classmethod
-    @asynccontextmanager
-    async def aenter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
-    ) -> AsyncGenerator[Self, None]:
-        async with super().aenter(config, graph, **kwargs) as value:
-            value.examples = [e async for e in value.aiter()]
-            yield value
-
-    def __call__(self, step: int, task: PregelTaskDescription) -> Sequence[V]:
-        return self.examples

libs/langgraph/langgraph/prebuilt/__init__.py

@@ -1,14 +1,10 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
-from langgraph.prebuilt import chat_agent_executor
-from langgraph.prebuilt.agent_executor import create_agent_executor
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
 from langgraph.prebuilt.tool_node import InjectedState, ToolNode, tools_condition
 from langgraph.prebuilt.tool_validator import ValidationNode
 
 __all__ = [
-    "create_agent_executor",
-    "chat_agent_executor",
     "create_react_agent",
     "ToolExecutor",
     "ToolInvocation",

libs/langgraph/langgraph/prebuilt/agent_executor.py

@@ -1,181 +0,0 @@
-import operator
-from typing import Annotated, Sequence, TypedDict, Union
-
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.messages import BaseMessage
-
-from langgraph._api.deprecation import deprecated
-from langgraph.graph import END, StateGraph
-from langgraph.graph.state import CompiledStateGraph
-from langgraph.prebuilt.tool_executor import ToolExecutor
-from langgraph.utils import RunnableCallable
-
-
-def _get_agent_state(input_schema=None):
-    if input_schema is None:
-
-        class AgentState(TypedDict):
-            # The input string
-            input: str
-            # The list of previous messages in the conversation
-            chat_history: Sequence[BaseMessage]
-            # The outcome of a given call to the agent
-            # Needs `None` as a valid type, since this is what this will start as
-            agent_outcome: Union[AgentAction, AgentFinish, None]
-            # List of actions and corresponding observations
-            # Here we annotate this with `operator.add` to indicate that operations to
-            # this state should be ADDED to the existing values (not overwrite it)
-            intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]
-
-    else:
-
-        class AgentState(input_schema):
-            # The outcome of a given call to the agent
-            # Needs `None` as a valid type, since this is what this will start as
-            agent_outcome: Union[AgentAction, AgentFinish, None]
-            # List of actions and corresponding observations
-            # Here we annotate this with `operator.add` to indicate that operations to
-            # this state should be ADDED to the existing values (not overwrite it)
-            intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]
-
-    return AgentState
-
-
-@deprecated(
-    "0.0.44",
-    alternative="create_react_agent",
-    removal="0.2.0",
-    example="""
-from langgraph.prebuilt import create_react_agent 
-
-create_react_agent(...)
-""",
-)
-def create_agent_executor(
-    agent_runnable, tools, input_schema=None
-) -> CompiledStateGraph:
-    """This is a helper function for creating a graph that works with LangChain Agents.
-
-    Args:
-        agent_runnable (RunnableLike): The agent runnable.
-        tools (list): A list of tools to be used by the agent.
-        input_schema (dict, optional): The input schema for the agent. Defaults to None.
-
-    Returns:
-        The `CompiledStateGraph` object.
-
-
-    Examples:
-
-        # Since this is deprecated, you should use `create_react_agent` instead.
-        # Example usage:
-        from langgraph.prebuilt import create_react_agent
-        from langchain_openai import ChatOpenAI
-        from langchain_community.tools.tavily_search import TavilySearchResults
-
-        tools = [TavilySearchResults(max_results=1)]
-        model = ChatOpenAI()
-
-        app = create_react_agent(model, tools)
-
-        inputs = {"messages": [("user", "what is the weather in sf")]}
-        for s in app.stream(inputs):
-            print(list(s.values())[0])
-            print("----")
-    """
-
-    if isinstance(tools, ToolExecutor):
-        tool_executor = tools
-    else:
-        tool_executor = ToolExecutor(tools)
-
-    state = _get_agent_state(input_schema)
-
-    # Define logic that will be used to determine which conditional edge to go down
-
-    def should_continue(data):
-        # If the agent outcome is an AgentFinish, then we return `exit` string
-        # This will be used when setting up the graph to define the flow
-        if isinstance(data["agent_outcome"], AgentFinish):
-            return "end"
-        # Otherwise, an AgentAction is returned
-        # Here we return `continue` string
-        # This will be used when setting up the graph to define the flow
-        else:
-            return "continue"
-
-    def run_agent(data, config):
-        agent_outcome = agent_runnable.invoke(data, config)
-        return {"agent_outcome": agent_outcome}
-
-    async def arun_agent(data, config):
-        agent_outcome = await agent_runnable.ainvoke(data, config)
-        return {"agent_outcome": agent_outcome}
-
-    # Define the function to execute tools
-    def execute_tools(data, config):
-        # Get the most recent agent_outcome - this is the key added in the `agent` above
-        agent_action = data["agent_outcome"]
-        if not isinstance(agent_action, list):
-            agent_action = [agent_action]
-        output = tool_executor.batch(agent_action, config, return_exceptions=True)
-        return {
-            "intermediate_steps": [
-                (action, str(out)) for action, out in zip(agent_action, output)
-            ]
-        }
-
-    async def aexecute_tools(data, config):
-        # Get the most recent agent_outcome - this is the key added in the `agent` above
-        agent_action = data["agent_outcome"]
-        if not isinstance(agent_action, list):
-            agent_action = [agent_action]
-        output = await tool_executor.abatch(
-            agent_action, config, return_exceptions=True
-        )
-        return {
-            "intermediate_steps": [
-                (action, str(out)) for action, out in zip(agent_action, output)
-            ]
-        }
-
-    # Define a new graph
-    workflow = StateGraph(state)
-
-    # Define the two nodes we will cycle between
-    workflow.add_node("agent", RunnableCallable(run_agent, arun_agent))
-    workflow.add_node("tools", RunnableCallable(execute_tools, aexecute_tools))
-
-    # Set the entrypoint as `agent`
-    # This means that this node is the first one called
-    workflow.set_entry_point("agent")
-
-    # We now add a conditional edge
-    workflow.add_conditional_edges(
-        # First, we define the start node. We use `agent`.
-        # This means these are the edges taken after the `agent` node is called.
-        "agent",
-        # Next, we pass in the function that will determine which node is called next.
-        should_continue,
-        # Finally we pass in a mapping.
-        # The keys are strings, and the values are other nodes.
-        # END is a special node marking that the graph should finish.
-        # What will happen is we will call `should_continue`, and then the output of that
-        # will be matched against the keys in this mapping.
-        # Based on which one it matches, that node will then be called.
-        {
-            # If `tools`, then we call the tool node.
-            "continue": "tools",
-            # Otherwise we finish.
-            "end": END,
-        },
-    )
-
-    # We now add a normal edge from `tools` to `agent`.
-    # This means that after `tools` is called, `agent` node is called next.
-    workflow.add_edge("tools", "agent")
-
-    # Finally, we compile it!
-    # This compiles it into a LangChain Runnable,
-    # meaning you can use it as you would any other runnable
-    return workflow.compile()

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -1,5 +1,3 @@
-import json
-import types
 from typing import (
     Annotated,
     Callable,
@@ -15,20 +13,18 @@ from langchain_core.language_models import LanguageModelLike
 from langchain_core.messages import (
     AIMessage,
     BaseMessage,
-    FunctionMessage,
     SystemMessage,
 )
 from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda
 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._api.deprecation import deprecated_parameter
+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
 from langgraph.managed import IsLastStep
-from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
+from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.prebuilt.tool_node import ToolNode
 
 
@@ -64,136 +60,6 @@ StateModifier = Union[
 ]
 
 
-@deprecated("0.0.44", "create_react_agent", removal="0.2.0")
-def create_function_calling_executor(
-    model: LanguageModelLike, tools: Union[ToolExecutor, Sequence[BaseTool]]
-) -> CompiledGraph:
-    """Creates a graph that works with a chat model that utilizes function calling.
-
-    Examples:
-        ```pycon
-        >>> # Since this is deprecated, you should use `create_react_agent` instead.
-        >>> # Example usage:
-        >>> from langgraph.prebuilt import create_react_agent
-        >>> from langchain_openai import ChatOpenAI
-        >>> from langchain_community.tools.tavily_search import TavilySearchResults
-        >>>
-        >>> tools = [TavilySearchResults(max_results=1)]
-        >>> model = ChatOpenAI()
-        >>>
-        >>> app = create_react_agent(model, tools)
-        >>> inputs = {"messages": [("user", "what is the weather in sf")]}
-        >>> for s in app.stream(inputs):
-        ...     print(list(s.values())[0])
-        ...     print("----")
-        ```
-    """
-    if isinstance(tools, ToolExecutor):
-        tool_executor = tools
-        tool_classes = tools.tools
-    else:
-        tool_executor = ToolExecutor(tools)
-        tool_classes = tools
-    model = model.bind(functions=[convert_to_openai_function(t) for t in tool_classes])
-
-    # Define the function that determines whether to continue or not
-    def should_continue(state: AgentState):
-        messages = state["messages"]
-        last_message = messages[-1]
-        # If there is no function call, then we finish
-        if "function_call" not in last_message.additional_kwargs:
-            return "end"
-        # Otherwise if there is, we continue
-        else:
-            return "continue"
-
-    # Define the function that calls the model
-    def call_model(state: AgentState, config: RunnableConfig):
-        messages = state["messages"]
-        response = model.invoke(messages, config)
-        # We return a list, because this will get added to the existing list
-        return {"messages": [response]}
-
-    async def acall_model(state: AgentState, config: RunnableConfig):
-        messages = state["messages"]
-        response = await model.ainvoke(messages, config)
-        # We return a list, because this will get added to the existing list
-        return {"messages": [response]}
-
-    # Define the function to execute tools
-    def _get_action(state: AgentState):
-        messages = state["messages"]
-        # Based on the continue condition
-        # we know the last message involves a function call
-        last_message = messages[-1]
-        # We construct an AgentAction from the function_call
-        return ToolInvocation(
-            tool=last_message.additional_kwargs["function_call"]["name"],
-            tool_input=json.loads(
-                last_message.additional_kwargs["function_call"]["arguments"]
-            ),
-        )
-
-    def call_tool(state: AgentState, config: RunnableConfig):
-        action = _get_action(state)
-        # We call the tool_executor and get back a response
-        response = tool_executor.invoke(action, config)
-        # We use the response to create a FunctionMessage
-        function_message = FunctionMessage(content=str(response), name=action.tool)
-        # We return a list, because this will get added to the existing list
-        return {"messages": [function_message]}
-
-    async def acall_tool(state: AgentState, config: RunnableConfig):
-        action = _get_action(state)
-        # We call the tool_executor and get back a response
-        response = await tool_executor.ainvoke(action, config)
-        # We use the response to create a FunctionMessage
-        function_message = FunctionMessage(content=str(response), name=action.tool)
-        # We return a list, because this will get added to the existing list
-        return {"messages": [function_message]}
-
-    # Define a new graph
-    workflow = StateGraph(AgentState)
-
-    # Define the two nodes we will cycle between
-    workflow.add_node("agent", RunnableLambda(call_model, acall_model))
-    workflow.add_node("tools", RunnableLambda(call_tool, acall_tool))
-
-    # Set the entrypoint as `agent`
-    # This means that this node is the first one called
-    workflow.set_entry_point("agent")
-
-    # We now add a conditional edge
-    workflow.add_conditional_edges(
-        # First, we define the start node. We use `agent`.
-        # This means these are the edges taken after the `agent` node is called.
-        "agent",
-        # Next, we pass in the function that will determine which node is called next.
-        should_continue,
-        # Finally we pass in a mapping.
-        # The keys are strings, and the values are other nodes.
-        # END is a special node marking that the graph should finish.
-        # What will happen is we will call `should_continue`, and then the output of that
-        # will be matched against the keys in this mapping.
-        # Based on which one it matches, that node will then be called.
-        {
-            # If `tools`, then we call the tool node.
-            "continue": "tools",
-            # Otherwise we finish.
-            "end": END,
-        },
-    )
-
-    # We now add a normal edge from `tools` to `agent`.
-    # This means that after `tools` is called, `agent` node is called next.
-    workflow.add_edge("tools", "agent")
-
-    # Finally, we compile it!
-    # This compiles it into a LangChain Runnable,
-    # meaning you can use it as you would any other runnable
-    return workflow.compile()
-
-
 def _get_state_modifier_runnable(state_modifier: Optional[StateModifier]) -> Runnable:
     state_modifier_runnable: Runnable
     if state_modifier is None:
@@ -231,7 +97,7 @@ def _convert_messages_modifier_to_state_modifier(
     state_modifier: StateModifier
     if isinstance(messages_modifier, (str, SystemMessage)):
         return messages_modifier
-    elif isinstance(messages_modifier, types.FunctionType):
+    elif callable(messages_modifier):
 
         def state_modifier(state: AgentState) -> Sequence[BaseMessage]:
             return messages_modifier(state["messages"])
@@ -261,7 +127,7 @@ def _get_model_preprocessing_runnable(
     return _get_state_modifier_runnable(state_modifier)
 
 
-@deprecated_parameter("messages_modifier", "0.1.9", "state_modifier", removal="0.2.0")
+@deprecated_parameter("messages_modifier", "0.1.9", "state_modifier", removal="0.3.0")
 def create_react_agent(
     model: LanguageModelLike,
     tools: Union[ToolExecutor, Sequence[BaseTool]],
@@ -472,7 +338,7 @@ 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):
@@ -608,7 +474,7 @@ def create_react_agent(
 
     # Define the two nodes we will cycle between
     workflow.add_node("agent", RunnableLambda(call_model, acall_model))
-    workflow.add_node("tools", ToolNode(tools))
+    workflow.add_node("tools", ToolNode(tool_classes))
 
     # Set the entrypoint as `agent`
     # This means that this node is the first one called
@@ -656,6 +522,5 @@ create_tool_calling_executor = create_react_agent
 __all__ = [
     "create_react_agent",
     "create_tool_calling_executor",
-    "create_function_calling_executor",
     "AgentState",
 ]

libs/langgraph/langgraph/prebuilt/tool_executor.py

@@ -5,6 +5,7 @@ from langchain_core.runnables import RunnableConfig
 from langchain_core.tools import BaseTool
 from langchain_core.tools import tool as create_tool
 
+from langgraph._api.deprecation import deprecated
 from langgraph.utils import RunnableCallable
 
 INVALID_TOOL_MSG_TEMPLATE = (
@@ -13,6 +14,7 @@ INVALID_TOOL_MSG_TEMPLATE = (
 )
 
 
+@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
 class ToolInvocationInterface:
     """Interface for invoking a tool.
 
@@ -26,6 +28,7 @@ class ToolInvocationInterface:
     tool_input: Union[str, dict]
 
 
+@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
 class ToolInvocation(Serializable):
     """Information about how to invoke a tool.
 
@@ -47,6 +50,7 @@ class ToolInvocation(Serializable):
     tool_input: Union[str, dict]
 
 
+@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
 class ToolExecutor(RunnableCallable):
     """Executes a tool invocation.
 

libs/langgraph/langgraph/prebuilt/tool_node.py

@@ -19,7 +19,7 @@ from langchain_core.runnables import RunnableConfig
 from langchain_core.runnables.config import get_config_list, get_executor_for_config
 from langchain_core.tools import BaseTool, InjectedToolArg
 from langchain_core.tools import tool as create_tool
-from typing_extensions import get_args, get_origin
+from typing_extensions import get_args
 
 from langgraph.utils import RunnableCallable
 
@@ -319,28 +319,16 @@ class InjectedState(InjectedToolArg):
         self.field = field
 
 
-def _get_injections(type_: type) -> List[str]:
-    def check_args(args: Sequence) -> bool:
-        return [
-            arg
-            for arg in args[1:]
-            if isinstance(arg, InjectedState)
-            or (isinstance(arg, type) and issubclass(arg, InjectedState))
-        ]
-
-    args = get_args(type_)
-
-    if get_origin(type_) in (Optional, Union):
-        # Pydantic will type Annotated[Any, InjectedState] as typing.Optional[Annotated[Any, InjectedState]]
-        return [inj for arg in args for inj in check_args(get_args(arg))]
-    return check_args(args)
-
-
 def _get_state_args(tool: BaseTool) -> Dict[str, Optional[str]]:
     full_schema = tool.get_input_schema()
     tool_args_to_state_fields: Dict = {}
     for name, type_ in full_schema.__annotations__.items():
-        injections = _get_injections(type_)
+        injections = [
+            type_arg
+            for type_arg in get_args(type_)
+            if isinstance(type_arg, InjectedState)
+            or (isinstance(type_arg, type) and issubclass(type_arg, InjectedState))
+        ]
         if len(injections) > 1:
             raise ValueError(
                 "A tool argument should not be annotated with InjectedState more than "

libs/langgraph/langgraph/pregel/__init__.py

@@ -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 (
@@ -361,7 +361,7 @@ class Pregel(
         with ChannelsManager(
             self.channels, checkpoint, config
         ) as channels, ManagedValuesManager(
-            self.managed_values_dict, ensure_config(config), self
+            self.managed_values_dict, ensure_config(config)
         ) as managed:
             next_tasks = prepare_next_tasks(
                 checkpoint,
@@ -393,7 +393,7 @@ class Pregel(
         async with AsyncChannelsManager(
             self.channels, checkpoint, config
         ) as channels, AsyncManagedValuesManager(
-            self.managed_values_dict, ensure_config(config), self
+            self.managed_values_dict, ensure_config(config)
         ) as managed:
             next_tasks = prepare_next_tasks(
                 checkpoint,
@@ -435,7 +435,7 @@ class Pregel(
             with ChannelsManager(
                 self.channels, checkpoint, config
             ) as channels, ManagedValuesManager(
-                self.managed_values_dict, ensure_config(config), self
+                self.managed_values_dict, ensure_config(config)
             ) as managed:
                 next_tasks = prepare_next_tasks(
                     checkpoint,
@@ -481,7 +481,7 @@ class Pregel(
             async with AsyncChannelsManager(
                 self.channels, checkpoint, config
             ) as channels, AsyncManagedValuesManager(
-                self.managed_values_dict, ensure_config(config), self
+                self.managed_values_dict, ensure_config(config)
             ) as managed:
                 next_tasks = prepare_next_tasks(
                     checkpoint,
@@ -504,7 +504,7 @@ class Pregel(
     def update_state(
         self,
         config: RunnableConfig,
-        values: dict[str, Any] | Any,
+        values: Optional[Union[dict[str, Any], Any]],
         as_node: Optional[str] = None,
     ) -> RunnableConfig:
         """Update the state of the graph with the given values, as if they came from
@@ -517,8 +517,36 @@ class Pregel(
         # get last checkpoint
         saved = self.checkpointer.get_tuple(config)
         checkpoint = copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+        step = saved.metadata.get("step", -1) if saved else -1
+        # merge configurable fields with previous checkpoint config
+        checkpoint_config = {
+            **config,
+            "configurable": {
+                **config["configurable"],
+                # TODO: add proper support for updating nested subgraph state
+                "checkpoint_ns": "",
+            },
+        }
+        if saved:
+            checkpoint_config = {
+                "configurable": {
+                    **config.get("configurable", {}),
+                    **saved.config["configurable"],
+                }
+            }
         # find last node that updated the state, if not provided
-        if as_node is None and not any(
+        if values is None and as_node is None:
+            return self.checkpointer.put(
+                checkpoint_config,
+                create_checkpoint(checkpoint, None, step),
+                {
+                    "source": "update",
+                    "step": step,
+                    "writes": {},
+                },
+                {},
+            )
+        elif as_node is None and not any(
             v for vv in checkpoint["versions_seen"].values() for v in vv.values()
         ):
             if (
@@ -577,26 +605,16 @@ class Pregel(
             apply_writes(
                 checkpoint, channels, [task], self.checkpointer.get_next_version
             )
-            step = saved.metadata.get("step", -2) + 1 if saved else -1
-
-            # merge configurable fields with previous checkpoint config
-            checkpoint_config = config
-            if saved:
-                checkpoint_config = {
-                    "configurable": {
-                        **config.get("configurable", {}),
-                        **saved.config["configurable"],
-                    }
-                }
 
             return self.checkpointer.put(
                 checkpoint_config,
-                create_checkpoint(checkpoint, channels, step),
+                create_checkpoint(checkpoint, channels, step + 1),
                 {
                     "source": "update",
-                    "step": step,
+                    "step": step + 1,
                     "writes": {as_node: values},
                 },
+                {},
             )
 
     async def aupdate_state(
@@ -611,8 +629,36 @@ class Pregel(
         # get last checkpoint
         saved = await self.checkpointer.aget_tuple(config)
         checkpoint = copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
+        step = saved.metadata.get("step", -1) if saved else -1
+        # merge configurable fields with previous checkpoint config
+        checkpoint_config = {
+            **config,
+            "configurable": {
+                **config["configurable"],
+                # TODO: add proper support for updating nested subgraph state
+                "checkpoint_ns": "",
+            },
+        }
+        if saved:
+            checkpoint_config = {
+                "configurable": {
+                    **config.get("configurable", {}),
+                    **saved.config["configurable"],
+                }
+            }
         # find last node that updated the state, if not provided
-        if as_node is None and not saved:
+        if values is None and as_node is None:
+            return await self.checkpointer.aput(
+                checkpoint_config,
+                create_checkpoint(checkpoint, None, step),
+                {
+                    "source": "update",
+                    "step": step,
+                    "writes": {},
+                },
+                {},
+            )
+        elif as_node is None and not saved:
             if (
                 isinstance(self.input_channels, str)
                 and self.input_channels in self.nodes
@@ -669,26 +715,16 @@ class Pregel(
             apply_writes(
                 checkpoint, channels, [task], self.checkpointer.get_next_version
             )
-            step = saved.metadata.get("step", -2) + 1 if saved else -1
-
-            # merge configurable fields with previous checkpoint config
-            checkpoint_config = config
-            if saved:
-                checkpoint_config = {
-                    "configurable": {
-                        **config.get("configurable", {}),
-                        **saved.config["configurable"],
-                    }
-                }
 
             return await self.checkpointer.aput(
                 checkpoint_config,
-                create_checkpoint(checkpoint, channels, step),
+                create_checkpoint(checkpoint, channels, step + 1),
                 {
                     "source": "update",
-                    "step": step,
+                    "step": step + 1,
                     "writes": {as_node: values},
                 },
+                {},
             )
 
     def _defaults(

libs/langgraph/langgraph/pregel/algo.py

@@ -25,9 +25,15 @@ 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 (
+    CHECKPOINT_NAMESPACE_SEPARATOR,
     CONFIG_KEY_CHECKPOINTER,
     CONFIG_KEY_READ,
     CONFIG_KEY_RESUMING,
@@ -214,6 +220,7 @@ def prepare_next_tasks(
     managed: ManagedValueMapping,
     config: RunnableConfig,
     step: int,
+    *,
     for_execution: Literal[False],
     is_resuming: bool = False,
     checkpointer: Literal[None] = None,
@@ -230,6 +237,7 @@ def prepare_next_tasks(
     managed: ManagedValueMapping,
     config: RunnableConfig,
     step: int,
+    *,
     for_execution: Literal[True],
     is_resuming: bool,
     checkpointer: Optional[BaseCheckpointSaver],
@@ -251,6 +259,7 @@ def prepare_next_tasks(
     checkpointer: Optional[BaseCheckpointSaver] = None,
     manager: Union[None, ParentRunManager, AsyncParentRunManager] = None,
 ) -> Union[list[PregelTaskDescription], list[PregelExecutableTask]]:
+    parent_ns = config.get("configurable", {}).get("checkpoint_ns", "")
     tasks: Union[list[PregelTaskDescription], list[PregelExecutableTask]] = []
     # Consume pending packets
     for packet in checkpoint["pending_sends"]:
@@ -270,7 +279,14 @@ def prepare_next_tasks(
                     "langgraph_triggers": triggers,
                     "langgraph_task_idx": len(tasks),
                 }
-                task_id = str(uuid5(UUID(checkpoint["id"]), json.dumps(metadata)))
+                checkpoint_ns = (
+                    f"{parent_ns}{CHECKPOINT_NAMESPACE_SEPARATOR}{packet.node}"
+                    if parent_ns
+                    else packet.node
+                )
+                task_id = str(
+                    uuid5(UUID(checkpoint["id"]), json.dumps((checkpoint_ns, metadata)))
+                )
                 writes = deque()
                 tasks.append(
                     PregelExecutableTask(
@@ -344,13 +360,18 @@ def prepare_next_tasks(
                         "langgraph_triggers": triggers,
                         "langgraph_task_idx": len(tasks),
                     }
-                    task_id = str(uuid5(UUID(checkpoint["id"]), json.dumps(metadata)))
-                    if parent_thread_id := config.get("configurable", {}).get(
-                        "thread_id"
-                    ):
-                        thread_id: Optional[str] = f"{parent_thread_id}-{name}"
-                    else:
-                        thread_id = None
+                    checkpoint_ns = (
+                        f"{parent_ns}{CHECKPOINT_NAMESPACE_SEPARATOR}{name}"
+                        if parent_ns
+                        else name
+                    )
+                    task_id = str(
+                        uuid5(
+                            UUID(checkpoint["id"]),
+                            json.dumps((checkpoint_ns, metadata)),
+                        )
+                    )
+
                     writes = deque()
                     tasks.append(
                         PregelExecutableTask(
@@ -384,8 +405,8 @@ def prepare_next_tasks(
                                     ),
                                     CONFIG_KEY_CHECKPOINTER: checkpointer,
                                     CONFIG_KEY_RESUMING: is_resuming,
-                                    "thread_id": thread_id,
-                                    "thread_ts": checkpoint["id"],
+                                    "checkpoint_id": checkpoint["id"],
+                                    "checkpoint_ns": checkpoint_ns,
                                 },
                             ),
                             triggers,

libs/langgraph/langgraph/pregel/debug.py

@@ -71,7 +71,7 @@ def map_debug_tasks(
             continue
 
         metadata = config["metadata"].copy()
-        metadata.pop("thread_ts", None)
+        metadata.pop("checkpoint_id", None)
 
         yield {
             "type": "task",
@@ -97,7 +97,8 @@ def map_debug_task_results(
             continue
 
         metadata = config["metadata"].copy()
-        metadata.pop("thread_ts", None)
+        metadata.pop("checkpoint_id", None)
+        # TODO: make task IDs deterministic in tests and reuse task IDs for payload ID
 
         yield {
             "type": "task_result",

libs/langgraph/langgraph/pregel/loop.py

@@ -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
@@ -100,6 +100,8 @@ class PregelLoop:
     checkpoint_config: RunnableConfig
     checkpoint_metadata: CheckpointMetadata
     checkpoint_pending_writes: List[PendingWrite]
+    # (thread_id, checkpoint_ns -> channel_versions)
+    checkpoint_previous_versions: dict[str, Union[str, float, int]]
 
     step: int
     stop: int
@@ -143,7 +145,10 @@ class PregelLoop:
                     **self.checkpoint_config,
                     "configurable": {
                         **self.checkpoint_config["configurable"],
-                        "thread_ts": self.checkpoint["id"],
+                        "checkpoint_ns": self.config["configurable"].get(
+                            "checkpoint_ns", ""
+                        ),
+                        "checkpoint_id": self.checkpoint["id"],
                     },
                 },
                 writes,
@@ -315,8 +320,35 @@ class PregelLoop:
                 # this is achieved by writing child checkpoints as progress is made
                 # (so that error recovery / resuming from interrupt don't lose work)
                 # but doing so always with an id equal to that of the parent checkpoint
-                id=self.config["configurable"]["thread_ts"] if self.is_nested else None,
+                id=self.config["configurable"]["checkpoint_id"]
+                if self.is_nested
+                else None,
             )
+
+            self.checkpoint_config = {
+                **self.checkpoint_config,
+                "configurable": {
+                    **self.checkpoint_config["configurable"],
+                    "checkpoint_ns": self.config["configurable"].get(
+                        "checkpoint_ns", ""
+                    ),
+                },
+            }
+
+            channel_versions = self.checkpoint["channel_versions"].copy()
+            if self.checkpoint_previous_versions:
+                new_versions = {
+                    k: v
+                    for k, v in channel_versions.items()
+                    if k not in self.checkpoint_previous_versions
+                    or k in self.checkpoint_previous_versions
+                    and v > self.checkpoint_previous_versions[k]
+                }
+            else:
+                new_versions = channel_versions
+
+            self.checkpoint_previous_versions = channel_versions
+
             # save it, without blocking
             # if there's a previous checkpoint save in progress, wait for it
             # ensuring checkpointers receive checkpoints in order
@@ -326,12 +358,13 @@ class PregelLoop:
                 self.checkpoint_config,
                 copy_checkpoint(self.checkpoint),
                 self.checkpoint_metadata,
+                new_versions,
             )
             self.checkpoint_config = {
                 **self.checkpoint_config,
                 "configurable": {
                     **self.checkpoint_config["configurable"],
-                    "thread_ts": self.checkpoint["id"],
+                    "checkpoint_id": self.checkpoint["id"],
                 },
             }
             # produce debug output
@@ -384,12 +417,13 @@ class SyncPregelLoop(PregelLoop, ContextManager):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: Optional[dict[str, Union[str, float, int]]],
     ) -> RunnableConfig:
         try:
             if prev is not None:
                 prev.result()
         finally:
-            self.checkpointer.put(config, checkpoint, metadata)
+            self.checkpointer.put(config, checkpoint, metadata, new_versions)
 
     # context manager
 
@@ -414,13 +448,12 @@ class SyncPregelLoop(PregelLoop, ContextManager):
             ChannelsManager(self.graph.channels, self.checkpoint, self.config)
         )
         self.managed = self.stack.enter_context(
-            ManagedValuesManager(
-                self.graph.managed_values_dict, self.config, self.graph
-            )
+            ManagedValuesManager(self.graph.managed_values_dict, self.config)
         )
         self.status = "pending"
         self.step = self.checkpoint_metadata["step"] + 1
         self.stop = self.step + self.config["recursion_limit"] + 1
+        self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
 
         return self
 
@@ -461,12 +494,13 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
         config: RunnableConfig,
         checkpoint: Checkpoint,
         metadata: CheckpointMetadata,
+        new_versions: Optional[dict[str, Union[str, float, int]]],
     ) -> RunnableConfig:
         try:
             if prev is not None:
                 await prev
         finally:
-            await self.checkpointer.aput(config, checkpoint, metadata)
+            await self.checkpointer.aput(config, checkpoint, metadata, new_versions)
 
     # context manager
 
@@ -493,14 +527,14 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
             AsyncChannelsManager(self.graph.channels, self.checkpoint, self.config)
         )
         self.managed = await self.stack.enter_async_context(
-            AsyncManagedValuesManager(
-                self.graph.managed_values_dict, self.config, self.graph
-            )
+            AsyncManagedValuesManager(self.graph.managed_values_dict, self.config)
         )
         self.status = "pending"
         self.step = self.checkpoint_metadata["step"] + 1
         self.stop = self.step + self.config["recursion_limit"] + 1
 
+        self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
+
         return self
 
     async def __aexit__(

libs/langgraph/langgraph/serde/base.py

@@ -1,17 +0,0 @@
-from typing import Any, Protocol
-
-
-class SerializerProtocol(Protocol):
-    """Protocol for serialization and deserialization of objects.
-
-    - `dumps`: Serialize an object to bytes.
-    - `loads`: Deserialize an object from bytes.
-
-    Valid implementations include the `pickle`, `json` and `orjson` modules.
-    """
-
-    def dumps(self, obj: Any) -> bytes:
-        ...
-
-    def loads(self, data: bytes) -> Any:
-        ...

libs/langgraph/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -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"
@@ -1760,13 +1748,13 @@ langchain-core = ">=0.2.2rc1,<0.3"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.22"
+version = "0.2.27"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.22-py3-none-any.whl", hash = "sha256:7731a86440c0958b3186c003fb9b26b2d5a682a6344bda7bfb9174e2898f8b43"},
-    {file = "langchain_core-0.2.22.tar.gz", hash = "sha256:582d6f929a43b830139444e4124123cd415331ad62f25757b1406252958cdcac"},
+    {file = "langchain_core-0.2.27-py3-none-any.whl", hash = "sha256:b12f58d4e3590e8e0b4b727acb0457b00e80862d1bf8b9d1ae36128adb08a7d0"},
+    {file = "langchain_core-0.2.27.tar.gz", hash = "sha256:5d2e4b9bc84285bbfe19864363b1a21dd04c2eed12d4a6b39f239ee02237ce40"},
 ]
 
 [package.dependencies]
@@ -1779,6 +1767,7 @@ pydantic = [
 ]
 PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-openai"
@@ -1829,6 +1818,57 @@ packaging = ">=23.2,<25"
 requests = ">=2,<3"
 types-requests = ">=2.31.0.2,<3.0.0.0"
 
+[[package]]
+name = "langgraph-checkpoint"
+version = "1.0.1"
+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 = "langgraph-checkpoint-postgres"
+version = "1.0.0"
+description = "Library with a Postgres implementation of LangGraph checkpoint saver."
+optional = false
+python-versions = "^3.9.0,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+langgraph-checkpoint = "^1.0.1"
+orjson = ">=3.10.1"
+psycopg = {version = ">=3.1.19", extras = ["binary"]}
+
+[package.source]
+type = "directory"
+url = "../checkpoint-postgres"
+
+[[package]]
+name = "langgraph-checkpoint-sqlite"
+version = "1.0.0"
+description = "Library with a SQLite implementation of LangGraph checkpoint saver."
+optional = false
+python-versions = "^3.9.0"
+files = []
+develop = true
+
+[package.dependencies]
+aiosqlite = "^0.20.0"
+langgraph-checkpoint = "^1.0.1"
+
+[package.source]
+type = "directory"
+url = "../checkpoint-sqlite"
+
 [[package]]
 name = "langsmith"
 version = "0.1.79"
@@ -1914,25 +1954,6 @@ 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 = [
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-]
-
-[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"
@@ -2519,6 +2540,92 @@ files = [
 [package.extras]
 test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
+[[package]]
+name = "psycopg"
+version = "3.2.1"
+description = "PostgreSQL database adapter for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "psycopg-3.2.1.tar.gz", hash = "sha256:dc8da6dc8729dacacda3cc2f17d2c9397a70a66cf0d2b69c91065d60d5f00cb7"},
+]
+
+[package.dependencies]
+psycopg-binary = {version = "3.2.1", optional = true, markers = "implementation_name != \"pypy\" and extra == \"binary\""}
+typing-extensions = ">=4.4"
+tzdata = {version = "*", markers = "sys_platform == \"win32\""}
+
+[package.extras]
+binary = ["psycopg-binary (==3.2.1)"]
+c = ["psycopg-c (==3.2.1)"]
+dev = ["ast-comments (>=1.1.2)", "black (>=24.1.0)", "codespell (>=2.2)", "dnspython (>=2.1)", "flake8 (>=4.0)", "mypy (>=1.6)", "types-setuptools (>=57.4)", "wheel (>=0.37)"]
+docs = ["Sphinx (>=5.0)", "furo (==2022.6.21)", "sphinx-autobuild (>=2021.3.14)", "sphinx-autodoc-typehints (>=1.12)"]
+pool = ["psycopg-pool"]
+test = ["anyio (>=4.0)", "mypy (>=1.6)", "pproxy (>=2.7)", "pytest (>=6.2.5)", "pytest-cov (>=3.0)", "pytest-randomly (>=3.5)"]
+
+[[package]]
+name = "psycopg-binary"
+version = "3.2.1"
+description = "PostgreSQL database adapter for Python -- C optimisation distribution"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
 [[package]]
 name = "ptyprocess"
 version = "0.7.0"
@@ -3905,20 +4012,16 @@ files = [
 ]
 
 [[package]]
-name = "typing-inspect"
-version = "0.9.0"
-description = "Runtime inspection utilities for typing module."
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
 optional = false
-python-versions = "*"
+python-versions = ">=2"
 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"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
-[package.dependencies]
-mypy-extensions = ">=0.3.0"
-typing-extensions = ">=3.7.4"
-
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -4179,4 +4282,4 @@ 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 = "4da3823fc4a530f306ab8bf29f11cd01e868310b23417665d776024617b2a22e"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.1.15"
+version = "0.2.0"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -9,7 +9,8 @@ repository = "https://www.github.com/langchain-ai/langgraph"
 
 [tool.poetry.dependencies]
 python = ">=3.9.0,<4.0"
-langchain-core = ">=0.2.22,<0.3"
+langchain-core = ">=0.2.27,<0.3"
+langgraph-checkpoint = "^1.0.1"
 
 
 [tool.poetry.group.dev.dependencies]
@@ -22,7 +23,6 @@ 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 +30,11 @@ 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}
+langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
+langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
 
 [tool.poetry.group.dev]
 optional = true

libs/langgraph/tests/__snapshots__/test_pregel_async.ambr

@@ -36,6 +36,127 @@
                 +---------+                
   '''
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.1
+  dict({
+    '$defs': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'anyOf': list([
+          dict({
+            'type': 'string',
+          }),
+          dict({
+            'type': 'null',
+          }),
+        ]),
+        'default': None,
+        'title': 'Answer',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/$defs/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.2
+  dict({
+    '$defs': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'anyOf': list([
+          dict({
+            'type': 'string',
+          }),
+          dict({
+            'type': 'null',
+          }),
+        ]),
+        'default': None,
+        'title': 'Answer',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/$defs/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_via_branch
   '''
                 +-----------+              

libs/langgraph/tests/checkpoint/test_aiosqlite.py

@@ -1,76 +0,0 @@
-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
-
-
-class TestAsyncSqliteSaver:
-    @pytest.fixture(autouse=True)
-    def setup(self):
-        self.sqlite_saver = AsyncSqliteSaver.from_conn_string(":memory:")
-
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {"thread_id": "thread-1", "thread_ts": "1"}
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {"thread_id": "thread-2", "thread_ts": "2"}
-        }
-
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-
-        self.metadata_1: CheckpointMetadata = {
-            "source": "input",
-            "step": 2,
-            "writes": {},
-            "score": 1,
-        }
-        self.metadata_2: CheckpointMetadata = {
-            "source": "loop",
-            "step": 1,
-            "writes": {"foo": "bar"},
-            "score": None,
-        }
-
-    async def test_asearch(self):
-        # set up test
-        # save checkpoints
-        await self.sqlite_saver.aput(self.config_1, self.chkpnt_1, self.metadata_1)
-        await self.sqlite_saver.aput(self.config_2, self.chkpnt_2, self.metadata_2)
-
-        # 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
-
-        async with self.sqlite_saver as sqlite_saver:
-            search_results_1 = [
-                c async for c in sqlite_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 sqlite_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 sqlite_saver.alist(None, filter=query_3)
-            ]
-            assert len(search_results_3) == 2
-
-            search_results_4 = [
-                c async for c in sqlite_saver.alist(None, filter=query_4)
-            ]
-            assert len(search_results_4) == 0
-
-            # TODO: test before and limit params

libs/langgraph/tests/checkpoint/test_sqlite.py

@@ -1,125 +0,0 @@
-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,
-)
-
-
-class TestSqliteSaver:
-    @pytest.fixture(autouse=True)
-    def setup(self):
-        self.sqlite_saver = SqliteSaver.from_conn_string(":memory:")
-
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {"thread_id": "thread-1", "thread_ts": "1"}
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {"thread_id": "thread-2", "thread_ts": "2"}
-        }
-
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-
-        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):
-        # set up test
-        # save checkpoints
-        self.sqlite_saver.put(self.config_1, self.chkpnt_1, self.metadata_1)
-        self.sqlite_saver.put(self.config_2, self.chkpnt_2, self.metadata_2)
-
-        # 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(self.sqlite_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(self.sqlite_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(self.sqlite_saver.list(None, filter=query_3))
-        assert len(search_results_3) == 2
-
-        search_results_4 = list(self.sqlite_saver.list(None, filter=query_4))
-        assert len(search_results_4) == 0
-
-        # 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 thread_ts < ?"
-        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):
-        # call method / assertions
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
-            await self.sqlite_saver.aget(self.config_1)
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
-            await self.sqlite_saver.aget_tuple(self.config_1)
-        with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG):
-            async for _ in self.sqlite_saver.alist(self.config_1):
-                pass

libs/langgraph/tests/compose-postgres.yml

@@ -0,0 +1,16 @@
+services:
+  postgres-test:
+    image: postgres:16
+    ports:
+      - "5432:5432"
+    environment:
+      POSTGRES_DB: postgres
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+    healthcheck:
+      test: pg_isready -U postgres
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 60s
+      start_interval: 1s